What Is Data Discovery and Why Do We Need It?

What Is Data Discovery and Why Do We Need It?

July 18, 2022

Information revelation, or from its industry term “data discovery,” is the forensic examination of information. Collated from different sources, its purpose is to reveal concealed patterns that tell the untold story of a business.  

“Information is a beacon, a cudgel, an olive branch, a deterrent--all depending on who wields it and how.”  Steven D. Levitt – “Freakonomics” 

Data discovery is the very first step in cementing a business’s information, to inform essential business choices. If ignoring your entity’s data is like flying blind, then data discovery is like having night vision. Through the data discovery process, data can be gathered and merged, then broken into usable portions.  

The goal, therefore, is to make your business's information justifiable and cleansed. But that’s not all, the tertiary use is probably more important than the first two, data protection. Understanding how an organization processes and transfers sensitive data is now a legal requirement in most countries.   

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Benefits Of Information Revelation 

Insights, without them, it’s pure guesswork. The ability to reveal insights and modify unstructured information into usable metrics is the most powerful tool any business can own.  

Solving The Infrequency Issue 

Most expository instruments are designed to expect data conformity. In short, they expect and look for standardization across a single channel. Humans, however, are not so standardized. Infrequent data input and broken connectors can easily render an existing system useless. Data discovery aggregates multiple sources of information, giving the user the ability to pull correct data in the right configuration. It can even make sense of the human element.

Pattern Recognition  

The information disclosure process allows new insights into crucial hidden data within unstructured information. Taking unstructured data and structured data, combining them and gleaning insights will reveal patterns that were obscured by dysfunctional data processes.  

Team-wide Application 

The flexibility of data means it can be used across multiple sectors of the business. Offices can take similar information, process it through the lens of their department and gain novel insights. Conversely, it shows the entire organization a single pin-point version of events that cannot be refuted.  

Recycling Data 

Since data discovery is about persistence, constantly reading, measuring and using data can cause revelation fatigue. On the other hand, by keeping the revelation process going it allows amalgamation of future information to gain insights at scale, time and time again, and at increased scale.  

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Challenges 

Powerful information revelation calls for exact and reliable sources of information. Therefore, any difficulties in disclosure usually arise from sourcing and storage of information.  

  • The sheer volume of data being processed and put away can hamper examinations and, in some cases, predispose the user to incorrect outcomes. An application needs to handle large data sets with ease 
  • As sources increase, so does the tendency for unreliable data collection. Specialized applications like NOWNow Privacy are built to withstand this 
  • Information velocity can also hamper correct data insights, the system must be able to withstand increased velocity of information gathering as new channels come online 
  • Consistency is crucial if everyone across the deck is to use the information correctly. Anomalies can upset the balance and push users to make improper choices. A solitary version of reality as new information is introduced must be maintained even when information is being pulled in real time 
  • Information gathered erroneously or in strange formats can throw data investigations through a loop. These minor issues can present genuine obstacles for accurate information revelation. 

The Five Phases: 

To understand the inherent value hidden within an organization, we must first see the data for what it truly is, the ability to predict outcomes.  

Phase 1: Assembly 

First up, we must assemble our data. Take the structured and unstructured elements of our hive-mind and bring them into a single product like NOWNow Privacy. This allows us to examine accurately, without interference. By combining multiple channels, the user can assemble a bird’s eye view.  

Phase 2: Cleanse 

While you can investigate crude information occasionally, it’s not a habit you should build over time. Your information should be cleansed and collated in a way that makes sense to your organization. It should be made-ready.  

Phase 3: Presentation 

Now the data has been gathered and cleansed, it can be presented to others in the organization. And despite this story being singular in essence, it can be adapted so many ways by different parts of the organization that it becomes a translation tool more than anything else.  

Phase 4: Examination 

With this data in front of them, users can examine it at will. Each portion of the organization will be able to glean insights to their own end and ultimately, draw their own conclusions going forward. It’s grassroots empowerment using data as the lever.  

Phase 5: Implementation 

By now, your organization has large chunks of highly accurate, highly relevant data. Sectors of the organization have it and have examined it. Now it’s time to implement it. Perhaps data gleaned from your CRM have shown that certain C-suite employees answer your emails more often on certain days. That information can be used to produce an accurate, updated best-practice document to share with your department. Obviously this is a simplified version, but your unique organizational issues can be solved through the implementation of discovered data.  

Brilliant data discovery is human-controlled, AI-assisted. This constant “question-and-answer” model is what makes data discovery so powerful, humans ask questions, and the machines answer them, accurately.

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