Eddie Reynolds

Union Square Consulting

Union Square Consulting is a GTM Strategy & Operations consulting firm. 

I spend the majority of my time:
1. Managing our Sales and Marketing Team
2. Managing our VPs of GTM Strategy & Operations Consultants
3. Providing GTM Strategy & Operations Consulting to Customers

We work with high-growth companies, primarily B2B SaaS companies to help them in five areas:
1. GTM Strategy and Planning
2. Go To Market Process
3. GTM Metrics and Insights
4. Systems Implementation and Ongoing Optimization
5. User Training and Enablement

We do this to help companies execute their Go To Market Strategy by:
1. Making sure your systems are working
2. Making sure your revenue team is using the systems properly
3. Making sure they are executing the right process to attract and retain customers
4. Analyzing the metrics to see what's working, what's not and what to do about it
5. Creating a strategy and a roadmap for all of the above to actually get it done

CEO and GTM Strategy & Operations Consultant

RevOps Corner

RevOps Corner is a podcast we produce at Union Square Consulting to share best practices for Strategic Revenue Operations. 

We do this by interviewing amazing guests, ranging from Revenue Operations Professionals to Revenue Leaders to Venture Capitalists and many other experts in the B2B SaaS community. 

We also share what we're seeing in the trenches every day in our work as Revenue Operations Consultants here at Union Square Consulting.

Listen to the Podcast on:
Spotify: https://open.spotify.com/show/1CM3rmelcdtm6gJVL0PtMK?si=00aae750ea7a4937
Apple: https://podcasts.apple.com/us/podcast/revops-corner/id1619571311

Host

Pavilion

Executive Member

Trumid

Trumid is a Unicorn Fintech Startup that has raised over $650 million in funding to date. 

I joined Trumid right when they had achieved Product Market Fit and relaunched their application. 

My role was to manage all our Revenue Operations:

1. Systems Implementation and Management
 - Sales, Marketing, Service and Data Analytics

2. Enablement
 - Provided training and enablement to the customer facing revenue team

3. Insights
 - Worked directly with the Co-CEOs to provide daily insights on revenue metrics

4. Process Mapping
 - Mapped our sales, marketing and service processes prior to architecting systems and data

5. Strategy and Planning
 - Planned, managed and executed our revenue operations roadmap

VP of Revenue Operations

Salesforce

My role at Salesforce was to work with current and prospective customers to explore opportunities to leverage technology to find more prospects, win more customers, keep more customers and enable their business to scale.

We helped customers accomplish this by improving business processes in marketing, sales, customer service and other departments, centralizing their data in one place and building operational efficiencies. This enables them to grow faster and gives leadership more visibility to make better strategic decisions.

I accomplished this by beginning consultations with the objectives of the business and only then discussing technology. My role was to quarterback a number of resources within Salesforce and the client's team to work together to uncover the best business processes to help them overcome obstacles and reach their goals. Then, we would map out how Salesforce and related technologies could be used to execute those business processes.

Account Executive

CrossBay Capital Partners

CrossBay Capital Partners is a boutique placement agent. We raise capital for established alternative investment managers with a primary focus on private equity.

 - Sourced investments by soliciting private equity firms seeking to raise a fund
 - Distributed investments by building relationships with institutional investors
 - Designed and implemented Salesforce.com to support these efforts for the entire team

Vice President - Institutional Sales

Altvia

Altvia Solutions provides a solution, built on the Salesforce platform, for institutional investors and fund managers to manage investing and fundraising processes.

 - Managed sales process from initial prospecting to negotiation and close for new business
 - Acted as my own sales engineer to provide highly customized and tailored presentations
 - Oversaw marketing efforts by working closely with CEO and our advertising agency
 - Worked closely with implementation team to scope project 
 - Helped implementation team customize solution for each new client

Account Executive - Private Equity Solutions

Yen Capital Management LLC

YEN Capital Management assists operating companies raising capital from private equity investors.

 - Built healthcare platform from inception by growing relationships with private equity investors and healthcare companies
- Closed mandates to raise $25 million and $40 million for two pharmaceutical manufacturers

Head of Advisory Services – Healthcare Banking Team

Wells Fargo

Wells Fargo Business Banking provides lending and deposit solutions to small and medium-sized businesses.

- Solicited and built relationships with hundreds of independent businesses across all industries
- Achieved sales production in the top 1% of bankers in the company

Business Banking

Over +20 years in sales, customer success and marketing, I’ve developed a passion for data-driven strategy and process. I’ve always been fascinated by the edge it can give an IC or an entire company. 

Ten years ago, I joined Salesforce as an AE covering high-growth, VC-backed B2B SaaS companies. I met with hundreds of executives and revenue leaders and they all wanted this edge. They wanted to hear how Salesforce and my other customers operated, not how the software worked. 

Our consulting partners, however, were solely focussed on the software, often missing the broader strategy. This inspired me to start a unique consulting firm, merging system implementation with data-driven GTM strategy. 

Since then, I’ve seen some of the deepest ingrained revenue growth problems plaguing B2B SaaS – and we’ve developed a system to solve them.

𝘾𝙍𝙊𝙨, 𝘾𝙈𝙊𝙨, 𝘼𝙉𝘿 𝙑𝙋𝙨:

Does this sound familiar?

💭“I don’t understand what my data is telling me. I’m not even sure it’s accurate.”
💭“We’re getting tons of leads but they’re not converting enough to outpace churn.”
💭“We’ve spent so much money on tech, marketing, training, etc. – but there’s no ROI.”

Or maybe even…

“I don’t want to end up like those guys ☝️ How do I get in front of this?”

𝙔𝙊𝙐’𝙍𝙀 𝙄𝙉 𝙏𝙃𝙀 𝙍𝙄𝙂𝙃𝙏 𝙋𝙇𝘼𝘾𝙀

I founded Union Square Consulting to help B2B recurring revenue brands increase profitability by targeting the source of their revenue engine issues (not just the symptoms).

𝙒𝙃𝘼𝙏 𝙒𝙀 𝘿𝙊

Fractional RevOps that delivers both systems implementation and go-to-market strategy.

𝙒𝙃𝙊 𝙒𝙀’𝙑𝙀 𝙃𝙀𝙇𝙋𝙀𝘿

High-growth brands from Series A to C and beyond like Techstars, Luminate, and Federato.

𝙂𝙀𝙏 𝘼 𝙁𝙍𝙀𝙀 𝘾𝙊𝘼𝘾𝙃𝙄𝙉𝙂 𝙎𝙀𝙎𝙎𝙄𝙊𝙉

Sit down with one of my Senior Revenue Strategists for a personalized Revenue Operations Coaching Session. Walk away with advice you can use immediately.

👉 CLICK HERE: unionsquareconsulting.com/workshop

𝙎𝙐𝘽𝙎𝘾𝙍𝙄𝘽𝙀 𝙏𝙊 𝙊𝙐𝙍 𝙍𝙀𝙑𝙊𝙋𝙎 𝙉𝙀𝙒𝙎𝙇𝙀𝙏𝙏𝙀𝙍

Get actionable and practical tips on optimizing your marketing, sales, and customer success engines.

👉 CLICK HERE: unionsquareconsulting.com/newsletter

Wow! This guy DM'ed me an hour ago and now I have a proposal. 

I got a random connection request this morning.

By pure miracle, this guy is pitching something that is #1 on my priority list this quarter and this morning. 

However, it's still a random guy on LI.

I didn't want a meeting.
He didn't ask. 

I just responded with "how much?"

He responded with 
"Happy to share. Can I ask a couple questions?"

I'm already like....
"Ugh....he's gonna ask for a meeting."

But no, he asked me a few basic questions over DM. 

Then he sent me a full Loom walkthrough of a custom proposal. 

Holy shit!

Okay, this is how I want to buy!

(And no, if you're buying from me, I'm not selling this way. Wish I could.)

But man.....where possible......this is the way!

✌️

CEO | GTM Strategy & Ops for B2B SaaS CROs

"What's the ROI of fixing our GTM Operations?"
I get asked this question constantly.

And every time I hear it, it feels like a slap in the face.

Here's the thing.
- You just told me your GTM is a mess
- Your forecasts are consistently off
- Inbound leads aren't being followed up on
- The pipeline is filled with dead deals
- NRR is slipping because there's no process
- No one has visibility because the systems are broken

But we can’t plug these revenue leaks until we “prove ROI?” 

Warren Buffett said it best: "You don't need to know if a man weighs 300 or 350 pounds to know that he's overweight."

When the problem is obvious, you don't need to overanalyze it.

But here's the real issue.
When your GTM is fundamentally broken, you can't even measure ROI.

Your Close Rate looks like 25%. But it's meaningless.
Some reps create opps after every meeting. Others wait for the verbal.
One group has a 5% Close Rate. The other has 85%.

The blended number tells you nothing.

You can't measure the ROI of fixing your sales process until you've fixed your sales process.

So let's think about this differently.
- What if you improved your Close Rate from 15% to 16%?
- That's $1M in additional ARR on a $25M new business goal.

That single improvement pays for your entire GTM Ops investment.

What if you improved inbound lead conversion from 1.0% to 1.1%?
- That adds $16.7M in pipeline. 
- At a 15% close rate, that's $2.5M in additional revenue.

What if you improved retention from 85% to 86%?
- On a $100M ARR base, that's $1M saved.

These aren't aggressive assumptions.
They're conservative.
And they compound.

Better lead conversion creates more pipeline.
Better close rates turn more pipeline into customers.
Better retention and expansion multiply the impact.

In tomorrow's 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰, I break down the exact math on why any improvement in GTM Ops pays for itself several times over.

But the real question isn't "What's the ROI?"

It's "Can we afford NOT to fix this?"

How much revenue are you leaving on the table with broken GTM processes?

🤔

Get the full breakdown (including the image below) in tomorrow's 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰
Subscribe to get it here: https://bit.ly/49RCm0h

✌️

How do you actually calculate the most important GTM Metrics?

Following the 𝗚𝗧𝗠 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 & 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 we published yesterday, we released a 𝗚𝗧𝗠 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗜𝗻𝗱𝗲𝘅 outlining how to calculate each of these metrics. 

We're coving the following types of metrics:

𝗚𝗧𝗠 𝗘𝗙𝗙𝗜𝗖𝗜𝗘𝗡𝗖𝗬 𝗠𝗘𝗧𝗥𝗜𝗖𝗦

We ask questions like:
 - How efficient is our overall GTM Engine?
 - What does it cost to acquire customers?
 - What does it cost to grow revenue by $1?
 - What's the lifetime value of our customers?

We answer them with metrics like:
 - CAC
 - CAC Ratio
 - CAC Payback
 - LTV:CAC
 - Magic Number
 - GTM Efficiency Ratio
 - GTM Efficiency Margin

𝗙𝗜𝗡𝗔𝗡𝗖𝗜𝗔𝗟 𝗠𝗘𝗧𝗥𝗜𝗖𝗦

We ask questions like:
 - Are we growing fast enough?
 - Are we growing profitably enough?
 - Do we have strong unit economics?
 - Are we producing enough cash flow?
 - Are we spending too much in sales/mktg?

We answer them with metrics like:
 - Rule of 40
 - Growth Rate
 - Gross Margin
 - Net Income
 - Etc.

𝗣𝗜𝗣𝗘𝗟𝗜𝗡𝗘 𝗚𝗘𝗡𝗘𝗥𝗔𝗧𝗜𝗢𝗡 𝗠𝗘𝗧𝗥𝗜𝗖𝗦

We ask questions like:
 - Are we generating enough pipeline?
 - Are we generating enough inbound leads?
 - Are we logging enough outbound activities?
 - How are leads and activities converting to pipe?

We answer them with metrics like:
 - Lead Generation (MQLs)
 - Pipeline Generation (# and $)
 - Sales Activities, Meetings Booked
 - Conversion Rates

𝗣𝗜𝗣𝗘𝗟𝗜𝗡𝗘 𝗠𝗘𝗧𝗥𝗜𝗖𝗦

We ask questions like:
 - Are we closing deals?
 - Are we chasing the right deals?
 - Do we have real pipeline to close?

In other words, do our reps actually know how to identify real qualified sales opportunities and do they know how to take all the right steps to close them?

