Self-service analytics has been a leading priority in the business intelligence (BI) space for years and is likely here to stay. With data-driven culture on the rise, analytics is no longer just for IT teams and data scientists. Self-service business intelligence tools make it possible for personnel across functions to perform analytics-related tasks themselves, dramatically reducing time to insight.
This article will explore self-service analytics in detail, covering use cases, proven benefits, common objections, and strategies for implementation.
What is Self-Service Analytics?
In the late 1960s, when databases were just getting their start, “only individuals with extremely specialized skills could translate data into usable information.” Stakeholders, most of them in finance at the time, had no choice but to send even the simplest analytical requests to IT and wait patiently for the numbers to come in.
Today, self-service analytics solutions enable business users to complete BI tasks themselves instead of sending them to IT. Such tasks can vary widely and can be as simple as applying a filter to a report or as complex as building an entire dashboard from scratch.
The goal of self-service analytics, according to the Business Application Research Center (BARC), “is to give the users of BI tools more freedom and responsibility at the same time.” Users have more freedom over when and how the tasks are performed, but they are also responsible for its execution. Empowering data stakeholders in this way allows enterprises to decentralize their analytics, thereby improving operational efficiency.
Self-Service Analytics vs Self-Service Business Intelligence
There is ongoing debate around the relationship between analytics and BI. Some industry experts view analytics as a type of BI while others maintain it’s the opposite. Still others say the two are entirely separate, but it ultimately depends on who you ask. For the purposes of this article, “self-service analytics’ and “self-service BI” will be used interchangeably.
Self-Service Analytics vs Ad Hoc Analytics
Self-service analytics is often confused with ad hoc analytics. An ad hoc analysis is one designed to answer a new, emergent question or use case as opposed to a routine one.
Unlike self-service analytics tasks, ad hoc analytics tasks can be carried out by anyone. What makes them ad hoc isn’t who is performing them but why. An IT manager might, for example, refer to a systems performance dashboard daily but need to build a special report in order to get to the bottom of a specific error the dashboard uncovered. This wouldn’t qualify as self-service BI because the IT manager isn’t a business user, but it would qualify as ad hoc because the use case is an emergent one.
Analyses can be both self-service and ad hoc, however. Consider a marketing manager curious about a recent spike in traffic to the company website. If none of the canned reports available shed light on the situation, she will have to perform an ad hoc analysis of her data to determine what caused the spike.
What is the Role of IT in Self-Service Analytics?
People sometimes misunderstand self-service analytics to be entirely independent of IT, but this is far from the case. Technologists work diligently behind the scenes to ensure that business users have everything they need to help themselves to data insights in the moment.
IT’s responsibilities will vary depending on the organization’s culture, tech stack, and self-service capabilities; but they generally include some amount of data management, analytics software administration, and canned report building. Data and analytics responsibilities may also be distributed across multiple teams. In any case, IT helps ensure that business users are never left with more responsibility than they require.
What Are the Benefits of Self-Service Analytics?
By decentralizing and democratizing business intelligence, self-service analytics yields the following benefits:
- More bandwidth for technical teams. Because they are not responsible for fulfilling everyday ad hoc analytics requests, technical teams — be they IT, Development, Support, or BI — have more time to devote to core responsibilities, mission-critical projects, and other tasks requiring their specialized skills. In many cases, this also lowers operational costs.
- Reduced time to insight for business users. Because data stakeholders can help themselves to insights instead of waiting for fulfillment from IT, they are able to make data-informed decisions more swiftly.
- The fostering of a data-driven culture. Self-service analytics, in conjunction with a data literacy program, can help organizations establish a work culture more accustomed to and accepting of data-informed decision making.
It’s important to note that self-service BI technology alone will not be enough to realize these benefits. Organizations should adopt self-service analytics tools as part of an overall strategy that includes new workflows, policy changes, and education.
