A Modern Guide To Data Mapping

4 min read
(January 4, 2023)

It’s 2 AM. Do you know where your data is? Mapping Data and Risks

The modern business landscape requires companies to be innovative, agile, and
flexible. At the same time, exploiting the next opportunity calls for speed and
accuracy. For example, suppose you are sharing crucial data to close a potential
business deal. The data must be accurate, compliant with relevant regulations,
and up to date.

But do you know where your data is?

The answer to this question is data mapping, a systematic process through which
organizations organize, classify, analyze, and understand unstructured and
structured information stored across multiple sites. It shows the data flows
throughout the company, the types of data stored and processed to drive daily
business operations, storage locations, and user access rights to different types of information.

In addition, data mapping plays a vital role in supporting successful data
processes. Erroneous data mapping led to ripple impacts across the company,
such as inaccurate analysis and replicated errors, which may cause poor decision-making. Since most business operations are data-driven, enterprises often move data from one system to another. A roadmap ensures the data gets to the intended location without errors or inaccuracies.

Why modern enterprises need data mapping

Data mapping enables successful integration, migration, and other vital data
management processes. Essentially, mapping data homogenize it to be accessible and available to decision-makers for analysis and derivation of key business insights. In addition, most organizations access data from multiple sources, and mapping data bridges the dissimilarities between distinct data models or systems to ensure data accuracy and usability once moved from one location to another.

Furthermore, data mapping is a vital function in successful data management tasks. However, due to the rapidly increasing data amounts, organizations require automated tools to perform key data management tasks. Improper management often causes data corruption and degrades data quality. Thus, effective data mapping practices ensure you get the most out of your data in various data management tasks, including data transformation, migration, and integration.

Data mapping ensures data integrity.

For companies to maintain data integrity, they must ensure that it is complete,
consistent, and accurate. That said, data mapping allows businesses to maintain
data integrity based on the following principles:

  • Trustworthiness: The mapped data must be trustworthy throughout its use
    and lifecycle. Specifically, the data mapping process must ensure data
    confirmability, transferability, credibility, and dependability.
  • Validation: The data maps must accomplish data integrity through reliable
    validation and frequent checks on map performance and data flows to
    ensure accuracy and consistency.
  • Continuous monitoring: The mapped information must reflect the current
    content during data flows and ensure consistency in the data’s primary and
    secondary use cases. Continuous monitoring of the data mapping processes ensures data integrity.

Data mapping to prevent data breaches

Data breaches continue increasing as hackers devise more advanced methods to
gain unauthorized access to sensitive information. In Q3 2022, more than 15
million records were exposed in data breaches worldwide. Also, a successful data breach costs an average of $4.35 million, which is enough to cause 60% of
businesses to shut down operations in six months. Data mapping can reduce the
risk of a data breach significantly.

1. Mapping the risks

The ability to detect, respond to, and prevent data breaches lies in how well a
company understands its risks. Mapping the potential risks involves: determining
the collected types of data; methods used to collect the data; the data storage
locations; users authorized to collect and process the data using various systems; data flows and interactions across the enterprise; and the company’s legal
obligations during a breach.

2. Implementing data protection safeguards

Different types of information differ in importance since some are more sensitive
than others. Through data mapping, you get to classify and categorize data based on sensitivity. You can then focus on implementing more robust data security controls for the most sensitive data, such as personal data, health information, and financial data. Besides, data mapping assists in optimizing resource allocation to ensure that the most effective data protection safeguards are implemented to protect confidential data.

3. Conforming to compliance regulations

Compliance regulations strengthen data security and build a company’s
reputation since most clients are likely to work with compliant entities. Data
mapping assists in understanding the data storage locations, defining the access
rights of different users, and determining the required protection mechanisms for each data type. For example, GDPR emphasizes the required security controls for personally identifiable information (PII), HIPAA outlines security requirements for health data, and PCI DSS provides measures for protecting financial information.

Data mapping challenges and solutions

1. It is a time-consuming process

Mapping the entire data environment is a tedious and time-consuming process
since the scope of the work involved is enormous. Fortunately, companies can
overcome this challenge by defining an effective information-gathering process,
such as interviews with data controllers, to understand the data flows within the
organization. The collected knowledge serves as a basis for starting with what you know and building out gradually. Then, leveraging modern technologies to
automate the data mapping process can ease the burden significantly. Such
software solutions scan data sources rapidly and identify sensitive information,
enabling a more effective data mapping approach.

2. Challenges in ensuring data maps are up to date

Data maps require constant evaluation, assessment, and updating to ensure
accuracy, usability, and quality. Failing to constantly assess and evaluate data
maps often results in outdated data that does not provide value, hindering
effective decision-making processes. Automation can address this challenge by
ensuring data flows continuously instead of manually updating, which is prone to
errors and time-consuming. Also, integrating data maps with asset management
systems can ensure data maps reflect current information.

3. Incomplete information when building data maps

Omitting relevant information when creating data maps is a common challenge
facing many organizations, and it causes data maps to be less useful. Therefore,
companies must engage all stakeholders before initiating a data mapping project
to gather feedback regarding the information to include in a data map.
Additionally, understanding the interactions between various business units can
guide the information to include in the data maps to ensure they are more
effective and each user knows where to access specific data.