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#234 Doing Data Work That Matters: Perspective From a Line of Business Head - Interview w/ Iryna Arzner
Episode 23425th June 2023 • Data Mesh Radio • Data as a Product Podcast Network
00:00:00 01:03:06

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In this episode, Scott interviewed Iryna Arzner, Head of Group Customer Growth, Retail Banking at Raiffeisen Bank International (RBI). To be clear, she was only representing her own views on the episode.

Scott note: I mostly use the phrase line of business or LOB instead of domain in this write up but they are mostly interchangeable.

Some key takeaways/thoughts from Iryna's point of view:

  1. As a line of business head, data has value but only in so far as they can use it. If it's not aligned to a use case or business need, data work can be more of a distraction than a benefit.
  2. It can be very interesting for a line of business owner to know how much their data is worth to other parts of the organization - that could drive funding for additional data work inside their LOB or even more funding than that because the LOB is core to driving business value at the organizational level.
  3. "You cannot be successful in your data strategy if there are no business leaders that understand the value of the data and are very much determined to uncover this value." Scott note: couldn't put it better
  4. A good way to get your business leaders more data fluent is to very closely pair with them. Sitting side-by-side on a project will up their fluency far better than any training course ever could.
  5. "How do we get these data insights that we actually need to fuel the business strategy?" It's crucial to understand the LOB business strategy and focus data work around that. Start from the business needs and work to the data, not the other way around. Scott note: PREACH!
  6. {For many senior business execs, they've operated on gut and limited data for literally decades. You need to partner with them to show why data makes their decisioning better/quicker instead of that the data will now make the calls. Data is a tool for them to be better, not their AI robot replacement :)
  7. You can't force a senior exec into leaning in on data. Start from their actual business goals and work backwards towards what could be better or what can't they do unless they have better data.
  8. It's easy to say your data work should have business value but actually get specific around driving to how your data work supports the business strategy - corporate and line of business strategy. Scott note: if it doesn't have business value in some respect, why do it?!
  9. Far too often, especially in advanced areas like AI, data people/teams want to do really cutting edge projects. But if they aren't attached to business priorities - or even if they are but just looking at the business challenge in a way that doesn't align to business leaders - it will likely be wasted effort.
  10. ?Controversial?: Please, please, please stop building platforms for the sake of platforms. LOB leaders don't care about what cool tech you use, they care about capabilities that drive business value.
  11. ?Controversial?: If your LOB execs aren't willing to share their strategies and use cases necessary to develop planned data work, you might have to resort to bringing things to them you believe are aligned and hope they bite. Scott note: if you can skip these execs at first, they'll come around if others are driving more value through data
  12. Related but very different, talk to LOB leaders about if they are interested in those potential insights that aren't directly tied to current business strategy/challenges. Some (e.g. Iryna) like to be opportunistic and have the cognitive load for potentially interesting if tangential insights. The key is to ask.
  13. !Controversial!: The "time is past" where business leaders can get away with not understanding tech or data. Business leaders need to understand the necessary data capabilities to drive to their use cases. They can't just wait for insights to be dropped in their laps.
  14. Data people need to help business leaders understand the value of a platform approach. Just because something can be addressed 'tomorrow' doesn't mean that it's the right call. What is sustainable and scalable? It's crucial to communicate that.
  15. ?Controversial?: It's okay to focus your data work on the pioneers, the data advanced domains. Yes, they might get even further ahead of the pack but they will be easier to work with. Scott note: this is actually a pretty big debate in data mesh as it can lead to building a platform only for advanced teams.
  16. Really closely partner between the business and data teams on use cases. If you requirements dump and the data team does work divorced from the business context, it will rarely drive good value.
  17. Part of delivering a good product or service is giving your customer something relevant to them. In data, that seems to be missing from lots of work - what is actually relevant to someone's needs and wants?
  18. There is a tendency by many data people to defer to the data first. Instead, think about leveraging your subject matter experts more to help define what data you need or what reasonable hypotheses to test :)

Iryna started out with a bit about her background and then jumped right into it. She said "it takes two to tango" relative to the business side and the data side collaborating. She understands the power and value good data work can drive for her line of business so she and her team are often pairing with the data team to figure out how to drive value.

For Iryna, a data strategy cannot be successful unless there are at least some business leaders that are leaning in - that understand the value of data and are also determined to actually capture that value. Specifically in her area, Iryna gave an example that while in the past, banking was often done via relationship with a banker - so insights about customers could essentially be managed in the head of the banker - as people move to mostly digital, there needs to be a lot more data insights to be able to offer them the best services to their needs at the right time.

