What the Consumerization of BI Means for Data Experts
Data Evangelist, Sigma
In 2005, it was predicted that consumerization would be the biggest trend to affect the world of IT over the next decade — and we saw that unfold right before our eyes.
The beautiful, UX-forward consumer applications people enjoyed using at home stood in stark contrast to the ugly, outdated enterprise tools they were forced to use at the office. Through amazing products like the iPhone and the iPad, Apple helped us see just how transformative a good user experience could be.
The mobile revolution put amazing processing power in the palm of our hands, literally. Slack’s gorgeous UI and microinteractions got us excited about chat again. And the cloud and big data continue to open up possibilities that would have been unfathomable just a few short years ago.
Companies of all types and sizes have embraced these sweeping changes, encouraging their workforce to select and use the tools they need to do more. This has led to a new breed of data-driven employees, eager to leverage technology to generate massive impact.
Data is in demand
One of the last areas to benefit from this consumerization trend has been BI. Once purely the dominion of those with SQL chops or advanced degrees in mathematics, advancements in visualizations and dashboards have made data and analytics tools more approachable for non-coders. Tech-savvy business users have even been able to start marrying their domain expertise with the data delivered to them, and making more informed decisions as a result.
In our fast-moving global economy, the confidence data adds to decision making is too attractive to ignore. From marketing to HR, customer success to accounting, data is changing the way every department operates and transforming foundational processes.
When people are able to ask questions of their data and actually get the answers they need, insights follow. Insight-driven companies are growing 8x faster than global GDP.
But while the benefits of opening up access to data and analytics through easier to use tools are obvious, there’s one question nobody seems to be asking — if anyone can access, analyze, and interpret data on their own, what does this mean for data teams?
There’s a lot of fear and uncertainty around the future of data science. A panel at SXSW a few years back was entitled the not-so-subtle “Data Science Will Be Replaced by Tools.”
Using history as our guide, we see that just about every technological advancement has disrupted some set of industries or roles. Fear and even violent backlash has often followed.
There was massive resistance from bankers against the automatic teller machine when it was first introduced. ATMs exploded in popularity throughout the 70’s and 80’s, but interestingly enough, so did the number of bank tellers.
Because many basic banking duties could be handled by ATMs, fewer bank tellers were needed in each branch. But less bank tellers, meant banks could afford to open more branches and hire more staff. Teller jobs increased even faster than the labor market as a whole. Their daily duties evolved from things like deposits and withdraws to things like customer service, but they were needed more than ever.
According to the latest data from the department of labor, demand for teller jobs will decline slightly over the next decade. But even with the proliferation of ATMs and an increase in cashless transactions, the role of the bank teller will exist into the conceivable future. It’s exciting to think about all the ways the role will expand and evolve as we zero in on the elements that are uniquely human and can’t be replaced by automation and AI.
The data team of the future
The value of a data team’s work isn’t connected to more output, it’s about the quality of their results. The consumerization of BI means data teams can be freed from an endless queue of low-level ad-hoc reports and other manual work. They can then use this time to focus on more complex and impactful data projects that add more value to their organizations and bring greater job satisfaction.
Many of these tools also enable the automation of the manual and tedious parts of data prep work that are huge time-sucks for data scientists. This shouldn’t be viewed as a threat to those in the data world, but a welcome relief.
As Reddit user TheLostModels so eloquently posted in r/datascience:“If data cleaning (?) and model selection are your favorite parts of data science, then there is probably more to worry about.”
If anything, the consumerization of BI and the rise of self-serve analytics allow data teams to focus on high-value projects like building new and better data pipelines, setting up data warehouses, and building better data models — work that requires research, critical thinking, and creativity.
Success can no longer be measured by the number of reports or dashboards they create for line-of-business leaders but on the value they deliver to the organization as a whole. This all requires a dramatic shift in their thinking.
Howdy, data partners
There’s a reason data science is ranked as the sexiest career of the 21st century. Data teams have an incredible opportunity to collaborate with others and help push the organization forward.
The best BI tools don’t just make data accessible to all, they enable everyone to get into the data conversation and work together to extract insights. Sigma bridges the gap between data teams and business users, allowing both to collaborate and make best use of their expertise.
Its familiar spreadsheet interface helps business users jump in and immediately feel comfortable while advanced tools like our visual data modeling and SQL runner allow the experts to flex their data muscles.
The ball is in your court, data teams. Are you ready to step up and help lead your organizations forward through data? If so, get in touch.
To help you get you started, download our latest ebook, “Data’s Inferno: How to Data Teams Can Escape Report Factory Hell.”