Marketing Ops Community

Head of Training and Certification

Indeed.com

Director of Marketing Strategy and Operations

MarTech Alliance

I am the lead instructor for a brand new 8 week course on all things Marketing Operations! Visit Martech Alliance online for more details.

Course Instructor

Amazon Web Services (AWS)

Global Marketing Operations Leader

Hitwise

Leads the marketing and demand generation teams in the Americas and APAC. 

- Oversee the planning and direction of marketing programs
- Partner and support sales to execute strategy for winning clients and upselling existing clients
- Report on key business metrics, campaign results, and drive optimization
- Development of Marketing ROI reporting and analytics 
- Lead nurturing, drip campaigns, lead scoring, sales and marketing alignment
- Oversees sponsorships, paid advertising, and online media spend
- Development and execution of campaigns across paid search and social, online advertising, webinars, events and content marketing
- Marketing automation and Salesforce implementation

Director, Global Marketing

American Marketing Association

Leads content marketing and communications strategy across all channels. 

Oversees communications and social media teams.

Manages writers and content producers for blogs, eBooks, and advertisements.

Director Of Communications

Leaf Group

Responsible for marketing and demand generation programs including marketing automation, lead nurturing, email marketing, messaging, sales enablement, and alignment across paid, content marketing, and event marketing programs. 

Leads and manages team of demand generation professionals.

Passionately implementing unified Marketo, Salesforce, Facebook Advertising and content marketing B2B programs. Marketo Certified Expert.

- Database Hygiene 
- Database Migration 
- De-duplication 
- Data Standardization and Data Normalization

Senior Marketing Manager

Trumpia

Drove 500% growth of new marketing platform through partner marketing and relentless adoption programs for AT&T Mobility’s 1,500+ business sales force. 

Established webinar lead generation program accounting for over 80% of marketing qualified leads 
Instrumental in product marketing, pricing, packaging, business development and go-to-market strategies. 

Online marketing including Google analytics, website and landing page optimization, marketing automation using Pardot, email marketing, collateral development and copywriting. 

Led event marketing team to successfully execute AT&T new product roadshow and events in 15 major US markets.

Digital Marketing 
Marketing Automation 
Google Analytics and Adwords 
Product Marketing
Customer Marketing 
Channel Marketing

Marketing Manager

Independent

Small business marketing consulting including: 

Digital Marketing 
Email Marketing 
Marketing Automation 
Social Media Marketing 
Online Marketing 
Website Design 
SEO/SEM

Marketing Consultant

Director of Marketing Strategy & Operations | Martech Leader | Speaker

Author and speaker. Co-host of the Humans of Martech Podcast.

Subscribe to the Marketing Operations Leader newsletter for MOPs strategies and insights every week: https://darrellalfonso.substack.com/

Highlights: 
- Ranked as one of the Top Marketers in the US by Propolis 2022
- Named one of the "Top Martech Marketers to Follow" 2020 by Martech Alliance 
- Keynote speaker for Martech Conference and Get Stacked B2B Marketing Conference 2021
- Awarded Fearless Marketer 2018 
- Marketo Champion 2018 and 2019 
- VP of Communications American Marketing Association - 2017-2019
- Marketo User Group Leader 2017-2019
- Leading Program Manager for Connect@Amazon (35,000 member Amazon organization)
- Frequent Martech Speaker and Author 
- Marketo and Salesforce Certified 

Posts are my own and do not reflect the views of my current or former employers.

Darrell Alfonso

Revenue leaders need both broad and deep data & insights to make better decisions.

Here are the 9 crucial reports that Chief Revenue Officers rely on to drive predictable, scalable growth—and how to turn insights into action.

1️⃣ Revenue & Pipeline Forecast
Expected revenue is how much in sales you expect to make in a given time period, while pipeline is the dollar amount of open opportunities in the same time period.

2️⃣ Revenue Contribution Breakdown
This report breaks down the proportion of revenue from new business, renewals, and expansions, as well as the split between marketing-sourced and sales-sourced revenue.

