How to Overcome the Insights Gap with AI-Powered Analytics
The best way to avoid blind spots and gain granular insights is by integrating artificial intelligence (AI) in data analytics. Here’s how.
- By Ramesh Panuganty
- September 28, 2022
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:
- You are looking at several dashboards and reports together to find a single insight
- You need to make repeated report requests for more updated information
- You are waiting for an analyst to test hypotheses and make sense of the numbers
- You know what happened in the past but don’t know what data to use to predict for the future
- You know the “What” but cannot find out the “How” and the “Why”
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.
AI is used in analytics to augment analytics’ value by providing intuitive insight discovery, faster time to insight, and higher insight quality. AI brings the benefits of machine learning, natural language processing (NLP), and natural language generation (NLG) to data analytics. AI can automate time-consuming data processes, find relationships within data, identify patterns, and test hypotheses faster than manual methods. It continuously learns from the past to predict upcoming trends.
AI-powered analytics bridges the insights gap by making granular insights accessible to everyone nearly effortlessly. The following are some use cases where AI is used in analytics:
- Predicting future trends and forecasts
- Finding analogies, correlations, and patterns in data
- Identifying anomalies and potential risks in advance
- Generating personalized and actionable insights
- Creating targeted recommendations and offers
- Summarizing findings in narrations and visual stories
- racking KPIs and business drivers
- Offering a unified view of data
The Benefits of an AI-Powered Analytics Platforms
An AI-powered analytics platform has AI features embedded in it and brings the following benefits to organizations of all sizes.
Faster time to insights. With AI-powered analytics, business users across departments and hierarchies can receive insights when they want and where they want, without delay or dependency. Faster time to insight ensures that decision makers can make faster data-driven decisions without missing opportunities.
Intuitive experience through augmented analytics. AI-powered analytics augments the entire analytics experience for business users by enabling self-service analytics through an intuitive user interface and automating insight discovery.
Easy understanding with summaries and interactive visualizations. Presenting insights as interactive visualizations and summarizing them as text narrations improves understanding and helps highlight important points to users.
Access to in-depth analysis. With AI-powered analytics, users can go beyond descriptive analysis to easily perform prescriptive, predictive, multivariate, and root-cause analysis. The ability to perform such advanced analyses themselves without technical expertise or a learning curve truly empowers business users in their everyday decision-making.
Improved decision-making using actionable insights. AI-powered analytics can provide actionable insights into segmenting customers, predicting churn, improving customer retention, generating personalized offers, or allocating campaign budgets.
Personalized insights. Acting as a personal business companion, AI-powered analytics brings personalized insights to each business user based on their role, interest, work, and past activities.
How to Evaluate an AI-Powered Analytics Platform
While evaluating AI-powered analytics platforms, look for the following essential capabilities:
Natural language search. Using natural language processing, AI-powered analytics platforms can understand users’ requests, convert them into machine-understandable syntax, and process them to get the correct answers. This empowers users to become self-reliant in their use of the platform.
Actionable insights. AI-powered analytics uncover granular insights such as analogies, anomalies, outliers, trends, clusters, distribution, correlation, and predictions. The ability to receive such advanced insights on time becomes essential for responding to changing business situations effectively.
Root-cause analysis. Going beyond the “What,” AI-powered analytics identifies the “How” and “Why” of changing business situations. Understanding why something happened helps to uncover key influencing factors. Decision-makers are in a better position to control these factors to achieve their objectives and prevent negative outcomes.
Personalization. Getting insights that matter the most for their work helps users stay on the path to achieve their goals. AI-powered analytics learns how users search and what they search for, considering their role and interests while offering personalized actionable insights. This helps reduce noise, ensuring that only relevant insights reach the appropriate users.
Interactive visualizations. AI-powered analytics brings the benefits of storytelling to analytics by presenting insights as interactive charts and audio-visuals. These formats provide different ways to visualize insights, ensure better understanding, and increase user engagement with data.
Text summaries. AI-powered analytics uses natural language generation to create text summaries of insights automatically. Interpreting a visualization becomes fast and easy with such summaries. Users don’t have to spend time analyzing the charts. Summaries also minimize the chance of users missing out on important data points because they are published upfront in text summaries.
Click-less updates. With AI-powered analytics, users receive the latest insights as they happen in data without actively searching for them. This capability ensures that users don’t miss any important events and are always updated about their business.
Dashboards and sharing. Users don’t work in isolation and need to frequently present and share insights with their teams. Search-driven analytics platforms that enable easy dashboard creation and sharing of insights drive collaboration, productivity, and unison within teams.
Embeddable and customizable analytics. Configurable AI and machine learning models provide better control over data and ensure that analytics can be adapted to each organization’s unique needs. AI-powered analytics capabilities that are embeddable in existing systems make integration faster and products stickier, without significant infrastructure overhauls.
Overcome the Insights Gap with AI-Powered Analytics
AI-powered analytics platforms can bridge the insights gap by augmenting insight discovery and making actionable insights accessible to everyone. Such platforms improve the overall data analytics experience by offering self-service capabilities, intuitive search interfaces, personalized insights, and click-less insight delivery. By adopting AI-powered analytics platforms, organizations can empower their business users to anticipate and prepare for challenges, and make informed insight-driven decisions confidently.