Data Audits: A Comprehensive Overview

Any company can solve immediate data quality problems with the help of data audits. Using data audits helps keep data collection and storage focused, accurate, and relevant thus enabling companies to increase operational efficiency.

Data audits, while often avoided, are important to ensure transparency about who is using sensitive data and for what purpose. This process is necessary to ensure the organization secures sensitive data, remains compliant, and translates that data into well-informed decisions.

This article provides an overview of data audits covering the following information:

• What is Data Auditing?
  A data audit evaluates who has accessed the data and for what purposes. In contrast to auditing funds, auditing data involves reviewing leading factors other than the total amount to make conclusions about the characteristics of a data set.

When conducting a data audit, you may scrutinize the data sources, how you created the data, and its structure to determine its quality, importance, and usefulness. Protocols for data auditing are supported and promoted by a wide variety of organizations and associations across a variety of sectors.

In addition, conducting research data audits has become an essential activity compliance component. Data privacy regulations, such as GDPR and CCPA, require organizations within their jurisdictions to create and maintain auditing logs. These regulations will likely increase in stringency and frequency in the future.

• Stumbling Blocks with Data Audits
  A data audit normally relies on a registry which serves as a space for storing data assets. This reliance is because registries provide a central location for the audited data. Locating a registry or repository, typically housed within a particular department of a company or organization, is one step in performing a comprehensive data audit. The maintenance and storage of consistently audited data are not costless in terms of time, energy, and resources.

In order to effectively complete a data audit engineers will sometimes have to audit information across many platforms. All of these platforms need to be compatible with each other in order to complete the data audit, further, the audit log may lack the relevant metadata thus requiring engineering teams to spend time and resources searching for this information. Altogether conducting a data audit can be a difficult and time-consuming process.

The purpose of an audit of data quality aims to enhance the accuracy of the data by locating and filling in any gaps, locating and correcting any inaccuracies, and eliminating any duplicate information. This procedure contributes to improved insight into the decision-making processes of an organization.

• How to Conduct a Data Audit
  To ensure compliance with necessary data protection standards, businesses must thoroughly understand the different types of data it processes. The effective use of data also requires that the data is accurate, organized, and up-to-date. To ensure data is compliant and effective, organizations use data audits to fully understand the current state of their data and associated data processes. But, how do you conduct a data audit?

Conducting a data audit follows the five-step process outlined below:

1. Plan the Data Audit

• Identify the sponsor and ensure senior ownership is secured. This step necessitates designating an individual as responsible for the audit as well as identifying the other essential personnel required for the audit.
• Determine the time employees can expect to devote to the audit and agree on who will have access to relevant persons, divisions, networks, and data.
• Create a plan for the audit and document the process you will implement.

2. Determine the Data Being Audited and the Method by which it is Handled

• When creating the documentation for the data inventory, be sure to include detailed queries pertinent to the company’s operations.
• Gather all of the required information and put together a complete inventory.
• Determine whether or not any analysis of special category data has occurred.

3. Monitor Data Processing and Ensure all Data Processes are in Compliance

• Find and record the requirement of lawful data handling that applies to all discovered data sets. Also, you should find and document all the data sites and the precautions taken to protect migrations.
• Identify and record how much corporate data is handled, including security precautions and access controls, and document all individuals and third parties with access privileges to the company data.
• Determine which aspects of the policy are being followed and which are not being followed.

4. Present the Findings, Offer Recommendations, and Implement any Necessary Adjustments

• Create a report evaluating the data audit. Describe what audited data is and how it is used.

• Mark the areas that require additional input and present suggestions.

• Obtain the audit sponsor’s validation of the conclusions and then transmit the results to the senior data management staff or committee.

• Discover who is in charge of the implementation, list the necessary tasks and delegate them to the right employees.

• Finally, make sure to monitor the implementation progress.

• 3 Data Audit Best Practices
  Auditing data is not difficult if you fully understand the process and follow a few best practices. We elaborate on these practices below.

1. Discover Data
Data discovery enables businesses to unearth all the data and clearly understand the nature and location of any issues. This option eliminates issues such as data silos, data swamps, or hidden information and creates a clear overview of all data processing activities.

2. Work Collaboratively
Make an effort to work with all the divisions, establish a standard data language, and coordinate the auditing and inventory systems for the data. This collaboration reduces the likelihood of ambiguity and hidden datasets. Ultimately, fostering teamwork will result in the production of singular and true data.

3. Establish Consistency in the Procedure
Establishing an internal data handling and management policy that all of the teams can refer to may be sufficient to accomplish consistent procedures. This policy may include individual roles, data terminology, best data practices involved in handling particular datasets, and accurate data field layouts.

Conclusion

To ensure high-quality, efficient, and compliant data, all data-related tasks must involve regular auditing. Through this, firms learn where their sensitive data is located and its integrity, security, utilization, and efficacy for management, statistics, and analytics.