3️⃣ Net Recurring Revenue Over Time
NRR tells you if you are growing steadily, or if customers are cancelling/churning so quickly that it impacts your total business growth.

4️⃣ Pipeline Analysis & Breakdown
Total pipeline is the sum of all opportunities in a given time period. Weighted pipeline is the dollar amount of the opportunities that have a high probability of closing. Healthy pipeline is the dollar amount of the minimum amount opportunities needed to close to hit revenue targets. 

5️⃣ Average Deal Size by Segment
This shows the mean sale price by customer bucket. 

6️⃣ Win Rate by Segment
Win rate is the percentage of deals you close versus lose in a given time period. This report shows win rate by various customer buckets.

7️⃣ Conversion Funnel Breakdown
 This visualizes lead progression through MQLs, SQLs, opportunities, and closed-won deals. A significant drop-off at any stage signals inefficiencies in lead qualification, sales execution, or marketing and sales alignment.

8️⃣ Renewal Rate Over Time
This tracks renewal percentage trends throughout the year. A declining renewal rate highlights potential issues with customer satisfaction, competitive pressures, or pricing misalignment.

9️⃣ Revenue Efficiency Metrics
A low LTV:CAC ratio indicates inefficient acquisition costs. A declining demo-to-close rate suggests qualification or sales execution gaps. A longer average time to close points to deal cycle inefficiencies that slow revenue growth.

This is the foundation for revenue leadership that delivers results.

Which reports do you agree with? What would you add?

This diagram was created by me and the Revops genius, Jeff Ignacio.

Big thanks to the revenue leaders who gave us direction on this diagram - Connor Fee Multi-CRO, Tess Baldwin CRO at SixFifty, Robert Livada CRO, and Steven Dunston CRO at StackMoxie. 

NOTE: This initially started with more marketing-related reports, however, we only included the top 9 in this exercise.

Make sure to subscribe to our weekly newsletters to keep leveling up your knowledge, search RevOps Impact Newsletter on Google for Jeff’s newsletter, and The Marketing Operations Leader for my newsletter. 

#marketing #martech #marketingoperations #revops #CRO

Over a year ago, I predicted the future of ops would be conversational. 

I knew the large marketing automation players wouldn't be able to do it. 

But I knew someone else would. 

I was invited to beta-test Knak's AI campaign creation capabilities, and my take is: it's ready. 

William Gibson said, "The future is already here, it's just not evenly distributed." 

And that's what I think about marketing. The future is already here, but not everyone is taking advantage of it.

Make sure to attend Knak's AI campaign creation launch event this week. Details in the comments via Nick Donaldson

#marketing #martech #marketingoperations #ai

Brand partnership • VP of Marketing Ops and Martech, Speaker

Sorry late notice everyone! 

We have an amazing marketing ops town hall where we'll dive into practical AI use cases for marketing ops. 

We'll have Lindsay Rothlisberger, Jomar Ebalida, Mike Rizzo and myself cover some awesome use cases, and we'll also open it up to the group to share what they've learned. 

See you all there, link in the comments to sign up to join us for free.

#marketing #martech #marketingoperations #ai

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

Account Executive

- 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.

- 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

You’re about to make a $200k mistake on your first data hire. Your first hire shouldn’t be technical. It should be strategic.

The CEO says: “We need to be data-driven.”
The CTO agrees: “We should hire a Data Scientist.”
 
So they do.
PhD. Brilliant. Technical wizard.
They join, ask for data… and walk straight into chaos.

Three CRMs with conflicting definitions.
Revenue numbers that don’t match Finance.
Dashboards that contradict every meeting.

Six months later, they’re spending 100% of their time cleaning up the past instead of building the future.

By month nine, they quit.
You’re back at zero - with a bigger mess and a smaller budget.

Here’s the blunt truth:
You don’t have a data problem.
You have a sequencing problem.

You started by hiring technical people before defining strategic direction.
That’s like hiring construction workers before drawing the blueprint.

