Your Data Product Will Fail: Here is How UX Can Help

2 Minutes Read

I am not being a pessimist.  Countless articles back this up.  Most data products fail to meet the expected business outcomes.  If you want to change this narrative, you need to approach your data projects differently.  In particular, you need to integrate the users of your data projects much earlier than you might think.  I argue, the expected users of your data product are the first people you need to talk to.  Before you start collecting data, start building pipelines, tweaking your algorithm, thinking about the UI - you need to understand what problems your consumers are trying to solve.

Here’s a summary of a recent conversation:

PUX: Are your stakeholders interacting with data?  

Client: Yes

PUX: Are you talking to your stakeholders before you start your data product development processes?

Client: No. We tried to improve adoption with a streamlined UI, more data, new algorithms, new metrics.

We see this situation time and time again.  Simply designing a nice UI and pushing out more data, new metrics or the latest algorithms isn’t enough.  A Gartner report from 2019 states, only 20% of analytic insights will deliver business outcomes. I would argue, it's most likely less than that.  There are a lot of reasons for this, but the lack of focus on UX is one the biggest reasons.  

UX principles need to be integrated throughout the process of designing data products.   The first step is understanding the users of your data product.  Before you start data projects, are you asking your stakeholders:

  • What are you trying to do achieve
  • What questions are you trying to answer?
  • What are your business goals?
  • How do they use the product / consume the data?

If you aren't asking these questions before you start your data projects, you are missing out on valuable insight that will guide your data product development.  UX is an integral part of product design, helping successful products become reality.  Data products are no different, UX principles are critical to help you successfully launch your next data product.  Understanding your users, what they are trying to achieve and how they are trying to achieve are critical to developing effective data solutions.

Now, you could forward this article along to your data team of data scientists and engineers and say “You need to talk to the users.”   They will most likely say, “We already talked to the users, we know what they want.”  And they are most likely telling you the truth.  They most likely did talk to users.  Their effectiveness in this endeavor shows up in the adoption of the data product.  If your data product adoption is lagging, they didn’t ask the right questions.  Sending out non UX trained professionals to gather user information creates the problem.  Most data team members are not trained researchers, they don’t know what questions to ask or how to not introduce bias, among other things.  We see this on a regular basis, data teams gather feedback reinforcing what they want to build.  After months of development you get a data product they love, but no one uses.  

Good UX researchers are hard to find, but invaluable.  The best UX researchers can help you uncover the unmet needs of your users.  They systematically develop questions that help data teams understand the feelings, emotions, and needs of the users.  They gather feedback on the problems and challenges the users are trying to solve.  And they uncover bias and perspectives.  Once you truly understand your users, only then can you start to devise solutions for them.  Otherwise you are just wasting money, and more importantly time. 

Most data products fail because they don’t understand the users. PUX specializes in uncovering the unmet needs of the data product users, if you want to increase your chance of success, reach out. 

Picture of Steve Stesney

Steve Stesney

Steve Stesney is a Senior Product Leader and Data Practice Lead at Predictive UX. Steve talks about Data Mesh, Data Fabric, UX Design, Graph Databases, Knowledge Graphs, and Product Leadership. His career spans more than 20 years with the last decade focused primarily on strategy and consulting on data solutions.