October 3, 2018

The Pitfalls Of Relying On Business Intelligence In Today's Data-Driven Environment

By 
Harry Glaser

Business intelligence (BI) has long provided a reliable way for nontechnical employees to dig into data and answer basic questions to do their jobs. From the emergence of BI in the ‘80s until around 2014, the impact of this type of analysis remained fairly constrained. Data was collected in small, siloed channels and used to answer straightforward questions. BI could be used to find out how much revenue a company made last year, or how many sales were made during a certain time period, but those were the limits of data analysis.

The Ascent Of Data Companies

For decades, Moore's Law has made the cost of computing and memory cheaper and cheaper. In the last five years, they've become cheap enough that companies have started collecting every piece of data they can get their hands on. Companies are now storing and analyzing all kinds of data on a huge scale.

Early movers started building specialized data teams to answer new, more complicated business questions, shifting their focus away from traditional BI. These companies are answering new questions every day. It's not just how much revenue or how many sales; it's how to route ships around the world more efficiently, how to track internet of things (IoT) device activations or even how to send the right text message to a person in crisis to avert a suicide. A few years later, the amount of data generated has grown exponentially, compounding and changing how we understand the landscape of modern businesses.

The clear winners of the explosion of data and data teams may not fit into the box we’d traditionally like to put them in. They are revolutionary companies that are reshaping legacy industries. Companies like Netflix, Facebook, Uber and Amazon were ahead of the game in building data teams to solve advanced business problems and create huge opportunities by leveraging data. Disguised as an entertainment company, a social media platform, a transportation provider and an online retailer, these companies used formalized data teams to start a data revolution. This has enabled them to emerge as the big winners in their industries. In reality, these companies have always considered themselves data companies. Their strategy was to harness their proprietary data, make better decisions across the whole company and leapfrog their competition.

What role has BI played in the shift to data teams? Not a big one. BI can still answer sales and marketing questions within small data sets, but those answers are becoming insignificant compared to the value provided by a data team analyzing vast data sets. BI questions tend to be backward-looking inquiries that satisfy status quo business metrics. They don’t harness any of the power that came from the data revolution. Businesses that use traditional BI to make decisions are falling behind, replaced by companies that hire CDOs and build data teams to investigate questions that couldn’t have been asked five years ago. The data landscape has evolved to a point where BI on its own isn’t enough.

The Perils Of Relying Solely On BI

Most traditional BI tools offer a drag-and-drop query interface, allowing nontechnical users to directly query data from data models that have been built by IT professionals. The promise of dragging and dropping to find fast answers is still possible, but in my opinion, the questions that need answering today are far too complex to simply drag and drop your way to an answer. Overestimating the importance of these simple questions is the lie of BI. The way I see it, this outdated model of data analysis lacks the speed, power and flexibility to give companies a competitive advantage.

I believe companies that claim to make “data-driven decisions” that rely solely on BI are fooling themselves. Any organization that doesn’t build a formal data team can’t honestly say that they’re letting the data drive. Imagine a company that claims to be sales-driven without a sales team, or customer-driven without a dedicated customer success team. They mean well, but they’re not putting in the work to make that goal a reality.

Data Teams Answer The Tough Questions

The loftier objective of handling data’s complex questions requires a team of trained data professionals, not just a tool that answers basic questions. The process should begin with leveraging a data team to create an environment for business users to explore on their own, without code, built on a single source of truth for all their data inputs. The findings that business users create from this process may seem independent, but in reality, they are an extension of the work that’s being done by the data team.

The most cutting-edge data teams can likely get even more non-data pros involved in that process by finding ways for them to drill down further on their own new questions as they come up. Those users can’t be left out of the recent revolution; they just need guidance from the data team to unlock their business value.

Ultimately, the value of BI is an extension of the value of the data team. If they create data sets where anyone can answer their own questions, they’ll be free to dive deeper into the inquiries that actually add value to their company. But no organization today can afford to stake its entire business on the backs of simplistic BI decisions. As I see it, a company that forgoes innovative data inquiries to spend resources chasing status quo metrics is just extending their long march into obsolescence.

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