#162 Creating Data FOMO and Keeping Close to the Business – Interview w/ Dacil Hernandez

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Dacil’s LinkedIn: https://www.linkedin.com/in/daciluhernandez/

In this episode, Scott interviewed Dacil Hernandez, Director of Data and AI for Northwest Europe at Nagarro.

Some key takeaways/thoughts from Dacil’s point of view:

  1. At the end of the day, tech is the easy part in data. Creating value though data is the hard part. And why do data work if not to create value?
  2. It’s incredibly easy and pretty common for IT and the business to get disconnected – or never be connected in the first place. Focus on creating and maintaining relationships with a steady flow of context between both sides.
  3. Dig into if your business partners actually understand what data ownership means, what it entails. They may be willing but not capable to own data at the start. Work with them to up their capabilities and understanding.
  4. Data ownership should not be treated like the “hot potato”, being passed to “anyone other than me”.
  5. Tell your business counterparts “I need your help to help you.” You can unlock far more value through collaboration than waiting for requests.
  6. Your data strategy should give people FOMO – fear of missing out. Give them incentives that makes it feel like they can’t miss out on the value you’re creating.
  7. !Interesting Idea!: use gamification to find data quality issues. Don’t make it a shameful thing, find it and get it remediated and celebrate the people who found it. Look to drive positive energy around your data.
  8. Talk to potential data consumers before creating a data quantum or anything in data. You won’t know what you can offer that will be valuable to them until you know what they want. So have the conversation.
  9. Bad requirements and/or bad requests lead to bad results and data. Ask why someone wants work done – what are you trying to accomplish, let’s focus on the outcome.
  10. It’s easy to lose sight that optimizing to turnaround time doesn’t typically optimize time to actual value and you create hard-to-support data assets.
  11. Dig into what data consumers are trying to achieve and why. Their idea of what they want may not be the most complete or best way to structure the data or insights. Again, have the conversation.
  12. When looking at data quality, tie your metrics and measurement to value. What aspects of data quality matter to that use case?
  13. Data trust is obviously crucial. Data quality issues will happen. When they do, remediate and put rules in place to prevent that same issue from happening again and show business partners that you fixed the cause. It was a data downtime incident, treat it like a software incident.
  14. To achieve actual sustainable change, you don’t have to make big, sudden shift changes. You can and should break change into much smaller pieces.
  15. Centralized governance might not be your data bottleneck. Or even if it is, if you aren’t mature enough to do federation and decentralization right, data mesh might put you in a worse spot. Really consider your challenges and maturity level.

Dacil started out by calling herself a purple person, meaning not red or blue, not only technical or only business focused but a combination. As part of that, she echoed many past guests – the tech is the easy part, creating value is the hard bit. It’s easy to lose focus on creating value and playing with the cool toys, building really amazing things. But do they create business value?

It is crucial to have business counterparts as key partners, key stakeholders, in your data mesh journey according to Dacil. It is so easy for IT and the business side to get disconnected, for use cases and needs to change but the data getting shared doesn’t change. And with everyone saying business first, we are still focusing on tech far too often. Dacil likened data practices to being a teenager – we keep hearing business first but don’t listen, we do what we want 🙂 And we need to really get crisp on what “business first” actually means.

Dacil believes it’s crucial to get out of the IT desk request -> data loop we’ve been stuck in for so long. The business needs to come to the table and we need to bring them to the table. If the business side isn’t part of the conversations, you can’t get them to understand what data ownership means. They may say they will take on the responsibility but they can’t really grasp how to do it well. So we need to partner with them to get what we need and we need to work with them to improve their understanding and their capabilities around data ownership.

“I need your help to help you” is something core to Dacil’s work with the business. Too often, IT is left to wait for requests instead of working together to get what people need. It can unlock a number of additional use cases too.

When creating or improving a data strategy, Dacil recommends including incentives that mean people see/feel the need to participate. Leverage a fear of missing out or FOMO. Make sure people understand what will happen and the rules of engagement. Find the incentives to get them to participate by adding value back to them. Of course, easier said than done.

Dacil mentioned an interesting idea a company is implementing: using gamification to find data quality issues. So instead of it being this really bad thing to discover data quality issues, the people who found the issue get rewards. So that also will drive better data consumer literacy and drive up trust – they are checking data for “does this make sense” instead of just consuming data and learning in the process. Where else could friendly competition/gamification work in data? Look to create friendly and positive energy around your data work and show how it contributes to company value too.

For far too long we’ve had a vicious and costly cycle of bad requirements and requests leading to bad results and data according to Dacil. So, we need to ask far more questions – why do you really need this. As Alla Hale mentioned in her episode, not in a push back way. She said “what would having this unlock for you?” So take what they are looking for and why and repeat it back to make sure you are on as close to the same page as possible. And then keep communicating while you are building. Drive to that small prototype to make sure you are driving towards value together and aligning on expected outcome.

There’s a maturity level to differentiating between what people want and what they say they want in Dacil’s experience. And it is also often hard to differentiate between what they say they want and what they are trying to achieve. Always dig into what are they trying to achieve or you will create lots of wasted work. Again, have the conversation.

It’s very easy to measure the wrong thing in data quality according to Dacil. She brought up an example where phone number was 100% complete for every record but most of them were not real numbers. So someone said they had perfect quality based on “there was something in the field” but it was unusable, wrong information. So when you look to data quality measurements, tie to value. What is actually valuable here? If it’s operational data plane, that might be speed. If it’s mailing address for sending out holiday cards to all your customers, accuracy is probably better than completeness. If a few clients don’t get a card, that’s probably better than sending out lots to fake addresses.

In Dacil’s experience, business is typically the first team to notice data quality issues. And that means their trust is broken. Trust is hard to build but much harder to rebuild. How can you be data driven if you don’t trust your data? People need to understand that issues will come up but put the rules in place – and show the business – to prevent that same issue from happening again. It was a data downtime incident, treat it just like you would with a software incident.

Other tidbits:

Data ownership is often like a hot potato, no one wants to catch it. You can’t throw that responsibility to the business and expect them to know how to handle it.

Talk to potential data consumers before creating a data quantum or anything similar in data. You won’t know what you can offer that will be valuable to them until you know what they want. So have the conversation, drill into what is of value and why, then collaborate together to drive to that value.

It’s easy to lose sight that optimizing to turnaround time doesn’t typically optimize time to actual value and you create hard-to-support data assets.

Break your changes into much smaller pieces. Big changes are more prone to failure and are harder. Make incremental, small-scale progress.

Measure if centralization is actually your challenge before you look at implementing data mesh. If it’s not, will data mesh be worth it for you?

Really think if you are mature enough to really do federated governance and decentralized data ownership. Centralized governance is a bottleneck but that will likely be far better than chaos if you aren’t ready.

Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or on LinkedIn: https://www.linkedin.com/in/scotthirleman/

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