#169 Sharpening Your Competitive Advantage With Data – The Solution Is Not Simple – Interview w/ Alexa Westlake

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Alexa’s LinkedIn: https://www.linkedin.com/in/alexandrawestlake/

Alexa’s Medium: https://medium.com/@westlakealexa

In this episode, Scott interviewed Alexa Westlake, Senior Data Analyst at Okta. To be clear though, she was only representing her own views.

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

  1. Data can be transformational but it is expensive to do the work. “Without literacy, all your analytics is is expensive.” So do the data literacy work to make your data work actually valuable.
  2. ?Controversial?: Don’t focus your transformation initiatives around a negative, focus on a goal/aspiration. It is hard to maintain momentum around pain, especially as it starts to ease with early wins. While pain points can pique attention, shared goals and collective outcomes will keep people bought in and motivated.
  3. ?Controversial?: Data is “not going to make or break you [in every case], it is there to help you be better, it is there to help you unlock your full potential.” Use it to sharpen your competitive advantage.
  4. As you scale your organization, if you do not prioritize data – the way you manage the people, processes, and tech around data – you will generate a ton of friction. It will create a “feedback loop of pain.”
  5. “Never jump and hope.” You need to make sure you have the support to get your initiatives going and then maintain momentum.
  6. !Important!: Data work is often not the number one priority for the stakeholders you serve. Understand that and keep close enough to make sure you are working on analytics to support their top priorities.
  7. Alignment is one of the hardest things to do organizationally and is far more crucial in data work than most believe. Be prepared to repeat yourself – repeatedly.
  8. The sunk cost fallacy very often results in throwing good money after bad in data, especially platform work. Look for signs you need to shift when the investment rises and the return continues to fall.
  9. People, especially in analytics related initiatives, often don’t know exactly ‘where the pain is coming from’. Work with them to understand their pain but that the data team and/or data work is not some magic wand that is waved and it’s all better. It’s tough but valuable work.
  10. It can be hard to get executive sponsorship to generate new data explicitly for analytics, but not doing that results in only analyzing the information you already have. How can we incorporate information capture into part of the business processes? What data is valuable – and reasonable/ethical – to generate?
  11. Get more specific when talking about data quality. “The data is bad” is essentially meaningless. Really drive towards specifics and then get people on the same page on measurement/monitoring of quality.
  12. It’s easy to focus on the data you have instead of the insights you can generate. When talking to business stakeholders, how often do they care about the data versus what it means? Focus on communicating in what matters, not the speeds and feeds aspects of data.
  13. A lot of data tech debt / challenges in data come from decision makers not understanding their internal data ecosystems. Adding urgency to that often results in band-aid solutions that degrade quickly.
  14. You can’t turn the ship too quickly. Trying to bet all your data transformation on one aspect or a big bang won’t work. Look for small wins to build momentum.
  15. It’s pretty easy to burn out your data team – make sure to give them interesting, important, and valued work.
  16. Communicate with the business about what matters. As a data person, that usually means more listening than speaking so you can figure out what matters to them. Applicable Scott phrase (to vendors): no one cares what you want to tell them, they care about what they want to hear about.

When asking people generally what do they think of data, Alexa has seen many fall into one of two camps: either data is transformational or data is expensive. And in truth, both are probably right. There is massive investment in data but most organizations are still struggling to scale.

“The data is bad” is such a common refrain across the industry but Alexa believes that is like data without context – essentially meaningless. What aspect is bad? What makes it bad versus good? Why is it bad? Do we need better collection processes in place? Do we need more expertise in transforming and analyzing data? And when answering those questions, it’s often very difficult to figure out what are the overarching problems that you can tackle instead of building point solutions.

Alexa has seen it can be a bit of a scary proposition to try to get exec sponsorship to generate new data specifically for the purpose of analytics. So most companies start with what data they have today, and don’t get overly ambitious, at least until they are proving they can deal with what they have now well. But, at the same time, many fall victim to the sunk cost fallacy of ‘we’ve spent a bunch on this platform already, we have to focus on scaling it out’, often throwing good money after bad. You can’t just constantly change your platform but ignoring problems simply because the decision was already made is a recipe for disaster.

According to Alexa, a lot of the challenges in data come from the decision makers not really understanding their internal data ecosystems so we need to make it easier for execs to make better decisions. That ML model might have 40+ pipelines in some form or fashion that feed into it, of course it’s likely to degrade. And there is often an urgency to solve the problem of today with a solution that addresses that challenge today for that specific use case – fighting the symptoms instead of tackling the cause of issues.

While something like data mesh – or any other large scale data transformation initiative – is a big change, Alexa believes we shouldn’t make that a giant leap instead of small steps. You have to get to small wins as you’re turning the ship to keep earning the right to steer the ship. And you shouldn’t revolve the entire transformation around a negative, a pain point. If you do, you’ll end up focusing on the pain too much instead of the goal – it’s hard to sustain focus and drive around pain. Look to focus on the motivation behind why you are doing data transformation work but also what are the incremental results.

A few ways to burnout your data team Alexa mentioned: not providing them work they feel is meaningful; putting low priority on data work in general but especially on the interesting insight generation; data not being part of the critical path to business success. Essentially, if people aren’t working on interesting, important, and valued work, they will leave.

Alexa believes it’s crucial to focus on your communication when working with the business – they aren’t well versed in data terminology or often even data concepts. So focus on communicating to them what matters and why instead of using data jargon. Most decision makers make so many decisions across many contexts, work to make it as easy as possible for them.

“Never jump and hope,” Alexa said about measuring and maintaining momentum. To do data work right, you can’t be an order-taker – taking the requirements, going away to build, and then presenting the ‘thing’ at the end and it’s done. Get closer to the decision makers, understand what their expectations and needs are. Are they shifting? Are they expecting too much? Have the constant flow of information bi-directionally to make sure you are still doing valuable – and valued! – work.

There is often a lot of pain around data for business stakeholders but they typically can’t directly identify the exact source of the pain in Alexa’s experience. Collaborate with them to figure out the actual pain so you aren’t treating symptoms. And work with them to explain that just like in medicine, exercise, diet, etc., there isn’t a miracle cure or improvement. Data work takes time. Explain why it will take time and drive to what actually matters to address. The common example is ‘what does real-time mean for you and what value does real-time drive?’ It’s often ‘2 hours is fine, just sick of 24 hour delay.’

For Alexa, it’s very easy to try to build out governance centrally because it maintains a feeling – if not a reality – of control. The reaction of most humans to the unknown is fear. But you can make slow improvements to build trust and momentum – as Laura Madsen also talked about in her episode. If you don’t invest the time to empower and enable your employees around data, you won’t get good returns from it.

To do any data work, but especially something like data mesh, right you need champions in the domains to help. Both to help you move things forward but also that constant communication loop as the world – and thus requirements – changes. Focus on them being your partner and treating them like a customer of your product.

It’s very easy to fall into the trap of putting too much emphasis on the data work, according to Alexa. It is not likely to make or break your organization but it can be a significant competitive advantage. Data can unlock your value potential and sharpen your competitive advantage.

Quick tidbits:

“Without literacy, all your analytics is is expensive.”

Alignment is one of the hardest things to do organizationally and is far more crucial in data work than most believe. Be prepared to repeat yourself over and over.

It’s easy – and often fun – for data people to focus on the raw data you have instead of the insights you can generate. When talking to business stakeholders, how often do they care about the data itself versus what it means? Focus on communicating in what matters, not the speeds and feeds aspects of data.

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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