Fleejo

Founder

MAN.Digital

Business Development Representative

Making sales assets to sell your b2b offer before the sales call

Most of the time there are two reasons you are struggling to close the calls : 
-the prospect does not believe in what you / software will do
-prospect does not not see a reason to choose you.

To fix this, you need sales assets.... pieces of content that are repeatedly used in the sales process to gain trust.

And I help make these pieces of assets... so you can make your
-outbound
-pre call flow
-closing call better

examples of these assets are : 
-SL / VSL (only works if you have a unique insight, not for commodity)
-Case Studies (works for any offer)
-Slides (for closing call)
-ROI calculator (really helpful in closing call)

Ali Gohar

Sebastian Hewing 🚀

Data Action Mentor

Building masterclasses and communities for data leaders and future data leaders who want to create outsized business impact with data & AI.

Sebastian Hewing Consulting

🚀 I build impactful, AI-first data infrastructure for VC/PE-backed consumer companies.

Previous clients include Zalando, Lazada, Global Fashion Group (Dafiti.com, Lamoda.ru, Jabong.com, TheIconic.com.au, Zalora.com), Linio, Jumia, Lieferando, eBay, Telefonica o2, HUK Coburg, Axel Springer, Lendico, OCC Assekuradeur, Creadi AG, Boerse Stuttgart, Bergfreunde.de / Backcountry.com, Sanity Group, Takeaway.com, Wasoko, Westwing, Beat81 and more

Owner

Locafox GmbH

Raised EUR 10+M from established Investors such as Holtzbrinck Ventures to digitize local retail and build Europe's largest omni-channel shopping marketplace

- Built up Business Intelligence and Analytics Team from Scratch
- Established a true Data Self Service Environment (Around 90% of Analytics Requests could be handled independently by Functional Departments)
- Managed and Led Market Entry to Spain
- Spain contributed 1/3 of the Total Revenues after 6 Months

Director Business Intelligence & International

Rocket Internet SE

Rocket Internet is the world's largest Internet incubator. Our team has been building online companies since 1999 and has created over 100 market leading companies in 50+ countries, dozens of which have been exited successfully. Examples: Zalando, Lazada, Delivery Hero, HelloFresh.

- Implemented and customised 7 Data Warehouses (DWHs) for Rocket Internet’s global portfolio
companies.
- Product Owner for Analytics Applications such as split-testing, catalogue
sorting algorithms, product recommendation algorithms, marketing attribution
modelling and predictive modelling.
- Built and led the global Data Team. Disciplinary lead for 10 central
employees and functional lead for 40+ employees in 7 Portfolio companies.
- Increased Revenue per Visitor of 3 Portfolio companies by 10-12% with the
help of a custom-built product recommendation algorithm.
- Management of the central Analytics Budget for 50+ Portfolio companies.

Head of Business Analytics

SHS VIVEON AG

One of Europe's leading boutique consulting firms for Data Analytics, Data Warehousing and Customer Management.

- Managed a variety of projects with focus on customer analytics; e.g. customer
segmentation, churn prevention and development of customer scorecards.
- Accountable for the development of the Multi-Channel Marketing practice area,
specializing in the field of Marketing-Performance-Monitoring and -Optimisation.
- Fast career progression, receiving semi-annual promotions.

Consultant

InsightAsia Research Group / DemandROMI (JV)

built up a pan-Asian consulting division focusing on Marketing & Sales Performance

Co-Founder

Accenture

conducted emprical study about success factors of Multi-Channel Management in Banking as part of final thesis

Business Analyst

Founder Data Action Mentor | Swiss Army Knife for Data & Analytics | Ex-Rocket Internet

I'm a mix of data engineer, entrepreneur, product manager and marketer. 🛠️🚀🧑‍💻🎯

I help tomorrow's data leaders create outsized business impact by building AI-first, 10X data teams.

-> Find out more at www.dataactionmentor.com

In the last 17+ years, I worked as a Data Practitioner and Data Leader for Startups, Corporates, and in Consulting across all continents. 

As Head of Business Analytics at Rocket Internet, the world's largest venture builder, I built and led Data & Analytics Teams and Infrastructure for 7 portfolio companies in 15 countries.

Rocket Internet scaled companies such as Zalando (Europe's leading online fashion platform), Delivery Hero (a leading food delivery platform), HelloFresh (the world's leading meal kit delivery company), Lazada (the Amazon.com of SouthEast Asia, exited to Alibaba) and Jumia (the first African company on the NYSE). 

As a consultant and advisor to Founders and CEOs, I built or re-built the first data team and data infrastructure for 50+ leading consumer companies.

P.S.: My biggest passion next to Data & AI is Traveling. I live on the beautiful island of Taiwan 🇹🇼 but I typically spend 5-6 months per year traveling to hang out with my awesome global clients. I have been to 100+ countries and territories.

Founder & Mentor

Solve all your Data- & Analytics Challenges and connect with experienced Data & AI Professionals for a price and time investment that every startup can afford

“Let’s define a data strategy.”

