Augmented Analytics – 3/4: The Rise of the Citizen Data Scientist
Part 3 of a series about Augmented Analytics
In many ways, technology is the great equalizer. A person with a minimal amount of knowledge but with the right tools can easily tackle tasks that taxed history’s brightest minds. A five-year-old child with a pocket calculator can tabulate products that would have frustrated Pythagoras. A nine-year-old with a few hours of training on Adobe Photoshop can create images that look like they were made by a professional graphic designer. And when someone uses technology enabled by AI, it’s hard not to look downright brilliant.
Like so many other fields, analytics will become increasingly democratized in the coming years. No longer will analytics be the reserve of big companies with deep pockets. AI-powered augmented analytics can automatically dig through a company’s data, analyze it, and offer actionable insights about the best means of addressing problems detected through that analysis. That means that small- and medium-sized businesses will be able to go toe to toe with much larger companies. Augmented analytics will level the playing field in countless industries.
It will also give rise to so-called “citizen data scientists,” whose primary work isn’t in data analysis but whose access to augmented analytics gives them the ability to gather actionable insights from their organizations’ data. The technology is so intuitive that people in sales, customer service, product design, and HR can make data-driven decisions and recommendations that are in line with those of the data experts. Automation will also allow citizen data scientists to analyze far more data than actual data analysts and data scientists can manage.
The rise of the citizen data scientist will coincide with the proliferation of collaborative decision-making. When everyone within an organization has access to the organization’s data and to the insights generated by augmented analytics, then conversations about strategy and tactics are better informed and more robust. And with the right communications platform and with full contexts derived from benchmark analytics (see our first post in this series), organizations can avoid the pitfalls that imperil those who can’t make sense of their data.
One of the greatest benefits of augmented analytics is the decentralization of data knowledge and decision-making. Because everyone who uses augmented analytics is a domain expert, everyone is entitled to a seat at the decision-making table. All those extra minds working the problem are a boon to an organization’s long-term health.
For those interested in seeing how augmented analytics can turn anyone into a domain expert, check out Kubit, an innovator in the field of AI-powered analytics. We’re doing things no company has ever done.
In our final post of this series, we’ll look at how augmented analytics is helping data teams do their jobs better.
This is part 3 in a series of 4 posts about Augmented Analytics:
Check out the next part of this series: 4/4: Unchaining the Data Scientist