Here’s what you should’ve done instead:

1. Define the Business Problem Before the Role

Most leaders start with:
“We need dashboards.”
“We need a data warehouse.”

Wrong starting point.

The right question is:

“Where are we losing money, time, or credibility because of bad data?”

That’s the anchor.
Until you can quantify that cost, every technical hire is just busywork.

2. Hire the Strategist Before the Builders

Your first hire shouldn’t be a Data Engineer or someone Specialized. 
It should be a Strategic Data Lead, or someone who can connect business goals to what the data team actually builds.

Their job isn’t to code.
It’s to define the map before you start driving.

In the first 90 days, they should:
Interview execs and department heads.
Quantify which data problems cost the most money.
Align on definitions. 
Prioritzie clear next steps 
Build a 90-day roadmap tied to ROI.

That roadmap will tell you who to hire next, and who not to.

3. Align Hires to ROI, Not Titles

If your biggest gap is broken pipelines → hire an Engineer.
If the problem is metric chaos → hire an Analyst or BI Developer.
If it’s governance → get an Architect.

But only after the roadmap exists.

Hiring a Data Scientist without strategyis like deploying AI on top of bad Excel sheets,  expensive, slow, and guaranteed to fail.

4. Measure Data Team Success in Business Outcomes

Stop celebrating activity, “We built 10 dashboards.”
Change to "Reduced fuel cost per shipment by 8%" 

5. Sit under where best 

Hot take: It doesn't matter where your data team sits. 
Seen it all work from IT, to COO to CEO. 
Put it where it brings the biggest impac

Here’s the formula I’ve seen work repeatedly:

Strategy first → Tools second → Team third.

That order alone can save you 6–12 months and hundreds of thousands in cost. 

If your first data hire is technical, you’ll get deliverables.
If your first data hire is strategic, you’ll get results.

Big difference.

-> Comment “Team” and I'll sent playbook on how to built a strategic data team in 2026 that will be your competitive edge.

Ali Šifrar

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

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

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.

You spent $500k on building the best engineers and data team to get an edge. But after 12-months, got more broken dashboards then employees. If your Data Leader says "Yes" to every request, you don't have data team but an "admin desk". 

You have a reactivity problem, and it’s killing your ROI.

Every data leader knows this spiral.

The week starts strong, then the chaos begins:

“Can we get this dashboard by Friday?”
“We need these numbers for the board deck.”
“Marketing needs a 360° view, ASAP.”
“Oh, and the CEO wants an AI demo next week.”

You say yes to all of it because saying no feels like political suicide.

Twelve weeks later:

The backlog has tripled.
Your team’s burned out.
Stakeholders still don’t trust the data.
And leadership wonders why the $300K investment isn’t paying off.

That’s not a technical issue.
That’s the death spiral of a reactive data team.


1. Set clear criteria

If you can’t see all demand, you can’t prioritize it.
Stop juggling Slack, email, and hallway asks.

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?

Some factors such as:

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

2. Turn Tickets into Data Products.

Every initiative must have:

A business problem (why this matters).
A user (who it helps).
A definition of done (what success means).

Force this before starting.
70% of noise disappears instantly.

3. Use the 2×2 Prioritization Matrix.

Score every initiative by:
Business Impact × Feasibility.

Deliver quick wins first (High Impact + High Feasibility).
Protect time for long-term bets.
Politely say no to low-impact noise.

This simple visual turns politics into logic.
The loudest voice stops winning.

4. Sequence, Don’t Stack.

Stop doing 12 things at once.
Do 3 that actually compound.
Each initiative should fuel the next, not reset your progress.

That’s how you move from activity → momentum → ROI.

5. Publish a “Stop Doing” List.

Leadership maturity = knowing what not to do.
Examples:

No new dashboards without an owner.
No mid-sprint “urgent” work that no one gone look.
No initiatives without a business outcome.

Your team, CIO isn’t slow.
They’re trapped in a system that rewards chaos.

The teams that win in 2025 aren’t the fastest.
They’re the most focused.

