What is data discovery?

Data discovery involves the collection and evaluation of data from various sources and is often used to understand trends and patterns in the data. It requires a progression of steps that organizations can use as a framework to understand their data. Data discovery, usually associated with business intelligence (BI), helps inform business decisions by bringing together disparate, siloed data sources to be analyzed. Having mounds of data is useless unless you find a way to extract insights from it. The data discovery process includes connecting multiple data sources, cleansing and preparing the data, sharing the data throughout the organization, and performing analysis to gain insights into business processes.

Today, nearly all businesses collect huge amounts of data on their customers, markets, suppliers, production processes, and more. Data flows in from online and traditional transactions systems, sensors, social media, mobile devices, and other diverse sources. As a result, decision makers are drowning in data but starving for insights. Insights are hidden within that data.

Data exploration and visual analytics is one approach that business data analysts use to uncover and investigate hidden but potentially useful insights in data. It is a methodology for digging into data looking for interesting relationships, trends, patterns, and anomalies requiring further exploration. Exploration and visual analytics enables the use of technology assisted analytical and pattern recognition software for visualization and drill-downs to turn data into knowledge and understanding.

Data discovery offers businesses a way to make their data clean, easily understandable, and user-friendly. A comprehensive solution should be able to be used by all members of the business. The main benefit of data discovery is the actionable insights that are uncovered in the data. These insights help users spot valuable opportunities before competitors without having to consult the IT organization. Visual data discovery can enhance this value, allowing line of business workers to find answers faster.

Today, companies are finding that the use of artificial intelligence (AI) is greatly enhancing the data discovery process. This process is also referred to as smart data discovery. In smart data discovery, AI can automatically discover data relationships and accelerate a company’s analyses with AI-powered recommendations. The underlying AI suggestion engine uses sophisticated AI algorithms that run against any type of data without the user being aware that processing is happening in the background. The AI engine identifies potential relationships such as correlations by employing trained learning algorithms. Leading analytics platforms utilizing AI offer recommended visualizations of related variables that users can choose to explore further.

There are several exciting directions for innovation in the area of AI-powered analytics including:

  • AI techniques can be used to suggest data preparation steps such as normalization, missing data handling, string pattern recognitions, and others.
  • Algorithms can be used to identify and draw attention to particular patterns or outliers in the data for groups of related variables.
  • Time series analysis has distinct needs and techniques for pattern recognition, anomaly detection, and series relationships discovery.
  • Behavioral data of expert users can be collected, analyzed, and used to influence recommended analysis actions.

AI suggestion engines and recommendations are increasingly used to augment analytics on an ever-expanding space of problems. This combination of human understanding with machine tirelessness enables business professionals to rapidly discover important relationships across vast amounts of data in time to take action.

Solving business problems with data discovery

Analysts are tasked with discovering insights in the massive amounts of data that businesses collect. Because it brings in data from so many different sources, data discovery enables businesses to use data in innovative ways. It helps users explore data in new and different ways and to find insights that were not apparent prior to data discovery. And, once new trends or patterns are made, data discovery makes it easy for users to drill down into the variables and come up with new questions and insights.

These insights can include identifying customer problems such as the following:

  • Unexpected customer churn
  • Customer relationship and management problems
  • Subtle product issues such as returns and failures
  • Price leakages due to excessive discounting
  • Promotional failures
  • Lost market share due to competitive actions such as aggressive pricing or a new product

Data discovery is enabling companies to capture a 360-degree view of their customers by compiling and assessing customer behavioral, transactional, and sentiment data across the many channels customers use to interact with companies.

Data discovery is invaluable in helping decision makers detect early warning signs about customer dissatisfaction. Data discovery helps business leaders gain a more thorough understanding of how customers view the company.

Text, sentiment, social, and speech analytics can be used to identify what customers are saying about your company across a variety of interactions, including social media comments and contact center interactions. Key word searches against customer sentiment can help business leaders identify where potential product or service problems may be coming to the fore with multiple customers.

Data discovery tools also offer banks myriad opportunities to learn more about their customers and act on these insights. For instance, data discovery tools can help bankers determine which products a particular customer is using (e.g., checking, savings) and then determine based on that customer’s income, lifecycle status, and other factors whether she might be a good candidate for a cross-sell or upsell offer (e.g., certificate of deposit).

With customer churn so high in financial services, bankers can also use data analysis and data discovery tools to determine the primary causes of customer defection among certain groups of customers and also to spot the warning signs when a customer is about to jump ship. Undetected and unaddressed, these problems can seriously undermine any business. Hence the urgency to find insights in the data and take action. With the right insights, companies can focus their efforts where they are needed to retain and delight customers rather than simply throwing customer-enticing tactics against the wall and see what sticks. Data discovery puts the power of big data into the hands of the everyday business user giving them the information that they need to make data-driven business decisions.

Data discovery chart

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