We answer them with metrics like:
 - Close Rate
 - ASP
 - Sales Cycle
 - Pipeline Velocity

𝗖𝗨𝗦𝗧𝗢𝗠𝗘𝗥 𝗦𝗨𝗖𝗖𝗘𝗦𝗦 𝗠𝗘𝗧𝗥𝗜𝗖𝗦

We ask questions like:
 - Are we retaining customers?
 - Are we expanding customers?
 - Are we onboarding them right?
 - Which customers are healthy?
 - Which can we try to expand?
 - Which are unhealthy?
 - Which are at risk of churn?

We look at metrics like:
 - Gross Retention Rate (GRR)
 - Expansion $ARR
 - Net Revenue Retention (NRR)
 - Customer Health Ratings
 - Onboarding Metrics

More on this in our 𝗚𝗧𝗠 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗜𝗻𝗱𝗲𝘅

Read the Framework and/or get the high-res image in the link in my comment below 👇

Or comment "High Res" below (and send me a connection request if we're not already connected) and I'll DM it to you. 

✌️

𝗚𝗧𝗠 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 & 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 separate Strategic CROs and RevOps Leaders from glorified sales managers and systems admins. 

Getting accurate metrics isn’t easy though. 

Getting real Insights from those metrics is even harder.

We need to:

𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿
Start by identifying the metrics that matter for your unique GTM motion. 

To help, our 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 will have a 𝗚𝗧𝗠 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗜𝗻𝗱𝗲𝘅 outlining the most common metrics in B2B SaaS and how to calculate them. 

𝗗𝗲𝗳𝗶𝗻𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝘁𝗵𝗮𝘁 𝗙𝗲𝗲𝗱 𝗠𝗲𝘁𝗿𝗶𝗰𝘀
Metrics are only as accurate as the process being executed. 

If our salespeople don’t know how to qualify deals, for example, our Close Rate will tell us nothing. 

We have to clearly define processes, train the team, and drive them to adopt and execute in order to be a data-driven organization. 

𝗨𝗻𝗯𝗹𝗲𝗻𝗱 𝘁𝗵𝗲 𝗙𝘂𝗻𝗻𝗲𝗹
It’s not enough to see our Lead Conversion Rate, or Close Rate, across our entire business. 

These numbers are blended together across different types of leads and lead sources, different segments (SMB, MM, ENT), geos, products, etc. 

To see what’s really happening, we have to unblend the funnel. 

More on this in our 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 

𝗜𝗻𝘀𝗽𝗲𝗰𝘁 𝗗𝗮𝘁𝗮 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗹𝘆
People only execute processes when held accountable. 

To get accurate data, we need to inspect the data regularly to be sure it’s accurate. 

To get real insights into our business, we need to analyze that data regularly to spot trends and take action.

This requires the right person/people with the right skills and uninterrupted time to do “deep work.” 

CROs usually don’t have time to do this well and RevOps can’t if they’re constantly harassed with system admin requests. 

𝗗𝗿𝗶𝘃𝗲 𝗔𝗻𝗻𝘂𝗮𝗹 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
As we get to this part of the year, finalizing our annual plan, it’s a bit too late. We have dirty data and have to make some BIG assumptions. 

If we’re in this boat, we need fix this for next year.

Additionally, Annual Plans aren’t set in stone. We need to track them and iterate as we hit or miss goals and milestones. 

More on this in our 𝗚𝗧𝗠 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸.

See the Framework and get the high-res version in the link in the comment below. 👇

If you want more breakdowns like this, subscribe to our newsletter at the link to get weekly insights directly in your inbox. 

✌️

The current GTM AI frenzy reminds me of the same shiny object syndrome that drove Predictable Revenue and Outreach and Salesloft. 

It's not that any of it's wrong. 
It's that GTM leaders overlook fundamentals. 

Here's what we all saw last time.
 - Predictable Revenue came out
 - Outreach and Salesloft came out
 - And it felt like a magic easy button
 - Just hire a bunch of outbound SDRs
 - Buy and implement Outreach or Salesloft
 - Slap together some cadences and templates
 - Reps press "Send All" and watch the money roll in

And this worked for some
And this worked for some time
But response rates ended near zero

Why?

Think about the fundamentals:

 - Do we have the right ICP?
 - And the right Buyer Personas?
 - Do we know our outbound capacity?
 - As in the number of prospects we can work?
 - Have we identified the best prospects to target?
 - And have we segmented similar types of buyers?
 - And created sequences for each of those segments?
 - And trained our reps to do actual research before send?
 - And built a reporting and review engine for management?
 - To identify what's working, what's not, and what to iterate?

If yes, this approach and these tools worked very well. 

If not, they didn't, at least not for long. 
 - Not after we burned through the first batch
 - Got a <1% response rate from our best prospects
 - And had to dig for scraps waaaaaaay outside the ICP

AI is not different. 

There's no magic prompt to write the perfect message to be all things to all prospects. 

If we want AI to work, we need to master the fundamentals. 

✌️

I've had 3 back to back calls with major PE firms facing massive pressure to implement AI in GTM. 

One told me "Even if these AI tools don't add value to GTM, they add value to the company."

I had to digest that one. 

What he was telling me is this:
 - If a company implements AI in GTM
 - And it produces zero impact or improvement
 - Just saying "we have AI" increases company value

I was shocked by this!
But I really shouldn't be. 

I've spent the last +20 years working with PE/VC investors and they're just as susceptible to shiny object syndrome as any other humans.

So I get it.

Everyone is facing this pressure. 

But, as we address it, we might as well create some real value too.  

To do this, we need to master the fundamentals:
 - Pipe Gen
 - Closing and Forecasting
 - Renewals and Expansion

Within each of these is a series of basic steps. 
Unique to each market, each product, each GTM team. 

Many have not defined these steps. 
Many don't know what "good looks like"
Making it extremely hard to replicate "good" with AI. 

For those that have mastered these fundamentals. 
It's FAR easier to look through each step for use cases. 

If you need some help, check out our GTM Efficiency Pyramid. 

This will outline the fundamentals for you. 

So you can quickly get to the shiny objects AND see real value. 

Link below in the comments 👇

✌️

Outbound is not dead!
 - It's working for us
 - It's working for our clients
 - What's dead is Spray and Pray
 - It's pressing "Send All" that's dead
 - Sending generic templates to everyone

Relevant/Personalized outbound works
 - Highly targeted messaging
 - To a very narrow niche of prospects

We see this w/ our clients
 - We see the reports
 - We see what's working
 - We see what's not working

Outbound works when reps reach:
 - The right people
 - In the right companies
 - With the right messaging

Here's how to do this "at scale"

𝗜𝗖𝗣/𝗕𝘂𝘆𝗲𝗿 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝘀

Most ICP/Persona definitions miss the "ideal" and cast far too wide a net, targeting broad industries and revenue ranges without getting granular enough to define the "ideal" customers. 

𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴

Reps have 2,000 accounts and can only cover 200. 

They're missing 90% of the best accounts and/or doing their own analytics to identify them. This is NOT an efficient use of seller's time. 

Instead, we should carefully analyze capacity and give reps the absolute best accounts they can work. 

𝗧𝗲𝗿𝗿𝗶𝘁𝗼𝗿𝘆 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴

Territories need to match capacity with the best accounts and contacts in our ICP/Personas, within that capacity. 

𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗧𝗮𝗿𝗴𝗲𝘁 𝗟𝗶𝘀𝘁𝘀

Break territories into segments so reps can focus on one person in one type of company at a time. 

Reps can share far better messaging in far less time if they're focussed on one avatar with one set of challenges. 

𝗖𝗮𝗱𝗲𝗻𝗰𝗲𝘀/𝗣𝗿𝗼𝗰𝗲𝘀𝘀

Define the process reps need to execute to maximize conversion rates on this extremely targeted outbound. 

Define where messaging needs to be personalized/tailored and where it can be targeted with highly relevant templates for each segment. 

𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁, 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴

Train the team and use reports to ensure they're targeting the right people with the right messaging and executing the process. 

This requires active effort and ongoing coaching from management. 

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

With this foundation in place, we can get accurate data and look at what's working, what's not, and where we can optimize the targets, messaging and process. 

Pipeline Council is just one way to do this in a more formal way. 

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻

Once things are working, we can scale that foundation with automation, AI, and other advanced motions. 

--------------------------

We broke this down in our 𝗢𝘂𝘁𝗯𝗼𝘂𝗻𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸

👉  See the link in comments to get it. 👇

𝗪𝗮𝗻𝘁 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵-𝗿𝗲𝘀 𝗶𝗺𝗮𝗴𝗲? 

Grab it in that same link. 

Or comment "High Res" below 
 - Send a connection request if we're not connected
 - We'll DM it to you

✌️

Improving your CRM won't fix your bad data.
Improving your data won't fix your poor reporting.
Improving your reporting won't fix your poor process.
Improving your process won't fix your poor GTM strategy.
And improving your GTM strategy won't fix your lack of focus.

Your focus is everything.

One buyer
One product
One GTM motion

Just get that right.
Ignore absolutely everything else.

If you're a $1M startup, there shouldn't be anything else.
If you're a $1B public company, you can't fix it all at once.

Your focus will lead to great GTM Strategy
Your focussed strategy will lead to great process
Your focussed process will lead to great reporting
Your focussed reporting will lead to great execution
Your focussed execution will lead to great GTM data
Data will then circle back up to further improve GTM strategy

✌️

I see a lot of CROs set up to fail.
It happens for a lot of reasons, but here's one. 

I met a CRO and CMO a little while back. 

Core Issues:
 - Pipeline mismanaged
 - Inbound leads being ignored
 - Outbound prospecting neglected

Potential customers in their ICP were raising their hands, asking for demos, getting zero response from sales. 

My 1st Reaction:
 - What is the issue?
 - You're the CRO and CMO
 - Just fix the process tomorrow
 - We can help you fix this, and fast

Unfortunately, they had other blockers. 

Second Layer:
 - The CRM is a mess
 - We have 500 custom fields
 - It's a nightmare for our reps

My 2nd Reaction:
 - We can fix this too. What's the issue?

This looked like a tech issue, but it wasn't. 

Third Layer:
 - The CTO built this
 - CRO and CMO are new
 - And CTO says he's got this
 - Except that he's slammed now

My 3rd Reaction:
 - Okay.....
 - The CTO built the CRM
 - And made an enormous mess
 - So much so that sales can't use it
 - Can't follow up with hot inbound leads
 - So you're loosing easy revenue every day
 - We need to get aligned with your CEO on this

What happened next?
 - We couldn't get the CEO into a conversation
 - We spoke with the CTO and ended as you'd expect
 - Leaving the CRO and CMO fighting to swim upstream 

The REAL Core Issue:
 - CEO is not committed to GTM

The CEO is committed to spending money
 - On a competent CRO
 - On a competent CMO
 - On a robust GTM tech stack
 - On marketing spend to drive leads
 - On salespeople to convert those leads

But the CEO isn't committed to understanding it
 - Or making sure it works

As a CEO myself, I get it. 
 - We don't have time to fix every problem

But revenue generation is never something to ignore. 

✌️

There's a good reason salespeople get paid more.
And it's not just that they're closer to revenue. 
It's also not just that it's (in theory) more measurable. 

It's about accountability. 

Think about every other job in a company. 

1. Finance
 - Customers paid late
 - "We followed up. What else can we do?"
 - They still get paid all/most of their comp

2. Product
 - Customers didn't like new features
 - They still get paid all/most of their comp

3. Marketing
 - Leads didn't convert to pipeline
 - We can blame sales for not closing
 - They still get paid all/most of their comp

Etc.

Now, contrast this with sales. 

1. Leads Not Converting
 - Even if they followed up
 - Even if everyone knows leads are bad
 - They lose half their income and/or their job

2. Customers Don't Pay Bills
 - They get clawbacks
 - Sometimes they have to chase them
 - They lose half their income and/or their job

3. Product Features Can't Compete
 - "You need to sell better."
 - "Sell value not features."
 - They lose half their income and/or their job

Sales is one of the few jobs where even when you're right, you can be wrong. 

The one job where no matter what obstacles are thrown in your way you just have to overcome them or deal with the consequences. 

The only job I know of that has more accountability than this....

Bootstrapped Founder/CEO

And sales is one of the best training grounds for it. 

Agree/disagree?

🤔

Most companies define ICP too broadly. 

They look at who they've already sold to.
Pull a report from the CRM.
Filter by company size, industry, revenue.
Call it done.