Translation: let’s argue about tools for 6 months.

Here’s the thing nobody wants to admit:

Most data strategies are just about data stacks.
Not actual strategy.

If you want a strategy that survives the next reorg, AI hype wave, or exec sponsor change - start here:

1. Problem: What are we solving?
2. Users: Who are you serving?
3. UVP: What's our data team's secret sauce?
4. Solution: What are we building and what are we NOT building?
5. Distribution: How will we get this in front of users without chasing them down?
6. Systems: What helps this scale without breaking us?
7. Outcomes: What will users actually do differently?
8. Costs: What’s the real investment? (Spoiler: it’s not just the software)
9. People: How will we grow a team that wants to stick around?
10. Vision: What guides our decisions when priorities clash?

Answer those 10?

You’ve got a data strategy.

Ignore them?

You’ve got a dashboard backlog and a stack no one uses.

♻️ Repost if you've ever watched a “data strategy session” turn into a Power BI vs Tableau debate.

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gt_2Gc4s

Sebastian Hewing

10x ROI with data & AI foundations for VC/PE-backed companies | Hands-on Data Strategist | Solopreneur & Travel Addict (100+ countries & territories) 🌏

We’re in the middle of a dashboard pandemic.

Symptoms include:

→ 173 dashboards across 3 tools
→ Nobody agrees on the numbers
→ “Can I get this in Excel?” - every day
→ Burned-out data teams trapped in JIRA tickets for minor tweaks

Diagnosis?

Chronic dashboard overproduction with decision fatigue.

Because somewhere along the way, we confused outputs with outcomes.

We thought: more dashboards = more insights = more value.

But here’s the thing:

-Dashboards don’t drive decisions
-Dashboards don’t change behavior
-Dashboards don’t fix broken processes

They visualize. That’s it.

Want to really move the needle instead of pumping out prettier charts?

Start with this:

→ What decision are we trying to support?
→ Who owns it?
→ What happens if we don’t build it?

It’s not easy.

But it’s the cure.

♻️ Repost if your dashboards are multiplying faster than decisions are being made

👉 And join 3,000+ data leaders who read my free newsletter for actionable tips on building impactful data teams in the AI-era:
https://lnkd.in/gMSUaAsx

Putting together a bunch of tools is NOT a data strategy!

A data strategy is this: 👇 (refined over 18 years)

1. A problem to solve

Start by uncovering and deeply understanding stakeholder problems (not with tools and data stacks!)

2. A human who has the problem

Learn about the jobs-to-be-done your users are facing. Sit on their lap, if necessary.

3. A unique value proposition (UVP)

Clearly communicate why your users should drop Excel to use your services.

4. A solution

Craft solutions that solve your user’s problem(s) and take away their anxiety to try them (be accessible)

5. A distribution strategy

The best solution is worthless if your stakeholders don’t know about it. Let the world know why your data team rocks!

6. A systems strategy

The best data teams spend more time building systems than being a slave in a system. Don’t be a dashboard factory! Build a problem solving factory!

7. Outcomes

Most data teams only produce output - tickets solved, dashboards built yada yada. You need to produce dream outcomes for your stakeholders!

8. Cost Structure

A necessary puzzle piece, but a lot of data teams worry about it too much. Worry more about creating value than reducing cost!

9. People Strategy

Have a hiring and people management strategy that reflects your principles and values.

That’s all.

Easy peasy, lemon squeezy, right? 🍋

♻️ Repost if you’ve felt the pain of building dashboards while your stakeholders export to Excel.

👉 And join 3,000+ data leaders who read my free newsletter for actionable tips on building impactful data teams in the AI-era: https://lnkd.in/grgzCkUf

Building impactful data teams for the AI-age | Data Strategist & Engineer | Advisor to C-Level in VC/PE-backed companies | Solopreneur & Travel Addict (100+ countries & territories) 🌏

The best CMOs would crush it running a data team.

Because they get three things many data leaders struggle with:

1. Speed over perfection
2. Business impact over tooling
3. Influence over insight

I used to think marketing is all gut feeling and creativity.

But the best CMOs I’ve worked with proved me wrong.

→ They were fast.
→ They were analytical.
→ And they were obsessed with business impact.

Tomorrow, I’m breaking down the 6 traits that make top CMOs unstoppable - and how data teams can borrow those traits to become true business partners instead of dashboard factories.

Here’s a taste of what I’ll cover:

✅ How marketers act with speed despite messy, conflicting data
✅ Where data teams lose trust and how to fix it
✅ And what it really takes for data teams to earn a seat at the table

If you want to influence the business, you need to understand how high-performing marketers operate.

And tomorrow I’ll show you exactly how.

I’m sharing with 3,000+ data leaders how CMOs win - and how you can adopt their playbook to earn real influence as a data leader.

📩 Sign up for my FREE newsletter if you don’t want to miss it:
https://lnkd.in/g3WJTejm

They don’t hate dashboards.

