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.

Founder

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

Everyone’s hyped when it’s time to talk tools.

But mention “solve business problems” and suddenly…

… Empty chairs.

… Half-asleep audience.

Say “Snowflake” or “dbt” and the data crowd lights up like it’s the Champions League final.

Say “spend time with stakeholders to understand their actual problems” and it’s crickets.

Because tools are shiny.

They come with swag, events, influencers, certifications.

→ But a tool doesn’t know why Sales doesn’t reach their goals.
→ Or why retention is dropping.
→ Or how your marketing team actually works.

And yet…

I’ve seen teams sink 6 months into setting up the “modern data stack”…

- Before talking to a single stakeholder.
- Before validating a single use case.

Here’s the thing:

- Tools ≠ Strategy
- Spend ≠ Value
- Dashboards ≠ Decisions

Start with the problem.
Start with the human.
Start with the why.

Then pick a tool.

♻️ Repost if you ever had more opinions on data architecture than on customer churn.

👉 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/giKF7kf3

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) 🌏

Most data teams don’t have a strategy. They have a recurring nightmare.

And it looks something like this:

→ “Last-minute KPI change from Marketing”
→ “Fix the broken dashboard filter (again)”
→ “Explain Revenue discrepancy to Sales - again”
→ “Build model for a feature we might never ship”
→ “Answer 5 conflicting questions from 3 execs”
→ “URGENT: Insight for CEO lunch”

Welcome to the chaos.

- You’re working late. But without impact.
- You’re helpful. But not heard.
- You’re busy. But not building leverage.

Let’s be real:

When everything’s on fire… the data team brings the marshmallows.

And then wonders why nobody sees them as strategic partners.

Here’s the uncomfortable truth:

If your day is a string of reactive tasks, you’re not driving the data agenda, you’re babysitting it.

So what is strategy?

It’s not:

- A Confluence doc no one reads
- A tidy backlog with 57 open tickets
- A pretty diagram of your stack

Strategy is a system for prioritising trade-offs.

It’s how you decide:

✅ What actually moves the needle
✅ What’s just noise in a shiny wrapper
✅ When to push back with confidence
✅ How to lead without asking for permission

You don’t earn a seat at the table by saying yes to everything.

You earn it by choosing the right problems - and solving them better than anyone else.

Want to stop reacting and start leading your data agenda?

👉 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/g4M2wyiP

♻️ Repost if your calendar is full of urgent data tasks… and no time to think.

“Your data is ALL WRONG!”, yells the Marketing Lead.

“No, you are justing using the wrong Metric.”, replies the Data Team.

If you work in data, you’ve definitely been here 👇
→ The business sees a wrong number.
→ The data team sees a wrong definition.

And 80% of the time…

It’s not a bug.
It’s a misunderstanding.

We love blaming the data team.

We love blaming dashboards and pipelines.

But most “data issues” come from:
→ Different metric definitions
→ KPIs that were never aligned
→ Filters people assume are applied
→ People thinking “Revenue” means the same thing to everyone

♻️ Repost if you’ve ever had to explain why “Revenue” can mean 5 different things.

→ And follow me, Sebastian, for daily tips on building impactful data teams in the AI-era.

“We just need a single source of truth.” 

Sure. But truth for who?

Here’s what I see every week:

→ Endless debates over definitions
→ Trying to standardise things that were never meant to be standard
→ One team strong-arming their version into every report

The reality is:

Your company doesn’t have one version of revenue.

It has many - each useful in different contexts.

The problem isn’t that Marketing and Finance report different numbers.

The problem is pretending they shouldn’t.

Instead of chasing a mythical “one number to rule them all,”
build shared understanding.

Let teams speak their own language -

just give them a good translator.

→ Clear definitions
→ Easy-to-access context
→ Mutual respect between teams

You don't need a universal metric.

You need alignment on when each one makes sense.

That’s how data drives clarity, not chaos.

Want to stop chasing the mythical “one version of the truth” and start creating real business alignment?

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/g7-hxTHk

♻️ Repost if you’ve ever watched Finance and Marketing argue over revenue like it’s a custody battle

There are 4 levels of data maturity.

Most companies never reach Level 4. Here's why:

❌ Trying to skip level 1-3.
❌ Starting at level 1 and stopping there.

The levels are:

Level 1: DESCRIPTIVE 

→ Questions answered: What happened? 
→ Tools: Excel/Google Sheets, Dashboarding 
→ Low Effort, low impact

Level 2: DIAGNOSTIC 

→ Questions answered: Why did it happen? 
→ Tools: Excel/Google Sheets sitting on top of a solid Data Foundation. Re-active AI-agents
→ Higher Effort, higher impact

Level 3: PREDICTIVE 

→ Questions answered: What will happen next? 
→ Tools: Excel/Google Sheets, Machine Learning, Pro-active AI-Agents with access to contextual information
→ High Effort, high impact

Level 4: PRESCRIPTIVE 

→ Questions answered: What should we do about it? 
→ Tools: Machine Learning & sending analytical data to operational systems to make automated decisions, Autonomous AI-analytics Agents
→ Highest Effort, highest impact

If you're currently working on a data and/or AI strategy, make sure you don't forget about levels 1-3 before spending big $$$ on level 4. 

Tomorrow, I'm sharing with 3,000+ data leaders:

👉 how one ultra-successful data team moved through these 4 levels rapidly
👉 and triggered revenue growth from $7M to $1,200M in 3 years.

✅ Join here for free if you don't want to miss it: https://lnkd.in/gQRFVHHA

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