Modern enterprises are driven by a large amount of data that influences many business decisions. Product improvement, marketing, advertising trends, business risk, and product performance – these are elements of business that rely on accurate data for high-quality decision-making.
Despite how critical data is to enterprises, there is no denying that business data can also be marred by inaccuracies that can lead to dirty data† recent Research suggests that dirty data incidents cost corporate organizations in the United States an average of $15 million a year. a shocking report in 2018 it revealed that Samsung lost about $300 million due to bad data.
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Subsequently, there has been a growing concern about data quality and how to ensure data integrity in organizations to avoid mistakes that can lead to terrible business decisions. There is also concern that dirty data could lead to data security vulnerabilities, which is a major cybersecurity concern for businesses.
What is dirty data?
Dirty data refers to customer or company information that is incorrect, duplicated, or missing. Dirty data can arise when a manager mistakenly duplicates a customer record, someone misspells an important data record, a data entry tool automatically fills in misinformation or spam emails, or a date format is applied inconsistently.
Due to human interaction with organizational data, it is nearly impossible to maintain the integrity and accuracy of data at all times, making data a weapon for attackers to target and abuse.
Types of Dirty Data
Below are types of dirty data that can compromise the integrity of most corporate databases.
Duplicate data
Duplicate data refers to data entries containing data that are identical to other data entered inadvertently into your database. Contacts, leads, and accounts are the most duplicated objects.
Outdated data
Outdated data is information that is no longer relevant. For example, obsolescence can come in the form of old server session cookies, web information that is no longer accurate, and when the organization is going through a rebranding phase.
incomplete data
Incomplete data can 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 Inaccurate Data
Generating field values outside the acceptable range of values may result in incorrect data. For example, a month field should only accept values between one and 12, and an address should be a real home or office location. When these acceptable values are missed, we can call it inaccurate data.
Inconsistent data
Data is called inconsistent when an item has multiple representations on other systems. For example, when entering a date of birth field in different versions, such as dob, DOB, or Date, or B.
A major problem with inconsistent data is that it affects analytics and hinders data segmentation when you have to consider all variables with the same title and industry.
Dirty Data Cybersecurity Issues for Business Enterprises
With the proliferation of data breaches across many industries, dirty data raises a number of new cybersecurity concerns. These concerns are highlighted below.
Misleading signals will target cyber fusion centers
Cyberfusion centers are collaborative projects set up to take on the task of cybersecurity to improve communication between different teams. Fusion centers combine automation techniques with data compiled from various sources to discover insights that support business and security decisions.
Unfortunately, there is a possibility that attackers can abuse the power cyberfusion centers have over commercial activities to manipulate data and spread false information.
More attackers targeting data poisoning
Attackers continue to test new strategies and perform more stealthy and targeted attacks to increase their success rate and evade law enforcement. They aggressively prey on false information to damage an organization’s reputation, mislead 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, and undermine the integrity of the data that organizations rely on to move their businesses forward.
Digital twins will double the attack surface
To collect data based on actual behavior, simulation and machine learning are used to create a digital twin of a physical object. Digital Twin usage is increasing among manufacturers in an effort to streamline product development, improve tracking capabilities and predict financial results.
Anyone with access to the twins can see crucial details about their physical counterpart, as digital twins use real-world data. Attackers can take advantage of digital twin vulnerabilities to cause production and supply chain downtime.
How organizations can protect themselves
Indicate critical assets
Listing essential information assets is the first step. Then focus on creating, implementing, and maintaining an organizational plan for dealing with data poisoning within these critical assets.
Consider deploying platforms with built-in data management capabilities, as they provide controls to monitor and troubleshoot all facets of data management, including data integrity.
Using automation
Pay close attention to the accuracy of data and intelligence input as the cyber fusion center evolves. There is a need to routinely review automation systems, especially their potential to cause disruptions. You should also set automation thresholds that do not contradict the requirements of reliability and security. Develop, practice, and categorize response strategies for a sudden problem with the data integrity of a cyber fusion center.
Use data cleaning
To further ensure the integrity of the data powering the cyberfusion center, you must: data cleaning procedures and establish policies that allow the business and IT teams to work together to improve the accuracy and effectiveness of the cyber fusion center.
Meet digital twins
Security teams will be better able to monitor and manage digital twins if they are familiar with them and how they relate to the larger company. Try establishing connections with digital twin providers to evaluate their security capabilities. Examine the software connections between digital twins and their physical counterparts for weaknesses.
