Increasing the perceived value of your data team with Aaron Wilkerson
AgileData Podcast #62
Join Shane Gibson as he chats with Aaron Wilkerson on ways data teams can increase the perception of the value they add to their organisation.
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Read the podcast transcript at:
https://agiledata.io/podcast/agiledata-podcast/increasing-the-perceived-value-of-your-data-team-with-aaron-wilkerson/#read
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Key Participants:
Shane Gibson: Host, Agile Data Podcast
Aaron Wilkerson: Director of Data Strategy and Products at Carhartt
Main Themes:
The Perception of Lack of Value: The podcast kicks off by acknowledging a common sentiment that many data teams are not perceived as delivering significant value to their organisations. This is seen as a problem, potentially exacerbated by recent economic downturns where data teams were impacted.
Quote (Shane): "there still seems to be this perception that data teams aren't adding value. And I think a lot of it comes from when the downturn happened, a lot of data teams got hit. Like a lot of good people lost their jobs and that reinforced this message or this perception that data teams aren't adding value."
Understanding and Managing Expectations: The perceived lack of value is often tied to unclear or misaligned expectations. Legacy businesses that "fell into data" may have initially had low expectations that have since grown with increasing capabilities. Newer teams, conversely, may face very high expectations from the outset.
Quote (Aaron): "I think the reason I think it really depends on the organization. I've been a part of a lot of organizations... They fell into data. They didn't really know what they want data to do. So they started with usually it's like a database, right?... I would say expectations I would say started pretty small around hey we just need you guys to do these couple things but then as you start to build out reporting... I think that you started to see just more expectations coming out of the data team."
Quote (Aaron): "I really think it depends on the leader and really what that mandate is. What were you brought in to do or what is your team being asked to do? But if you don't know then I really then that's also on the leader to define that and say hey this is what I think you guys want us to do. This is what I think is important. Am I right or or am I wrong?"
The Need for Proactive Engagement and "Selling" Value: Data teams often operate in a reactive mode, waiting for requests. To increase perceived value, they need to be more proactive, understand the organisation's strategy, experiment with data to identify potential value, and then "sell" those possibilities internally.
Quote (Shane): "What I see with data teams is often they sit in cupboard. They sit behind this wall of data work and they never really go out and showcase what they've done and so they don't sell it."
Quote (Shane): "if they spent a bit of their time for understanding the organization strategy like where's it going what is its goals and thenffect ly playing with data as data experts to say, hey, given that goal, if we use this data in this way, it may have value. And then going back and and treating as something they have to sell internally, I think it'd be really more fun for them..."
Quote (Aaron): "a lot of us we came into this job because we wanted to be coders or programmers where you believe that hey I'm just showing up to write code and make to build something but it's not my job to sell people on what I'm doing to tell people."
Shifting from Ticket-Based Work to Value-Driven Outcomes: The traditional model of waiting for "geo tickets" hinders the perception of value. Data teams need to focus on understanding the business problem and how data can solve it, adopting a more product-centric approach.
Quote (Aaron): "The process is we get a ticket that comes in, we write it and we give it to someone else and someone else's job is to sell what we do. And I think that's where it's come to our detriment."
Quote (Shane): "look at product management right if you look at those types of roles which you we're starting to see that role of data product manager come in into teams now and it's not sales and marketing it's basically understanding the business problem and then trying to figure out ideulate how you could solve it with data discover which option is actually the best one and then go and implemented."
The Data Catalog Misconception: Data teams often believe a data catalog alone will showcase their value. However, business stakeholders don't typically engage with it. The focus should be on how data is surfaced and the value it provides in a user-friendly way.
Quote (Shane): "Our our store is the data catalog. And I'm always intrigued when data teams believe that having a data catalog, pumping more data into the catalog and and then that's the end of their job. Somehow everybody's going to come shopping in the catalog. Whereas actually data cataloges are only designed for data people. You go talk to a stakeholder, they don't want to use your data catalog. They don't care. There's a bunch of data in there. There's no value to them. They're not data people."
Understanding the Business Context and Processes: Data teams need a deep understanding of business processes to effectively leverage data for improvement, not just reporting. The increasing complexity and automation of business processes within SaaS and ERP systems mean that even business users may not fully understand the underlying data flows.
