The world of data analysis is constantly changing and evolving, and sometimes it can be hard to keep up with. I had the pleasure of moderating a panel discussion on The State of Data Analysts at Datanova with Charles Wilson, Senior Manager of Consumer Insights and Analytics at New Balance, Parker Dillion, Senior Consultant in Data and Analytics at Slalom, and Manav Gupta, Head of Analytics and Insights at Adobe Stock. We discussed everything from dashboards and the future of data analysts to the impact of artificial intelligence, and even whether data analysis is an art or a science.
Dashboards: A thing of the past?
For years, data users have relied on dashboards to provide easy access to pertinent information. My book, The Big Book of Dashboards, is filled with references to all different types and models of dashboards that organizations have used. However, increasingly, their lack of efficiency is being noted. The overall consensus among the panel was that dashboards are slowly going to decline in use. Even though they may still play a role in anchoring data to a finite set of pre-defined business questions, analysts find that they only fulfil a small role in a successful data culture.
There is a push to use data sets, not dashboards, to answer new questions as they arise.. Adobe’s Manav made an interesting prediction about the changing role of dashboards and role of data analysts in using them. He says analysts will spend less time on building dashboards and “more time on doing exploratory stuff and solving newer questions.” In addition, data driven insights and predictive analytics will also become more popular. Because “dashboard” is such a broad term that encompasses so many different models, these methods will allow for more specific analysis and data visualization. We concluded with a dashboard usage prediction: By 2026, data analysts will be spending only 20% of their time on dashboards. I’d love to revisit that in five years to see where we stand.
A Thought Partnership: The Future of Data Analysts
Data is getting into the hands of data analysts at a faster rate, largely due to the emergence of SQL-based MPP engines. Therefore, analysts will need to learn how to best prepare data for scalable analysis and acquire some more technical skills. One of the biggest changes noted among panelists is the need for analysts to be proactive and embedded in the business unit as a whole. The power of analytics is being realized by many organizations; therefore, there is a greater need for curiosity, creativity, and problem solving among data analysts.
Manav described this as a thought partnership: data analysts need to understand the business thought process behind the data use in order to better create recommendations for the larger organization. Charles from New Balance added, the data analyst of the future will be “able to take ambiguous business requests, translate that into a scope of work, and then communicate that back to the business team in the form of recommendations.”
Is Artificial Intelligence inevitable?
Talk of artificial intelligence is everywhere, but how will this blooming technology impact data analysis? There is a general consensus that the usefulness of AI is inflated. Parker from Slalom says, “AI and ML are a bit overhyped. I think we all know the power of it, it can be leveraged and used to create incredible things with regards to new products or efficiency, but it’s still very hard to execute in practice.”
However, artificial intelligence could be helpful in assisting the new curiosity aspect of data analysis. The sophistication of AI allows new questions to be discovered, and curiosity as a whole to be encouraged. Hypothesis testing and answering difficult questions could be a job for AI.
Is data analysis an art or a science?
Art? Science? Artist Science? Scientific Art? Where does data analysis fall in these categories? Some thought that it was strictly a science just with creativity and thought behind it. The foundation of data analysis is in structured statistics, thereby making it a science. However, there is so much curiosity and freedom that makes data analysis an art.
Manav draws a comparison between artists’ tools and the statistical science foundation of data analysis. He says, “For art you use the set of brushes which are kind of standard, so, to me, the tools that we’re using for analysis are the set of brushes or paint… but curiosity [creates] the most impact”. Because there is so much creativity involved in creating data visualizations and projections, it is more of an art than science. But, it could also be both. A science of creativity or an art with a science foundation. Either way data analysis combines both of these worlds in the technical skills involved and the curiosity and creativity that fuels the never ending quest for data analysis. What sets it apart from just being a strict science is the qualitative results that an organization can see through their data analysts. They influence big creative decisions and are essential in assisting in the success of a business.
The role of a data analyst is becoming more important than ever. Because of the availability of big data and our increasingly efficient means of analyzing it, more is being asked of data analysts. Instead of merely being a science, data analysis is evolving and data analysts are being asked to think more creatively and integrate themselves into the business world. Critical thinking, problem solving, and asking questions are new skills that data analysts need to add to their palette.
