Enterprises are driven by a high volume of data that influences many business decisions. Product improvement, marketing, advertising trends, business risks and product performance — these are elements of business that depend on accurate data for quality decision-making.
Despite how crucial data is to enterprises, there is no denying that enterprise data can also be marred by inaccuracies that may result in dirty data. Recent research suggests that incidents of dirty data cost business organizations in the United States an average of $15 million annually. A shocking report in 2018 revealed that Samsung lost about $300 million due to bad data.
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There has subsequently been a growing concern over data quality and how to ensure data integrity in organizations to avoid mistakes that could lead to terrible business decisions. There is also a concern that dirty data may lead to data security vulnerabilities, which is a top cybersecurity concern for business enterprises.
What is dirty data?
Dirty data refers to customer or business information that is erroneous, duplicated or missing. Dirty data can arise when a manager erroneously duplicates a customer record, someone misspells an important data record, a data entry tool auto-fills wrong information or fills up with spam emails, or a date format is applied inconsistently.
Due to human interaction with organizational data, it’s almost impossible to maintain data integrity and accuracy at all times, making data a weapon attackers can target and exploit.
Types of dirty data
Below are types of dirty data that can mar the integrity of most enterprise databases.
Duplicate data refers to data entries with data identical to another entered unintentionally into your database. Contacts, leads and accounts are the most frequently duplicated objects.
Outdated data is information that is no longer relevant. For instance, outdated could come in the form of old server session cookies, web information that is no longer accurate and when the organization passes through a rebranding phase.
Incomplete data could be a record that is missing important fields from master data records. Some important fields include first names, last names, industry types and phone numbers.
Inaccurate or incorrect data
When field values are generated outside of the acceptable range of values, it could lead to incorrect data. For instance, a month field should only accept values between one and 12, and an address must be a real house or office location. When these acceptable values are missed, we can call this inaccurate data.
Data is termed inconsistent when an entry has multiple representations on other systems. For instance, when a Date of Birth field is entered in different versions as d.o.b, D.O.B, or Date of B.
One major issue with inconsistent data is that it affects analytics and hinders data segmentation when you have to consider all variables of the same title and industry.
Dirty data cybersecurity concerns for business enterprises
With the rise in data breaches across numerous industries, dirty data raises some emerging cybersecurity concerns. These concerns are highlighted below.
Misleading signals will be targeted at cyber fusion centers
Cyber fusion centers are collaborative projects created to take on the duty of cybersecurity to increase communication between various teams. Fusion centers combine automation techniques with data curated from a variety of sources to uncover insights that inform business and security decisions.
Unfortunately, there is a possibility that attackers will take advantage of the power cyber fusion centers have over commercial activities to manipulate data and spread false information.
More attackers will focus on poisoning data
Attackers continue to test new strategies and conduct stealthier and more focused attacks to increase their success rates and elude law authorities. They aggressively hunt for false information to harm an organization’s reputation, deceive consumers or alter the course of an event.
There is a chance that threat actors will turn their attention to illegal data manipulation to undermine the integrity and legitimacy of information, undermining the integrity of the data that organizations rely on to advance their businesses.
Digital twins will double the attack surface
In order to gather data based on actual behaviors, simulation and machine learning are used to create a digital twin of a physical object. Digital twin usage is picking up speed among manufacturers in an effort to streamline product development, improve tracking capabilities and forecast financial results.
Anyone with access to the twin can see crucial details about its physical counterpart since digital twins use real-world data. Attackers can take advantage of digital twin vulnerabilities to cause downtime in the manufacturing and the supply chain.
How organizations can protect themselves
Indicate critical assets
Enumerating essential information assets is the initial step. Next, focus on creating, implementing and maintaining an organizational plan for handling data poisoning occurrences within these critical assets.
Think about deploying platforms with built-in data governance features, as these provide controls for monitoring and troubleshooting all facets of data management, including data integrity.
Pay close attention to the accuracy of data and intelligence inputs as the cyber fusion center evolves. There is a need to routinely review automation systems, especially their potential to cause disruption. You should also set automation thresholds that don’t contradict the demands of reliability and safety. Develop, practice and categorize response strategies for a sudden cyber fusion center data integrity issue.
Employ data sanitization
To further ensure the integrity of data feeding the cyber fusion center, employ data clean-up procedures and establish policies that allow the business and IT teams to collaborate on improving the accuracy and effectiveness of the cyber fusion center.
Get to know digital twins
Security teams will be able to better monitor and manage digital twins if they are familiar with them and how they relate to the larger company. Try to establish connections with digital twin providers to evaluate their security capabilities. Examine the software connections between digital twins and their physical counterparts for weaknesses.
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