How to Overcome the Insights Gap with AI-Powered Analytics | Transforming Data with Intelligence

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The best way to avoid blind spots and gain granular insights is by integrating artificial intelligence (AI) in data analytics. Here’s how.

In the race to become data-driven, organizations are rushing to stock up on various point solutions and analytics data stacks. They are gathering the required data, the tools to process that data, and the personnel to analyze it. However, their business users and decision makers are still unable to get clarity on business situations. Self-service analytics does give business users some control over data and simplifies search, but what is more important is the granularity of insights and the self-service ability to find them easily from data.

Granular insights emerge from detailed examination of even the smallest factor that influences a change or an event. Such insights become crucial for creating strategies, making targeted adjustments, and taking focused actions. Business users will continue to miss opportunities and make poor decisions because they lack direct access to granular insights. They have to either put in manual efforts themselves or depend on analysts for deeper analysis. This leads to an insight gap.

What is an Insights Gap? How Do You Know If You Have One?

An insight gap arises when you have all the facts extracted from data but there are no interpretations available to understand how and why they influence your business activities. Traditional BI inundates you with many dashboards based on historical data. These dashboards give you the numbers but little in the way of required insights into customer preferences, campaign outcomes, churn predictions, or resource allocations.

Suppose you are a sales manager looking at a sales dashboard for Q3. It shows the total sales and distribution across stores and products. You see that Store A has the highest sales. You pull up another dashboard for Q2 to compare Store A’s sales. You calculate the increase and it’s a sharp one. You decide to check its sales for the past 2 years to see if there were similar increases, but that’ll require another report request. The insight gap begins.

You want to know why Store A’s sales surged in Q3. Was it due to an increased demand for a specific product or the popularity of a coupon code? Did the store start catering to a new customer segment? Was it seasonal or just an anomaly? The insight gap widens. Identifying the correct success influencer is just the insight you need to make decisions about maintaining product inventories, allocating resources to stores, understanding purchasing behaviors, introducing targeted offers, and revising store strategies.

Unfortunately, traditional BI reports and dashboards cannot provide such granular insights. Such types of analysis take considerable manual effort, can be prone to errors, and take days -- if not weeks -- to complete and reach the decision maker. By then, valuable opportunities have already slipped through the insight gap.

You know you have an insight gap when:

How to Extract Granular Insights from Data

Granular insights include analogies, anomalies, outliers, trends, clusters, distribution, correlations, and predictions that can have a direct impact on business. The simplest way to know granular insights starts with search. An analytical search that is simpler and powerful can boost the chances of finding insights with minimum efforts.

The best way to avoid blind spots and gain granular insights is by integrating artificial intelligence (AI) in data analytics. AI-powered analytics has the ability to learn from past behaviors, identify patterns, and proactively provide comprehensive insights without users asking or actively searching for them. AI extends the potential of data analytics platforms by bringing scalability and agility to analytics and surfacing insights automatically.

AI-powered analytics can be found everywhere in daily life -- from unlocking your smartphone with facial recognition to using voice assistants, from receiving product recommendations on e-commerce sites to watching suggested content on a streaming video service, from using auto-reply suggestions in email to reading articles written by AI. AI increases the speed of completing tasks, reduces manual efforts, offers convenience, and improves the overall user experience.