That's a smart place to start.
But a dangerous place to stop.

Because our easiest wins aren't always our true ideal customers.

We recently had Brady Jensen, CEO of Clear Go-To-Market, on our podcast to talk about how to actually validate ICP.

Not just define it. Validate it.

Here's what he told us:

ICP isn't something we engineer in a conference room.

It's something we discover through direct conversations with the people we're trying to sell to.

Not just our best customers.
Not just the people already in our pipeline.
But the prospects who match our criteria and haven't yet bought.

Brady's process is straightforward:
- Start with a hypothesis based on who buys fastest, expands most, stays longest
- Talk to people who fit that profile
- Test whether they experience the problem
- Test whether they value the solution
- Test whether they would actually buy

You're not just looking for fit.
You're testing for signal.

And here's where most companies fail:

They define ICP once and freeze it.
They don't pressure-test it in the real world.
They don't learn from the "no's."

"You have the ability to learn a ton from people saying 'no'," Brady explains. "Oftentimes, the 'no' is not 'I don't like your product.' It's 'I don't believe I have the problem you say I have.'"

That feedback loop between our ICP hypothesis and the market's response is where high-leverage refinement happens.

But it only works if we're disciplined enough to say no to the wrong customers.

Even when we need the revenue.
Even when the deal is right there.
Even when leadership is pushing for more logos.

"The problem we often see is organizations will just sell to everybody," Brady says. 

"And then they have a bunch of unhappy customers, and they're like, 'I don't know why they're unhappy.' Well, because they were never a good fit."

That lack of discipline trickles down through the entire organization:
- Product builds for the wrong use cases
- CS struggles with misaligned expectations
- Onboarding becomes custom every time
- Renewals suffer
- Team morale tanks

And the biggest thing I've seen from making this mistake myself:

You're less valuable to the right buyers
 - You've underinvested in your real ICP
 - The product isn't as good for them as it could be
 - Nor the marketing materials and sales messaging

Real GTM clarity comes from pairing data with direct customer conversations.

When we understand our buyers really well, we can build a business that serves them really well.

How often are you validating your ICP assumptions with real buyer conversations?

🤔

Read more in tomorrow's 📰 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛  📰

Or listen to the full episode on the🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙋𝙤𝙙𝙘𝙖𝙨𝙩🎙

See comments below for links 👇

✌

What's the first thing I'd do as a new CRO?
I'd hire VAs for every person in GTM

Yep, my first thing would be hiring VAs
 - It wouldn't be AI
 - It wouldn't be fixing the CRM
 - It wouldn't even be the annual plan

All of those things are important. 

But I'd hire a VA for every single person in GTM
 - VAs for every AE
 - VAs for every BDR
 - VAs for every AM/CSM
 - VAs for every marketer
 - VAs for everyone in RevOps

Let's say this costs $10/hr.
That's net, not gross.
No office space.
No benefits or taxes.
Just a straight $10/hr.

Now, imagine the immediate impact.
 - AE has a $1M quota
 - How much time do we need to save?
 - To justify $10/hr for a VA? 
 - AE costs $500/hr in sales quota
 - So a VA needs to save them 1 hour
 - For every 50 hours that VA works for them
 - That's some pretty obvious ROI right there

How about the BDR? 
 - Half that? 25 hours?

How about the AM/CSM?
Or the marketing manager?

Just imagine if the VAs were:
 - Trained in using ZoomInfo/Clay
 - Trained to do basic things in SFDC
 - Trained to do basic things in Hubspot
 - Helping to do manual account research
 - Helping to manually create prospect lists
 - Helping to schedule and confirm meetings

Well I don't have to imagine. 

I've been doing this for ~9 years. 

I've had multiple VAs
I also have a NYC based EA

I cannot even describe the ROI we get. 

And after doing this, I can do the bigger things. 

Knowing what good looks like, we can look at each task and leverage AI. 

But there are always gaps with the AI. We need a "human in the loop."

Do I want that human carrying a $1M quota?

Or do I want someone at $10/hr doing the first pass?

🤔

There's a reason CROs aren't getting what they want from RevOps.

I was on a call like this the other day... 

CRO:
 - RevOps is purely reactive
 - They're not bringing me insights
 - They're not driving revenue growth
 - RevOps doesn't have any North Star

Me:
 - Who do you have on your team

CRO:
 - We have a Salesforce Admin
 - We have a Marketo Admin
 - And we have another admin

Me:
 - Who does RevOps report to?

CRO:
 - They report to this person
 (This person has zero GTM experience)

Me:
 - Asks detailed questions about GTM

CRO:
 - We have poor pipeline hygiene
 - No one trusts it and we can't forecast
 - We're very inconsistent with outbound
 - We don't follow up with inbound leads well
 - We are using over 40 different GTM tools now

Me:
 - I see the issue
 - Unfortunately we're at time
 - I'd like to schedule another call
 - To narrow in on the key priorities here

CRO:
 - Yep!
 - Work with {name} on that.
 - I'm really slammed at the moment. 

Me:
 - The person with zero experience in GTM?

Huh?

🤔

Let me share the real issue here:

1. RevOps has no North Star
 - Because the CRO has no North Star

2. RevOps is purely reactive for a few reasons
 - The CRO isn't investing time with them
 - They also don't have the right experience
 - And reporting to someone with GTM experience

3. There's no prioritization
 - Admins spend all day in tools
 - Because that's what admins do
 - And even if they wanted to stop
 - There's no prioritization to guide them
 - They're flooded w/ requests to update tools
 - With no clear priorities to decide when to say no

4. The GTM Process is broken
 - And none of these folks can fix it
 - Pipeline is a mess bc of the sales process
 - No amount of SFDC config will fix this issue
 - You have to change the process and train people
 - Then you have to drive them to change their actions
 - And I've never once seen an SFDC Admin do this alone

✌️

This guy DM'ed me and I had a full proposal an hour later. 
And I just signed a contract with him.

This was the smoothest buying experience.
 - I get a LI Connection Request with a DM
 - I'm actively searching for this solution
 - I accept it and respond to his DM
 - But I don't know this guy at all
 - I don't want to "jump on a call"

I ask if I can ask questions over DM
 - He says yes
 - So I hit him with questions
 - He asks me some questions too
 - We're essentially into a disco session
 - But then I have to jump into a meeting

When I get back
 - I have another DM
 - With a link to a full proposal
 - With a personalized video walk through

I'm basically sold at this point
 - He understood all my needs
 - The proposal reflected those needs
 - The pricing was very fair and attractive
 - And he shared more details in his video
 - He shared past successes, testimonials, etc
 - He built a lot of credibility in this ~5 min video

So I was sold
 - But I was vetting 3 vendors in total
 - So I took a couple of weeks to evaluate
 - And circled back and signed with him last week

This is how we all want to buy
 - Meet me where I'm at
 - Ask thoughtful questions
 - Answer my questions asap
 - Share details as soon as possible
 - Make it very easy for me to buy from you

✌️

How do you flip a GTM motion from 95% outbound to 95% inbound?

And close deals 4x faster than your competitors while doing it?

We recently had Aviv Canaani, CMO/CRO of Datarails, on our Podcast, to break down how his team built a revenue engine that grew new sales 90% YoY.

Here's what they did:

They engineered speed into every part of the motion.
- Five-minute SLA on demo requests
- Next-day AE availability
- Sales cycles a third of the industry average

They built predictable forecasting systems.
- Every dollar mapped to meetings, opps, revenue
- Conversion rates tied to historical data
- Quarterly targets they could actually hit

They treated GTM like a factory, not separate departments.
- Marketing, sales, RevOps as one machine
- Each team doing their part
- No silos, just coordination

The result?

AEs stopped prospecting. They didn't need to.

The inbound engine was feeding them qualified pipeline.

And their biggest deal in company history?

The lead source was ChatGPT.

This isn't about copying someone else's playbook.

It's about thinking like a systems builder instead of a department head.

When you connect every lever, 
- From inbound to outbound
- From content to conversion
- From SDR to AE...
…you don't just grow faster.

Your revenue becomes more predictable.

How repeatable are your systems today?

🤔

More on this in tomorrow’s 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰
Subscribe to get it here: https://bit.ly/49RCm0h

Listen to the full podcast episode on 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 📰 here: https://bit.ly/4agM6Bo

✌️

How do you get an ROI from GTM Ops?
A 0.6% improvement in Close Rate could be enough. 

That's it!

We’re often held back in GTM. 
 - We have broken processes
 - Our team isn’t executing consistently
 - And we don’t have visibility into our GTM

But we can’t get budget to fix it. 
 - Because hiring a sales rep has a “clear ROI” (in theory).
 - And GTM Ops feels harder to measure. 

Let me share a simple way to look at this.

Say we expect to close $25M in new biz w/ a 15% Close Rate.
 - If we improve Close Rate to just 15.6%...
 - We would close an additional $1M in ARR

Here’s the math:

$166.67M of Pipeline
x 15% Close Rate
= $25M Closed Won ARR

$166.67M of Pipeline
x 15.6% Close Rate
= $26M Closed Won ARR

ROI => +$1 million of additional ARR

Now, let’s look at our current situation:
 - Pipeline is very messy
 - Reps chasing unqualified deals
 - Reps are way too busy as a result
 - Reps missing critical steps in sales process
 - We’re not getting access to the right stakeholders
 - We’re not asking the right questions in the journey
 - Mgmt has zero visibility into this in pipeline reports
 - So it’s harder for them to spot these issues in real time 

Could fixing this improve Close Rate by 0.6%?

The answer is pretty obvious.

Now, what about the rest of GTM?
 - Our inbound lead conversion?
 - Our outbound prospecting conversion?
 - Our onboarding, health, and renewal rates?
 - Our generation and closing of expansion opptys?

This is an EXPONENTIAL RETURN
 - More qualified leads
 - More qualified outbound mtgs
 - Higher conversion rates for both of them
 - Means more qualified pipeline for us to close
 - With higher Close Rates, ASPs, and shorter cycles
 - Means more new business this year and the next
 - With higher renewal rates and more expansion opptys
 - Meaning more revenue from each new customer
 - On top of more new customers

It’s all pretty obvious.

And yet, I see GTM teams NOT fix these issues.

Because they’re worried about the ROI…..?

🤔

But I get it.

We get asked this question, because CROs, CEOs, CFOs, get asked this question. So we broke this down in fine detail, with math and visuals, based on EXTREMELY conservative assumptions, to help everyone map out the ROI in GTM Ops. 

Check it out in yesterday’s 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰

Get it here: https://bit.ly/3M6IbQu

✌️

Most CROs are stuck playing Whac-A-Mole.

Pipeline dips. They panic. They spin up new campaigns.

Deals stall. They blame the team. They change the comp plan.

Forecast misses. They scramble. They push harder on the same broken levers.

This is what happens when you lead departments instead of building systems.

We recently had Aviv Canaani, CMO/CRO of Datarails, on our podcast. 

His team grew new sales 90% YoY.

Not because they worked harder.

Because they stopped thinking like department heads.

Here's what that actually means:

They connected the entire revenue chain:
- Marketing didn't just generate leads
- They mapped spend to meetings to pipeline to revenue
- Sales didn't just work opportunities
- They fed insights back to optimize the top of funnel
- RevOps didn't just run reports
- They turned data into capacity planning decisions

When meeting volume spiked, they didn't let it become a bottleneck.
They pushed HR to hire faster.

When demo requests came in, they didn't let them sit.
They built a five-minute SLA and next-day availability.

When forecasting felt like guesswork, they didn't accept it.
They built conversion benchmarks tied to historical data.

Every part worked together.

Like a factory line, not separate departments.

Most revenue leaders are optimizing individual functions:
- Better marketing campaigns
- Better sales training
- Better CS processes

But they're not connecting them into one engine.

So when one part improves, another breaks.

When demand spikes, capacity can't handle it.
When data is needed, it's not trustworthy.

That's the difference between tactics and architecture.

Tactics fix today's problem.
Architecture prevents tomorrow's fire.

Are you building systems or managing departments?

🤔

More on this in our last issue of 📰𝙏𝙝𝙚 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰
Get it here: https://bit.ly/4inXUXG

Listen to the full podcast episode on 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 📰 here: https://bit.ly/4rgmRYO

✌️

Nothing feels more meta than consulting in RevOps while operating as CEO and CRO in my own company. 

