Metadata isn’t just something that concerns business intelligence and IT teams; but lawyers are extremely interested in it as well. Metadata, it turns out, can win or lose lawsuits, send politicians to jail, and even decide medical malpractice cases. It’s not uncommon for attorneys who conduct discovery of electronic records in organizations to find that the claims of plaintiffs or defendants are contradicted by metadata, like time and date, type of data, etc.
If a discovery process is initiated against them, an organization had better be sure that its metadata is in order. All it would take for an organization to lose a case would be for an attorney to discover a discrepancy in different databases – a different time stamp on some communication, a different job title for a principal in the case. Such discrepancies could lead to accusations of data tampering, fraud, or worse – and would most definitely put the organization in a very tough position versus a judge or jury.
Metadata errors are difficult to spot
The problem with that, of course, is that catching metadata errors is extremely difficult. In large organizations, data is stored in repositories that are spread throughout the organization, maybe even the world – in different departments. Each department is responsible for maintaining its own database, and the metadata in it; and on different cloud storage repositories, which may have their own system of classifying data.
An enterprising attorney could have a field day with the different categories and tags data is stored under, making claims that the organization is trying to “hide something.” The organization’s only defense: We’re poor administrators. That may not be enough to impress the court.
Types of Metadata
Metadata is “data about data,” and comes in three flavors:
System Metadata, which is data that is automatically generated from the computer and includes specific labeled criteria, like the date and time of creation and date a document was modified, etc.
Substantive Metadata reflects changes to a document, like tracked changes.
Embedded metadata is data entered into a document or file but not normally visible, such as formulas in cells in an Excel spreadsheet. All of these have increasingly become targets for attorneys in recent years.
Metadata has been used in thousands of cases – medical, financial, patent and trademark law, product liability, civil rights, and many more. Metadata is both discoverable and admissible as evidence. According to one New York court, “General information about the creation of a document, including who authored a document and when it was created, is pedigree information often important for purposes of determining admissibility at trial.” According to legal experts, “from a legal standpoint metadata is evidence… that describes the characteristics, origins, usage, and validity of other electronic evidence.”
The biggest metadata-linked payout until now – $10.8 million – occurred in 2017, when a jury awarded a plaintiff $8 million (eventually this was increased to nearly $11 million) after claiming he was fired from a biotechnology company after telling authorities about potential bribery in China. The key piece of evidence was the metadata timestamp on a performance review that was written after the plaintiff was fired; with that evidence, the court increased the defendant’s payout for violating laws against firing whistleblowers. In that case, records claiming that the employee was fired for cause were belied by the metadata in the performance review.
That, of course, was a case in which there was clear wrongdoing by an organization. But the same metadata errors could have cropped up in any number of scenarios, even if no laws were broken. The precedent in this case, and others like it, might be enough to convince the court to penalize an organization based on claims of a plaintiff.
How can organizations defend themselves from this legal bind of metadata
The answer would seem obvious; get control of your metadata and make sure it corresponds to the data it represents. With that kind of control over data, organizations would discover for themselves if something was amiss that could cost them in a settlement later.
But execution of that obvious answer is a different story. With reams of data to pore through, it would take an organization’s business intelligence team months, or even years, to manually sift through the databases. And because to err is human, there would be no guarantee they hadn’t missed something.
Clearly Business Intelligence and Data Analysis teams need some help in doing this. One solution would be to hire more staff, expanding teams at least temporarily to make sense of the data and metadata that could prove problematic. There are services that will lend their staff to an organization to do just that, and for companies that prefer the “human touch,” adding that temporary staff may be the best solution.
Another idea is to automate the process, with advanced tools that will do a full examination of data, both across systems and within repositories themselves. Such automated tools would examine the data in the various repositories and find where the metadata for the same information is different – pointing BI teams in the right direction and cutting down on the time needed to determine what needs to be fixed.
Using automated metadata management tools, companies can ensure that they remain secure. If a company is being sued and discovery has commenced, it will be too late for the organization to fix anything. Honest mistakes or disorganized file keeping can no longer be corrected, and the fate of the organization will be in the hands of a jury or a judge. Automated metadata management tools can help Business Intelligence and Data Analysis teams figure out which metadata entries are not consistent across the repositories, ensuring that things are fixed before discovery takes place.
There are a variety of tools on the market, with various strengths and weaknesses. Companies will need to decide whether a data dictionary, a business glossary, or a more all-encompassing product best answers their needs. They’ll also need to make sure the enterprise software they currently use is supported by the metadata management solution they are after. As the market develops, AI will be a huge distinguishing factor between metadata solutions, as machine learning will reduce the cost and manpower investment of solution onboarding significantly.
With the success of recent metadata-based lawsuits, you can be sure more attorneys will be using metadata in their discovery processes. Organizations that want to defend themselves need to get their data in order, and ensure that they won’t end up losing lots of money because of their own errors.
Amnon Drori is the Co-Founder and CEO of Octopai and has over 20 years of leadership experience in technology companies. Before co-founding Octopai he led sales efforts at companies like Panaya (Acquired by Infosys), Zend Technologies (Acquired by Rogue Wave Software), ModusNovo and Alvarion.