Data Mesh Radio #187 Maximizing the Value of Your Data Through Data Products - Interview w/ Bruno Aziza
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Bruno's LinkedIn: https://www.linkedin.com/in/brunoaziza/
Bruno's Medium: https://medium.com/@brunoaziza
Bruno's YouTube (Carcast videos): https://www.youtube.com/@brunoaziza
In this episode, Scott interviewed Bruno Aziza, Head of Data and Analytics at Google Cloud.
Some key takeaways/thoughts from Bruno's point of view:
- The end goal of your data strategy should be to reliably and scalably turn data into value. The best way to do that is by creating data products. How you get there might be different but don't lose focus on turning data into value.
- "The number one barrier to your ability to drive value of data is not your technology, it's your people and how you organize your team."
- Focus on the point of what you are trying to deliver, not the actual output. It's not about delivering a dashboard, it's about creating a sustainable way to explore, share, and consume information/insights, whatever form that takes.
- !Controversial!: There are 3 phases to getting to data driven; 1) is building a data lake or ocean, 2) is data mesh, and 3) is getting to a data product factory equivalent.
- It's easy to try to put the cart before the horse in data. Before doing something like data mesh, you have to think how you will develop data as a function in your organization.
- Understanding the data product manager role and leveraging data product managers well is crucial to building an effective data product strategy and practice. They are your data product CEOs.
- A CDO's effectiveness depends on if they have a true seat at the exec table - can they create the necessary change - and how many people in the organization are "devoted to the data opportunity."
- ?Controversial?: "Really smart data leaders hire for the business department." Understanding if someone cares about data and if you can work with them is important. If that potential head of marketing hire doesn't care about data or has low data fluency, will it be possible to work together?
- The companies doing the best on data literacy are making data a crucial part of their culture. Their daily practices increase data fluency across the entire organization rather than in a centralized data team. It's not about just training, it's about changing habits.
- Data driven companies are "162% more likely to surpass their revenue goals."
- "We're seeing people kind of rushing into migrating and not thinking about governance."
- ?Controversial?: There are two components that significantly increase the chances of successfully transforming to being a data driven organization: 1) a true organizational mandate to become data driven and work on data products and 2) does your organization have the attitude and aptitude to drive towards being data driven.
- There isn't a clear pattern yet for the best way to find your data product managers - teach the business aspects to the data people or vice versa. But it's clear that understanding what value data products drive - not just the ins and outs of the data product itself - is crucial.
- More and more, the centralized data team model is getting swamped in large organizations. But too many are too happy to fully decentralize, which also causes many issues. Federated - decentralized control and work but centralized collaboration and practices - is the approach seeing the most success.
- ?Controversial?: There are 3 types of data products: 1) internal domain-focused (data on the inside, not very reusable), 2) core, centrally managed, and 3) everywhere in between :)
- A major issue in the organizations that focus on empowering domains without interoperability is that - surprise, surprise - there are different semantic meanings and so the data becomes very difficult to integrate/interoperate.
- Focusing too much on quick wins will mean you miss out on the places where data can add a lot of value. Quick wins are typically not big wins or you find the big wins fast and then all your following quick wins are moderately sized at best.
Bruno started off with something you don't often hear a vendor say: "The number one barrier to your ability to drive value of data is not your technology, it's your people and how you organize your team." So while you can't buy your way to a data mesh, you also can't just flip a switch and be doing data mesh. You need to build your organization's capabilities to a degree they actually can derive value from their data.
It's also not easy for a data leader to necessarily create the necessary change per Bruno's conversations with data leaders. Many don't get a true seat at the executive table. And even if they do, if there aren't enough people "devoted to the data opportunity," it will be a very hard road to drive the data function to where it can add significant value. Bruno also dove into what he's seeing that makes for a high data literacy rate at customers - changing the day to day interaction, the habits of working with data. Making data part of many more people's roles and making it an intentional part of the company practices/habits builds an incredibly deep bench of data talent across the organizations. So his three components to positive change in your data approaches as a company are a strong data leader, the proportion of people committed to data work, and daily practices involving data.
While we know being data driven has an advantage - data driven companies are 162% more likely to surpass their revenue goals per a study - Bruno sees a few reasons why only 27% of companies are actually data driven now. To be data driven, you need to reliably produce data at scale, hence creating data products. And to do that, you need to build out the capabilities to handle data at scale - and not skip the governance :) But the end goal is to provide a reliable way to create value from data. That's really it. The best way to reliably do that is via data products in his view.
Bruno is seeing people go through three phases in getting to a reliable, scalable way to turn data into value. Phase 1 is the data ocean - it's not a lake, that's landlocked. The second stage is data mesh, allowing people to autonomously innovate with data but relying on central resources. And the third stage is a data factory. Scott note: the factory analogy might be rough because 1) feature factories are a very bad software pattern and 2) factories are notoriously about producing the same things at scale. And while we want scalable ways of creating data products, they should be more fit for purpose to use cases (but of course reusable as well) in my view.
The data product manager role is crucial to getting data products right according to Bruno. You need someone to be the CEO for you data product, that is focused on the actual value the data product drives and how reliable is the data product creation/maintenance. What more should be added to the data product? How is it used? To drive that cultural shift, you need a strong leader of the data organization that is empowered to make the right changes.
For Bruno, there are two factors that significantly increase the chance of an organization successfully becoming data driven. The first is an organization-wide mandate that data matters and that people must participate in the change and leverage data. Especially if the CEO is bought in on the data opportunity and the need for more and better data for themselves especially and the organization more broadly. The other is the attitude + aptitude to actually go out and build a scalable capability to build data products. And that's far easier said than done. That can be driven centrally or in a distributed way but you need people to step up and own the data.
The centralized data team model is becoming harder and harder for companies to scale according to Bruno. The team needs to be constantly ahead of the curve and they don't have the ability to learn all the necessary context so they quickly get overwhelmed by requests. This was a key factor in Zhamak creating data mesh as a concept. But the teams that are just fully decentralizing are creating data silos and making it increasingly hard to answer cross domain questions. So the organizations that are doing a federated approach with a strong sense of overall collaboration are winning - there are things that are centralized and things that are decentralized and each organization needs to figure out what works for them but balance is crucial. Find the right approach for the job.
Bruno talked about those companies that are focusing more on empowering domains than on the bigger picture of how domains can also work together. No major surprise but it creates data silos because everyone has different definitions so nothing is easy to integrate/interoperate. This is leading to the rise of the idea of the universal semantic layer.
Quick tidbits:
Data leaders should be involved in the hiring process for business people. That way, you can start to build a relationship early and help select someone who values data and has a decent data fluency. You don't want to be left out of the process.
It's absolutely okay to have domain-only data products that are very specialized to that domain - basically data on the inside in a data product. It's also - per Bruno - okay to have very centralized data products that are pretty core across the organization. But look for places to build reusable data products to get the most leverage from your data work.
To do data products and data product management right, you can't only focus on the data product launch. Maintenance and growth/evolution are crucial aspects of product thinking.
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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