How to Create a Self-Service Analytics Strategy
The first step in building a self-service BI strategy is understanding what types of users you will be serving. Eckerson Group identifies four general types. Before you craft personas around your user base in specific, be sure to familiarize yourself with them and their needs. This step will help you determine what software functionality you will need to serve your personas.
Survey your user base and begin grouping individuals according to their needs. Consider:
- What data they will need to access. This will help you curate your data sets in a user-friendly way.
- What types of canned reports and dashboards they will want. Providing helpful content from the start will go a long way toward transitioning users to self-service.
- To which of the four user groups they belong. Again, this will make it easier to find software with the right capabilities.
- What their current data practices are. This will add dimension to the user group question and help identify any gaps in the services you are planning.
- How they feel about self-service analytics. A self-service analytics strategy isn’t just about providing tools! To transition a workforce, you’ll want to know who your champions are so they can help facilitate. Understanding the root cause of any mistrust or cynicism will also help inform your policy and training.
- What their priorities are. This will add dimension to the canned dashboards and reports question as well as identify keys to a positive user experience for that persona.
Once you have your users grouped, give each group a name and detailed description. Demographic information can help make the persona feel more “real” but isn’t a requirement. Simply knowing Mandy the Manager’s needs, priorities, and attitudes towards data analytics will help keep your strategy focused over time. Remember to validate your personas periodically and update them if needed.
How to Select Self-Service BI Software
We cover self-service analytics features in other articles (see The Seven Self-Service BI Essentials and, for capabilities by user type, Data Consumers vs Data Explorers), so we won’t go into detail here. Just know that, as important as technology is to a self-service BI strategy, it’s usually not the limiting factor when it comes to the success of the overall project.
In a recent recording of the Data Talks podcast (subscribe or check here to catch the episode when it airs!), data literacy expert Jordan Morrow maintains that tools often get blamed for low adoption rates when the problems lie elsewhere:
“I could tell you right now, it is one hundred percent a skills gap. A hundred percent a skills gap. It’s not tools and technology, which get blamed so often. Tools and technology are fine — they operate fine, they work fine, but they get blamed so often for why adoption does not occur in data and analytics.”
So while we would of course encourage you to get in touch with us about Exago BI’s self-service capabilities, we’d also advise giving plenty of thought to your plans for educating, training, and incentivizing users.
How To Educate and Incentive Self-Service Analytics End Users
We have several resources to get your cultural strategy off to a strong start.
First, you will of course need to train your end users in the BI software solution you select. Check out 8 Tips for Designing Your End User Training Program for ideas sourced directly from Exago BI customers.
Next, you’ll want to equip users with general data literacy training. This episode of Data Talks offers a great primer and links to data literacy courses offered by TDWI. Low data literacy might sound like a minor priority, but it can have serious consequences for your BI project if left unaddressed.
And finally, Encouraging Top-Down BI Adoption in 5 Simple Steps will help you effect a smooth transition from your legacy workflows to the new self-service system. The more your users understand and experience the benefits of self-service analytics, the more eager they will be to adopt new practices.
As always, plan on validating and revising your strategy as it plays out. As data literacy guru David Langer reminds us, “moving the needle is hard.” We shouldn’t expect to get it right on the first try. Maybe your personas turn out to be inaccurate or your training program needs a redesign. That’s okay! Anytime an effort fails, you walk away with the consolation prize of new information. And in a data-driven world, that’s as good as gold.
Ready for real-life self-service analytics success stories? Learn how Exago BI helped a midsize SaaS company free up 200+ developer hours a month.
About Exago BI:
For SaaS businesses looking for embedded BI solutions, Exago supports providers and their users by making it easy for business professionals to access and manipulate their data, no matter their technical qualifications. Exago BI integrates seamlessly with web-based SaaS applications to bring ad hoc reporting and analytics to any multi-tenant environment. Our dedication to customizable integration allows us to prioritize usability without compromising on power. Contact us today for more information.