Click to learn more about author Nabil Lodey.
Most companies, whether in finance, retail, or government sectors, are seeking to make the most of their data to gain a competitive advantage. But not many organizations are making effective use of metadata.
Arguably, data on its own can be meaningless, but when combined with metadata, it turns into information that can be exploited and, when aggregated with other datasets, delivers the insight that every organization needs to improve decision-making.
A recent Veritas Report on unlocking the value of data found that, on average, employees lose two hours a day searching for data, resulting in a 16 percent drop in workforce efficiency. For an organization of 1,000 workers that are dependent on data, the inability to find the right data at the right time costs that organization £16m a year.
If run correctly, a metadata project will deliver significant time and cost savings in the short-term and enhance the effectiveness of data projects in the medium-to-longer term.
Therefore, all companies should ask themselves: Why not explore a metadata project to see how it can deliver savings and unlock future value from data?
The importance of having rich metadata will grow as artificial intelligence/machine learning (AI/ML) and autonomous applications become increasingly common in every organization.
Effective metadata enables data to be discovered by users, systems, or AI/ML applications, whereas without it, a manual and time-consuming process is required to physically interpret whatever data is available and decide if it’s relevant or not. This is open to human error with the likelihood of missing key datasets.
A great example of this is within enterprise search platforms, such as AWS Kendra (launched earlier this year), where data sources are managed through metadata/tags. This means that data stored in different data sources, such as manuals, reports, internal websites, or applications such as Salesforce, SharePoint, OneDrive, and ServiceNow can be accessed across one platform, and the use of machine learning algorithms enables the context of any question to be understood, with either the answer or the relevant document itself provided.
Another advantage is that only the metadata itself needs to be shared across departments and organizations in an open data environment to know what information is held, and where, but the data itself doesn’t have to be open to everyone. This reduces the security and cost implications of migrating data from legacy IT systems and keeps commercial agreements intact.
Metadata Management is a process that organizations need to be proactive in following if they are hoping to become truly data-driven, looking to extract information to improve decisions, and “future-proofing” to enable automation and AI/ML applications.
This will naturally require the company to invest in a metadata program and drive cultural change by following certain metadata standards and protocols that can, of course, be tailored towards the best fit for the organization.