Quote (Aaron): "I think the the challenge is it's the next level of the four steps of analytics realm. I think the scriptive, diagnostic, predictive and prescriptive. So our value then becomes how do we not only just tell them the story we tell them why is going on how can we do things better..."
Quote (Shane): "if you work with a data team, we love to do data lineage. We love to do this idea of nodes and links to see the flow of the data through our system. And why do we do that? Because that map helps us visualize the works being done, understand what's happening where, understand where we can fix it, where it's broken."
Over-Engineering and Complexity: Data teams can sometimes over-engineer solutions and overcomplicate explanations, making it difficult for business users to understand the value. Simplifying language and focusing on business outcomes is crucial.
Quote (Aaron): "I think that's also one of our challenges around value is that we've overtoled and over complicated a lot of the work that we do and it was very difficult for us to give the business simple answers to question..."
Quote (Shane): "We love we as data people, we love to argue semantics. What is a semantic layer? What is a data product? What is a data contract? We we get intrigued by engineering the the words and somehow we think exposing that to our stakeholders is a good idea. It's argue amongst yourselves, but give them simplicity. Just give them a definition."
Accountability for Value: While data teams provide the data, the stakeholders requesting it are ultimately responsible for delivering business value. However, data teams need to understand and advocate for the potential value of their work and push back on requests that lack clear purpose.
Quote (Shane): "somehow we're held accountable for the value of that data, not the stakeholder who's trying to make that business change."
Quote (Aaron): "I always think the answer the question right if my CEO came to me and say hey what are you guys working on I tell her and she said what's the value. I don't know. I'm giving it to someone else for them to figure out. It just it doesn't come off across as a good answer, right?"
Data Teams as Optional vs. Essential: Data teams are often perceived as "nice to have" rather than fundamentally essential to the core business operations, unlike functions like finance. Embedding data deeply into operational processes can help shift this perception.
Quote (Shane): "we are still perceived in most organizations as being optional."
Quote (Aaron): "I still think that organizations are still trying to figure out what data teams are how to get value out of it because it's just so different organization... unless you really embed your data team into the operational nature of and say, 'Yeah, we actually can't run without the data team.' That's more like operation use cases. I think that's really the challenge that we're still proving our worth so to speak in many different organizations."
The Pitfalls of Constant Replatforming: Frequent technology changes without a clear link to business value can erode trust and the perception of value. Stakeholders don't necessarily see the benefit of repeated "kitchen renovations."
Quote (Shane): "Replatforming every couple years cuz we think it's cool doesn't do us any favors if you're sitting outside the data team and saying what value have they added to us this this year."
Quote (Aaron): "I've been guilty that myself in my career is trying to argue for the replatform... I just need 12 18 months and $3 to $5 million to replplatform it. But at the end of that, I promise you... I think that's where a lot of us have gotten where we do this 18-month transformation and most likely by the end of that your leaders leaving..."
Time to Value and Cycle Time: Stakeholders' perception of time starts from when they make a request, not when the data team begins work. Optimising the entire cycle time, from request to delivery, is crucial. Managing the intake queue and setting realistic expectations are also important.
Quote (Shane): "we often start the clock ticking from when we picked the work up. Oh yeah, it was great. Picked the work up like cra I smashed it a couple of days and it was in their hands yet it's been sitting in that queue for 3 months."
Quote (Aaron): "They're like, 'That's great.' But I asked for this like 6 months ago. So, they don't see it's you're getting like back to out of debt."
The Importance of Roadmaps and Transparency: Communicating the data team's plans and progress through clear roadmaps helps manage expectations and demonstrate value delivery over time. Time horizons rather than fixed timelines are recommended due to inherent uncertainties.
Quote (Aaron): "road maps have been very big to me right now trying to figure out what's the best way to create and visualize road maps to show our business partners because I think to your point like they just we just have to be honest with about hey you're not going to get this for a year."
Quote (Shane): "what they tend to do is they tend to do time horizons... So they tend to say, we'll have a a dot on the page and work that's closer to the dot is more likely to be done and then maybe there's another time horizon and then stuff out there will probably get done at some stage..."
Visualising Delivered Value: Creating visual representations of the value delivered, perhaps by "colouring in" domains on an organisational map as data capabilities are built, can help stakeholders understand the team's impact over time, especially given leadership turnover.
