What Does the Future of Self-Service Analytics Look Like?

Dataiku Product, Scaling AI Catie Grasso

Business intelligence (BI) first came on the scene in the early to mid 1990s, helping organizations deliver more standardized enterprise data analysis capabilities. Then, the rise of self-service BI came in the late 1990s and early 2000s when there was an appetite for these insights beyond just decision-makers and executives. Since then, there have been various waves of self-service analytics that were promised and reinvented and the notion just keeps coming back to the surface. It never fully goes away, nor is it ever fully capitalized on without roadblocks.

→ Get the Ebook: Building Self-Service Analytics in the Age of AI

With that being said, how can organizations ensure that their self-service efforts aren’t thrown to the wayside (read: either not utilized or taken advantage of by the business or failing to generate actual value)? 

In the video below, hear from Shaun McGirr, EMEA RVP of AI Strategy, and Stephanie Griffiths, AI Evangelist, as they share some insights on the role self-service analytics plays in the age of AI and why — often because of semantics and lack of alignment across teams — self-service analytics has frequently left organizations flummoxed and fumbling for a clear path forward.


So, What Actually Is the Path Forward?

Let’s say someone on your central data team is struggling to service a lot of requests and prioritize the highest value work. The idea that they can get other people involved is very attractive. So, for IT-driven buying and thinking, that’s why self-service analytics keeps rearing its head — it’s a huge win. The business users, though, need to find the time to do the work themselves without the expertise of the data experts, so it’s a hard sell to those stakeholders, especially because “self serve” as a concept doesn’t necessarily scream “positive value story” and might make them feel like they’re on an island.

What’s the key, then, to unlocking that value and getting buy in from the business? The answer, like many challenges in data science and AI, lies in empowering people. We’ve reached a point where people (such as an analyst or someone on the business side who is doing much more with data than ever before) want to drive additional insights. They’ve created a dataset, merged it, cleaned it, made data visualizations, and they still want to go further to answer a business objective (and probably ask new questions as well). In the end, it’s a win-win for them too because they can:

  • Get the autonomy to create data projects, all without having to learn to code or put even more strain on IT 
  • Put data quality and governance measures in place to avoid data chaos
  • Capitalize on their existing BI data pipelines and turn them into AI data pipelines by creating AI products (which, in turn, fosters data asset reuse and saves time to scale faster)
  • Start out with one simple insights question and hopefully use that as a springboard to discover new initiatives 

To go even further on the future of self-service analytics — and uncover an easy to remember analogy for what it should look like in practice — be sure to download the full ebook

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