The Evolution towards Data-Oriented Systems

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#251 Panel: Tying the Business Strategy to the Data Strategy and Data Work (and Vice Versa) - Led by Burce Gültekin w/ Ghada Richani, Beth Bauer, and Michael Toland

Burce's LinkedIn: https://www.linkedin.com/in/burcegultekin/

Beth's LinkedIn: https://www.linkedin.com/in/beth-bauer-102449/

Ghada's LinkedIn: https://www.linkedin.com/in/ghada-richani/

Michael's LinkedIn: https://www.linkedin.com/in/mjtoland/

Michael's 'Super Chicken' reference talk by Margaret Heffernan: https://www.ted.com/talks/margaret_heffernan_forget_the_pecking_order_at_work

In this episode, guest host Burce Gültekin, Chief Data and Analytics Officer at FrieslandCampina facilitated a discussion with Ghada Richani, Managing Director, Data & Technology Strategy & Project Management Office at Bank of America (guest of episode #206), Beth Bauer, CEO at her own consulting company PosiROI (guest of episode #218), and Michael Toland, Senior Product Management Consultant & Coach at Pathfinder Product Labs. As per usual, all guests were only reflecting their own views.

The topic for this panel was how do we tie the business strategy all the way down to the data work via the data strategy and vice versa of making sure the data work is tied into the business strategy. It's a complex topic so a lot of this is about driving to good ways of working and keeping sight of the actual overall business goals.

Scott note: As per usual with panels, I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually.

Scott's Top Takeaways:

1. It's easy to lose sight of the data work when focusing on a data strategy. Make sure to understand and layout your understanding of what work will need to actually happen - at a high level - to deliver on your data strategy. Essentially, make sure you can execute on your data strategy via work and not words. And don't you dare forget your governance 😎
2. In many organizations, there is some - maybe a LOT of - cultural change necessary before the data strategy influences the business strategy. You need to prove the data work drives business outcomes and is aligned to the business strategy for a while - maybe 2-3 years - before the data strategy can be a key factor in shaping the business strategy.
3. Think about layering your data strategy to include what you can accomplish, what value you can drive today based on past investments and what you are trying to build for the future, where additional investments could drive more value down the line. If all you focus on is areas for additional investment instead of reinforcing the value already generated, it can feel like past investments haven't really paid off. If you don't share about what capabilities you've built and the value, why would someone want to invest in future capabilities?
4. Well executed data work - at its core simplicity - helps people to make better decisions. Lean into how your data work will help people to be better at their job. If you are pitching data as the key decision maker, most business execs will at best laugh it off but probably feel threatened. Show them how data makes them better at making the key decisions, not making the decisions for them.
5. The business strategy and data strategy are frameworks for making decisions - help your organization understand how data will make them better at making decisions. Not just by being more informed but by having more focus on what information matters. There are so so many inputs to consider, people are information overloaded. The data strategy should be about finding what matters and elevating that so leaders can focus on execution, not sorting through and absorbing information from 150 dashboards.
6. To develop an effective strategy, you have to understand where you are as a business. How can you know where you want to go and how you want to get there if you don't? Look to assess your high-level organizational capabilities as well as at the line of business level so you can help people align on the work to be done. A low data literacy team trying to do advanced AI is a bad strategy 😅
7. To create a good data strategy, you need to actually understand what a data strategy is and how it applies to your specific organization. A data strategy isn't only about desired outcomes but the entire information lifecycle from data acquisition/generation through to management and then leveraging the information gained to drive business decisions and outcomes. And say no to data for the sake of data!
8. Data leaders should help their business leaders to get data fluent to a degree where they can leverage data and know when they are out of their depth relative to data and should call in the experts to help them. We don't need every executive to be highly data literate as long as they recognize where they have weaknesses and lean on the right people to help them with data when it is called for.

