4 Reasons Self-Service Analytics Still Matter

Ben Bausili
Global Experience

Practice Director
March 2, 2023

When I first entered the analytics industry, we built everything in legacy tools and code. It was slow, inflexible, and required a lot of specialized expertise. This stagnant business intelligence world was ready for disruption, and the arrival of Tableau was a bolt of lightning. Suddenly we were delivering projects at 10% of the cost and excitedly telling people about the power of data and self-service analytics.

A recent post about the death of Big Data reminded me that the industry often is a pendulum swinging from one thing to another. The pendulum swings within business intelligence too. Self-service and empowerment of users were all the rage a decade ago, but the excesses of it turned many platforms into the wild west with junk reports scattering the landscape. We've gone from "data to the people" to more talk about strict governance and controls. Of course, these aren't binaries, but sometimes the zeitgeist makes it feel like they are.

So, Why Is It Still Worth It?

Self-service means giving business users access to the data and the tool needed to make data-driven decisions without being locked behind the specialized resources of IT, analytics or developers. Self-service analytics is vital to any data culture. If you want your people to be informed when making decisions, the data needs to be accessible, timely and trustworthy. The fastest way to destroy a data culture is to ensure their questions will require weeks or months to get answers. 

Let's break down four reasons why self-service analytics is worth investing in, and then we'll talk about some ways you can start your journey.

  1. Time-saving: The fastest path is a straight line. You're speeding up data-driven decisions by giving business users direct access to data instead of waiting for IT departments to process requests. This quick access to data can help organizations make decisions faster, leading to better results and improved competitiveness.

  2. Increased efficiency: Business users know their data. Centralized groups often do not and thus require back-and-forth with business users to get accurate, actionable reports built. We shorten the analysis time by giving business users the power to analyze data themselves. This increased efficiency can help organizations to make more informed decisions, leading to increased profitability.

  3. Improved collaboration: With self-service analytics, business users can collaborate more effectively. Instead of relying on IT departments to interpret data, business users can analyze data themselves and share their insights with colleagues. This collaborative data analysis approach can help organizations make better decisions and find new insights.

  4. Increased agility: Self-service analytics gives businesses the agility to respond quickly to changing market conditions. With access to real-time data, organizations can stay ahead of the competition. This increased agility can lead to better results, improved customer satisfaction and increased profits.

What To Do With Self-Service Analytics Now

If you're on board, it's time to take stock of your current state and plan a path forward. Tools like Tableau have grown based on many of the self-service capabilities they deliver. Still, the industry landscape continues to evolve, so it's essential to consider which tool or combination fits your organization's needs and existing skillsets. For example, tools like Thoughtspot provide a search interface to make it easier for people to answer data questions; this is especially useful when you've reached the end of your existing dashboard and still have questions. Some tools like Altery's Auto Insights or Metabase take datasets and begin generating insights automatically. This automated approach turns data exploration on its head; instead of building analysis and exploring blindly, you start by scanning through generated reports and visualizations to see what jumps out. You can then use those outliers and insights as your jumping-off point. In many cases, the correct answer will involve:

  • Taking a best-of-breed approach.

  • Selecting the best tools from across vendors.

  • Combining them into a single-user experience with a tool like Curator.

There's a lot to consider, so if you'd like some help getting started, we'd love to help! Reach out if you'd like a free consulting session to discuss how we can help you form the right strategy, enable your teams and build the modern data platform that will take you into the future.