The Importance of Confidence

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.

Benny's LinkedIn: https://www.linkedin.com/in/bennybenford/

Benny's Substack: https://www.datent.com/p/elevating-data-to-a-profession-why

In this episode, Scott interviewed Benny Benford, the former CDO at Jaguar Land Rover (JLR) and who is currently building out a community around data transformation. To be clear, he was only representing his own views on the episode.

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

1. !Controversial!: As a data leader, if you want to be effective, you will have to focus much more on what role data work plays in the organization rather than the data work itself. It's about partnering and communication, not SQL and LLMs. That can be a hard lesson but it's crucial to being successful.
2. ?Controversial?: It's easy to try - and common to want - to separate data culture and organizational culture but it doesn't really work. "Data culture is … an aspect of your organizational culture that you're trying to build and create."
3. A guidepost for your transformation journey: "If you want to go fast, go alone; if you want to go far, go together."
4. The data team can be leaders in transforming the organization but they must do it in collaboration, not just going out as pioneers 'to set an example' or something. Absolutely partner with your transformation org if you have one.
5. When you align your data culture transformation to the overall organization transformation messaging, you aren't fighting for attention to a different message. You are using the momentum of the overall org's transformation strategy. Don't try to fight the tide if you don't have to. Try to get that data transformation as part of the org's strategy and messaging.
6. Given the emerging attention to data in many orgs, it can be tempting to try to go off and try to set culture. But that's almost certainly not what the company is asking a data leader to do.
7. ?Controversial?: "Agile and data culture are two sides of the same coin." They are very close just approaching things from different angles. Scott note: I mostly agree. The big challenge is when people are overly rigid with their approaches to Agile.
8. If you try to force people to share data, it often stokes fears of 'can this data be used against me?' And that is a rational fear in an overly competitive org. You need assurance from senior management/the board of directors that trying to undermine other lines of business/domains will not be tolerated. If you want an open data sharing culture, you need to create clear consequences for internal political misuse.
9. Specifically at JLR, when they opened up a lot more access in their Tableau environment, it felt risky. But because of their well-defined and broad reaching initiative, it was nothing but positives, leading to far more/better collaboration.
10. Being frightened of change is totally normal. Don't try to solve every aspect ahead of time. Oftentimes you have to move with uncertainty and have a good framework for adjusting as you learn more. 'Bravery isn't a lack of fear, it's moving ahead despite your fear.' Scott note: this should be at the top of every data mesh strategy document…
11. Very few data leaders have strong transformational backgrounds and it can be hard to drive transformation without that. Look to educate yourself on how to drive effective transformation.
12. Champion's forums for your data efforts are very effective. It can inspire others but often, it can also draw out existing challenges you might not have known. Leverage those who will share their experiences to make it less scary and improve processes for those that follow in their footsteps.
13. All organizational choices essentially have puts and takes. Don't try to hide the weaknesses or challenges of whatever route you are taking. Be honest.
14. When decentralizing data work and ownership, many people in the centralized team may worry about their role and their future with the organization. Provide clarity and information on happy paths for them inside the organization including their career development.
15. Try to explain how the transition is happening "with you" not "to you". Easier said than done.
16. When thinking about data transformation, it can be helpful to think of it as splitting into projects and program. You need someone minding each aspect. One person trying to own both - the day-to-day execution and the long-term change in how your data org works - can be pretty difficult. It's a LOT.
17. ?Controversial?: Transformation happens when a few people's main focus is leading that transformation. If you have it as a bit part of many people's roles, it's unlikely to amount to a cohesive change effort.
18. You don't need a huge training budget or permission to train everyone in the organization. As a data team, you can start a small training program and as that sees success, you can build more and more. There is a lot of great free content out there, make sure to organize it for people but you can get going now.
19. Get people used to the idea of imperfection in data - and then give them permission to do imperfect things. Don't be cavalier and sloppy but it's okay that things provide value without being 'perfect'.
20. EVERY organization is far more immature with data than they show publicly. Everyone feels like they are behind. Meet your peers and embrace sharing reality instead of the Instagram view of your data transformation journey. It will lead to better information sharing and probably also better sleep at night.
21. It's crucial for data leaders to find allies across the organization. You can't transform a 5K+ person organization by yourself. Other teams can provide you leverage and support. Partner with those willing to partner.

Benny started off talking about data culture and organizational culture. He doesn't believe you can separate the two but it is a common desire among data leaders to do so. Data culture is something you build and create as an aspect of your organizational culture. Otherwise, you will always be fighting against the tide of your organizational culture. It all needs to be integrated change/transformation. Otherwise the organizational changes will be in conflict with your data culture changes. And people are employees of the organization, not only the data team, at the end of the day.