RevOps
 - We work with CROs and CEOs
 - We try to help them build processes
 - We try to help them get insights from data
 - We try to help them use data to plan better
 - We try to help them build a repeatable machine

CRO
 - I try to practice what I preach
 - We've built all these processes here
 - We have all the data our customers want
 - We review that data in our weekly team mtg
 - We plan carefully based on this data 

CRO (Small Business)
 - I don't have a big team
 - So I'm also selling and marketing
 - And also doing some CS work as well
 - Working on the front lines every single day
 - I'm creating marketing content all by myself
 - And also working with our team to create more
 - And tracking all marketing through to won revenue
 - And taking inbound leads, selling, closing deals myself
 - And outbound prospecting and pipeline mgmt every day
 - And hiring, training and managing our salespeople as well
 - And tracking our NRR and working with my SVP of Delivery

CEO
 - And then I step out of GTM
 - And it's the same types of things
 - How is the rest of the business working?
 - When do I work ON the business, not just IN it?
 - Do we have defined processes and metrics in place?
 - What are we spending and what revenue is it generating?

And ALL of this comes down to just a few things.

In somewhat reverse order of importance:

1. Process
 - Do we have clear process?

2. Execution
 - Are we executing that process?

3. Efficiency
 - Is this profitable?

4. Focus
 - Are we focused on the right things?
 - Or trying to do too many things at once?

Notice I didn't mention strategy. I still don't know what that word means. I think, whatever it is, it comes from focus, from thinking about the same thing day after day after day until you figure it out, or decide to walk away. 

✌️

Your outbound motion isn't working.
Is it a message problem or an audience problem?

Most teams assume it's the message.

So they rewrite the sequences.
Test new subject lines.
A/B test the CTAs.

But the real issue is simpler.

They're targeting the wrong people.

Brady Jensen, CEO of Clear Go-To-Market, puts it this way: "If your outbound motion doesn't work… you either have a message problem or an audience problem."

Here's how to tell the difference.

If you're reaching out to people who:
- Don't believe they have the problem you're solving
- Don't think the problem is worth solving
- Aren't actively looking for a solution

No message will fix that.
You're talking to the wrong audience.

This is why ICP validation matters.

It's not about building the perfect persona deck.
It's about testing your hypothesis against real market resistance.

Brady's approach is straightforward:
- Talk to prospects who match your ICP criteria but haven't bought yet
- Don't pitch. Listen.
- Look for patterns in the "no's"

"You have the ability to learn a ton from people saying 'no'," he explains.

The friction you hear tells you:
- Whether they recognize the problem
- Whether they've tried to solve it before
- What triggers them to evaluate new solutions
- Who inside their org feels the pain most

That feedback loop between your ICP hypothesis and the market's response is where the real refinement happens.

It's how you avoid building strategy around people who look right on paper but aren't ideal customers in practice.

And it's why your outbound motion is actually a discovery tool.

When prospects consistently say no, they're telling you something.
The question is: are you listening?

What's your outbound response rate telling you about your ICP?

🤔

More on this in the latest 📰𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰
Listen to the full podcast episode on 🎙𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚🎙

Links below in the comments 👇

✌️

There’s a BIG hidden cost to complex comp plans. 
Reps lose motivation because they don’t understand them. 

Any more than 3 variables loses reps. 

Let me walk through a personal example.

Variable 1:  You earn 10% of all contracts.
 - Simple enough
 - If I close a $100K deal
 - I get paid $10K in commission
 - That hits my bank on February 1
 - And I can book that ski trip if it closes!

This is motivating for a rep.
They can visualize the outcome.

Variable 2: 5% for one-time deals
 - So now it’s….
 - 10% for recurring
 - 5% for one-time deals
 - My deal was $80K SaaS
 - And 20K for implementation
 - So now it’s…
 - Harder to do the math
 - 10% x $80K = $8K
 - 5% x $20K = $1K
 - Total = $9K

Okay, I got there.
The ski trip could still be on!
Let’s close this deal as soon as I can!

Variable 3: 2X boost for multi-year
 - So now it’s….
 - 10% for recurring
 - 5% for one-time deals
 - And another 10% for 3yr deals
 - Okay, so I’ve got that in my deal here
 - 10% x $80K = $8K
 - 5% x $20K = $1K
 - 10% x 80K = $8K
 - Total = $17K

Alright, that was harder to calculate but I’m still here. 

Variable 4: 1.5X boost if Product X is included
 - Okay, I give up.
 - I’m working 15 deals
 - Some have Product X
 - Some have multi-year
 - Some have implementations
 - But I’d need an excel sheet to do this math

I personally experienced this as an AE at Salesforce.
 - We got comped on….
 - Percent of Closed Won $
 - Accelerators at different levels of quota
 - And more accelerators for multi-year deals 
 - And some other stuff that I never remembered

I vividly remember some spiff where I was like….
“I have no idea if focusing on this will make me money or cost me money so I’m just going to ignore it and keep my head down and work the plan I understand.”

And FWIW, I am a math nerd.
But I couldn't justify spending all day in Excel
When I had an extremely difficult quota to hit

This is a common comp failure we see.
 - Companies over-engineer comp plans.

The other common failures?
- No deal reporting standards (every rep records deals differently)
- No commission cutoff process (deals close after payroll runs)
- No formal approval chain (nobody knows who approved what)
- No process documentation (comp rules exist in someone's head)

And all of these stem from that complexity. 
Reps are confused.
Managers are confused.
RevOps is also confused.
Finance is confused as well.

So, instead of motivating reps.
We’re frustrating them and burning valuable time. 

What’s the worst comp plan you’ve seen?

🤔

More on this in yesterday’s 📰 𝙂𝙏𝙈 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘽𝙧𝙞𝙚𝙛 📰

We broke down the common mistakes companies make building commissions plans and how to fix them.

Get it in the link below in the comments 👇
Or click “View my blog” at the top of this post 👆

✌️

aztela

We bridge the gap between Business and Data to transform your data into actionable insights.  Here are some results how we helped companies decrease churn, increase profits just by using their data  ↓

Founder & CEO

Savvy Social

Alignment of GTM departments for a +20% uplift in profitability, and decrease in cost due to data-driven initiatives. EMEA hit the highest month in terms of MRR. We hit over +278K in MRR in Summer which should be the toughest period. 

Projects 

- Building Revenue Operations in the Sales team for regional teams in EMEA, North America, and Asia
- Accurate reporting  across every touchpoint through the funnel
-  Automate & Operationalize  lead lifecycle for frictionless processes
-  Dashboard for sales, executives, and the marketing team
-  Automation, workflows, and operation for sales teams and fulfillment sites across the organization
- Sales projection, forecasting, and data analytics** for EMEA, NA, and Asia regions
- Aligning departments (marketing, sales, and CS)
- Implementing CRMs and integrating with other GTM tech tool

Head of Analytics

HYPCCCYCL

Member
HYPCCCYCL is an invite-only, professional community where top B2B revenue leaders cross-train their GTM skills with VCs.

Brand Ambassador

RevGenius

Member

Astela

< Open the vault >
< put quality in your pipeline >
< Revenue engine that lasts forever NewEra of GTM >

We are like your Outsourced CRO aka Revenue engine. 

•Market penetration
•GoToMarket strategy 
•Fastest way to Impact 
•Fixing the old way of doing outbound with  TheVault (Outbound2.0.0) which is customer centric overall aligning bot revenue engines sales and marketing. 

We created that demand so outbound feels like inbound. 

Revenue focused > Meetings.

Co-Founder

Spendesk

- Started seeing results after 2-3 months.
- Been for few months as full cycle rep. 
- 980k ARR sourced
- Shift happened after seeing that volume was not gone help 
- Started to pursue quality in targeting and picking accounts, personas & ICP. 
- As it was early, the company also went in marketing to understand things that could help me. 
- Been involved a lot with the marketing and CS team all the time. Also working closely with the manager.

Senior Sales Representative

Etiket Agency

- Establishing their and runing their paid advertisement performance campaigns for B2B clients.
- Managing their campaign budgets for B2B clients. 
- Running SEO and FB campaigns mostly. 
- Running marketing team full of 14 people at the end started only with 2.

Marketing Manager

- Researching for clients campaigns. 
- Managing CRM. 
- Working closely with marketing and sales and CEO. 
- Admin work.

Sales And Marketing Specialist

Head of RevOps | Recently helped SaaS organisation 19% faster growth, +10-20% sales productivity, and 30% lower GTM costs with Revenue Operations

My Name is Ali and I love building things. Whether that be data solutions, my business, or helping other organizations at the end what I'm doing is building myself into the person I want to become. 

Our thesis is that data is the lifeblood of any organization and will only work if it's made custom to your goals, needs and priorities at any stage of your growth.

Unfortunately, many businesses don't have that as they 

-  Rely on off-the-shelf solutions that are not custom to their needs and don't adapt 

- Their data is all over the place resulting in not able to make any data driven decisions.

- Can't rely on their data to make rational logical decisions. 

- They track surface-level KPIs and not actionable insights that would give them a competitive advantage 

- They don't know how to bridge the gap between business and data expertise.

Well at aztela we are that bridge that connects business and data together to develop a moat inside of your business resulting in: 

- One of our clients generated over 20x ROI since starting working with us resulting in over $10m in revenue attributed 

- Increasing profits due to seeing everything at one place which would normally take you 100h a month

- Decreased churn and increased LTV able to see bottlenecks, and root cause as opposed then spinning in circles 

- Fixing inefficient operations due to removing data silos 

We do that by utilizing data you already have and turning it into actionable insights. 

We implement custom analytics dashboards (BI) and advanced solutions to achieve business results just by utilizing data you already have.  

If you want to create a moat, competitive edege in your market click on this link to speak to our team- > 
https://calendly.com/alizu/15-minute-meeting-aztela?month=2024-04I

Ali Z

Your data problem didn't start in your warehouse. It started in that free-text 'Region' in your ERP. Spending $1M modernizing your stack, won't fix your data. 

Everyone wants accurate data.
But when you dig you realize their processes were never built to produce good data.

They’re trying to analyze chaos.

A few months ago, we were talking to finance company. 
They’d just spent 14 months modernizing their stack.
They hired the data engineers.
Millions spent.
Hundreds of dashboards.

And yet:
“Revenue” in Salesforce included refunds.
“Customer” in Marketing meant prospects too.
Operations had 15 different “regions” spelled 8 different ways.

The tech wasn’t broken.
The process was.

Their CRM, ERP, and sales systems were designed for convenience, not for data.

Every time a sales rep skips a CRM field..
You create a leak in your data foundation.
Until your warehouse is garbage. 

If your processes weren’t designed with data in mind nothing will save you  

Here is how to go about stopping bad data

1. Design Every Process as if Data Were the End Goal

If you’re setting up a CRM, ERP, or even a Google Form, build it like a data engineer would.

Even if it's yet. Develop a process with data in mind. 
As down the line, you will need, and rather than waiting 3-5 months to get data. 

Replace free-text fields with controlled dropdowns.
Enforce mandatory fields that align with business-critical metrics.

Executives say they want clean data but approve workflows that guarantee mess.

In my opinion, data should be clean from the source. 
Becuse if it's not, managing pipelines, modelling becomes a nightmare. 
And even that can't save it 

2. Treat Metrics Like Products 

Agreeing on definitions is not easy at all. 
People change, leave. 
2 VPs can't agree on it so they create their own spreadsheet. 

Every metric you report on should have an owner, version history, use case and single definition across the company.

If found in a situation can't agree, ask "What finding this info enables you" 
If can't answer it, archive it. 
Or if can't agree on metric. 
Seperate and define clear use case where each. 

3. Asssign Owner & Build Feedback Loops

Bad data comes from the frontlines, reps skipping CRM fields, creating custom objects in Salesforce. 

Assign owners of the metrics.

Answer: 
Who owns the data?
Who manages the inputs?
Who's keeping operational systems clean? (Data stewards)

If no one is accountable or owns it, how do you thing it will get fixed.
Tie accuracy to incentives. 

4. Enforce Standards, Not Opinions 

Everyone uses their own definition of “good data” 
Define how data should look: formats, naming, validation rules.

If “Region” is free-text in CRM, you’ve built chaos by design. 

5. Data quality isn’t a project or a one-time thing  

Start where it's most important. 
Track exceptions, expose results, fix patterns.
Embed it in the system, so it's proactive rather than reactive.

