| |DECEMBER 202319CIOReviewManagers: AI is just the tip of the icebergvisualizations, running machine learning models, data analysis, etc. Recent researches say that between 40 to 80 percent of a data analytics professional time is still spent either collecting or cleaning data. This includes different professions like data analysts, business analysts, data scientists, and others. This is a huge efficiency problem. Before thinking of algorithms, models, problem-solving, insights, and recommendations, these professionals ­ who can be very expensive ­ spend 40-80 percent of their time collecting and cleaning data.Data accessibility is just the foundation stone of what is commonly referred to as a data analytics maturity framework. Simply by searching for this term online, it is possible to find multiple sources where a horizontal axis shows different levels of data analytics maturity. It starts with descriptive (what happened?), diagnostic (Why did it happen?), predictive (What will happen?), prescriptive (How to make it happen?), and cognitive (AI). On the vertical axis is business values. An exponential line chart links both axes and shows how much value is generated for a business when climbing the maturity level. Well, when most of the attention is on AI, who is going to ensure daily business questions like `What happened?', `Why did it happen?' and so on are answered? This still is a struggle at different organizations at different levels and forms.The data analytics journey through the maturity levels can be bumpWy depending on the data analytics culture of an organization. It is hard to determine who came first, a strong culture that built a mature data analytics platform, or vice-versa. But the best question is: what is a well-established data analytics culture? Business stakeholders are usually the most empowered and knowledgeable people to make decisions in their own business, which is kind of obvious. Data analytics is not around to replace that expertise. But to empower these business teams with access to actionable insights derived from data through visualizations, reports, dashboards, research, and analyses, which serve as resources for making decisions. When most business decisions are made like that, then we can say we are in front of a well-established data-driven culture. This usually requires constant effort from business professionals at all levels.The role of the `middle-person': data analytics professionals. As mentioned above, there are many types of roles in data analytics. If we were to combine all of them in one data unicorn, it would be a professional who can connect and translate problems, questions, and solutions, both ways, end-to-end, from technical to business. This is a very valuable resource, but many organizations have this ignored, and their data analytics strategy ran by a technical or a business department. It can work, but not ideal.I will conclude this analysis by directing messages (provocations, actually) to different stakeholder groups to push them out of their comfort zones. If you get to this point, I invite you to reflect on them.IT/Tech professionals: Big data and analytics platforms and solutions are fascinating and usually drive most of your interest and attention. I invite you to spend just a little more time talking about business problems with business teams.Data analytics professionals: we are fascinated by solutions, models, and platforms as well. I invite you to spend more than just a little time talking about business problems with business teams.Business teams: you usually know what numbers, charts, or reports you need. I invite you to bring up the questions you are trying to solve first. Then we'll discuss numbers and reports.Managers: AI is just the tip of the iceberg. Roberto FranceschiniIn an interview, Roberto Franceschini, Director, Commercial Analytics and Insights at Spinrite discusses his thoughts on the evolving AI landscape.
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