68% of data collected by organizations isn’t used at all. Gathering insights from this dark data can drive organization-wide changes for the better. These changes can usher in a slew of benefits both from the perspective of the organization and their customers.
With the amount of data collected increasing by almost ten times between 2013 to 2020, the amount of data that isn’t used has also increased. There’s never been a better time to make the analysis and processing of dark data a priority.
This blog explains dark data and lists the potential risks of being unaware of its existence. It also discusses the organization-wide benefits of analyzing and processing this data.
Before we explore what dark data is, let’s understand the types of data.
Data, in the form of facts and numbers – classified as qualitative or quantitative data – is all around us. Common types of data include messages, emails, files and folders on all types of devices, be it mobile phones, tablets, desktops and laptops.
As mentioned earlier, organizations collect large volumes of data, with data types that vary in format and intricacy. With traditional systems being unable to process this complexity of data at speed, this is where ‘big data’ comes in.
Big data also classifies data into three types based on its formatting. Commonly referred to as structured, semi-structured and unstructured data, each of these types of data is stored differently.
Relational databases can store structured data as tables, rows and columns while unstructured data has no predefined format. Since relational databases might store unstructured data poorly, nonrelational or NoSQL databases can work instead. Speaking of which, a large amount of unstructured data is classified as dark data.
As for semi-structured data, this data type can appear to be structured or unstructured or both. Social media and sensor-based data fall into this category.
Now, to explain dark data, here’s an analogy: When you’re moving home, you’ll move several boxes from one house to the next. In completing this move, you might leave several unopened boxes in the attic. After a while, since they’re out of sight, you might forget that these boxes even existed.
Organizations collect and store a huge amount of data. Like those boxes, this data may be forgotten over time and never used for its original purpose. It rapidly turns stale, losing value and relevance.
When an organization has large volumes of data but is not generating equivalent data insights, this is called “Analytics deficit”. However, this analytics deficit isn’t the only problem since there are other risks associated with ignoring ‘dark data’.
Storing unused data might seem that there’s no risk associated with inaction. Nothing could be further from the truth. There are definite costs associated with storing unused data well past its expiry date.
Storage costs can prove to be expensive, but there are regulatory risks involved if organizations store sensitive data improperly or fail to use it for its stated purpose. In addition, simply storing data without proper security controls can violate customer privacy laws.
Failure to comply with regulations can prove harmful to an organization’s reputation and bottom line. For this reason, partnering with big data experts is advisable.
There are also business benefits that make addressing the issue of dark data a strategic priority.
Finding the right partner who can process dark data with specialized tools is the first step. This approach offers benefits that can change an organization’s fortunes. Here are five of them:
Leverage better insights to drive business decision-making
With relevant dark data processed, the accuracy of the insights obtained increases. This is because, with a larger dataset at their disposal, one can sample more data. These insights will reveal where your business stands, leading to accurate, well-informed decisions.
Reduce risks associated with litigation, fines and regulatory sanctions
There’s much risk associated with storing obsolete, sensitive data. A simple data breach can result in records being accessible by unscrupulous individuals. If this data isn’t properly used, protected, or cleansed or purged in a timely manner, regulatory sanctions, fines and lawsuits will follow. It’s vital that all information stored should meet privacy guidelines set by law. The GDPR (General Data Protection Regulation) which applies in the UK and the EU comes to mind. For the US, adhering to HIPAA (Health Insurance Portability and Accountability) is necessary.
Prevent the loss of sensitive or identifiable information
Finding dark data in less secure areas is possible, which can lead to companies losing this data. This is because both authentication and authorization aren’t taken seriously, so people who should not have access can misuse personal, sensitive data. Once you locate and process that dark data, eliminating this risk becomes a reality.
Back up all important information to prevent data loss
Enterprise databases store and back up data for the purpose of generating insights. With the location of dark data forgotten over time, not making copies of this critical production data can be costly. In processing dark data and backing it up, one can prevent data loss even in the event of a serious database crash.
Reduce costs associated with storing large amounts of obsolete data
With so much dark data stored on an organization’s databases, a lot of it might be obsolete. So, how will an organization know whether they should keep such data or dispose of it? By analyzing and processing this dark data in the first place. Once this is done, you can get rid of it for good. After which, you can use the unused memory on your database to store relevant data. By freeing up this memory, you don’t have to requisition for more storage space either.
A prominent retail store conducted an audit of its data assets. Based on its findings, it did not use 80% of the data collected. This included emails, chat conversations, website logs and inventory management data.
Now, the company faced a number of challenges when handling its dark data. First, there was far too much data to manage and analyze. Second, it did not have the tools or the expertise to manage and analyze the data. Third, each department used a different system, so it was hard to combine data from all these sources.
Now, these are the steps that the retail store took to analyze its dark data:
Finally, the retail store benefited in three ways by processing its dark data:
Qualitest has over 26 years of experience, 8200 engineers and 563 active clients. It’s safe to say that we’re the largest AI-driven quality engineering company in the world. Are you looking for a quality engineering company that analyzes dark data? Qualitest can help. Speak to an expert now.
Dhruv Khandelwal is a Senior Specialist at Qualitest. With test automation as his forte, he has substantial experience in the Information Technology and service sectors. In particular, he specializes in automated testing for the UI, API and Database layers. With his data analytics and data testing experience at the fore, he currently contributes to a slew of data-driven projects.
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