Ali Šifrar

CEO @ aztela | Fractional data leader helping scaleups+ mid-market, gain control, value from their data to accelerate their data, digital & ai journey.

Everyone trying to 'become AI-driven.' But less than 10% of you actually are. You can't even trace where your data is coming from or what is difference between 2 metrics. 

Let’s call it what it is:
Most AI or ML initaitives fail because your data isn’t ready.

When we walk into mid-market organizations we see the same pattern every time:

Sales, finance, and ops each define metrics differently.
BI and AI pull from different data sources.
Data pipelines are built in silos, no lineage, no ownership.
80% of the data is still trapped in PDFs, or spreadsheets.
AI pilots stall in POCs 

Few months ago one firm came to use after burning through $400,000 on “AI enablement.” 

When they tried to build AI use cases (recommendation engines), the models failed.

Their models couldn’t differentiate between 'accounting-adjusted revenue' and 'store-reported sales'. There was no business meaning attached. 

Here’s the Data That Should Scare Every CIO

Over 55% of companies cite inconsistent data architecture as the #1 blocker to AI readiness. But only 14% invest in Data Strategy, the part that makes all investment pay off. 

Here is how to do it 

1. Impact over complexity  

Start with use cases, data products, not ML or AI. 
Don't due AI for sake of it. Don't skip foundational work.
Most use cases I saw are hidden logical statments.

Data products deliver business outcomes. 
Then design data products, curated, governed datasets aligned to those outcomes.

The smartest organizations aren’t moving data,  they’re federating it.

They build logical data products that query where data already lives, wrapped in a semantic consumption layer that abstracts complexity from the business. 

2. Semantic & Consumption layer 

A semantic layer starts by translating business concepts into reusable logic.

Instead of 20 “revenue” tables with different definitions, you get one trusted “Revenue” concept everyone agrees on defined once, reused everywhere. 

3. Add Business Context, Metadata & Lineage

Don't just add metadata. Add meaning. 
If not, AI will just assume. 

Column names like rev_net_v3_final don’t help at all. 
You need business metadata, written in the language your teams use.

You need business metadata, written in the language your teams use. 

Answer: What does each data point mean in business terms?  Where from? Where used etc?

This is not only for AI but also for trust and adoption. 

4. Don't need 99% data quality but 

The companies that are winning treat AI readiness like data hygiene.

Data doesn’t stay clean.
Set process ensuring data accurate in upstream. 
Freshness checks ensure data timeliness. 

5. Simplify to Scale.

Complexity kills agility.
If your data architecture diagram looks like a Gartner Expo, it’s already broken.
Every component should have a measurable purpose or be removed.

Start fixing the foundation that actually makes it work.
AI Readiness is a byproduct of good data enviroment.

You hired $200k engineers to clean your data. But a 3-year-old spreadsheet is more trusted than your $1M data warehouse. Here is why 99% accuracy doesn't matter 

“We can’t trust the data.” said every 'data-driven' org, so they

Buy another observability tool.
Hires two more data engineers.
Runs another “data cleaning” project.
Trys to change the logic in the warehouse. 

Six months later?

MD notices 2x higher revenue before the meeting.
Dashboards don’t match finance.
VPs trust his 4-year-old sheet more than the 1M warehouse. 

Here’s the truth 
80% of data problems have nothing to do with pipelines, tools, or code.

They start upstream, in the way your business creates the data.

We worked with a cement manufacturer running plants in multiple.
One plant was 100 years old. The other was new.

Different equipment, processes.

Everyone recorded data differently.
One used a shared Excel file.
Another logged reading in a legacy system.
A third called maintenance over the phone, logged in free text field in ERP.

By the time that data reached the warehouse, already broken.
Mismatched units, timestamps, duplicate entries.

The failure happened long before ingestion.

Most data quality mistakes are people, process, and trust issues. 

Most often that we see are: 
-No validation because “it looks fine.” 
-Treating quality standards as static.
-Ownership of IT.
-Logic scattered from BI to semantics.
-Long data lags not up to date. 

Data always fails, silently but that's to late.

Here’s How You Actually Fix Bad Data Forever

1. Map Where Data Is Born

Where does each data originate

Who enters it?
In which system?
How often?
You’ll find that half of it starts in Excel or free text fields in CRM.
Start where there is the highest impact, like regulatory etc. 

2. Standardize the Process.

Map out the process, ensuring that bad data is not possible to be entered. 

Standardize the process:
Define what, when, and how data is captured.
Document it.
Enforce it across.

Having a free text field in CRM is perfect for bad data, replace it.

3. Validate at the Source

Don’t wait until Snowflake to check for errors.
Build validation rules where the data starts. 
Catch mistakes before they hit the warehouse.

4. Assign Real Ownership

Every dataset, metric needs two people:
Owner: A business person who cares about accuracy.
Steward: A person who ensures reliability.

If no one owns it, it won’t get fixed.

5. Don't forget trust 

Accurcy ≠ Trust. 
Seen VPs trust old Excel file more than $1m warehouse. 
Most treat DQ as reactive play, fix it when it fails
But by then, trust is broken

Need to be always getting feedback from end users for trust.

Tip: Know important dates. This ensures specific QA done prior important meetings

Most data issues are reactive, when VP presents 3x higher revenue on board meeting.

Comment "DQ" and I'll sent you a detailed playbook on how to ensure to stop the bad data that results in trust and adoption in weeks

You promised to build source of truth, add predictive, dashboard annoying VP but in 12 months got 313 broken dashboards. If your Head of Data says "Yes" to every request, fire them

I can walk into any $20M, $1B org and I’ll find:

-89 dashboards nobody trusts.
-A “modern stack” that costs more than it saves.
-A data team is exhausted from chasing every request.
-CEO asking, “Why we still didn't fix this data mess?”

Most fail because leaders try to fix everything at once.

“Let’s rebuild the warehouse. Add AI. Roll out dashboards. VPs screaming "We need this...." 

When everything’s a priority, nothing is.

Most act as 'ad-hoc' service desks like. 
Trying to please everyone, say YES to all usless requests. 

After 6-months you spent 80% building usless things, that don't solve the problem.

You can’t modernize your architecture, add AI all at once.

12-month 
They care about whether something finally works and gets results.
And fast. 

Here is how to balance and prioritize accordingly 

1. Create clear priortization criteria

Revisit the business pain points identified in your earlier assessment

Pick one high-visibility, high-value problem 

Define Clear Scoring Criteria

Evaluate every initiative using these dimensions:
Business Value - Quantify the ROI or risk reduction.
Complexity - How mature is your current data environment?
Urgency / Risk - What happens if we don’t solve this?
Frequency - Are these data products used recurring daily or quarterly?
Reach - How many departments or users used by?
Time-to-Value - How fast can we deliver?

Additional Factors:

Staffing & Availability 
Budget & Capital Constraints 
Competing Current Projects
Upcoming Milestone 

You can use AI to help but need to feed a lot of context

2. Map it out, Rank, Communicate, and Confirm

Now score rank each use case
Validate this with your executive sponsor:

If it doesn’t meet the criteria, it doesn’t make the cut.

High impact, low complexity → Do now
High impact, high complexity → Plan next
Low/low → Park it
Low/high → Drop it 

3. Create a “Stop Doing” list.

Think in terms of data products.
If the 'quick thing' that VP asked doesn't have an end user, outcome, timeline, current way, definition of done assigned, don't do it. 

Thats why your data strategy or priortization roadmap needs to be reflected monthly. 

At the same time, you don't need to plan 12 months, Focus on now 3-6 months MAX

Things change fast 

4. Sequence, don’t scale

Your first few wins buy trust and set foundation for future strategic initiatives.

For example 
Start with a data product where the finance team spends 50+ hours manually reconciling data. Fixing this builds your pipelines, and a semantic layer,  this create foundation for next initiatve

For example can't do 'predictive' prior having a foundation set. Start with quick wins that enable that

→Comment "Matrix," and I'll send you a Priortization Matrix Playbook on how to balance short vs long term and setup data as growth function.

Your $1M 'modern data warehouse' is world's most expensive junkyard. Tracking 2TBs of usless data 'just in case' but still takes 2-weeks to answer simple question.

Every company says the same thing:
“Let’s capture everything, for down the line”

That sentence has quietly destroyed more ROI, Snowflake credits, and data trust than any bad tool ever has.

A few years ago, I met a retail data leader that proudly told me,
“We’re tracking everything. Every click, scroll, hover, API event.”

A billion events a day.
Storage bills through the roof.
And still, the FP&A didn’t trust a single revenue number. 
Irony right

Analysts couldn’t even tell if someone added or removed an item from a cart because every event stored the entire basket state.

So the engineers were drowning in data.
The business was starving for insight.
And leadership thought the solution was… to collect even more for 'in case'.

That’s not data maturity. That’s data garbage.

Most mid-market firms believe more data = more answers.
But that logic is backwards.

When you collect everything:

Your warehouse becomes a landfill.
Pipelines grow, meaning more debugging.
Your team spends 80% of time cleaning, 20% analyzing.
Governance dies because no one knows what matters.
Every metric becomes a debate, not a decision.

“Collect everything” doesn’t make you smart, it makes you slow, expensive, and untrusted.

Here's how to fix this

1. Start with the business question, not the data source

Before tracking another event, ask: “What question are we trying to answer and who needs it?”

If it doesn’t tie to a decision, action that enables the end user to achive their objective 
Don’t collect it.

Data only valuable if used, storing it it's worth $0. 

2. Move from data hoarding to event intent.

Stop dumping entire states  
Start tracking facts, atomic events one line that captures who, what, when, and why.

They represent a business action or customer intent.

For example:
- Don't “Full basket snapshot” → 200 useless fields.
- Do “Product_Added_to_Cart” → who, what, when, how much.

That’s where clarity starts.  
This is gone build foundation for it as AI needs context, not just more data.

3. Build a tracking plan that acts like a product spec.

Define which events are allowed, what’s mandatory, and who owns them.
If it’s not in the plan, it doesn’t get into your warehouse.

Most warehouse look 'modern' outside but 'legacy' inside. 
You didn't fix nothing, just made it worse. 

4. Kill the “just in case” mindset.

You don’t need another terabyte of noise.
You need confidence in the 1% of data that drives 99% of value.

One org had TBs of data, queries that cost  > $5,000 month, that they didn't need at all. 

If it takes three department, two weeks to answer “What’s our active customer count?” 

You’re not data-driven. 

Companies winning with AI and analytics aren’t hoarding petabytes.
They’re the ones who know exactly which 1% of their data actually matters.

Data Architecture is worthless. You just spent 12 months fighting with pipelines and $500k building data platform. But then VP announced move to HubSpot, and now all your work is useless. 

Most “modern data platforms” break the moment your business changes.

You spend 12 months modernizing, Snowflake, dbt, BI, governance, the works.
You spent hours on meetings aligning on definitions. 
You finally set processes in CRM so data gets accurate in the source. 

But then suddenly…
→ The CEO changes the pricing model.
→ VP says "We migrating to Hubspot" after you spent last 5-months combating with existing sources. 

Suddenly, everything you worked on became usless 

Half your dashboards are wrong.
Data models break.
Pipelines you built are usless.
New source + process needs to be redone.

You didn’t build a data architecture right way. 

Most companies build static systems in dynamic businesses.
You’ve got cloud storage and transformation pipelines…

But every change request still takes 6 weeks.
Every definition debate lasts 5 meetings.

You are communicating throught some 3rd persona that all value gets lost through translation. 

Building 'data architerture' in itself is not valuable. 
That's step 0. 
Step 1 is actually using it and generating ROI. 

Best data leaders and teams are building adaptive data architectures and evolve with the business. Not the other way around. 


Here is how to build data architerture that ensures trust, agility and long term ROI.

1. Map business capabilities, not systems

Forget the org chart,  map what your business actually does for each business area. 
From finance, sales and so on how do they work. 

This becomes the foundation of your data platform design.

2. Architect for change, destruction, not perfection.

Keep architecture modular.

Data Integration: Connect and standardise data
Data Layer: Raw → curated → business model
Smart Data Services: Reusable data products and APIs
Search & Consume: Self-service tools and governed access 
.
You should be able to change and add without breaking or rebuilding. 

3. Build data products, not pipelines.

Each product serves multiple teams,  with clear ownership and measurable value.