Starting from the business strategy and the use cases and working backwards to what data is necessary to support those use cases is crucial in Iryna's view. It's proving to be the case over and over in data mesh that starting from the use case is key to getting value out of the data work because you need to know what would actually drive business decisions and actions and then drive into the fine details. But it's easy to get lost in the data work AND it's just as easy to get lost in the business strategy - you need to actually get specific about the data products necessary to support use cases.

Iryna discussed that most senior execs have been operating much more on gut than on data for years to decades - you aren't going to change that overnight and you need to partner with them to make their lives easier via data if you want them to lean in. And the best way to do that is to partner on their business challenges, looking for opportunities or challenges they can't address right now without better data. And don't start with the biggest challenges first, think small and quick to prove out value as you build to bigger and better.

A common if understandable failure point Iryna mentioned was data people's desire to do really challenging work - fairly often irrespective of if that really matters from a business priority standpoint. Or even if some AI work is aligned to a key business objective or challenge, if it isn't aligned to the way the business leader thinks about the business, it often won't leveraged because they have to change their mental model of their business just to "get" it. So again, start from the conversation with the business leaders and work backwards to significantly increase your chances of your data work driving large-scale business value.

Iryna mentioned she's "allergic" to platforms built for the sake of building a platform - 'it's what everyone is doing, so we should too!' Or wanting to use fancy tech instead of focusing on capabilities. Those are red flags that the data team aren't aligned to business value or learning from past mistakes - past projects didn't fail because the platform just wasn't cool enough, it was because the platform didn't make it easy to do what was needed!

Many execs in Iryna's view won't want to engage much on data that isn't tied to their strategic initiatives/challenges. But, there are still many like her that are happy to be opportunistic. Actually have the conversations and ask the leaders where they come down on that. Some want people laser focused on what they are trying to do but you can build interesting and highly valuable partnerships with the flexible and opportunistic ones like Iryna.

"This is the past," is something Iryna said about business leaders having no clue about technology or data. Leaders need to understand at least the basics and upskill to be better able to leverage the new data capabilities from new tools being developed all the time. The data team will be needed to identify - "in a nice way" - business leader skill gaps and help them close those gaps. And it's important for business leaders to understand the benefit of platform and product thinking in data. Just because a challenge can be 'solved' tomorrow doesn't mean it will stay solved! Sustainability and scalability of solutions in data are crucial.

Iryna believes it's good to at least start your data initiatives working with the "front-runners and pioneers". That's because then you will save time and hassle - they are already bought in and capable. Seeing those front-runners drive lots of value with data will lead others to want to follow. Scott note: there is a lot of controversy around this topic - will you only build for teams that are already advanced? How far do you go with this strategy?

As to where lots of data projects go wrong, as past guests have also noted, Iryna pointed to the requirements dump pattern - the business stakeholders and data team meet, information is exchanged and then the data team goes off and works on the use case and comes back a month or two later and it's not really what the business side wanted and it doesn't deliver the value. Instead, as Ghada Richani also pointed to, the business and data side need to pair closely throughout the process as the use case and needs evolve and as incremental value is created. The data team can actually get a big boost to happiness from seeing the actual business value get created from their work. Make this whole process mutually tied and mutually beneficial.

When thinking about products and good customer interactions, Iryna says it's important to bring your customer something that is actually relevant to them at that point in time. We should look to do the same in data. And that should honestly be easier in data because your customer can - and should - literally tell you what they care about and want. And you can prototype together instead of having to deliver a final product - you need to communicate that but it's more of a partnership than just a customer relationship. Start to work together on the vision of what success looks like.

Iryna gave a great recent example of when the data isn't going to necessarily give you the best information. They are looking at a specific financial product and how to sell it better based on certain life event triggers. So, instead of making guesses and random hypotheses, why not ask the people on the ground for what they look for, what are their hypotheses as to why they are selling when and what are the best triggers to use to sell the financial product? This is how data and business processes can build off each other - you don't have to treat your data as if it should tell you the information - extract context from subject matter experts!

While she might not be the most typical line of business head in a data mesh implementation - she's more altruistic than what we're often hearing - Iryna and her team are simply excited to understand what value their data has for the rest of the organization. That might even lead to insights that get shared back to her team for additional use cases. But proving out the value of her domain's data is also likely to lead to more funding for their data work so it should have a positive impact all the same.

In wrapping up, Iryna urged listeners to tie their data work more closely to the business strategy - yes at the corporate level but even the line of business objectives. Every bit of your work should be tied somehow to driving business value and you should have visibility to that. Yes, platform work can have that but you should understand how that platform work actually drives value. And also, you should sit side-by-side with your business leaders in your data work - that will up their data fluency far more than any formal training program ever could.

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