If your data team is stuck in firefighting mode and you’re ready to turn it into a strategic growth engine,

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

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

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.

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.

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

Every manufacturer says they’re “data-driven.”  You are capturing 200,000 live machine signals every second.  But your Plant Manager still relies on a spreadsheet to run the shift.

Dashboard or warehouse doesn't make data into profit. 
You do it by turning 200,000 live machine signals into a single, trusted truth.

Most manufacturing plants are swimming in data they don’t trust.

The historian says one thing.
MES says another.
Finance has a third version in Excel.

And every plant manager walks into the Monday OEE meeting with a different “truth.”

One industrial firm we worked was no different, until we asked a smarter question:
“Why do all these systems tell different stories about the same line?”

The answer: context.

Every torque spike, temperature reading, or batch number existed in isolation.

They had signals, not meaning.

So instead of buying another tool, they built a context layer, a unified data model that linked every machine tag, batch, and process to the business logic behind it.

Once that layer existed, every dashboard pulled from one definition.
OEE, downtime, yield,  calculated once, reused everywhere.

They didn’t “modernize.”
They standardized.

Before:

Operators got hundreds of alerts a day.
Engineers wasted hours reconciling versions of “truth.”
Leadership didn’t trust data.

After:

Every sensor was mapped to its line, batch, and product.
All plants used one version of OEE.
Operators could query live data without waiting on IT.

Result?

8% productivity lift.
Downtime detection dropped by 94%.
Savings +$100,000 
1,200+ hours saved annually in manual reconciliation.
Fewer false alarms.

A data foundation trusted from the floor to the boardroom.

Not because of AI or 'digital twins'
Because they fixed context.

Here’s the same 5-step model we deploy with manufacturers who want to stop drowning in signals and use data for profit. 

1. Inventory your data sources.
List every PLC, historian, MES, ERP, and spreadsheet. If you don’t know where data lives, you’ll never control it.

2. Map your relationships.
Tie each signal to its machine, line, batch, and business process.
This creates the skeleton for your context layer.

3. Define semantics once.
Build a semantic layer one OEE, one yield, one definition of downtime  version-controlled, governed, and reused everywhere.

4. Architect for latency.
Keep real-time processing at the edge; push contextualized data to the cloud.
Insights should reach operators in seconds, not hours. 

5. Tie back into action

A “single source of truth” is meaningless if it doesn’t feed back into operations.
Detect → Diagnose → Recommend → Act → Learn.

Every manufacturer wants a “digital twin.”
But if your data is still siloed, inconsistent, or delayed, you don’t need a twin 
you need a single version of the truth.

Best firms are leveraging data for profit, not reporting.
They connected their context.
This when being a cost center and get a competitive edge.

You spent $1m to modernize your data infrastructure. Only to be rebuilt again in the next 8 months. You don’t need 12 data tools. You need one foundation. 

If you’ve “modernized” your data stack in the last two years… you’re probably planning to do it again next year.

And everyone’s pretending it’s innovation.
It’s not. It’s a reconstruction with new branding.

Here’s what we see in 90% of mid-market and enterprise teams:

A new CTO, CIO joins or data leader.  
Declares, “Our data stack is outdated.”
Spends $2–5M rebuilding it on new tools, new warehouse, new ETL, new BI layer.

Everyone celebrates the “modern data stack" and "AI readiness" 
18 months later… it’s “outdated” again.

Why? Because the foundation was never built to last.

The tools changed, the architecture never evolved.

Let’s call out the real causes.
They’re not technical, they’re architectural and organizational.

Tool obsession.
You don’t need 12 tools; you need one foundation.
Every “modernization” adds integration points, not stability.

Bespoke engineering.
Custom pipelines no one documents.
The moment your lead engineer leaves, you’re rebuilding.

Rigid data models.
Built for tables, not text, imaging, or AI embeddings.
Every new data type = new project = rebuild.

Governance bolted on, not built in.
Different access layers. Different policies.
Governance debt compounds until someone says, “We need to start over.”

