For as long as I’ve been working in data analytics — going on 15 years now — the industry has been talking about self-service analytics and BI. Back then, it was more of a pipe dream than a reality.
Today, it’s both. But the nature of the pipe dream has changed, and it’s time to start thinking about self-service differently.
When I started in this field, it was pretty normal to see “Excel proficiency” on a resume. People were proud of that skill set because it was uncommon. Interest in self-service analytics was also uncommon, as I learned in my first job as an actuary.
My role was to provide insights to an executive team and answer their questions about the business. But when I would try to show them the data or the math behind it, they would quickly move on.
I was a kid fresh out of college. I knew nothing about their business or their industry, but they accepted my recommendations without question. It made me extremely uncomfortable, but that was the analytical paradigm of the time.
Since then, we’ve seen steady progress toward widespread data literacy. If you saw “Excel proficiency” on a resume now, you’d probably chuckle, because today we expect people to have some degree of data literacy and self-service analytics proficiency.
Today, we have far more sophisticated analytics tools than Excel. Many of them claim to be self-service. And that’s where the pipe dream comes in.
It’s easy to get caught up in the idea that self-service is simply arming users with some software that has an intuitive interface and turning them loose. But underlying that notion is this assumption that business people will go and get what they need, and make decisions on their own, intelligently and responsibly — without negatively impacting their peers.
That’s a pipe dream. What business decision, made by an individual or group all by themselves, doesn’t impact everybody else? The entire concept often leads to tool or data ownership scuffles.
Take visualization tools, for example. I’ve seen too many situations where IT tries to jettison responsibility for them over to the business, or the business tries to take them away from IT. Instead, this is something that both teams should work on together.
These kinds of struggles have done damage to the idea that the business can sit at the table with technical folks and have a shared experience around data. But this is exactly what needs to happen. Instead of focusing on features and interfaces and what people can do on their own, organizations need to focus on building a culture of collaboration around data.
To get meaningful insights, you have to blend art and science. You need both business acumen and analytical know-how to effectively scrutinize the situations where the numbers tell you one thing, but your knowledge of the business tells you another.
That’s what made me so uncomfortable in my job as an actuary. I constantly worried that the numbers I knew so well weren’t telling the whole story.
Blending art and science requires organizations to think beyond software and systems. It’s not enough to simply “allow” the business to participate in analytics. You have to do it in a way that empowers people to collaborate, build bridges, and take on more responsibility. To that end, we have to disrupt our current thinking and practices around self-service analytics and BI.
In the analytics space, we’ve had a lot of innovations, but precious few disruptions. I define innovation as a better way of doing the same thing. Arguably, SQL Server Reporting Services (SSRS) and Crystal Reports were innovations that improved the analytic process.
Disruption, on the other hand, fundamentally changes the way we do things. I would contend that Tableau was disruptive. It carved out an entirely new market, and Gartner responded with a new Magic Quadrant.
More importantly, it changed the way that the business interacted with data by finally offering them an equal seat at the table through data visualization. This disruption significantly impacted the prevailing culture around data.
Collaborative analytics is the next foreseeable disruption to influence an organization’s culture around data. It calls for a community-driven approach to business intelligence, and uses a combination of BI software and collaboration tools to allow various teams in an organization to participate in data analytics. It also requires the proper foundation to enable it, because such a culture is built upon data being accessible, transparent, and performant.
Collaborative analytics around transparent, fast, and available data is not a pipe dream. We have the tools to enable that now. Unified Data Analytics Platforms (UDAPs) provide a place for business and technical people to work in the same environment, with the same data.
UDAPs make it easier for business people to ask questions and query the data themselves. They enable self-service data access and dashboard/report creation, and put guardrails around places where users could unintentionally drive off the road. As a result, users are self-sufficient when it comes to data access and dashboard and report creation, but are well-supported by the community-driven approach to the data.
An environment like this fosters conversations around data, sharing of information and insights, and collaborative decision making. When that happens, organizations are able to realize exponential value from analytics. This is what we should really be aiming for.
The goal is not just giving people tools to visualize data or move it from point A to point B. It’s not about saying, “Here, businessperson, go do it yourself,” which seems to be the reigning definition of self-service. The bigger question is, what does the tool do to the culture?
Self-service analytics and BI is a pipe dream because no one is self-made. Everyone is community made. The idea is to build an environment where everybody can go to the well. They all see the same thing. And they can all have a conversation about it.
It is a function of people and technology, and you have to move both together.
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