Data discovery is the process used to collect and evaluate data from different sources. It is often used to understand trends and patterns in data. Data discovery requires a series of progressing steps that organizations can use as a framework to gain a thorough understanding of their data. 

Data discovery is commonly associated with business intelligence (BI) and allows organizations to make informed business decisions by bringing together disparate, siloed sources of data for analysis. 

Mounds of data are completely useless unless you can find a way to extract valuable insights from the data. Data discovery involves connecting many data sources, cleansing and preparing data, sharing it throughout your organization, and analyzing the data to gain valuable insights into business processes.  

In today’s world, virtually every business collects massive amounts of data on their suppliers, customers, markets, production processes, and any other process in the customer journey you can think of. 

Data comes in from online and traditional transactions, sensors, mobile devices, and social media, among other diverse sources. As a result, decision-makers may find themselves drowning in data but hungry for insights. The insights are hidden within that data. How do you get them? With data discovery. 

Studies show that 79% of enterprise executives believe that companies that fail to leverage big data appropriately will lose their competitive position and may even ultimately face extinction. What’s more, 83% of executives have pursued big data projects in order to gain a competitive advantage. Undoubtedly, one of the most effective ways to drive maximum value from metrics, insights, and information is by using data discovery. 

Data analysts have the task of discovering insights that live within the enormous amounts of data that organizations collect. Since data discovery has the ability to bring in data from so many sources, it allows businesses to use data in innovative ways to find insights that weren’t apparent before data discovery. Once new trends or patterns emerge, data discovery makes it simple for users to drill down into the different variables and develop new questions and insights to identify customer problems, including: 

• Unexpected customer churn. 
• Price leakage caused by excessive discounts. 
• Promotional failures. 
• Customer relationship and management issues. 
• Lost market share caused by competitive actions like aggressive pricing or new products. 

With data discovery, businesses are able to capture a 360-degree view of their customers by compiling and analyzing customer behavior, transactions, and sentiment data across the many different channels that customers use to interact with businesses. 

Data discovery is invaluable when it comes to helping decision-makers detect early warning signs of customer churn. 

Data discovery helps leaders gain a much deeper understanding of how their customers view the company. 

Data discovery can analyze text, sentiment, social, and speech analytics to pinpoint what customers are saying about your organization across a range of interactions, including social media comments. 

Using data discovery to conduct keyword searches against customer sentiment helps leaders identify when potential service or product issues are emerging. Then, companies can fix the problems before they get out of hand and result in crippling financial losses. 

The two main types of data discovery are: 

Manual Data Discovery  

Within the past couple of decades, before machine learning advancements, data specialists had to map their data using only their brains. These people used critical thinking to find and analyze data. Organizations monitored metadata and data lineage discovery to gain insights on data categorization and flow. 

Smart Data Discovery  

Thanks to the recent advancements in technology, the definition of data discovery now includes automated ways to present data and, in turn, reveal deeper business insights. Smart data discovery uses augmented analytics and machine learning. Artificial Intelligence (AI) prepares, conceptualizes, integrates, and presents hidden patterns and insights. 

Data Discovery Tools 

There are some tools out there that use business intelligence software to help people read and interpret the data they are discovering. Data discovery tools provide visual presentations in the form of geographical maps, charts, graphs, and so on. 

These tools should: 

• Be easy to implement. You shouldn’t need a statistical degree just to use the tools. 
• Be adaptable. Anyone should be able to gain insights from the data across all departments without needing to call IT experts to get the info. 
• Be quick: You should be able to understand precisely what you must improve in order to boost your decision-making abilities without having to wait to get the information you need. 

Efficient data discovery and data security work together to protect a company's most sensitive data from external and internal threats. 

Data loss prevention (DLP) is a set of tools and processes used to ensure that sensitive data is not lost, misused, or accessed by unauthorized users. Data loss prevention helps you assess and manage your compliance risks with robust data discovery, monitoring and protection to keep your important data safe. 

Data discovery allows you to identify company data and ensure the appropriate controls are in place to not only secure the data but to ensure the company is complying with data regulations such as GDPR-- which are regulations that require businesses to protect the personal data and privacy of citizens of the European Union for transactions that happen within EU member states. Non-compliance can be financially devastating for companies which is why it's important to use a strategy such as data discovery to guarantee around-the-clock compliance. 

How to Discover Data 

Follow these steps to begin discovering key data and insights: 

Identify Your Pain Points 

On your quest for data enlightenment, you must identify your pain points — or the obstacles preventing you from becoming a smarter business entity. 

Mix Varied Data Sources for Deeper Insights 

As we have mentioned, data comes from a diverse range of sources, both structured and unstructured. When you gather data from your existing structured or unstructured data sources and look at them in a new way, you are likely to discover new insights that lead to innovation and drive your business forward. 

Develop a Data Discovery Model 

A data discovery model is a strategic approach to using your data. These models include data collection, curation, and analysis in addition to data-driven actions businesses take upon discovering new insights that prove integral to the development of the organization. 

Tell Stories with Your Data 

One of the most effective ways to ensure your organization is entirely data-driven is by creating easy-to-follow, inspirational stories with your data. It’s important that everyone within your organization is able to understand the story you are trying to tell with the data — no matter their level of technical skill and competency. 

Data discovery products and tools are the gateway to fostering a more efficient, productive, and insight-rich organization. It’s important when choosing a business intelligence platform and/or tools that you choose a company that has all of the attributes we discussed above.