Quote (Shane): "What we can do is we can wireframe out what we think we're going to build and then color it in as we build it... and what we're doing is we're visually showing this map of the value we've added over the last couple years because people forget..."
Incremental Delivery and Building Trust: Providing value in smaller increments allows for feedback, builds trust, and ensures alignment with evolving business needs. Waiting for a large, year-long project to complete increases the risk of delivering something that is no longer needed or doesn't meet the current requirements.
Quote (Aaron): "you definitely want to show incrementally over time because you want to make sure that people feel comfortable that this are going to pay you all the different installments and you have a good experience at the end..."
Quote (Shane): "by showing them something early, we increase their fluency as much as anything else and we get feedback and we get the ability to change before we've gone and build all the pipelines to feed that dashboard or whatever the way we deliver that product."
The Role of Data Storytelling and Data Product Managers: Effectively communicating insights and the value of data through storytelling is becoming increasingly important. The emergence of data product manager roles reflects the need for individuals who can bridge the gap between technical data work and business understanding.
Quote (Shane): "one of the key takeaways for me is this idea of a storyteller we starting to see data storytelling becoming a thing where instead of just giving them back a list of data, we're starting to tell them a story about that data uh in business context."
Quote (Aaron): "that's where you're seeing a lot of the data product managers right you're hearing much more about that role coming out because you realize that that's the don't know it's the data storyteller plus uh storyteller plus your product manager who can tell you about it but they can also figure out if anybody's using it requirements I think that's where you're starting to see these roles get created..."
Sales as a Natural Part of the Role: Demonstrating value requires data professionals to embrace a degree of "sales," not in a pushy way, but through clear communication and highlighting the benefits of their work. This is essential for career longevity and ensuring the team's continued relevance.
Quote (Aaron): "I realized that sales is a natural part of our jobs and our life, right? If I lose my job, my new job is I have to go sell myself to a new company... So I think we have to realize that sales is a part of..."
Quote (Shane): "Describing the value we've added to the organization we work in and keeping adding that value and therefore keeping our jobs is much easier and cheaper than losing our job and having to go into a recruitment round where we have to sell the value we could offer rather than the value we have delivered."
Iterating on Value Communication: Just as data work itself requires iteration, so too does the communication of value. Data teams should consciously experiment with different ways of articulating their impact and gather feedback on what resonates with stakeholders.
Quote (Shane): "I think I'm probably going to add One more layer into that now which is how are data teams experimenting and iterating with describing the value they've added that feedback loop to their stakeholders."
Quote (Aaron): "That's also something I'm working with my team on is doing retros to say okay three months ago in the last quarter we worked on this thing and no one used it or everyone used it. So why did they or did they then not use it? And that should be an input into the future work..."
Most Important Ideas/Facts:
The perception that data teams lack value is a significant challenge that needs to be actively addressed.
Proactive engagement with business stakeholders, understanding their needs and the organisation's strategy, is crucial.
Data teams need to move beyond simply fulfilling requests and focus on delivering measurable business outcomes.
Clear communication, transparency through roadmaps, and visualising delivered value are essential for demonstrating impact.
Incremental delivery and continuous feedback loops help build trust and ensure alignment.
Embracing data storytelling and potentially dedicated roles like data product managers can enhance value communication.
"Selling" the value of data work is a necessary skill for data professionals to ensure their relevance and the team's success.
Iterating on how value is communicated and learning from past experiences is vital for continuous improvement.
Conclusion:
This podcast provides valuable insights into the challenges surrounding the perceived value of data teams and offers a range of actionable strategies for improvement. By shifting from a reactive, task-oriented approach to a proactive, value-driven mindset, and by focusing on clear communication, collaboration, and a deep understanding of the business, data teams can significantly enhance their perceived value and become indispensable partners in achieving organisational goals. It's not just about the data itself, but about the story of impact that the data team can effectively tell.
«oo»
Stakeholder - “Thats not what I wanted!”
Data Team - “But thats what you asked for!”
Struggling to gather data requirements and constantly hearing the conversation above?
Want to learn how to capture data and information requirements in a repeatable way so stakeholders love them and data teams can build from them, by using the Information Product Canvas.