Leadership churn.
Every new executive wants their “own stack.”
So the company pays for ego re-platforming.

You don’t have a modernization problem.
You have architecture debt disguised as innovation.

Every rebuild costs as much as a new drug discovery initiative or an entire data team’s annual budget.

And worse, it resets progress.
You’re not compounding value; you’re compounding technical debt.

You don’t stop rebuilding by buying better tools.
You stop rebuilding by designing a foundation that doesn’t break every time something changes.

Here’s the playbook we deploy for clients:

1. Keep it simple. 

Build a universal data foundation that supports the most urgent needs now. 
Think in terms of data products, use cases, and each fuels another one. 
On the grand scheme, you're building a data foundation but on lower level you getting impact. 

This is how you get value, ROI in weeks, but manage long-term plans. 

2. Governance embedded, not bolted.

Access control, lineage, and versioning built into the core.
Governance shouldn’t depend on the tool, it should outlive it.

3. Build Modular, Iterative 

I see organizations take 6 or 12 months to build something. 
Built iteratively with feedback and iteration.  

4. Don't chase perfection 

If you're chasing "done," that will never come as needs in data change weekly. 
You start small, build data products, aligning current business needs. 

You don’t need another vendor demo.
You need a universal foundation that can outlive tools, trends, and org charts.

We saved a manufacturer $420k per quarter. You are capturing 200,000 machine signals and have best "data stack". But half of team is relying on 3-year-old Excel file and can't answer where you are losing money.

Every COO I meet says the same thing:
“We’ve got dashboards everywhere, and we still can’t see what’s happening.”

That’s not a data problem.
That’s a context problem.

We worked with a mid-market food manufacturer, 400 FTEs, six plants, modern MES and historian stack.

Every line was instrumented.
Thousands of tags. Real-time feeds.
They’d invested heavily in digitization.

Yet they were bleeding 2% in yield losses every quarter, and no one could explain why.

Ops blamed maintenance.
Maintenance blamed suppliers.
Finance blamed “bad data.”

But when we got under the hood, the data wasn’t bad,  it was blind.

Here’s what we found:

26 PLCs reporting machine data, none linked to product or cost center.
MES logging shifts ,  but not correlated to batch or SKU.
ERP tracking margin, but disconnected from line events.
Everything was technically right, and strategically useless.

They didn’t need “more data.”
They needed to connect machine → process → product → profit.

How we rebuilt context from the ground up

We didn’t start with tools.
We started with translation.

Here’s the process we ran:

Step 1: Map the Data-to-Decision Chain

We traced every signal from PLC to dashboard and asked:
“What decision does this data actually inform?”

Turns out, 70% of data flowing into dashboards had no decision path.
It existed because someone could collect it, not because anyone could act on it.

Stop "creating dashboards" and start creating data products. 

Step 2: Build a Shared Asset Model

Every sensor, machine, and line was mapped to a product, batch, and cost center.

This was the aha moment for leadership, they could finally trace a temperature spike to a specific SKU and margin.

A single asset hierarchy tied production metrics to financial KPIs.

Step 3: Contextualize Before Centralizing

We moved context closer to the source. Use UNS.
Instead of dumping raw historian tags into the warehouse, we transformed:

Example:
Before: “Tag_004B > 80°C”
After: “Dryer deviation - line 3 - product X - batch Y.”
Outcome: Reduced reporting noise by 92%, improved root-cause traceability.

Step 4: Build the Semantic Layer

We defined what “good” means, once.

Downtime. Yield. OEE. Scrap rate.
All standardized. All tied to the same definitions across OT, IT, and finance.

CFO, COO, and plant managers finally looking at the same metrics, same numbers, same meaning.

Step 5: Translate Context into Behavior

Instead of waiting for weekly reviews, supervisors got automated alerts when temperature deviations exceeded tolerance for 15 minutes.

Maintenance teams intervened in hours, not days.
Yield loss dropped from 2% to 0.5% in 90 days.

