The impact of AI on the data analyst

In this special guest article, Glen Rabie, CEO of Yellowfin, believes that while many analysts fear being replaced by automation and AI, the role of the data analyst will gain in importance for the company and the range of skills required. Yellowfin is an analytics and business intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics. Prior to founding Yellowfin, he held various positions at National Australia Bank, including senior e-commerce consultant and global head of employee self-service. Rabie holds a master’s degree in commerce from the University of Melbourne.

The introduction of AI, automation, and data storytelling into the world of analytics has not only had an immediate impact on analytics end users, but also on the people who work on it. ground. While many analysts fear they will be replaced by automation and AI, I believe the role of the data analyst will grow in importance for the business and the breadth of skills required.

Data analysts have traditionally spent a great deal of their time performing mundane and repetitive tasks, such as preparing data for analysis, creating reports and dashboards, and then using them to manually find significant changes. in their data. With traditional business intelligence and analysis tools, analysts simply cannot explore all combinations or permutations of their data. And if they find something of interest, how do they determine if it’s statistically relevant and of significant benefit to the business? The introduction of automated data discovery addresses these issues. This cuts down on the time it takes to find information, subsequently leaving analysts much more time to add value by interpreting their findings. This will require analysts to become business savvy (understanding the business, not just the data) and storytellers with enhanced literacy skills to better communicate their findings.

The role of the data analyst today encompasses a wide range of data management and analysis activities. These include obtaining, preparing, cleaning and modeling data, then creating reports and dashboards to analysis tailored to the business to support decision making. Of all these activities, the real value to the business are those that relate to identifying critical changes or trends that are impacting the business and interpreting this information to determine the possible impact on the business. the company.

The dilemma facing business analysts is that while interpretation is the most valuable activity they undertake, it is the one where they spend the least time. Most data analysts spend only 20% of their time analyzing real data and 80% of their time on low-value business tasks, such as data research, cleansing, and modeling, which is very inefficient and adds little value to the business.

It’s not just the data preparation that’s inefficient. Traditional data analysis and visualization tools require an entirely manual approach to data discovery. Users have to choose from a wide variety of fields and filters, then slice and slice the data to find patterns, changes in trends, and anomalies. This manual process is extremely time consuming and very prone to human error and bias, especially in today’s data-rich world. The result? Identifying critical changes in business data is accidental rather than something that will happen with certainty. This creates risk for business leaders who want certainty in the data they use for decision making.

AI and automation promise to radically change this paradigm. Applied to analysis and business intelligence, many of the tedious and time-consuming processes will be done by machines. Intelligent data preparation that uses machine learning to streamline the profiling, matching, and data cleansing processes will dramatically reduce the time analysts spend preparing data for analysis. This, combined with AI-powered data discovery, which applies a range of sophisticated algorithms to the data, will reduce tedious data mining and uncovering relevant business information.

These advancements do not mean, however, that AI will replace the data analyst. AI is great for automation, but it has fundamental limitations. Machines cannot understand the context. Only humans have the ability to contextualize data in complex terms such as organizational environment, external market factors, customer dynamics and much more. For example, the ability to make sense of a declining trend in product sales based on the anecdotal surge in a competitor’s marketing is much more than what AI can handle, but it is relatively easy for a human to do.

The result of this change will see data analysts spending a lot more time doing what machines can’t – provide context and interpret data. Data analysts will be elevated to important business partners, where they will use their data literacy skills to help the business interpret data, contextualize uncovered information, and tell compelling stories with that data. The result of this will be that today’s data analysts have to become much more savvy in business and develop their skills to develop stories.

This doesn’t mean that repetitive data analyst tasks won’t go away. For data analysts whose primary focus is preparing data and building dashboards, their time will come sooner rather than later. However, organizations will rely more on those with the skills to provide insight into the meaning of the data. Data analysts will rely on AI-powered tools that facilitate the mundane aspects of their jobs, so they can devote more time to high-value activities like data interpretation and storytelling. As a result, they will be able to provide meaningful analysis to the business to make better data-driven decisions.

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Sean N. Ayres