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Three Augmented Data Management Flavors

What is Augmented Data Management?

The term augmented data management has become a hyped topic in the data management world. “Augmented” is used here to describe an extension of the capabilities that are now available for doing data management.

We have outlined the three core principles below:

  • Inclusion of Machine Learning (ML) and Artificial Intelligence (AI) methodology and technology to handle data management challenges that until now have been poorly solved using traditional methodology and technology
  • Encompassing graph approaches and technology to scale and widen data management coverage towards data that is lesser structured and has more variation than data that until now has been formally managed as an asset
  • Aiming at automating data management tasks that until now have been solved in manual ways or simply not been solved at all due to the size and complexity of the work involved.

How do we apply this?

Augmented data management can be applied to all the data management disciplines we know. In the following I will have a look at three data management disciplines where we today see solutions and implementations emerging. These are:

Augmented Metadata Management

The word metadata has been around for ages. The importance of metadata management as a prerequisite for proper data management is commonly agreed on among data management professionals. However, the concrete examples of successful enterprise-wide implementations are sparse. Even more, examples of solutions that are governed and maintained over time are rare.

Lines of augmented data flowing in golden colours

Metadata management is a daunting task. Doing a snapshot of the metadata in play within an enterprise just now is hard enough. Maintaining this as new data types are utilized, applications are replaced, the organization changes, new standards are adopted, and more is even more daunting.

Leverage AI and Machine Learning

So, here augmented metadata management comes with a promise of automating this task by providing active metadata management. This is enabled by using machine learning and artificial intelligence components and relying on graph approaches that are able to picture complex relationships between metadata.

Managing Augmented Master Data

Master Data Management (MDM) solutions are being implemented around the clock in large and midsize organizations. As these solutions becomes a part of business processes there are people responsible for controlling and maintaining master data. Some of this work can be automated through Robotic Automation Processes (RPA). However, there is still a substantial amount of work that relies on decision making not easily solved that way. Add to that, that more and more data will become part of MDM solutions.

So, here augmented master data management comes with a promise of automating these tasks by using machine learning and artificial intelligence components that where feasible can rely on graph approaches that is able to picture complex relationships between master data.

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Augmented Data Quality

The promise of automating data quality tasks through machine learning and artificial intelligence is not new at all. For decades this approach has been tried out in areas as for example data matching and product classification.

What we see now is that this approach has matured and is more widely utilized including going from being standalone specialty solutions to being components in broader data management solutions.  

Abstract Data Management in boxes

An Example

One example of how data quality, master data management, and metadata management is supported by augmented data management in a mature solution is showcased in a video embedded in the Semarchy blog post How Augmented Data Management Adds Value to Your Business.

How can this apply to your business?

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This post was initially published on by Analyst Henrik Liliendahl.