E.g., “Customer Revenue Product” used by Sales + Finance + Marketing = one truth, three users. 

Everyone can accsess lowest hanging data, aligned to their objectives, which allows agility, but remains control. 

4. Make governance adaptive

Governance that moves at the pace of business,  flexible, iterative, and owned by both data and business.

Governance = light guardrails, clear ownership, fast iteration

5. Build feedback loops

Your architerture should adapt to your business. 
Attack lowest hanging fruits, only build according to value. 

Identify a business outcome
Deliver a small, testable solution
Measure impact
Iterate

When you build adaptively, everything accelerates

Your business changes every quarter.
Your data architecture should adapt, scale with you

Your machines are collecting millions of data points. But PLC says “Pump_4A” while your ERP says “Equipment_1403.” Your market edge is trapped inside 20 year old machine and 2008 Excel file. 

Pumps. Sensors. PLCs.
They’re all generating thousands of data points every second.

The problem? 
No one’s listening.

Not because the data isn’t there, it’s everywhere.
It’s because your systems, teams, machines don’t speak the same language.

Machines OT are speaking MQTT or OPC-UA.
Corporate IT systems are speaking SQL and APIs. 
The business speaks “Excel and PowerPoint.”

So you’ve got noise.

I sat with a COO & CIO of a 300-person industrial manufactuing firm.
Their “digital transformation roadmap” - AI, predictive maintenance, digital twins,

But when asked:
“Can your systems tell you in real time when a batch is about to fail?”

Silence.

Here’s what was happening behind the scenes:
-Quality results took 6+ hours to show up in reports. 
-Each factory ran different generations of equipment and software. 
-Production systems (SCADA, MES) weren’t connected to corporate IT (ERP)
- Engineers couldn’t access business data, finance couldn’t trust operational numbers. 

Because here’s the truth:

Most industrial or heavy operational orgs already own the data they need.
They just can’t use it - because it’s locked inside 20-year-old machines, local servers, and 2008 Excel templates.

The result?

Every report was hours late, every meeting a data debate, and AI pilots stayed stuck on PowerPoint. 

All while you are losing $260,000-$2M per hour due inefficency

Here is how to combine OT and IT data into a single, trusted layer to create moat

1. Start with a Unified Namespace.

Stop wiring point-to-point systems.
Standardize how data talks MQTT, OPC UA and normalize it at ingestion.

If one site logs “pump_2B_temp” and another “Pump_2B_Temperature" its problem

If every new sensor needs a custom connector, your architecture is already broken. 
All pulled from UNS from there. 

2. Unify OT and IT data in one foundation.

Make finance, operations speak the same language.

If finance’s “efficiency” and operations’ “efficiency” don’t match, losing money.

3. Govern before you automate.

Everyone wants AI. No one wants accountability.
Assign data owners, document definitions, build lineage.

Every dataset without an owner becomes a liability.
Governance isn't seperate project its part of the architecture. 

4. Democratize, but with guardrails.

Give operators, analysts, and managers access to governed data through a portal or tool they like. Excel not enemy.

It's not giving them all data, but one aligned to their needs. Federate impact, give flexibility.

5. Prioritize by value, not hype.

Start with quality prediction, maintenance alerts, where value is visible and measurable.
If a project doesn’t have a impact, defintion of done, stop.

You have data worth millions

When your OT, IT, and finance speak one language,  you can create a moat

Everyone wants AI and predictive. But most of your data is locked in 1990s ERP system, 31 spreadsheets, and Joe shouting across the hallway. Moving from legacy ERP won't make you data-driven. 

Most mid-market run on systems older than their CEOs.

That “temporary” ERP you implemented in 2010? Still there.
The homegrown Access database that powers finance? Still critical.
Half your ops? Run on Excel, emails, and shouting to Joe 

You don’t need to replace your legacy systems to move forward.
Moving from legacy ERP won't make you data-driven. 
After 18-months gone be in bigger data mess but in cloud

You just need innovate around it. 

Was talking to CIO at huge industrial company facing this nightmare.

Their ERP was from the 1990s. 
Their factories ran on siloed systems.
Data lagged 6 hours behind reality.

Replacing the ERP wasn’t an option. 
So they built around it.

They built an integration “hub” so all systems could talk without touching core ops.

They normalized data at ingestion.
They governed every metric from the start.

And suddenly, they had real-time visibility, data to leverage a 24-month delay.
They led with value, not tech. 

Here is how to deal with legacy systems

1. Stop Thinking in Systems. 

Stop treating your ERP as your source of systems
Legacy record transactions. 
Build innovation around them, not inside them.

You don’t fix legacy by buying new tools.

You fix it by decoupling the old stack from the new.
Build a central data layer (lakehouse/warehouse) that real source of truth, and let your ERP, CRM, and OT systems feed into it. 

2. Introduce an Integration & Translation UNS Layer.

If your ERP, legacy, OT systems can’t talk API, don’t panic.

Add a lightweight API gateway or event-driven architerture 
Make every legacy system publish standardized events through that. 

If ERP calls it “Material ID", Iot some code and MES calls it “Product Code” you need apply consistent data model, naming conventions

That one layer eliminates point-to-point chaos, everyone speak same language  and gets data with context. 

3. Normalize Upstream, Not in Dashboards.

Most firms want AI but have "machine_21" in warehouse but "M12" in ERP. Not gone work

Standardize naming conventions, master data, use canonical schemas , validate

If we can't align on metrics, nothing will happen

4. Go Hybrid, Not All-In Cloud

Not every system needs to move or even can

Keep sensitive or legacy systems on-prem if that case
Send structured, validated data to your cloud lakehouse
Sync it in near real-time if have clear value there

5. Govern Before You Scale.

When someone pulls “Margin,” they need to know:
What it means
Where it came from
When it was last refreshed 

Assign data stewards per domain  
Define data flows between systems.
Establish business definitions for critical metrics

Don't let your legacy systems stop you

Comment “LEGACY” and I’ll send you the blueprint how to deal with legacy, without breaking the business

Most data teams are set up for failure. They create bureaucracy, not value. You can hire best engineers, buy best tools and still fail.

One mid-market company finally approves their “data investment.”
They hire their first data engineer. Maybe even head of data.

Spin up Snowflake. 
Maybe dbt.
Add 2 BI tools with an AI feature.

Six months later: same issues amplified

The engineer is buried in random Slack requests.
The CFO says, “We’re not getting ROI.”
Leadership starts whispering: “Maybe we just hired the wrong person.”

If your dashboards aren’t trusted and your data team looks “slow”
It’s because your operating model.

The truth is, 70% of data teams are set up to fail.

Seen this across our clients and 90% of the time, the problem wasn’t the stack or the people. 

It was how the team was positioned inside the business:

-No clear ownership model (who owns what data? who maintains it?)
-No definition of what success looks like beyond “more dashboards.”
-A data team buried under IT with 100 bosses and no mandate.
-Endless overengineering, copying Netflixs stack with a team of three.

By the time they realize it, the engineer’s burned out, adoption is low, fixes takes 12 months and the CEO’s lost trust data.

You can have every tool, hire the best engineer, and still have the same data problems. Tools and people just amplify an already good data team 

Most data teams create bureaucracy then value.  

Here is how to set up the data environment for succsess 

1. Start with problems, not roles.

Don’t hire anyone until you can name the 3–5 ways data can impact business directly. 
Most hire data scientists 

Otherwise, you’re building a reporting factory.

2. Define ownership before tools.

Map who owns what, by name. 
Every key domain (sales, finance, ops) has one accountable owner. 

No “shared responsibility.” 
That’s how data chaos starts.
The data team also decides and has the final word. 
No more hidden spreadsheets and so on. 

3. Set outcome-based goals.
Data teams shouldn’t be measured by tickets closed.

They should be measured by business impact.
That’s what executives actually care about.

4. Move Reporting to the Business.

This one is hard as I've seen working everything under the sun. 
If your Head of Data reports to IT, they’ll always optimize for infrastructure.
Move them under the people who actually care about ROI and impact. 

5. Centralize Data. Federate Execution.

Have one central team that defines rules, security, and data architecture 
but let embedded data partners execute inside the business units. 

They build one standardized data platform, then give teams freedom within that system with correct guidrails. In short, self-service right way. 

This way, you maintain speed, agility and control. 

The failure started in how you structured ownership, expectations, and accountability long before the first hire.

Your $1M data team is either your biggest edge or biggest cost center. Your $400k engineers are spending 80% time cleaning Excel files. Hiring won't fix data chaos, it just amplifies it. 

Your data people aren’t quitting because Google pays better.

No engineer wants to spend their days fixing pipelines held together by duct tape.
No analyst wants to reconcile “revenue” across six dashboards.
No data lead wants to explain, for the 50th time, why “the numbers don’t match.”

“We just can’t find or keep good data people.”
Most are set up to fail.

Here’s what they see:

Seven disconnected systems.
A dashboard graveyard no one trusts.
Constant firefighting and finger-pointing.
Zero clear ownership of data.

So they leave. 

Data team can be your biggest edge or biggest cost center. 
Great talents leaves for better environments, and where data actually aligns to business 

The best data teams can be your moat or cost center.

Here’s how to build and scale your data team.

1. Hiring is an amplifier 

You can’t fix chaos by hiring more.
Hiring is just amplification. 
It can be good or death. 

Start with this audit:

Do we have a single source of truth?
Do we know who owns each dataset?
Do we have consistent definitions for core metrics?

Rationalize your data stack.
Kill duplicate tools.
Unify your metrics in a semantic layer.
And for the love of ROI, document ownership.

Most new data hires are pushed into chaos and set up to fail with unrealistic expectations. 

2. Align it to Business Impact

Top talent wants to innovate, not babysit broken pipelines.
They want to solve business problems, not chase data discrepancies.

Reframe your projects to data products:
-“Build a new dashboard.” to "Reduce delivery time by 15%"

Measure the team on business outcomes. Not activity, maintenance. 

Where the data sit is important.
Is that CTO, CIO, CEO, it don't matter. I seen all the work. 
Make sure it sits where data has the biggest business impact 

If your data team spends 80% time cleaning, that's janitorial work.

3. Treat Data Like a Product

Your data team should own outcomes, not tickets.

Create data products with owners, roadmaps, and measurable ROI.
Stop letting random ad hoc requests drive your priorities.

4. Hire for Outcomes, Not Tools 

I've seen orgs hiring becuse someone knows Databricks. 
Then they wonder why they end up with 3124 dashboards. 

Hire not for titles as "Data Engineer" or tools. Hire for outcomes. 
Make sure each role owns responsibilities and clear succsess criteria.

4. Don't Turn Into Bureaucracy 

Most data teams, as scale turn into bureaucracy. 
Do more harm than good. 

The best data orgs are not choosing between federated and centralized they are choosing matrix. 

You centralize standards (platform), federate the impact. 
This way you get agility, speed while remaining control & impact

Comment “Team” and I’ll send you the playbook on how to build, scale your data team for max business impact.

Most data teams find out their data is wrong after the VP finds a $400k discrepancy 2-min prior meeting. You can have 99.9% uptime. But if the VP finds the error before you. Your data team failed 

The phrase "Data is accurate" has quietly cost companies millions and trust.

Because “fine” isn’t proof.
It’s an assumption.
And assumptions are where bad data hides best.

A mid-market data team told us proudly,

“Our dashboards are up. The data looks fine.”

Three weeks later, Finance caught a $400K “phantom revenue” error a few minutes prior most important meetings. 

Same dashboards. Same pipelines. Everything still looked fine.

The issue?
A small logic change in Salesforce never made it to dbt.
Every deal with a certain tag was double-counted.

Nobody noticed because:

-The data pipeline didn’t fail.
-The dashboard loaded.
-The metrics trended “normally.”

When they finally caught it, trust collapsed overnight.
Executives stopped using data.
The data team spent a month reconciling instead of delivering value.

The CFO said it bluntly:
“If we can’t trust the numbers, everything else is irrelevant.”

Most leaders think data breaks loudly.
It doesn’t.
It breaks quietly

Dashboards still work.
Queries still run.
The data just drifts, slightly wrong, every day, until it’s completely disconnected from reality.

Every “fine” assumption creates downstream waste:

-Finance reconciling numbers that don’t match.
-Ops running forecasts off incomplete data.
-Execs delaying decisions because they don’t trust dashboards.

