Self-service has been a hot topic in the analytics space for many years already, and it won’t be going away any time soon. Given that, it is reasonable to ask: why is it that with all the focus it has received, self-service capabilities are still far off from where organizations want them to be? Is this due to the self-service concept being overhyped? Poor execution and implementation? Insufficient supporting technology? Something else? Let’s dig in and discuss.
The Challenges of Self-Service Analytics
We’ll start by outlining some of the common concerns and objections about self-service analytics before covering why these concerns and objections can be overcome and self-service made a reality.
Realizing the vision of self-service is not a simple task. Self-service is about enabling people to easily do things that they don’t know how to do themselves using the experts’ toolsets. For example, those without SQL knowledge can still generate queries through an interface. Or, those who don’t know machine learning tools can still build a basic predictive model through a guided template. Thus, a self-service tool must take something complex and make it easy for someone with limited knowledge or understanding of the underlying process to safely make that “something” happen. Of course, without proper guard rails, a self-service tool can clearly be a dangerous thing to hand over to users. Much like putting a bunch of ingredients on a table and letting children start trying to make cookies, you have to be careful to predefine views of data and recipes for models so that the self-service users are channeled down the proper path.
Another challenge with self-service tools is that they are configured to enable specific sets of analytical logic to be executed. However, there is a limitless number of business questions that can be asked and a limitless number of ways that analytics can be created to address those questions. As a result, no matter how strong a self-service tool might be, it is easy to identify questions that it just cannot answer. And that’s okay! Self-service analytics aren’t meant to be all-encompassing. They are meant to facilitate a broader range of users and a more efficient allocation of resources.
Yet another reality is that business users — especially senior executives — don’t have a lot of time and want their questions answered via the path of least resistance. This often leads to the executive requesting that a member of the analytics team execute an ad hoc analysis instead of using a self-service tool to do it themselves. This frustrates and distracts the expert from more complex work, but they can’t turn down the request. However, if a self-service tool is configured to easily answer questions so that no custom work is needed, the expert can quickly get the answer with limited distraction.
The Successes of Self-Service Analytics
Now let’s look at the ways that self-service analytics can become a highly valuable reality.
First, self-service may not be perfect, and it certainly isn’t all encompassing. However, don’t sell it short. It is tempting to take the glass-half-empty view and declare that self-service tools are insufficient since they can’t handle as many cases as we’d like. A better, glass-half-full view acknowledges that self-service tools can answer a range of questions, even if they can’t answer all of them — every question answered in a self-service fashion is one less question that the experts have to handle!
Next, if you take the time to look around your organization, there’s a good chance that you’ll find a wide range of self-service successes. Don’t let those successes be forgotten or minimized just because self-service hasn’t solved everything. Self-service will never be fool-proof and it will never be complete. If forward movement is occurring and your organization is effectively expanding its self-service capabilities, embrace the progress as you push for more.
Finally, in the drive to enable more self-service, your organization will achieve a lot of positive things: better understanding of the breadth of data available, more complete cataloging and definition of both data assets and business problems, and increased usage of analytics as your organization becomes more data-driven. Accept that your self-service journey will never be complete and instead focus on all the waypoints that you pass as you continuously improve your capabilities.
Defining Your Self-Service Journey
To be successful with self-service, an organization must find the right mix of automated and standardized processes that can be deployed alongside a team that can handle any questions not yet automated. It must be recognized that not everything will be automated. A new, never-before-asked question might need some personal handling before it can eventually be automated and made available in a self-service fashion.
The key to success is to have a common platform that can handle all your self-service and ad hoc requests equally well. If ad hoc requests are being answered using the exact same underlying data, governance, and security infrastructure as self-service requests, then it becomes that much easier to migrate a new process from manual ad hoc status to fully self-service. Getting scalability and consistency in place is a crucial first step to a successful self-service journey.
One option to enable all of this is a data operating system such as DataOS from The Modern Data Company. A data operating system can identify, catalog, and govern data from any type of system from one central entry point. Better yet, it can apply consistent governance and security protocols to all data requests regardless of their source. This makes managing and implementing self-service tools easier than ever before. To learn more about how a data operating system like DataOS can help your organization democratize data and enable more self-service, download our white paper A Paradigm Shift in Data Management - Generating Reports.
