This post originally appeared on Dataconomy.com
Among the many things I loved about the latest Star Wars movie was our introduction to the unexpected rise of Rose Tico – a humble mechanic who gets thrust into the heart of the action following a chance encounter with Finn. By the end of the movie, she’s flying ski-speeders in one of the film’s most critical battles. As the going gets tough, Rose’s role expands and gets more complex and demanding, but she never wavers from fighting to get the job done.
Many data professionals can certainly relate to Rose’s character. The role of data analysts and data teams has become unrecognizable compared to what it was five years ago. As data proliferation increases in both size and complexity, the responsibility for utilizing it is moving from dedicated specialists – including data integrators, scientists, modelers and business analysts – to cross-functional data teams that blend all of their skills together. Today’s data professionals can no longer get away with delivering basic business metrics and performance indicators. They now have to work collaboratively to create sophisticated analyses that allow a business to predict the future success of the business.
The Transformation of Data Teams
Traditionally, the role of prediction has fallen to the elite cadre of data scientists. A business analyst might have focused on measuring churn, but rarely would have been tasked with predicting which companies might churn in the future. Other sophisticated tasks, such as natural language processing or model construction, have been reserved for a fairly elite group of data professionals with strong engineering backgrounds.
Today, however, market pressure is forcing these forward-looking analyses to become a regular part of business operations, which means every member of the data team is expected to acquire these essential skills. Data manipulation languages like R are rapidly emerging, and a quick glance at Stack Overflow’s survey of the most popular developer languages in 2017 shows Python – a language often used for advanced analysis – among the most used languages. Additionally, job listings for data teams have a very different set of skills required than they did just a few years ago. Like Rose, each member of today’s data teams must adapt to new expectations and build their expertise to match what’s needed for their role…or risk getting killed by the First Order.
Data And Business: Apply It or Die
As if learning new languages and tools wasn’t enough of a challenge, data teams must now consider the visualization of their results more than ever before. Until recently, machine learning models were consigned to the backend of a server, facing lower expectations for visualizing the results for business use.
Now, insights created from machine learning are designed for consumption by business leaders and operations teams, so they have to be clearly and creatively presented. The result has been a need to develop predictive tools that combine the power of Python and R with the reporting and dashboarding capabilities that analysts’ stakeholders are familiar with.
These changes are placing big demands on the data community – if you’re not using modern data analysis tools for predictive analytics and folding them into your regular business metrics, you are going to get left behind. Companies that can analyze churn, retention and social media trends – and how each will change over time – will have huge advantages over companies who ignore these crucial metrics.
Getting the Job Done
As big of a challenge as this has become, it’s an exciting time for data teams to make significant impacts on business results. Whether it’s professional trainings, acquiring new skills, working with new tools or hiring more resources, data teams must adapt and do what it takes to get ahead. To my friends in the data community: may the force be with you.