#199 Finishing Your Data Marathon – Driving to Action from Data – Interview w/ Brent Dykes

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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.

Brent’s website and book: https://www.effectivedatastorytelling.com/

Brent’s LinkedIn: https://www.linkedin.com/in/brentdykes/

Brent’s Data Analytics Marathon Forbes article: https://www.forbes.com/sites/brentdykes/2022/01/12/data-analytics-marathon-why-your-organization-must-focus-on-the-finish/?sh=2af698743c3b

In this episode, Scott interviewed Brent Dykes, Chief of Data Storytelling at his own firm, AnalyticsHero. Scott asked Brent to be on after João Sousa pointed him to Brent’s content.

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

  1. Focus on the so-what, what should people take away and do from the insights, not the sausage making of the insights. Execs want to eat the dang cake, not hear about how you made it!
  2. !Controversial!: You want to get to a place where you remain more neutral until the data informs your view. It can cause more cognitive load to update our views instead of waiting for the data to speak first.
  3. Many organizations lose steam in actually driving action on analytics, they don’t drive change with data – they fail at least one of the following: generating actual insights, communicating their insights well enough to drive action, and/or actually acting on the insights.
  4. The analytics marathon: data collection -> data processing -> data visualization/reporting -> data analysis -> insight communication -> take action.
  5. Many companies stop the data marathon after getting to the visualization/reporting step. They aren’t driving the results they want so they focus more time on collection and processing instead of finishing the race and get caught in a loop.
  6. Really consider why are you doing data work. It’s not to simply do analytics, to build the dashboards and reports, it’s to take action on the data and affect change through more informed decisions.
  7. It’s easy to like the data when it supports your narrative. A strong analytics culture bends decisions and thinking to the data instead of the other way around.
  8. Companies with good analytics practices, that take action on data, typically have at least an executive sponsor around analytics if not buy-in from the entire leadership team. There is an understanding that actions are driven by data when possible. And there is a test and learn culture.
  9. Executive support and a test and learn culture are what drive results from analytics – many companies buy the same tools and have vastly different degrees of success with data.
  10. If your organization isn’t doing analytics well, the best way to drive towards doing analytics well is get to wins from analytics and build momentum to drive to higher and higher exec sponsors.
  11. ?Controversial?: Sometimes to drive necessary understanding, in documentation – or other ways of bringing data users up to speed – you really need to show the lineage all the way back to how the data is even collected.
  12. The more you share data, the more likely information is to be misunderstood. Beware the difference between what a metric means and what people _think_ it means.
  13. ?Controversial?: To get to scale, we use passive communication – read mostly documentation – about our data. But to truly drive to understanding, we also can’t shy away from active – read person-to-person – communication. Scott note: Episode 150 w/ has some interesting insight on how far documentation should go.
  14. ?Controversial?: Storytelling is often the easiest way to sway people with data because human brains have evolved to accept information via stories. We’ve been doing it for 1000s of years.
  15. Mature organizations understand the data can be wrong and prepare for that. Move fast and make incremental moves instead of a big bang approach. You learn more and can do better the next time even on actions that weren’t as valuable as expected.
  16. ?Controversial?: Business analyst roles should evolve to be more like personal trainers – helping people learn how to do good analysis and then communicate their insights. They won’t work themselves out of a job, merely get to a place where they focus on the bigger, harder, deeper, more valuable questions.

Brent started with a bit about his background and why he titled his book “Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals.” There are a few places many organizations fall down in driving change via their analytics whether that is a failure to generate actual insights, a failure to communicate insights well enough to drive action, or a failure to actually take action on the insights – he’s most focused on the communication of insights, a place often overlooked. You can find the best insights in the world but if you can’t communicate those insights well enough, no one will understand them and/or understand the potential impact of acting on them. Communicate well enough to drive change!

The analytics marathon is one of Brent’s big analogies for explaining where organizations fail along the path to taking action on their insights. There is data collection, which pretty much all organizations do. Then data preparation into data visualization. But this is where many orgs fall off because they are simply reporting on what’s happening, the descriptive analytics and not actually driving to diagnostic analytics. Instead of doing deeper analysis, they believe their problems lie in what data is collected so they try to collect more data, thinking it’s simply a lack of information instead of a lack of analysis. And then of course, once you do the analysis, you still have to communicate and take action.

For Brent, a few common indicators an organization will likely have a good analytics practice include: 1) an executive sponsor for being or becoming data driven. Possibly the entire leadership team. 2) a general commitment to driving actions from data where possible. It’s “how we do things.” 3) A test and learn culture in the organization that’s supported by data.

If an organization isn’t yet data driven, isn’t doing analytics that well, Brent recommends getting to wins and slowly moving your executive sponsorship up the ladder. It might start at a Director level and then after you build momentum, people will take notice and you can climb to VP level and then C-Suite level. It’s about showing the value of analytics and plugging along so you have proof points when you move the conversation higher in the organization. Rome wasn’t built in a day and neither is a good, organization-wide analytics practice.

As data initiatives have become more ambitious, it’s often meant ownership has become more murky according to Brent. What was once data that was essentially only for the generating team is now a potential core value asset and driver for the organization. And that opens you up for much more misunderstanding. Focusing on making sure information is understood – not just data is made available – is crucial to making good decisions with your data. There’s often what the metric means and what others assume it means.

Brent shared his views that we need both active and passive ways of sharing context around data. Passive is the metadata, the documentation and the like. If we want to scale, passive is crucial. Self-service can’t just be a pipe dream. But too often, people in data want to only do passive and ignore the people-to-people conversation. But often that’s key to nuanced data or crucial to working with key people making big decisions based on data.

For 1000s of years, humans have been passing information via stories – human brains have evolved to share information via stories. We inherently want to know where the story goes. For Brent, mastering that storytelling with data and about data is the best way to convey the information we generate and discover with our data. If you don’t communicate insights to those who can take actions, in a way they can understand, they won’t take those actions :)

For Brent, execs rarely want to hear how the sausage was made via data. You want to show them what you’ve discovered and what they should do with that, not how you came upon that. It can be important to show people the sausage making isn’t that hard though, especially trying to enable a team to do self-serve analytics. Really consider which is more appropriate to the situation.

It’s pretty easy to like what the data is saying and point to the data as backing you up when you agree with it in Brent’s experience. But a truly data-driven culture will focus on updating their thoughts and processes based on what the data says, improving their understanding via the data instead of trying to bend the data to support our hypotheses. It’s about getting to a place where you try to remain more neutral until you hear what the data says and shape your vision around that.

On data-driven versus data-informed as semantics of what we’re trying to get to, Brent likes the idea of data-driven. For him, it means really leaning into the data. Part of that is understanding and accepting that sometimes the data is wrong or we didn’t ask the question in the right way – that’s just getting to data maturity. And data-driven companies recognize the value of learning – there is still value from experiments and moves that didn’t have as much benefit as expected when digging into the why. You have incremental understanding even if not direct incremental business value. And it sets you up to do better on the next iteration.

When asked about where will business analysts fit in data storytelling in the future, Brent sees them like personal trainers – they won’t be doing the work but showing people how and assisting them until they can get to a level the BAs aren’t as needed. That’s for the analysis and especially the insight communication. Pure self-service analysis is nice in theory but you need a way for people to get help and make sure they aren’t hurting themselves :) If more people are far more capable, that means the BAs can focus on the more valuable, large-scale questions.

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/

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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

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