Introduction to Dark Data: its Uses and Benefits

Dark data is all of an organization’s unused, undiscovered, and untapped data. It includes all from machine data to server logs to unstructured information derived from social media. It is produced due to users’ daily online interactions with numerous devices and systems. Organizations might view this data as obsolete, redundant, incomplete, or restricted by a format that can’t be used with current tools. They frequently aren’t even aware that it exists.

Dark data, however, might be one of a company’s most valuable untapped resources. Data is becoming an important organizational asset, and businesses that want to be competitive must realize their full potential. Furthermore, stricter data regulations might call for total data management. The following article looks at dark data, how it can impact your business, how to find, access, and analyze your organization’s unclear information, and how to develop a comprehensive data strategy.

What is Dark Data?

Dark data is the information assets businesses gather, process, and store during routine business operations but rarely utilize for many other uses (for example, analytics, business relationships, and direct monetizing). It frequently makes up the entirety of information assets in most organizations, much like dark matter in physics. As a result, organizations often keep dark data for legal compliance needs only. Data storage and security typically cost more money (and carry higher risk) than they are worth.

The dark data on a social media site like Instagram would be:

• How many logins does the user currently have?
• Does their user activity tend to be concentrated at particular times of the day?
• How many users with sizable user networks liked the post? (To assess a user’s influence.)
• Where was the picture taken from?
• When the photo was posted, where was the person?

To see so much data can be overwhelming for some people. Keep It Simple, Stupid is the mantra of conventional design, and white space is seen as its primary virtue. Instagram even reduced the amount of information it displayed by changing from a highly specific 134,392 to just “Thousands” when describing the number of views a photo would receive. It refers to unstructured data that is not analyzed whenever the users are the engineers. The data that is eventually left lying around to satisfy the industry’s statute of limitations or is kept because data storage is so affordable is the data that is stored through different networking processes on servers and in data lakes.

Dark Data Assessment 

Every day, every business deals with enormous amounts of data, most of which are used, stored and then forgotten. Nobody recognizes who the owner is or whether the data is relevant to business, which is why Gartner refers to this lost information as “Dark Data.” Data that is lost creates a significant security risk. If used wisely, dark data can have a positive effect. Information security risks wasted IT expenses, and storage costs should all be anticipated if mishandled. Although the impact of unchecked data proliferation may not be felt right away, it can quickly become overwhelming due to escalating costs, inefficiencies, and compliance issues. Additionally, Splunk dark data challenges the IT operation and slows down the servers.

According to a recent report by Gartner, 50% of enterprise data is deemed to be of “indeterminate” value, and 30% of it is redundant. You can reduce risks, stop storing redundant data, and start saving money on storage with Pacific DataCom Dark Data Assessment. You can determine the proper data classification and identify data risks with the help of Pacific DataCom’s Dark Data Assessment. The Dark Data Assessment is a minimally invasive method of scanning your unstructured data, gathering analytics, compiling them, and publishing reports that give you an understanding of what data is available, how it is being used, who owns it, and who has access to it.

Benefits 

With the aid of Pacific DataCom’s Dark Data Assessment, you can better understand the state of your data and learn how to manage it more effectively while lowering risk. Using the evaluation, you can:

• Enhance Data Management: Find stale data and eliminate redundant and unnecessary data
• Protect Data from Risk: Monitor data usage and discover open shares
• Identify the data holders and organize information for retention as well as retrieval to achieve compliance

Uses of Dark Data 

Dark data discovery will play a crucial role in powering AI-powered solutions. As the amount of data AI can analyze increases, AI tools should produce even more profound and precise insights. There are numerous distinct use cases. Developing new, more effective enterprise business strategies will be one of the biggest. It includes assisting organizations in identifying which departments within the organization own which types of data and the data that management and leadership should own. Additionally, it can enhance quality assurance procedures, find and fix process flaws, and scan for privacy gaps, security weaknesses, and potential compliance violations. Splunk dark data can be used proactively to develop new data management strategies around quickly evolving technologies like IoT and provide the basis for short- and long-term trend analysis to show quantifiable results to managers, directors, and leadership.

Discovering Dark Data 

Discovering dark data is the first step. Data can be dark in almost any storage repository, making this step of the process the most difficult. Hard drives within individual servers, storage arrays or subsystems, hosted or collocated remote systems, and even storage instances across public clouds and SaaS providers are all possible places for them to reside. Dark data discovery, therefore, rarely manifests itself. To locate, identify, and correct it, deliberate — and frequently manual — effort is needed.

Such deliberate efforts frequently include formal assessments. A company will typically approach the hard work internally with IT staff to audit the agency’s storage content, even though external consultants can help lead the project. A business could form a picture of the data types and quantities available and the applications or devices produced by correlating data to applications and devices through an audit.

The discovery and identification of DD statistics cannot be automated with currently available tools. IT administrators must take the following actions to speed up an assessment:

• comprehend the business’s various applications and data sources
• acknowledge the storage resources allocated to those sources of data; and
• start by concentrating on the evaluation of those assets.

How to Manage Dark Data? 

Mitigation presents the third difficulty in dealing with dark data. It is a vast body of unstructured, unrelated data that originates from various enterprise applications, device logs, and other sources. Making wise choices regarding existing and potential dark data assets is the trick for contemporary businesses. The four standard steps for dark data mitigation are as follows:

1. Recognizes sources – Every piece of dark data statistics should have a reference. The audit to find dark data should identify its origins, including IoT device streams, system and network logs, or records of customer transactions.
2. Determine importance – It is sincere to say that only some data benefits the company. Even valid data has a limited shelf life. Business and IT leaders must decide which data sets to keep and for how long because keeping all data forever is not a wise policy for security, compliance, or infrastructure.
3. Set retention and deletion procedures – Tools for data retention are used by businesses to establish and enforce storage and security guidelines for set intervals and securely delete data after those intervals have passed. To reduce storage costs, guarantee analytics tools only process timely and pertinent data, and keep the business secure and compliant by erasing outdated data following a consistently established policy, these tools should include dark data and its sources.
4. Turn off unwanted sources – The business may not require or desire data simply because a machine or application generates it. Data collection and storage for future use is not a wise compliance, security, or infrastructure strategy. Disable the corresponding data source if the company does not require a specific data set. Applications and IoT devices, for instance, have configuration options to turn off particular actions like logging.

As business data sets expand, dark data statistics will continue to exist. However, enterprises and IT leaders should take proactive measures to find, use, and handle dark data now and for the foreseeable future.

Conclusion

The basis of the business world is data. Large amounts of data are routinely collected by businesses and used for various purposes, including direct monetization and business analytics. However, only some of the information gathered and stored will be put to good use for the company.

Instead, companies need more time to pay attention to specific data. It is the term for this information. IT and business leaders must comprehend dark data, know the risks and challenges it presents for the company, and develop an effective plan to deal with it across the entire organization.