That's $420K quarterly savings, achieved with 0 new tools or rebuilts. 

Data without context, is usless

AI is no longer a competitive advantage. Companies winning in 2026 won't be the ones with the best AI models. But those who use their proprietary data as an edge. 

Everyone’s chasing “AI transformation.”
But no one’s asking the obvious question:

If every company is using the same foundational models, where’s your advantage?

Let’s be blunt.
Foundational models (GPT, Gemini, Claude, etc.) are trained on the public internet, Wikipedia, code repos, Reddit posts, and PDFs.

That data doesn’t know your business.
It doesn’t know your machines, your customers, or your operational context.

It’s smart, but not you-smart.
This is especilly seen a lot in industrial vertical

One manufactuer we worked faced the same challenge.
300,000+ employees, hardware and software across industries, decades of know-how buried in CAD files, production logs, and design blueprints.

Instead of building another LLM, they built GenX,  a foundational model trained on their own proprietary industrial data.

The result?
An engineer can now feed a design file into the system, and 80% of the manufacturing plan is auto-generated in hours, not months.

That’s not just productivity, that’s a moat.
Because no public model can replicate it.

The model doesn’t just know “how to manufacture.”
It knows their IP. 

Right now, 90% of enterprises are doing the exact opposite.

They’re buying “AI strategy workshops,” fine-tuning open models, and calling it innovation.

They’re feeding prompts to ChatGPT instead of mining the data they already own.

The irony?
Your internal data is your real differentiator, not the model you rent.

Every product design
Every customer log
Every maintenance record
Every engineering note

…is proprietary know-how your competitors don’t have.
But most of it’s unstructured, unlabeled, and ignored.

The Playbook: Turning Proprietary Data Into Competitive Advantage

Here’s how industrial leaders are doing it, and how mid-market enterprises can follow suit:

1. Inventory What You Actually Own

List every data source structured and unstructured.
ERP, PLM, sensors, CAD, field reports, PDFs.
If it’s unique to your operations, it’s an asset.

2. Clean and Classify It Like IP

Govern it.
Tag it.
Define who owns it.
This isn’t “compliance.” It’s protecting your data capital.

3. Build Data Products, Not Pipelines

Turn that data into reusable products, governed, standardized, and ready for both analytics and AI.

4. Build Your Private AI Layer

Use open models as a base, but fine-tune internally with your data.
Never send your proprietary knowledge to a public API.

5. Operationalize Governance

Automate lineage, access, and version control.
If you can’t trace it, you can’t protect it.

Your data is your moat.

In 2026 and beyond data data-driven orgs are not gone be with best AI tools are tools, or platforms. 

But the ones that use their proprietary data to it's fullest. 

→ Comment & Repost if you think this is true.

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

Your 2026 data strategy will dictate whether your data is your competitive edge or a cost center. 

Most “modern” data teams aren’t modern. 

They’ve swapped SQL for dbt, on-prem for Snowflake, and reports for dashboards, but are experiencing the same issues as before.

We’ve seen this movie dozens of times:

A company finally “gets serious” about data.
They hire a Head of Data/CIO rebuild the stack, and buy another three tools to “modernize.”
By month 18, dashboards are broken, business trust is gone, and the data team is buried in requests.

The CFO’s asking why Snowflake costs more than AWS, and the COO is asking why decisions still take weeks.

CEO was expecting "AI-readiness" and a competitive edge, but got inaccurate data and dashboards.

So, they rebuild again.
New VP. New consultants. New stack. 
Same story.

80% of Mid-Market Firms Will Rebuild Their Data Teams by 2026
Not because technology failed, but because strategy did.

The pattern is brutally consistent:

No operating model.
Data teams are set up like IT help desks, not product teams.
They react. They don’t drive outcomes.

No ownership.
No one actually owns data quality, definitions, or adoption.
Everyone “uses data.” No one’s accountable for it.

No alignment.
Data priorities live in a vacuum, disconnected from revenue, margin, and growth metrics

The teams that win in 2026 will look nothing like this.