Here’s what smart data teams to catch data issues proactivly, not reactive 

1. Validate, Don’t Assume

Don’t check if the pipeline ran; check if the numbers make sense
Run regular data profiling on key datasets: nulls, duplicates, and outliers.

Get important dates of VPs. Run proactive changes prior to that 

If something “feels fine,” challenge it.

2. Define What ‘Fine’ Actually Means 

“Fine” needs metrics, completeness %, timeliness, and consistency.


Set clear thresholds and treat them like SLAs.

3. Audit Business Logic Quarterly 

Your business changes every quarter.
Your metrics don’t, unless someone updates them.

Schedule recurring audits between Data + Finance + Ops to realign definitions and rules.

Don't be surprised if next month the VP says that doesn't trust the data. 

4. Build Trust Checks 

The most efficient place to fix data quality is upstream.

Examples:
Mandatory fields for pipeline-critical data.
Drop-downs instead of free text. 

Prevent upstream.

5. Make Data Quality Part of Business 

This isn’t IT’s job.
It’s an org-wide accountability problem.
Domains own data. 

By the time a dashboard is obviously broken, the real damage is already done:

Missed revenue, wrong decisions, lost confidence, and wasted months of work.

You don’t need more DQ tools.
You need validation, ownership, and accountability.

Because once trust is broken. It doesn't matter if your data is 99% accurate.

Everyone says they have "Single Source of Truth." But your Imaging data is in S3 buckets. Commercial in Snowflake. Genomic arrays across 6 other systems. You don’t win with more data, you win with usable data. 

Everyone says they are “data-driven.”

Here’s the reality:
12 systems. 6 clouds. Zero trust in data
Not an AI failure, an architecture failure 

Walk into any mid-sized biotech or pharma right now and you’ll see the same thing:

R&D runs on legacy genomics platforms
Clinical data lives in proprietary systems.
Imaging data sits in S3 folders with no metadata.
Commercial analytics lives in Snowflake or Databricks

Everything’s in silos but nothing talks. 

So when your head of research says, “Let’s run a cross-study model to predict responders,”

The data team goes silent

They know it’ll take three months just to merge datasets.
Half that time will be spent just defining what “sample” even means across systems.

That’s duct-tape engineering, it kills velocity 

Here’s the trap everyone falls into:

You “modernize” by adding another tool.
Each tool solves a local problem (ETL, storage, analytics).
Integration complexity doubles.

Everyone blames the data team, but it’s data architecture debt. 

The next generation of life sciences, pharma companies aren’t chasing “modern stacks.”

They’re building universal data architectures,  one foundation that handles every data type, every team, and every workflow.

That means:

Tables + images + genomics + vectors → one data structure.
Governance + lineage + access → one policy layer.
Analysis + AI → one compute engine.

Designed to work together from day one, that’s the universal model 

Here’s how top biotech and pharma teams are pulling this off:

1. Map the chaos.

Inventory every dataset, every format, every owner. 

Group them: 

Tabular 
Scientific arrays  
Imaging  
Text 
AI/Vector data 

Quantify what’s actually being used vs. what’s just rotting in cloud storage.

2. Standardize definitions

Most treat governance as a separate initiative. But it makes all work

“Sample,” “gene", all defined once, globally.
Centralized authentication across all datasets.
Version control, enabled by default
Unified access policies that span data types.

When an auditor asks, “Where did this result come from?” you can answer it instantly. 

3. Universal infrastructure

Move from “stacks per department” to one universal foundation that can store and query any data type.

With multidimensional arrays - a data structure that can represent any modality can be stored and processed

This eliminates the need to constantly convert between formats. 

4. Align to ROI

Once your data lives in a unified structure, you can query across modalities in seconds 

For example: 
Combine genomic data (arrays) with clinical trial data (tables) to identify responders
Or imaging + transcriptomics to accelerate biomarker discovery 

If dealing with multi-modal complex data, repost and check our newsletter on it

Your complex $1m data architecture is built in 2015 and looks like vendor expo at a Gartner event. You are spending more time maintaining it than driving impact. 

Tightly coupled. 
Over-engineered.
And allergic to change.

Doing litterly more harm than good. 

You built data foundation like it’s 2015.

Meanwhile, the defense sector, the one with more risk, regulation, and bureaucracy than anyone, is doing it differently.

They call it data flexibility and being agile. 

Was talking to the data leader of a big defense manufacturer and realized their operations were being slowed by 200+ systems. 
They didn’t buy more tools, created more pipelines. 
They stopped, looked at the mess, and asked one hard question:

“How can we move faster without rebuilding everything?”

Flexible scalable data architeriture.
They built a foundation that can absorb change,  not crumble under it.

What they did differently: 

1. Rationalized first.

They killed redundant systems before they modernized.
No more patchwork of “legacy + shiny new.”
One unified platform, one source of truth, one model.
Centrelize, Standradize data, but federate the use of it. 

Stop chasing perfection, and 'future tense'. Data only valuable if used.

2. Standardized everything.

Every pipeline, schema, and permission model followed the same rules.
No cowboy connectors. 
No hidden spreadsheets 
No redunded tables or pipelines ( like v3 or v3_realfinal)

Create a semantic and consumer layer where you centrelize all, but remain agility.

Most teams spent 80% time uplifting then providing impact. 

3. Built governance into the foundation.

I hate 'governance' documentation. 
Not as policy docs, but as code.
They automated trust: lineage, access, compliance.

Hits all from trust, adoption, scale and impact. 

4. Designed for agility.

They didn’t just centralize data, they made it modular.
Imagine like building legos. 
Thats how you should you think about data foundation. 

Add a new source, new data product, deploy an AI use case…
without ripping up the foundation each time.

Most teams spent more time fixing rather providing impact. 

5. Agility, People & Literalcy 

They invested in data literacy, not just data engineering.
Literlcy is not training or workshop. 
It's providing clear ideas, feedback on what is possible with data, ai etc. 
End users could  trust, and act data without anyone. 

Compared to others, and one one manufactuerer who recently audit said: 
“We have everything, but nothing works together.”

Their architecture looked like a vendor expo at Gartner event. 
Data team spends 80% time firefighting pipelines instead of delivering impact.

You’re slow because you’ve built rigid systems. 
You need fewer tools and scalable architecture.

Architeirture needs to adapt to business, not the other way around. 

→ Comment below "Scale"  and will sent you full playbook on building a scalable agile data architecture without risking never-ending rebuilds

You spent $1m on AI strategy. But your 'Revenue' number changes depending on which dashboard you open. AI readiness isn’t "We have a data lake". It's someting else

Every boardroom 
“We need to be AI-ready.”
“Let’s invest in data infrastructure.”

Yet 80% of AI pilots fail not from bad ai models or missing talent, but from broken data models. 

Most companies sit on terabytes of data that can’t answer a single question confidently.
Their “revenue” number changes depending on which dashboard you open.

So when someone says, “Let’s build an AI use case,” here’s what happens:

Data scientists clean 80% of the time.
Finance and Ops argue over numbers.
Leadership loses patience and priority. 

But it’s not the AI that failed, it’s the foundation.

A retail CIO told me they tracked over 1B events daily every click, basket change, and impression. 

On paper, it looked advanced.
In reality, users couldn’t even tell if a customer added or removed an item from the cart because the system tracked states, not actions.

That’s what bad data modeling looks like at scale.

How To Fix The Thing That Is Killing Your AI & ML Plans 

1. Redefine the Purpose of a Data Model

A data model isn’t a schema it’s a representation of your business logic.

If your teams can’t align on “what’s a customer, what’s an order, what’s revenue,” you’re not ready for AI.

You’re ready for rework.

2. Move from Raw Data to Modeled Information

Don’t dump “state snapshots” (like every cart update).
Model “facts”  real business actions: 

“Product Added to Basket.”
“Payment Completed.”
“Account Churned.”

Each event should represent a meaningful business action.

3. Build a Semantic Layer 

Your AI doesn’t understand tables, it understands meaning.

A semantic layer defines core business metrics once and enforces them everywhere, dashboards, APIs, AI models.

No more “marketing’s revenue” vs “finance’s revenue.” 

4. Make It Modular

AI thrives on flexibility. 
Domains evolve independently (customer, transaction, product). 

Your model should have:

Decoupled domains  
Version-controlled schemas (so changes are tracked).
Automated tests for data quality. 

When your data model is tightly coupled every change breaks everything

5. Ontology provides meaning 

This is where advanced companies pull ahead.
Your data model isn’t just tables and joins 

Do this

Define relationships explicitly: “A Customer places Orders,” “An Order contains Products.”
Translate this into your data model structure  
Document these relationships using dbt docs.
Start mapping these into knowledge graphs.

Your AI doesn't know what "final_21" means. 
It understands relationships, meaning, and context. 

AI readiness isn’t “we have a data lake.”
It’s when your execs can make decisions from trusted, modeled data without asking an analyst to debug the pipeline.

If you can’t define your key entities and metrics with confidence,
you’re not ready for AI, you’re barely ready for basic reporting

Moving to new ERP won’t fix your data. You can spend $500k on Odoo, SAP, or NetSuite. But if your plant manager "fixes" inventory in a spreadsheet. No "data cleaning" project will solve it. 

We worked with a manufacturing firm that had 15 years of data in Sage, Excel, and a few random Access databases.

They thought the new ERP would solve their data problems.
But they just migrated the same bad data, definitions, and processes they had before. 

Seen it across manufacturing, food, and ag. 
They were migrating to Odoo and thought:
“Let’s clean everything. New ERP will give us a new start; it will remove bad data.”

So they did what everyone does:
Froze the ERP for “data cleanup"
Built a dozen Excel trackers and migrated.

Six months later?

Operations slowed to a crawl.
Finance couldn’t close the month.
Nobody knew which version of truth was real.

The business was bleeding time and trust.

They treat 'data cleaning' as a one-time event.
They think moving to a new ERP will solve all their data issues

Bad ERP data isn’t a technical issue. 
It’s a behavioral one.

-It’s a sales rep skipping CRM fields.
-A plant manager “fixing” inventory manually.
-Finance entering workarounds to close on time (Excel file name '_finalv3')

So when you try to clean or migrate to a new ERP it all at once without fixing ownership or process you’re moving problems with you.

Here’s the Playbook We Use  

We’ve done this across manufacturing, agrifood, and logistics firms 10-year legacy data.

1. Audit What Matters, Not Everything

Don’t clean everything. Start where bad data costs money (risk vs value)

Ask:
“If this data were wrong, what would it cost us?”

If it doesn’t move P&L or decision-making, it doesn’t make the first round.
Clean the 20% of data that drives 80% of your business.

2. Assign Ownership at the Source

Bad data comes due to bad process. 
Ops uses the wrong codes. Skipped fields in ERPs. 

Your data won’t stay clean until someone owns it.
Assign data stewards. 
Create a process so that bad data is impossible to happen. 
Make data quality part of the goal in that domain. 

3. Clean in Iterations, Not in a Big Bang

Forget the “6-month cleanup project.”
All clean-up projects are band-aid fixes.

Start with the lowest-hanging fruit first 
Audit → Clean → Validate 

Each iteration delivers visible ROI, faster close, and cleaner SKUs.

Momentum matters more than perfection.

4. Automate the Guardrails

If the VP notices the error before you. You lost.
Set up automated checks to catch mistakes before they spread.

Duplicates? Missing values? Invalid ranges?

Flag them in real-time, notify.
Fix it at the source, not six reports later.

5. Build the “Semantic Layer”  

Chaos returns fast if definitions differ.
Build a semantic layer a unified model defining what metrics mean 
Your legacy ERP is not the source of truth.

You don’t fix 15 years of bad data by "cleaning up" one time.
You don't fix it by migrating to new ERP.

Your spent $1M "data modernization" for AI-readiness and market edge. But ended up with more then 898 dashboards than employees that nobody uses. Thats not a data problem, that's adoption problem. 

Perfect Databricks setup.dbt pipelines humming. BI dashboards polished. Models training.  

And yet
Revenue teams were back in spreadsheets.
Finance couldn’t reconcile bookings to actuals.
Executives said, “We don’t trust the dashboards.”

The CIO asked us why adoption never took off.

Our answer was simple:
“You built a data platform. You never built data products.”

This is the quiet failure happening across 80% of enterprises we walk into.

The pattern is always the same:

Leadership funds a “modern data stack" or "ai readiness" 
IT builds pipelines, warehouses, dashboards.

