Data integrity, defined

Data integrity refers to how valid, complete and consistent data is and how safe and secure the information is regarding regulatory compliance.

A data set’s integrity is maintained by a collection of processes and standards, which ensure the data remains complete and accurate no matter how long it’s stored or how many times it’s been accessed. To prevent data loss or leaks, companies should emphasise data integrity. By implementing processes to check for errors within data sets and ensuring internal users are handling data correctly, organisations can reduce the risk of outside forces gaining access to sensitive data.

When looking at data integrity, legal professionals are often concerned with the trustworthiness and accuracy of the data throughout its lifecycle. This concerns when the data is entered into the system, how it’s stored, where it’s transferred and more.

You’re most likely to encounter the concern of data integrity in litigation when looking at issues regarding compliance or privacy protocols. Accurate and well-managed data is essential for meeting compliance regulations. Additionally, data integrity is vital for litigation and internal company investigations, as data is often required to be recoverable, searchable and traceable.

If a company ensures its data is of high integrity, investigations can run more smoothly. Data is more easily recoverable, remains accurate throughout processing and produces robust evidence a case can rely on.

With different data definitions to be aware of, it’s easy to confuse data integrity with other meanings, such as data quality. Data quality is only a part of data integrity, with data integrity encompassing all aspects of what defines data quality and more.

Data quality, defined

Data quality refers to a data’s age, relevance and reliability. Data quality is an aspect of data integrity, with the latter going further by implementing rules and processes which determine how a company’s data is entered, stored and transferred.

Whether or not data is defined as high quality can depend on its purpose. For example, if a business uses data to influence business decisions, it must contain relevant, accurate and reliable information to drive these outcomes. Poor data quality can lead to several stumbling blocks for businesses, such as inaccurate analytics and missed opportunities. Inaccurate data can also lead to financial ramifications, including lost sales opportunities due to incomplete customer records or fines for failing to meet regulatory compliance.

So, where does data quality matter in litigation? Of course, if a client’s data is of high quality, this can make litigation processes simpler and evidence easier to produce.

However, law firms must also ensure their data is of high quality. Much like in wider business, poor data quality can mean the information your team is provided is inaccurate, resulting in lost time, money and possible reputation damage.

Data quality issues can be caused by several factors, such as human error or inconsistent processes within two separate departments at your firm. These problems can cause long-lasting harm to your firm, so the first step should be to identify where the issues lie and look to implement effective data governance processes to rectify them.

Data analytics is essential to any firm and its strategy, so now is the time to implement data policies if you haven’t done so already.

Data accuracy, defined

Data accuracy is an essential standard of data quality and refers to error-free records that can be used as a reliable source.

Data accuracy is the first and most crucial step in achieving high data quality within most frameworks. Accurate data is key to effective planning, strategy and budgeting; your goals and plans for the future could fail if they’re based on inaccurate data.

To achieve accurate data, specific processes must be implemented and adhered to by those who handle data. For example, a simple data standardisation rule a business could implement would be how to format dates.

So, what can cause data to be inaccurate?

• Poor data entry — An organisation that doesn’t practice data governance is more likely to experience data being inputted in multiple formats and styles, leading to inconsistencies.
• No accessibility regulation — If several different departments can access a system, this can lead to issues if data isn’t governed correctly. These systems can become a hub for inaccurate information if not appropriately managed.
• Outdated systems — Issues such as poor form design can lead to inaccurate data. Plus, with manual input processes, human error can occur, which is arguably the most common cause of data inaccuracy.

In law, the accuracy of data presented in court is essential. The Data Protection Act 2018 defines ‘inaccurate’ as “incorrect or misleading as to any matter of fact.” Whether the data’s inaccuracy is purposeful or not can still present issues, which once again reinforces the need for high data quality and effective data governance processes.

Inaccurate data can be caused by data spoliation, which is the act of rendering potential evidence invalid, either purposefully or through negligence. For example, the spoliation of a document would refer to the destruction of the file, altering or hiding its contents.

A recent example of spoliation in a high-profile case was the ‘Wagatha Christie’ case between Coleen Rooney and Rebekah Vardy. Data spoliation had a strong presence in this case, as Caroline Watt, Vardy’s agent, was said to have dropped her phone into the North Sea to destroy evidence.

Data accuracy is one of the key components of high data quality, leading to good data integrity.

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