"If you want to go fast, go alone; if you want to go far, go together." For Benny, that emphasizes the long-term transformation efforts you need to drive to a better data culture. You can go somewhere quick but if everyone is going somewhere quick, there is no cohesion, no real concerted change to the organization and thus the culture. If you have a general transformation team, align with them. Align on the overall corporate goals and leverage them to drive your data culture transformation. Yes, slightly harder than just creating your own but far more effective and long-lasting :) Yes, you want to get your data transformation initiatives as part of the org strategy but it doesn't have to be the main focus. And it might take a bit of time to make it a core pillar of the strategy but that's how you do effective change management: building to better over time.

Benny made the excellent point that while it can be tempting to leverage the attention data is getting in many orgs to try to drive major organizational culture change, it's almost certainly not what the org is asking a data leader to do. Just because there is some 'juice' there, it's not where a data leader should focus. Ask if your job is really leading the organizational change or is it about delivering on data related objectives?

While pressing for an open data sharing culture is great in spirit, Benny has seen it can stoke a lot of fears - what happens if someone looks deeply into my line of business/domain to try to find mismanagement or incorrect decisions? And that could happen in a very competitive organization. That is why the cultural guidance needs to come from senior management and the board of directors around what would be considered unethical internal use. Assuring people there will be consequences for trying to undermine instead of help other lines of business can reduce a lot of fears. When JLR opened up their data sharing, it felt like a risky proposition but really, because the guidelines were clear and it was about opening up much more information access, it went very smoothly and significantly improved collaboration.

Benny pressed on the idea that we can solve every eventuality, every challenge in a data transformation ahead of time. Of course, we all know that's not possible. People will be afraid of change, that's normal. But having a good transformation strategy incorporates that fear and addresses it by having good ways to uncover and address emerging challenges. Assure people that while you don't have every answer, you have their back.

It's also very common for data leaders to really lack experience in transformation initiatives according to Benny. While data leaders are really good at data, that lack of transformation experience can be hard. Lean into this isn't about going it alone, we're in this together :)

Benny is a big proponent - a champion you might say - of champion's forums. Essentially having a place where champions of your data initiatives can exchange information with each other and your data leadership team are crucial because you can find your existing challenge points much easier. If you take in their feedback, they feel seen and heard and lean in even more. You also have great points of leverage to inspire others based on the success of your champions.

When moving from a centralized to a decentralized structure for the data team, Benny saw a lot of fear from those in the centralized team. Would they even have a role in the changed organization? What was their career path? You don't want to lose your data talent - it's so expensive and hard to replace - so be clear about the path forward for them. There is always uncertainty but try to show them they are and will continue to be valued.

Another experience from JLR that worked well for Benny and team was that when he was hired as a data leader, he realized he probably couldn't lead all of the transformation of focusing on both the day-to-day execution on data projects and the overall change program. So he got in a second leader to lead the projects while he focused on the program aspect. Transformation doesn't happen in a vacuum, people need to be focused on that specifically. That's the program aspect. Just executing well won't transform your team and culture.

Similarly, Benny shared how they built a transformation flywheel around data - at first, it was training a few people as part of the central data team's time, training them to act on their own in their lines of business. They didn't even have budget for this training specifically in year one, two, or even three as it continued to grow to 100s of people. They started seeding the organization with data capable champions that pushed others to take the data training. And after a few years, the training was so much in demand, it was too big for the data team to own so they brought in external trainers. But you can start small and have a big impact.

Benny recommends when getting started with - or really any point along the way of - a data transformation initiative, a big benefit is to get people used to and comfortable with the idea of imperfection in data. You can capture great value even around something that isn't perfect. And it will never be perfect. That's okay. Nothing in this world that is complicated ever gets to perfect!

Deep partnering with other parts of the organization might seem obvious but in Benny's experience, it isn't all that commonly done by data leaders. The data team should have partnerships with HR, Finance, Sales, Marketing, etc. And if you have a transformation office, that should be your number one best friend internally. You need partners to move the organization forward. And that also means data leaders need skills that aren't just data skills.

Benny wrapped on that point - what is a data leader's role in a large organization? Their role is rarely to focus on the data work itself anymore and that can be a bit of gut punch for those who love data. But it's about building the bridges to the rest of the organization and helping them do their work better. That means you're still doing the data work, just with the lens of bringing it into your business partners' context. Still, it can be frustrating and hard to give that data work up. That's not unusual or unexpected, it's just part of what it means to take on that data leadership role.

Quick tidbit:

Data culture and Agile culture, Agile transformation end up being pretty similar in many respects. Look to how organizations are successfully implementing Agile transformations to inform your data transformation strategies.

Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about

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

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

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