Other Important Takeaways (many touch on similar points from different aspects):

1. As Burce said in her wrap-up: "Communicate, communicate, communicate and make sure everyone has the right incentives in place so that their behavior is moving [the business and data strategies] forward."
2. Think of data as feedback from the real world about what is happening and what you are trying to do. If we purposefully stop people from thinking of data as this mystifying, all powerful force, what more could we do? How many more people would be willing to use data more frequently?
3. If the data strategy is its own wholly separate plan/approach, you're probably doing it wrong. It must be closely aligned to the business strategy or you are likely headed for trouble. A good data strategy is an enabling function to allow data and data work to support the business strategy.
4. If your data strategy isn't tightly aligned with your business strategy, your data work to execute on your data strategy is far more likely to be seen as a cost center. Make sure the rest of the organization understands how the data strategy supports their work and key business targets for data to be seen as a value generator.
5. Many executives think of data as mostly powering their applications and interactions with customers/partners. It's crucial to get them to understand how data can help make better decisions looking forward and not merely reporting on what happened. Especially because they need to help generate the data to use to make those forward-looking decisions.
6. Before doing data work, you should always ask what is the business justification. Things might not turn out how you expected but it's important to share with others and also make sure you are focusing on work that should drive business value. It also makes it easy to show the value you delivered instead of people believing the value happened elsewhere. Data work enables value, look to show that often.
7. If you want a data strategy of becoming more data driven and more data fluent to succeed, you have to take into account the organizational and cultural change aspects. You don't have to solve for everything but technology isn't going to solve your data literacy challenges for you!
8. When focusing on the data work, think about the small steps. You don't need to map every single step out but you can't try to take giant leaps and expect things to work out well.
9. When building out the data strategy, look at timelines to value and making people understand when value will come. If you only focus on quick wins, you will see your potential for data wins dry up over time. You have to balance near-term wins and building long-term capabilities to create far more capabilities for wins in the future. But only focusing on the 2-year out horizon probably won't win you any followers, friends, or funds.
10. A potential good leverage point to bring business execs in on the data strategy is around risk. What risks are they looking to take and how can data better enable them to understand and quantify those risks to ensure the potential rewards are actually worth the risk?
11. Throughout the data strategy and data work, always tie things back to specific business problems. Not having a good data platform isn't a business problem, not being able to handle your data at scale to reliably and quickly make decisions on certain challenges is a major business problem and not having a good data platform causes that business problem.
12. If you provide data without context, your business partners won't really care. Same for data work. Think about the business context of what you are trying to do and communicate that to drive alignment and buy-in.
13. Data leaders should certainly want to be part of the conversations shaping the business strategy. But unless you've got a proven track record in your company of data driving your business results, it might take time to earn your seat at the table. While unfortunate, that is totally normal.
14. Cultural change unfortunately typically takes a long time. If your company is not heavily focused on data, it may take a long time before the business strategy is influenced by data. You need to get more and more momentum and change people's view of data and their experience with leveraging data to do their jobs better before you can expect data to be integral to the business strategy. So start building the momentum now!
15. The data strategy is just half the battle. You need to incentivize people to actually execute on it, to do the data work. Incentives matter far more than we typically give credence to in data.
16. Find your advocates, your data champions. Those are your best tools to really drive cultural change, the people sitting alongside other business folks and showing them the power of embracing data.
17. If you are tying the data strategy to the data work, you need to understand how you already do data work. Coming up with a grand strategy perfectly aligned to the business strategy that completely changes how data work gets done is going to be a real mess.
18. If the people crafting the business strategy aren't data literate, are they going to create a strategy that relies on data to drive results? Probably not. Try to help them get to a decently data literate level.
19. When building your data strategy and focusing on the data work, you should consider what work ties to which parts of the business strategy. If the company isn't trying to do real-time data interaction and analysis, do you really need that streaming ML solution? If you can tie your investments directly to business objectives, it's easier to get funding and to keep the attention of business leaders around data initiatives.
20. It's okay - and probably a best practice - to iterate on your data strategy as you do more data work. Are you getting more or less aligned with the business strategy? Even if the business strategy is a once a year proclamation from the CEO and Board, you don't have to live with that time table. Test, learn, adapt.
21. The data work alone never adds value. If you deliver to an amazing data strategy and the organization can't act on, can't take advantage of the work, then it was a waste of time and effort. Don't get too far ahead of the organization while you improve what they are doing relative to data.
22. Well-functioning data teams do innovation work that may or may not pay off. Try to keep that in mind when you build your data strategy. If no one is trying and potentially failing, you are probably playing things too safe. Keep your blast radii small if things blow up, but create space for innovation.
23. When talking about data work and value, business value is not always in dollars. Make sure people understand that. Ask your business partners how much they value certain outcomes.
24. The world is changing so much faster than it ever has before. Execs need inputs at a much quicker pace than historically to stay on top of things and ahead of the competition. Work with them to understand their pain and align on data work to address it instead of focusing on the cool tech aspects.

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All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf