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The 7 Habits of Effective Data Driven Companies

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1. Start searching use cases with value & impact: without use cases, nobody will want to draft a data strategy​ Where do you want to go? Draft a clear Customer Experience that you want to create and think about the organization & data strategy to get there!​ 2. Get Tech (data scientists and engineers) and Business (Product Management & Commercial) on the same table: create a solid foundation.​ 3. Start with communities of practice to learn & experiment together and build the capability.​ 4. Stop talking about data. Start experimenting and doing. ​ 5. Product Management needs to get real about data. (start training these capabilities)

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The 7 habits of Effective Data Driven Companies JURRIAAN BERNSON & GIOVANNI LANZANI
MISTER PRODUCT MISTER DATA
Focus on building the right things BUT HOW DO WE KNOW?
Effective Data Driven Companies All decisions are based on data, and they can be automated It’s a journey, as you never get there 100%. But how do you start?
The 7 Habits of Effective Data Driven Companies
#1 Be Proactive Be Proactive is about taking responsibility for your organization. Don’t let the environment stop you, don’t be afraid to kill a product, learn from the mistakes through data
F&B company Ruthless 6 weeks cycle to validate idea’s If the model doesn’t deliver the expected results in the experimentation phase → add more data, change course, or kill it If it does, give it another 6 weeks for proof-of-value If they can measure impactin 6 weeks → scale The rest goes to the graveyard
#2 Begin with the end in mind Focus time and energy on things that can be controlled. Therefor have a data strategy in place. Being data driven often means changing products, processes, people, skills. Is your organization ready for the journey if you don’t know where you’ll go?
Airfrance-KLM They wantedto start with AI Strategy sessions for VPs, data discovery workshops, and severalPoCs They startedembedding a data-drivenway of working within the organization Today data-driven from financial forecastingto predictive maintenance
Loyalty program retailer THEY WANT TO STAY RELEVANT FOR CUSTOMERS THEY NEED TO COLLECT DATA & CONNECT IT TO CUSTOMER PROFILES SO THEY CAN CREATE BETTER EXPERIENCES
Don’ts example: Global FI THEY STARTED WORKING ON PRODUCTS BECAUSE “IT WAS COOL AND INNOVATIVE” SEARCHING A BUSINESS OWNER AFTER EUR 2M OF INVESTMENT RESULTED WAS HARD THEY HAD BUILT A SOLUTION WITHOUT AN UNDERLYING PROBLEM EPIC FAIL
#3 Put First Things First DATA IS THE ENERGY OF YOUR TRANSITION TO BECOME DATA DRIVEN, BUT DON’T LOSE SIGHT OF YOUR MISSION
NPO The NPO wants to become a personal broadcasting organization But transparency is their north star They build a personalization algorithm that uses only what the users want to share, nothing more!
#4 Think Win-Win Think about data products that add real-valueto you, your customers, and the environment
Quby REDUCING ENERGY WASTE BY INTRODUCING DATA APPLICATIONS THE CLIENTS SAVE MONEY, WITHOUT GIVING THEIR PRIVACY AWAY THE COMPANY OFFERS A SERVICE THE COMPETITORS DO NOT OFFER
Bakkersland REDUCING BREAD WASTE BY ACCURATE FORECAST DECREASE WASTE, OPTIMIZE OFFER, HAVE CLIENTS NEVER RUN OUT OF THEIR BREAD BETTER UTILIZATION AND INVENTORY MANAGEMENT, SUPERIOR PLANNING
#5 Understand, Then to Be Understood Don’t build problems in search of a solution/Use data to understand your users, and then build products to delight them
Suitsupply Opening brick and mortar stores based on data.
Don’ts Example: NU.nl Facebook reader app. Showing your reading behavior based on data.
#6 Synergize Becoming data-driven takes the whole team Business should be in the driver seat, no tech party But tech has a seat at the table!
Analytics as a business model ING wants to become a bank with analytics as a business model They worked out with a strategy starting with one thing in mind: people will do things tomorrow differently than today They immediately started a reskilling program for 50.000 people Analytics sponsor and business translators most important personas in the org → all projects have a clear impact!
Heineken: start from the value Heineken was able to — in a short time — work on more than 20 project based on a value driven approach How? They picked up all the use cases from the business but had a strong process and core team dedicated to make a success Business in the driver seat
Don’ts Example: B2B online platform A company active in the B2B online market started a new recommender project The Analytics department did it all without ever contacting IT At the end of the project, they asked IT to “just” implementit IT said “we’ll put it in the calendar next year”
#7 Sharpen the saw YOU’RE IN FOR THE LONG RUN DON’T DO QUICK AND DIRTY SPEND TIME ON FUNDAMENTALS
KPN iTV
NS They had outstanding models to predict when trains were going to break down But they needed days to get data from trains, so it was often too late They build an analytics platform that delivered live data from the trains Every 5’ they can run a model that tells them which trains will break down within 1-3 weeks
Don’ts Example: B2B Forecasting A company starteddoing forecasts for his B2B clients They very quickly saw the model was adding value They used the PoC code to onboard the first 15 customers They modified to onboard the next 100. And then the next 400. They have accumulatedso much technical debts, they need to spend half a million to rewrite the whole system
How you can start tomorrow
5 things to start with 1. Start searching use cases with value & impact: without use cases, nobody will want to draft a data strategy Where do you want to go? Draft a clear Customer Experience that you want to create and think about the organization & data strategy to get there! 2. Get Tech (data scientists and engineers) and Business (Product Management & Commercial) on the same table: create a solid foundation. 3. Start with communities of practice to learn & experiment together and build the capability. 4. Stop talking about data. Start experimenting and doing. 5. Product Management needs to get real about data. (start training these capabilities)