Adoption flatlines. Nobody opens nothing. ROI vanishes.

Then panic hits add tools, hire a Head of Data, run AI pilots. Nothing moves. 

Because the core problem never changed:
They confused infrastructure for outcomes.

Data lake, warehouses, modernizations don't equal 'business impact' 
It's what you do with data, what makes it valuable. 

One global hospitality brand engaged recently didn’t make that mistake.
They didn’t stop at “centralized data.”

They turned it into products:
Loyalty models that forecast guest retention.
Privacy filters that automatically enforce data laws by country.

They built data capabilities, not data.

If you want adoption, trust, and ROI, here’s the blunt playbook:

1. Stop Building Everything. Pick One Business Outcome.

Start with biggest problems. Don't glorify complexity. 

That’s your first product. Not your first dataset or you think is 'nice'
Start there. Kill the rest (for now).

2. Write a Product Brief, Not a Jira Ticket.

Every initiative needs a product brief, not “give me sales data.” 

Make sure have:
- End User 
- Timeline
- Problem 
- Outcome 
- Definition of Done (Most miss this) 

3. Deliver a Minimum & Fast  

Your goal is not perfect "data model" in 12-18 months 

Your goal is prove the value and impact.
Ship fast, in iterartive cycles 

Ship something tangible in weeks.
70% ready now beats 100% in 12 months. 

Adoption starts when your product generates results, not when delivered. 

4. Feedback Makes Adoption 

Adoption doesn’t happen from launch day. It happens from feedback loops.

Set up a 30-minute weekly sync with your end users.
Gain feedback, then iterate quickly.

Slowly, the team won't be able to live without it. 
That’s your adoption signal. 

5. Build For Reeuse.

Stop solving the same problem five times.

All three depend on the same foundational data: cust

Instead of building three separate pipelines, build a Customer Data Product clean, defined, and reusable across every use case.

Best data teams that win treat data like a portfolio of products, not a pile of assets.
They measure business outcomes not number of tables, pipelines, or dashboards.

Every company has that one custom pipeline that breaks right when VP looks at it. If your engineers are spending 70% of time fixing pipelines, you don't have a data team. 

Every company has that one custom data pipeline.
Built by the smartest engineer on the team.
Works (most of the time).
And supposedly “saved” you hundreds of thousands in vendor fees.

The problem?
It’s a time bomb.

When I talk to mid-market firms, I always hear the same story:

“We built it ourselves because it was cheaper, needed something fast to work”

Six months later…

That “free” pipelines has turned into a full-time maintenance job.
Your data engineers spend nights fixing schema drift.
Your new hires waste weeks trying to understand undocumented code.
It breaks at times right when VP looking at the data, 2min ago worked fine.
And your CFO still doesn’t trust the numbers in the dashboard.

Let’s do the math:
Two engineers, three months of work.
$150K annualized salary.
You just spent ~$30k to “build” it. 

But here’s what you didn’t factor in:

30–40% of those engineers’ time now spent keeping it alive.
2–3 months of onboarding every time someone leaves.
Countless hours lost when it breaks right before an exec review.
Missed opportunities because your team’s firefighting instead of innovating.

That $75K build?
Turns into $1.2M+ in lost productivity and opportunity cost over three years.

Here is what to do rather. 

1. Audit the True Cost

What is your data team working on? 
What is the requests, and data initatives (data products) they are shipping? 
Calculate time spent of your data team

Most data teams 70% of time spent on maitainance rather then driving impact.

2. Kill the Zombie Tools

Every organization has 2–3 internal tools or legacy pipelines no one dares to touch.  

Archive, replace, or sunset anything that:
Has no clear business owner. Don't bring ROI. Was built to just have smth.  

Seen recent finance org running redundent pipelines, they didn't need. 
They took more time to maintain, for 0 impact. 

3. Buy the Plumbing, Build the Differentiation

Your engineers should be building the data products that drive growth - not re-inventing connectors to Salesforce.

Buy what’s solved. 
Build only what creates a business advantage.
Buy boring. Build brilliant.

4. Force ROI Discipline

Before any “custom build,” or "data product"  ask one question:

“What’s the measurable business outcome?”

If there’s no answer, it’s not a project, it’s aligning on wrong outcomes.

Can't start building nothing, if dont have outcome, data, problem solving, definition of done, why and sign off. 

5. Move From Maintenance Mode → Innovation Mode

If your engineers are spending more time fixing than delivering, you don’t have a data team  you have a repair crew

Automate the repeatable
Buy managed infrastructure
Let your team focus on data, AI, and decisions that move the business

Your “free” pipeline isn’t free
It’s costing you growth, talent, and trust

Everyone wants AI to talk with their data. But every meeting sounds the same: ‘Wait… where did that number come from? You have trust issues.

What they actually want is confidence in their numbers and forecasts.

But somewhere between the Snowflake bill, the dashboards, and the AI hype deck… that confidence disappears.

And every board meeting sounds the same:

“Wait… where did that number come from?”

At that point, everything breaks.
Your $250K data stack.
Your shiny new AI pilot.
Your data team’s credibility.

Because if no one believes the output, the input doesn’t matter.

AI isn’t your next frontier. Trust is.

Here’s the uncomfortable truth:
Most data programs don’t fail technically

We’ve seen this play out across dozens of mid-market firms:

Dashboards no one logs into.
AI models that contradict intuition.
Executives reverting to Excel, because it’s “more trustworthy.”

This isn’t about tools or pipelines.
It’s about belief.

When your team says, “We have a data quality issue,” what they really mean is:

“No one trusts the numbers.”

So how do you fix it?
Not with another tool.
Not with a new dashboard.
But by rebuilding organizational trust in your data.

Here is what to change. 

1. Shift the Mindset: Trust is an Output 

You don’t “implement trust.”
You earn it.

Stop running weekly “data quality reviews” and start assigning data ownership at the source.
If Sales owns sales data, and Finance owns finance data.  

2. Replace ‘Single Source of Truth’ with ‘Single Source of Trust’

Truth is technical. Trust is emotional.
You can have the perfect data model and still lose credibility.

Fix that by making transparency visible.
Add lineage, timestamps, and ownership right. 

Executives don’t need more tables. They need assurance.

3. Kill the Shadow Data Systems - Openly

Those private Excel sheets and “Finance’s version” of the truth?
They’re not harmless, they’re silent trust killers.

Don’t ban them.
Reconcile them live.

Compare, explain, document, and publish the aligned metric.
You’re not fixing a formula.
You’re fixing belief.

4. Assign a Name to Every Metric

Every data point should have a human next to it.

Business Owner → defines what it means
Data Owner → ensures it’s right.
A metric without an owner is an orphan - and orphans aren’t trusted.

5. Make Governance Invisible 
:
Governance shouldn’t feel like paperwork.
It should feel like safety.

Automate your data validation and lineage tracking.
Keep it out of sight — but never out of control.

If governance feels visible, it’s already slowing you down.

AI fails for the same reason any data product fails, no one believes it.

AI isn’t a tech problem.
It’s a credibility problem.

If leaders don’t trust the source data, they won’t trust the model.
Especilly as models like to agree on a lot of things. 

You don’t have a data problem.
You have a trust problem.
And AI won't fix that.

You spent $1m on “AI analysts,” “data copilots,” licenses and call it digital transformation. Congratulations. You just spent a fortune to automate your chaos. Here is why it's a trap.

Ask a question in plain English, get answer and be "data-driven".

But here’s the truth no one wants to say out loud: 
AI doesn’t fix your data problem. It just amplifes them. 

Leaders think “AI analysts” are the missing link to being data-driven.

They’re lipstick on fragmented data.

I’ve seen this movie a dozen times:

An exec team is frustrated. 
Want to be data driven.
Data requests take weeks.
So they buy the next shiny thing, an “AI analyst” call it "digital transformation".

Now, suddenly, anyone can “ask questions” in plain English:

“Show me revenue growth for Q3 by region.”
“Summarize top-performing therapies by adherence.”

The AI spits out charts and insights instantly.
Everyone cheers.

Then it happens.

The CFO asks one question:
“Where did this number come from?”

Silence.

And the lineage? Untraceable somewhere across 8 systems. 
Context nobody knows what links to what. 

Congratulations, you’ve automated your chaos, spent $1m on license. 

AI doesn’t understand truth.
It predicts patterns.

If your data is fragmented, biased, or inconsistently defined, AI doesn’t fix it it just reveals data issues. 

AI didn’t fail.
The data strategy and data foundation did.

Here’s what real data-driven companies will do in 2026 to win.

1. Fix Ownership Before You Automate Anything.

If Finance doesn’t own Finance data and Sales doesn’t own Sales data, your “AI analyst” has no single source of truth.

Feeding raw, undefined data to AI or a VP never ends well. 

2. Build a Semantic Layer.

Without standardized definitions, your AI will pick whichever “revenue” field it finds first

It won't know is it 'rev_v2' or 'rev3_final'  
Need to have clear consumption layer. 

Define metrics, enforce one version of truth.

3. Context, Lineage.

If someone is asking "Where this from?",  I bet they trust their wrong excel file over your new ai pilot. 

Metadata and lineage aren’t compliance they’re the foundation for trust and AI.

If it doesn’t know how entities connect, what defines them, or what each metric means

AI without context is usless. 

4. Run AI on Trusted Data, Not All Data.

If you can’t defend the dataset in a board meeting, your AI shouldn’t be querying it.
Set clear guidrails, alerts and fix data in source. 

Certify your Tier 1 datasets and restrict everything else.

5. Start Solving Imporant Problems 

Data is only value if used to impact end user. 
Create consumption marts layers aligned to end user need and built data products. 

You don’t need more dashboards.
You need reusable data products that solve real end-user problems:  

"Detected 80% of patients on a failed therapy within 30 days
preventing 8 out of 10 from ineffective treatment."

The orgs that win in 2026 will be which use trusted data as market edge.

Your global data strategy failed. You have 200 different definitions of 'Revenue' and every regional team swears its version is correct.  

Every global company says they want to “be data-driven.”

Then reality hits:
20 countries.
More dashboards than FTEs
200 different definitions of “Revenue.”

Each regional team swears its version is correct.

Sound familiar?
That’s the governance agility trap.

You either centralize and kill innovation or decentralize and kill trust. 
Here’s what every global organization should copy today.

When you operate across 10, 20, or 30 markets, you hit the same wall.

Global wants consistency, governance, one version of truth.
Regions want speed, flexibility, control. 

If you standardize too much, you kill agility.
If you decentralize too much, you lose trust. 

Most companies end up bloated governance decks and Excel chaos.
That’s bureaucracy at scale.

One huge telecom org faced this exact problem.

20+ OpCos across Europe, Africa, and Asia each with its own data stack.
Revenue in Portugal didn’t match Germany.
Regional teams, were constrained. 

They launched a global data modernization to standardize without killing agility. 

Most companies fail right here.

Here’s why:
Don't start by standardizing tools.
Start by standardizing meaning.

Before migrating a single dataset, they built a Universal Semantic Layer 

It defined what core parts meant across every market.
Once those definitions were locked.

That became the single governed layer every market could build from.

Global = consistent.
Local/Regional = flexible.

Teams could finally speak the same language, while remaining flexible top to bottom.

Governance became invisible, built into the design, not enforced.

1. Build a Semantic Layer, Not Just a Cloud Layer,

Start with 10–15 global metrics every market must align on. 
Standardize parts on the Group level; you don't align on everything at a high level as impossible.

Define business logic once and reuse it everywhere.
That’s how you scale governance and self-service.

2. Automate Governance

Every metric definition, data transformation, and business rule needs to be version-controlled and reusable.

The result:
One governed layer.
20+ autonomous markets.
Zero manual governance overhead. 

3. Federate Ownership in Guidrails

Let markets own local regional extensions, products, and metrics as long as they build on the global foundation.

Have a regional/local consumption layer for their uniqe needs. 
That keeps truth, control, and flexibility aligned top to bottom.

This way, global and regional don't clash.

Create a shared foundation, not a shared dictatorship.
Let markets innovate on top.

4. Measure Adoption, Not Dashboards

Instead of forcing global "data" or "dashboards", focus on data consistency and transparency.
Every query is pulled from the same governed layer.
Every number was explainable, traceable, and identical all across.

Focus on trust, adoption before moving

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