Self-Service BI: What is it, Benefits, & Historical Concerns

Whether you call it self-service analytics or self-service business intelligence (BI), there has been much discussion about the perils, myths, promises, and prospects of successfully building self-service capability. Going forward, I’ll use the phrase “self-service BI” but you are welcome to substitute the words “self-service analytics”. So, is self-service BI actually attainable or just snake oil? If it is attainable, then what do you need to know to ensure your self-service BI endeavors are successful? In this article, I’ll walk you through the key benefits of self-service BI and some of the top concerns. I’ll also share what you need to do the build a successful self-service BI program.

Self-service BI is a business capability that enables business users, to ask questions of their data, get insights, and analyze data without relying on IT, BI specialists, or SQL; for the purposes of taking business actions. Now, let’s break this down just a little.

Most data engineers and analysts are familiar with the typical data analysis request that I call the “cycle of insanity.”  A stakeholder makes a request for dashboards or to have questions answered, followed by the realization that the data doesn’t exist in the system, data engineering needs to ingest, secure and govern the data, and the analyst buildout the dashboard, only to have the request refined to answer the next question.  

While this approach may provide value to the business and the data teams may feel valued, the problem is scale. This process does not scale, not even for small companies. The business wants visibility into its operations, to intuitively use the data they have, and to focus on the business operations. Analysts overwhelmingly want to provide insights that drive value for the business and not on mundane joyless data extracts or more deadboards.  

Data volumes are growing, business leaders are demanding more insights, and each new question yields additional questions. This in turn creates more and more data requests from data teams; which is simply unsustainable. This is where self-service BI finds its sweet spot, backlog reduction; freeing data engineers, analysts, and business users from the never-ending cycle of insanity and dashboard hell.

Let’s take a look at a few of the primary benefits of self-service BI and the chief concerns associated with them. After that, we’ll look at a few things to consider when building out your self-service BI capabilities.

Self-service BI solves the problem of scaling your business intelligence capability. Enabling users to answer their own questions, ask the next question, get their own insights, and take action, does a few key things.

Historical concern: Empowering business users that may not have the skills or even desire to take on such tasks, may reduce the value of self-service BI.

Identifying and realizing cost reduction opportunities is often a critical factor in getting leadership support for new programs and initiatives.  Here are three areas where self-service BI can help you realize actual savings for your organization:

Historical concern: Integrating and securing new technologies may add cost and delay the time to value of self-service BI.

Forward-thinking business and data leaders know that futurist Jim Carroll is right, when he declares, “the future belongs to those who are fast.” Markets and industries are changing and are reacting to market forces faster than ever. For example, when the pandemic disrupted supply chains and there was uncertainty in the auto finance industry, auto lenders, wholesalers, and dealers that pivoted quickly to the pre-owned car market actually increased their revenue and profits while laggards missed the opportunity. This illustrates two of the major benefits of self-service BI:

Historical concern: Users may not fully understand the data or know how to make inferences from the data.

You have seen the primary benefits of self-service BI; you are ready to embark on a self-service BI journey; to ensure you have a successful self-service BI program, do these three things:

I’ve already mentioned Jim Carroll earlier but truer words have never been written. Think big about what you want your organization to be – it’s a must. Start small by picking use cases that provide quick wins and business value. They also build momentum. And scale fast by showing these quick wins across the organization. 

Business leaders are competitive; it’s human nature. We all want the best for our customers and employees. So, seeing how co-workers are winning with self-service BI will drive adoption and increased funding across the organization. While business users may initially understand how self-service BI fits into their processes, seeing their co-workers winning will be a huge motivating factor. 

Maybe you have strong data-driven culture, but few organizations do. Building a data-driven culture is part of any self-service BI capability and will require an intentional effort for improvement, an analytics champion, and support from senior leadership. According to New Vantage Partners, 92% of organizations attribute the “principal challenge to becoming data-driven” to people, business processes, and culture, with only 8% identifying technology. 

For your self-service BI program to be successful, consider building a data literacy and enablement program. Regularly scheduled “Office Hours” is a common practice that solves problems and builds trust. Gamification is also common; it often includes user-led presentations demonstrating “I built this”, awards and badges for milestones, such as “1st Liveboard” or “10th Insight” generated, and a leaderboard for answers and liveboards. All of these create a sense of trust, support, and community within the organization. 

The analyst of the future is slightly different from the analyst that sits in the center of the cycle of insanity. Future analysts will optimize analytic data and data models to enable self-service BI and enterprise scalability. They will focus on the most challenging analysis, not the most mundane. While most of the tools and skills remain the same, optimizing for scale and self-service BI will be an additional skill.

To fully unlock the potential of self-service BI, analysts will need to create data models that answer questions for a larger domain rather than ad-hoc reports or dead dashboards. Data models in self-service BI will dovetail with existing governance, security, and business logic to ensure that non-technical business users have the right data to make the right decisions.

Self-service BI is not snake oil and it can be implemented by organizations that embrace the modern data stack. Yes, it’s attainable but like anything of value, it’s worth a bit of planning, organization, and process. Your BI capabilities will depend more heavily on the people and process than it does on the technology.  However, if the technology for your data experience layer doesn’t support augmented intelligence, search-based analytics, and AI-driven insights you’ll have a hard time achieving the benefits listed above.   As for the people and process components, focus on building a data-driven culture for your organization, and remember that well-governed and well-modeled data will enable scale within your lines of business.

So next time you or your team is caught in the cycle of insanity, rest assured that breaking the cycle is possible and let your business stakeholder know there is a way out!