Michael is CEO and cofounder of Virtualitics. A data scientist and entrepreneur with a background in finance and physics.
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Virtually no business today makes any strategic decision without considering their data; yet, it’s still all too common for organizations to make decisions using the wrong data.
Ironically, that misstep often happens because companies have too much data. According to IDC’s predictions, the amount of unstructured data will reach 175 billion zettabytes by 2025. For perspective, if you loaded that amount in the world’s largest hard drives, you would need 12.5 billion drives to fit it all.
In the hopes of making sense of these vast pools of available data, business analysts build reporting dashboards and data visualizations meant to communicate insights to business leaders. But the larger the enterprise, the greater the number of dashboards and visualizations, resulting in a deluge of data that makes it nearly impossible for analysts to find truly business-changing insights.
As business analysts become increasingly more adept at wrangling and interpreting data, innovations in AI technology offer one solution to help them overcome this challenge and find answers.
How AI Can Help Deliver Insights
The wide adoption of generative AI, like ChatGPT and Bard, has been groundbreaking. It paved the way for more acceptance of AI as a tool for non-data scientists to leverage, while also helping businesses understand the more practical applications of AI. In fact, generative AI is a crucial part of the new analytics process that gets insights into business leaders’ hands faster. Here are a few benefits to consider:
• AI can do a lot of the heavy lifting of ingesting and sifting through large datasets, so analysts can locate actionable insights more efficiently. Analytics platforms that leverage generative AI, explainable AI (XAI) and intelligent exploration technology can automatically find insights for the analyst and suggest, in plain language, what to do next to explore the data more in-depth.
• AI also democratizes data. For the analysts, AI-guided insights and suggestions help them uplevel their data science capabilities, while business consumers with non-analytical backgrounds can rely on the technology to help explain findings, answer questions or, in some cases, produce 3D visualizations that make data easier to interpret.
• AI can also help analysts deliver reports that are more accurate since some models can highlight errors or even predict potential issues before they happen. With cleaner data, company leaders feel more confident that they’re making decisions from the right datasets and, therefore, are pursuing the best solutions for the business.
In practice, what does this all look like? Imagine that an AI-supported business analyst wouldn’t have to spend time constructing a series of customer segmentation analyses from various datasets to understand the key factors that drive retail sales (Is it staffing? How about inventory volume? Does store square footage make a difference?)—a task that takes a ton of effort and often, the time and expertise of a data scientist.
Instead, the analyst can simply ask the AI a question like, “What’s driving sales?” The technology then evaluates the entire width of the dataset and ranks each feature’s importance in driving sales. This enables analysts to not only see which features are truly behind sales growth, but which ones also work in concert to make this result happen.
Going Beyond AI
With data science talent becoming harder to find, you want to support the curious, hard-charging business analyst in upping their game in this realm. However, supplying them with the right analytics and AI technologies is only one part of this equation. Business leaders can also support analysts by:
• Giving them space to solve strategic problems. Analysts are typically charged with creating and maintaining BI dashboards and spreadsheets for data consumers. However, the volume of this work prevents them from exercising their talents on advanced analytics projects. Reallocating some of the maintenance work to a different resource or spreading out the work across a team of analysts can free up more time for all of them to spend on strategic analysis using AI.
• Allow them to share their findings in compelling ways. Whether it’s a 3D visualization or a report guided by AI, make sure your analysts feel empowered to go beyond a dashboard to help their stakeholders understand what the data is trying to communicate.
• Applaud the work they do throughout the journey. As analysts level up their data science skills with AI, it’s important to not just celebrate their successes, but reframe mistakes as great learning opportunities that will make them stronger and better at their practice.
By really investing in your analysts, you’ll propel your business forward this year and beyond.
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