They hate feeling stupid.

You built the scalable pipeline. The modular dbt models. The layered dashboards. The forecasting model.

And what do they do?

👉 “Can you export that to Excel?”

You think:

“Why do they ignore everything we built?”

But here’s a better question:

“Why did we build something they don’t want to use?”

Let’s be honest.

The villain isn’t the stakeholder.

It’s the complexity we’ve come to worship.

→ We throw AI at problems a simple SQL statement would solve.
→ We hide insight behind dropdowns, tabs, and filters - then wonder why nobody clicks.
→ We act like we’re building for Google.
→ But we’re a 200-person scale-up with 3 analysts and a CEO who checks Slack at 6am.

Complexity looks smart.

But it kills trust.

Simplicity is the real flex.

♻️ Repost if your CEO’s favorite BI tool has a green X icon.

👉 And join 3,000+ data leaders who read my FREE weekly tips on building impactful data teams in the AI-age:
https://lnkd.in/gHvQuqMc

“Our data stack has 16 tools.”

But stakeholders still don’t trust a single number.

It’s like owning 16 pairs of shoes and still feeling like you have nothing to wear.

You don’t have a tooling problem.

You have a clarity problem.

Data leaders keep falling into the same trap I once fell into:

→ Hire more engineers
→ Buy the fancy tech
→ Integrate all the data
→ Build a beautiful, scalable pipeline

…and still get weekly “WTF is this number?” emails from the CEO.

Here’s why:

🧠 You start with a tech stack, not with your users’ actual problems
🧩 You build infrastructure, but no one knows how it maps to business goals
🛠️ You design systems your team loves but your users hate

All of that makes your data team technically impressive and practically invisible.

Because trust isn’t built in dbt.

It’s built in moments like:

- “Ah, I get it now.”
- “That’s exactly what I needed.”
- “This matches what I see in Hubspot.”

If your data products don’t trigger these reactions, it doesn’t matter how “modular”, “real-time”, or “compliant” they are.

The goal isn’t to impress your peers.

The goal is to help your stakeholders live easier, calmer, better lives.

Want to become a true business partner with your data team?

👉 Join 3,000+ data leaders who read my free newsletter for weekly  tips on building impactful data teams in the AI-era:
https://lnkd.in/gETTkzRd

♻️ Repost if you’ve ever watched a stakeholder open Looker… and immediately go back to Excel.

The “single source of truth” is NOT a myth.

I used to think it was a fantasy too.

One clean, trusted dataset that aligns every team.

Like:
→ unicorns 🦄
→ dragons… 🐲
→ ...and matching revenue numbers across teams.

But here’s what changed my mind:

I realized most metric debates aren’t about the data.

They’re about perspective.

The “truth” is built from:
→ A measure: e.g. ARR
→ A filter: e.g. New ARR
→ A dimension: “Finance ARR”: invoice date, “Sales ARR”: deal closed date.

Sales wants to show momentum → “Closed deal date”

Finance wants precision → “Invoice date”

Same Measure.
Same Filter.
Different dimension.

Here's how I think about the single source of truth today:

Treat your metrics like Legos. Provide Measures, filters and dimensions in a single source of truth.

Then every domain can build their version from the same bricks.

You get governance + agility. 

Metrics aren’t magical. They’re just combinations.

♻️ Repost if you’ve ever lost sleep over Revenue definitions that almost matched

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era:
https://lnkd.in/gJUCHAGz

Everyone wants to use AI to reduce headcount.

But here’s the inconvenient truth no one wants to hear:

👉 The biggest driver of bloated data teams isn’t lack of AI tools - it’s lack of foundations.

→ If your single source of truth is chaos…
→ If definitions and metrics change depending on who you ask…
→ If data governance resembles a police checkpoint (or doesn’t exist…)

Then AI won’t make you lean.

You’ll just end up automating the mess and hiring more people to clean it up.
Let’s get specific.

Here's a realistic, right-sized setup for a late-stage scale-up, assuming solid foundations:

- 173 Users (consume data via dashboards & reports)
- 17 Super Users (can self-serve simple insights) → 10:1 ratio
- 6 Analysts / Data Scientists (handle complex questions) → 3:1
- 2 Analytics Engineers (curate data for analysis) → 3:1
- 1 Data Engineer (builds the technical platform) → 2:1
- 1–2 Product Owners / Data PMs (the glue across domains)

That’s 11 people in the data org, or 5.5% of employees.

Let’s recap:

🚫 AI won’t save you from foundational debt
✅ AI can amplify your impact - if your data isn’t broken
Otherwise?

You’ll be automating confusion and scaling chaos.

♻️ Repost if someone just told you AI will replace analysts and fix your data problems magically.

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gVEHHj_T

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Ali Gohar

Sebastian Hewing 🚀

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The Battle

Ali Gohar

Sebastian Hewing 🚀

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Sebastian Hewing 🚀's LinkedIn Performance

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