Disrupting Data Discovery with Automated Metadata Mapping

Automated Metadata Mapping

Dec 1, 2019

Did you know, on any given day, over 2.5 quintillion bytes of data are generated by different sources? But what’s even more fascinating than this sheer amount is the velocity with which it’s being processed. In today’s dynamic economy, timing is everything and in order to stay relevant, companies must leverage this data in the right away and at the right time. This is just what Automated Metadata Mapping helps you with.

What is metadata and why is it important?

To understand metadata and its role in data discovery, let’s take a simple example. Do you remember those catalog cards in libraries which recorded information like title, author, copyright, location on shelf, etc. to help us find the book(s) we were looking for? That’s exactly what metadata is; it’s data that offers information about data. This includes source of the data, standards used, size of the data, when and where it was created, quality of the data, what purpose the data serves, who’s the author of the data.

All kinds of data (structured, semi-structured, or unstructured) have metadata information linked to it. In fact, accurate mapping is key to a number of data-related functions, from data warehousing to master data management.

Automated Metadata Mapping

Even though metadata mapping is critical for effective data processing, most organizations are still doing it manually. A major part of data scientists’ time is spent on its manual maintenance, be it identifying sources or tracking inconsistencies. Plus, there’s always room for error, no matter how carefully it’s done. This, in turn, results in a huge loss of time and effort spent on projects that are centered on inherently flawed data.

This is where automated metadata mapping comes in. It helps map business terms to their respective metadata, establishing automatic links between the two. This allows organizations to understand which data point represents what part of the information within the business.

In short, automated metadata mapping facilitates the instant discovery of data and its lineage — a process that can take weeks, or even months, when done manually. What’s more, automation completely eliminates the possibility of human error. This means that data scientists can now focus solely on generating real-time actionable insights.

DataBlaze: Taking data discovery to the next level with Automated Metadata Mapping

DataBlaze uses automated metadata mapping to build knowledge graphs, as opposed to traditional databases. Here’s a snapshot of how automated metadata mapping has turned DataBlaze into a truly next-gen product. It allows DataBlaze to:

  • Map knowledge graph terms with the metadata of the table ingested from the source system. This enables data professionals to understand and locate cross-connections without complete knowledge of the data landscape
  • Extract metadata automatically for Natural Language Processing (NLP).
  • Update changes in metadata across different data governance domains— whether that’s data modelling, business process, or enterprise architecture
  • Build data lakes, creating a central repository to store and manage metadata in a way that makes data processing far more efficient

Automated Metadata Mapping has the potential to transform data management processes by saving a lot of time and money of the business. The user need not invest time in manually mapping the data as DataBlaze does it automatically and subject matter experts simply need to approve the terms and labels. So, isn‘t it time you got in touch with us for a demo of DataBlaze!

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