More Related Content

The 7 Habits of Effective Data Driven Companies

  • 1. The 7 habits of Effective Data Driven Companies JURRIAAN BERNSON & GIOVANNI LANZANI
  • 4. Effective Data Driven Companies All decisions are based on data, and they can be automated It’s a journey, as you never get there 100%. But how do you start?
  • 6. #1 Be Proactive Be Proactive is about taking responsibility for your organization. Don’t let the environment stop you, don’t be afraid to kill a product, learn from the mistakes through data
  • 7. F&B company Ruthless 6 weeks cycle to validate idea’s If the model doesn’t deliver the expected results in the experimentation phase → add more data, change course, or kill it If it does, give it another 6 weeks for proof-of-value If they can measure impactin 6 weeks → scale The rest goes to the graveyard
  • 8. #2 Begin with the end in mind Focus time and energy on things that can be controlled. Therefor have a data strategy in place. Being data driven often means changing products, processes, people, skills. Is your organization ready for the journey if you don’t know where you’ll go?
  • 9. Airfrance-KLM They wantedto start with AI Strategy sessions for VPs, data discovery workshops, and severalPoCs They startedembedding a data-drivenway of working within the organization Today data-driven from financial forecastingto predictive maintenance
  • 10. Loyalty program retailer THEY WANT TO STAY RELEVANT FOR CUSTOMERS THEY NEED TO COLLECT DATA & CONNECT IT TO CUSTOMER PROFILES SO THEY CAN CREATE BETTER EXPERIENCES
  • 11. Don’ts example: Global FI THEY STARTED WORKING ON PRODUCTS BECAUSE “IT WAS COOL AND INNOVATIVE” SEARCHING A BUSINESS OWNER AFTER EUR 2M OF INVESTMENT RESULTED WAS HARD THEY HAD BUILT A SOLUTION WITHOUT AN UNDERLYING PROBLEM EPIC FAIL
  • 12. #3 Put First Things First DATA IS THE ENERGY OF YOUR TRANSITION TO BECOME DATA DRIVEN, BUT DON’T LOSE SIGHT OF YOUR MISSION
  • 13. NPO The NPO wants to become a personal broadcasting organization But transparency is their north star They build a personalization algorithm that uses only what the users want to share, nothing more!
  • 14. #4 Think Win-Win Think about data products that add real-valueto you, your customers, and the environment
  • 15. Quby REDUCING ENERGY WASTE BY INTRODUCING DATA APPLICATIONS THE CLIENTS SAVE MONEY, WITHOUT GIVING THEIR PRIVACY AWAY THE COMPANY OFFERS A SERVICE THE COMPETITORS DO NOT OFFER
  • 16. Bakkersland REDUCING BREAD WASTE BY ACCURATE FORECAST DECREASE WASTE, OPTIMIZE OFFER, HAVE CLIENTS NEVER RUN OUT OF THEIR BREAD BETTER UTILIZATION AND INVENTORY MANAGEMENT, SUPERIOR PLANNING
  • 17. #5 Understand, Then to Be Understood Don’t build problems in search of a solution/Use data to understand your users, and then build products to delight them
  • 18. Suitsupply Opening brick and mortar stores based on data.
  • 19. Don’ts Example: NU.nl Facebook reader app. Showing your reading behavior based on data.
  • 20. #6 Synergize Becoming data-driven takes the whole team Business should be in the driver seat, no tech party But tech has a seat at the table!
  • 21. Analytics as a business model ING wants to become a bank with analytics as a business model They worked out with a strategy starting with one thing in mind: people will do things tomorrow differently than today They immediately started a reskilling program for 50.000 people Analytics sponsor and business translators most important personas in the org → all projects have a clear impact!
  • 22. Heineken: start from the value Heineken was able to — in a short time — work on more than 20 project based on a value driven approach How? They picked up all the use cases from the business but had a strong process and core team dedicated to make a success Business in the driver seat
  • 23. Don’ts Example: B2B online platform A company active in the B2B online market started a new recommender project The Analytics department did it all without ever contacting IT At the end of the project, they asked IT to “just” implementit IT said “we’ll put it in the calendar next year”
  • 24. #7 Sharpen the saw YOU’RE IN FOR THE LONG RUN DON’T DO QUICK AND DIRTY SPEND TIME ON FUNDAMENTALS
  • 26. NS They had outstanding models to predict when trains were going to break down But they needed days to get data from trains, so it was often too late They build an analytics platform that delivered live data from the trains Every 5’ they can run a model that tells them which trains will break down within 1-3 weeks
  • 27. Don’ts Example: B2B Forecasting A company starteddoing forecasts for his B2B clients They very quickly saw the model was adding value They used the PoC code to onboard the first 15 customers They modified to onboard the next 100. And then the next 400. They have accumulatedso much technical debts, they need to spend half a million to rewrite the whole system
  • 28. How you can start tomorrow
  • 29. 5 things to start with 1. Start searching use cases with value & impact: without use cases, nobody will want to draft a data strategy Where do you want to go? Draft a clear Customer Experience that you want to create and think about the organization & data strategy to get there! 2. Get Tech (data scientists and engineers) and Business (Product Management & Commercial) on the same table: create a solid foundation. 3. Start with communities of practice to learn & experiment together and build the capability. 4. Stop talking about data. Start experimenting and doing. 5. Product Management needs to get real about data. (start training these capabilities)