They’ll be lean, embedded, and outcome-driven.
They’ll measure business impact, not ticket volume.
They’ll be architected around decisions, not datasets.
And they’ll run like product lines, not projects.

They don’t rebuild every 18 months because their foundation is flexible, not fragile.
They can pivot tools, AI layers, or priorities without tearing the whole thing apart.

Every exec thinks they’re modernizing.
But what they’re really doing is re-platforming.

They’re buying tech to fix symptoms created by a broken strategy.
They’re automating chaos.
They’re integrating, hiring faster… into dysfunction.

This is why “Data Strategy 2026” matters,  because the fix isn’t another migration or AI tool.

It’s a full reset of how mid-market firms design their data strategy 

The high-performing data teams we work with share the same five traits:

1. Business ownership of outcomes.
Data exists to drive P&L impact, not dashboards.

2. Clear operating model.
Every function knows what data it owns, what it produces, and how it’s consumed.

3. Modular architecture.
Replaceable components. Reusable pipelines. Zero rebuild anxiety.

4. AI-ready foundations.
They focus on governance, semantics, and trust,  not LLM buzzwords.

5. Short-cycle delivery.
They show measurable wins in 90 days, not 9 months.

That’s the future. 

You don’t have a “data” problem.
You have a strategy problem to turn your data into a market edge.

Comment "2026" for our 2026 Data Strategy executive report on what next-generation, data-driven organizations are prioritizing in 2026.

You spent millions moving to the cloud and called it "digital transformation." You didn't modernize. You just finished Step 0. 

Every week, we meet leadership teams who proudly say,

“We’ve moved to the cloud. We’re modern now.”

But when we ask what’s actually changed in the way they make decisions, deliver insights, or use data, it’s dead silence.

Their Snowflake or BigQuery bill tripled.
Dashboards still aren’t trusted.
Analysts still firefight.
And leadership is still waiting for ROI that never arrives.

Let’s be blunt:
Cloud migration ≠ transformation.
You didn’t become modern. You just outsourced your old mess to someone else’s infrastructure.

A $200M mid-market firm spent nearly $600k moving from on-prem to Snowflake and dbt.

They moved fast.
They had vendors.
They had engineers.

But a year later?
No measurable improvement in decision-making, reporting accuracy, or time to insight.

The CFO was furious:
“We spent all this money, and we’re still manually reconciling revenue in Excel.”

They’d done what every company does, lifted and shifted every broken process, messy definition, and mistrusted dataset straight into the cloud.

They didn’t fix how the business used data.
They just gave their old problems a new address.

1. Stop Migrating. Start Modernizing.

Moving terabytes isn’t transformation.
Transformation is changing how your organization uses data.

Before another file moves, ask:

What business problem will this solve?
How will our decision-making change?
How will success be measured in ROI, not uptime?

If you can’t answer those, you’re just rearranging servers in someone else’s basement.

2. Build the Connected Foundation First.

You can’t scale chaos.
Every trusted analytics org starts with a connected data foundation one source of truth, clear ownership, and agreed definitions across Finance, Sales, and Ops.

Without it, your “modern stack” is just a more expensive version of the same problem.

3. Treat Your Cloud Like a Product, Not Infrastructure.

High-performing companies treat data as a product with customers, outcomes, and iterations.

Create forward-deployed data teams embedded with the business.
Launch in weeks for specific use cases, not endless “phases.”
Ship small wins in weeks, not big promises in quarters.
Everything starts with problem, outcome and clear defintion of "done" before touching any code or tool. 

Each data product, use case fuels the next one. 
All together is building and creating "digital transformation" 

If you don’t have one business user saying, “This changed how we work,” nothing transformed.

Your “cloud journey” or "buying a new tool" isn’t transformation.
It's step 0. 
Data is only valuable when it's used, trusted and adopted. 

Until you fix your foundation, align ownership, and connect data to real business outcomes, you’re not transforming.

You’re just paying rent on the same old problems.

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