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In this episode, Scott interviewed Liz Henderson AKA The Data Queen, Executive Advisor at Capgemini. To be clear, Liz was only representing her own views on the podcast.
Some high-level takeaways/thoughts from Liz’s view:
- To drive buy-in and engagement in a data strategy – especially with people outside the IT/data team – focus on the “why”. Why are you doing this initiative or approach? What business goals is it supporting?
- Also on driving buy-in for a data initiative, start by listening instead of selling/pitching. Focus on the business needs and work backwards to show how data can help address those needs.
- To be successful with a large change-management data initiative, you need the patience, leadership, courage, and will to push forward. And the budget – don’t forget the budget 😀
- You can’t have an effective data strategy if it isn’t directly tied into the business strategy. Really consider how data can help to support and execute on the business strategy. Data strategy in a vacuum away from the business is a recipe for trouble.
- It’s very easy to get overly focused in data on what you are delivering instead of why you are delivering it and who it is supposed to serve. If you want to be successful, you need to focus on the latter two. And look to deliver continuous incremental value rather than a back-end loaded value delivery.
- Change management is very easy to get wrong in data. Really consider if you can not only get the ball rolling, but keep it rolling and in the right direction to implement a large change. Loss of momentum can mean loss of funding.
- If data mesh follows a similar pattern to data literacy, it’s likely to be 3-4 years from initial large swell in hype around data mesh – whether that was late 2021 or more now – until we really see a clear picture of how more organizations have implemented. There needs to be a time for trial and error.
- Data literacy can only get you so far – you need data storytelling and visualization as the business people need to be able to understand what the data is saying to drive decisions.
- The 3 most common ways organizations go wrong with data are 1) technology-first / expecting to buy your way to a solution to challenges; 2) not asking the “why” questions; and 3) not having a data strategy.
Liz started the conversation talking about how data is an asset to the company – not treating data as an asset, an important differentiation – and started with a common theme for the conversation: “why?”. Why is data important to the business? Why are we doing the data work we are doing? What is the purpose?
When speaking with company executives, especially those outside the IT/data team, Liz starts every conversation by digging into what is the business trying to do. What is the business strategy? How does data currently play into that strategy? What role do they want data to play in the business strategy? What role might data play if everything were perfect? You can’t have an effective data strategy without it tying directly to the business strategy.
When considering any aspect of a data strategy, whether that is data mesh or something else, Liz again recommends starting from the why. In some conversations, IT leaders have said “we want to do data mesh” but when Liz dug deeper, they couldn’t answer why. Huge red flag. So when considering a data initiative, think about what is the target impact of the work. How would executing on the data strategy impact the business, not the IT environment? Always ask “so what?”. Why is this the right path forward for the business and how is data pushing the business results forward?
For Liz, if you are speaking with executives outside of the IT/data team about a data initiative like data mesh, to drive buy-in, don’t start by selling, start by listening. What are the challenges they are having and work backwards to address those challenges through data. What are the business needs and wants, and how can data help them to address them? That should heavily inform your data strategy.
From experience, Liz knows it is quite easy to deliver a very large, costly data initiative that no one really uses or benefits from, AKA a “white elephant”. So people play probably an even bigger role in data initiative and data strategy success than most would assume. The cultural aspect is crucial to doing something like data mesh well – if the data consumers still only want to use spreadsheets, it doesn’t matter if you are delivering the best data products in the world, there won’t be the demand to make the work worth it.
Liz gave a specific example of a recent conversation with a company wanting to do data mesh when talking with IT leaders. When she started to dig in, the business wasn’t involved at all with the decision to do data mesh. And a big part of doing data mesh is, you know, the business teams owning the data. It’s the first data mesh principle! So if you run across a situation like this, you should ask why they want to implement without the business’ involvement. Will that be change for the sake of change instead of driving business results?
Patience, leadership, courage, and will are all necessary to effectively execute a data change management initiative or overarching data strategy in Liz’s view. And don’t forget the budget – both for getting going and for the continued improvement and maintenance. It’s often easy to get data change started but maintaining the momentum, especially in the right direction, can be quite difficult. And if momentum starts to falter, budget can go away quickly in many orgs. Really consider if you are ready for a large-scale change before moving forward. As Zhamak has said many times, “Think big. Start small. Move fast.”
Liz shared her insights into how it often takes 3-4 years for a new, large-scale approach to how people work in data to go from everyone talking about it until we see how people are actually implementing it. Data culture and data literacy were all the rage 3-4 years ago but there wasn’t much info about how to actually implement a data literacy strategy – we are just starting to see adoption stories being shared. It might take that long for data mesh.
So what happened with data literacy where it is now relatively widespread? For Liz, a lot of it was the general data and analytics industry maturing with a strong general awareness of the concept and need for data literacy rather than any one point or push. With data literacy, employees can understand how data impacts their role and thus how it impacts the business. So that is possibly how data mesh might evolve – broad awareness and the brave bleeding edge folks helping to mature the concept and find the useful patterns and anti-patterns but it still takes quite a while.
Circling back on how crucial the people aspect is for data mesh – or any data initiative – Liz is aligned with the research that people are the silent success or failure point for most data initiatives rather than technology or architecture. Technology-led initiatives in data are quite likely to fail.
Liz recommends that companies looking to be more “data driven” or “data informed” really show to employees how data impacts the overall business and encourage them to consider “the art of the possible” relative to data. And consider ways to take feedback and data requests to enhance the business but not in a single data run for one person type of way, but new data products to share that information with many more people. That way, people know that their insights might actually mean something and might result in a new service or opportunity. And it encourages people to speak up more about what might be interesting additional information to have, leading to better data products.
Data storytelling and strong visualization are an often overlooked part of doing data right per Liz. Having that data translator is crucial to take the information and make it so 1) people can understand what the data is telling us – the insight – and 2) what that might mean – the potential action. Just sharing information without the understanding is overwhelming and confusing.
Liz wrapped up her thoughts with 3 key points on where people go wrong in data: 1) using a technology-first approach – stop picking solutions to try to be a silver bullet, really consider what you are trying to do first; 2) not asking the why, especially about “why do we want to be data driven”; and 3) not having a data strategy at all – you need a compass to move forward.
Liz’s LinkedIn: https://www.linkedin.com/in/lizhendersondata/
Liz’s website: https://lizhendersondata.wordpress.com/
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