What does augmented data management mean for the enterprise?

Together, artificial intelligence and machine learning are transforming data management, increasing efficiency and productivity as they go

Machine learning (ML) and artificial intelligence (AI) are both tools that famously drive modern enterprise. Simultaneously, data is also at the forefront of business initiatives to enhance growth today. Combine the two, and you have some serious power in your hands: augmented data management.

AI is like the steroid of all things technology, powering up whatever you started with. Augmented data management is no exception to this. The augmented data management process uses ML and AI to automatically refine data or, as Gartner puts it, self-configure and self-tune. In doing so, ML and AI drive efficiency and productivity in the workplace.

Efficiency is hugely important in the data science realm. Data scientists notoriously spend a large portion of their time collecting and preparing data, which quashes the potential of their efficiency and productivity. Thus, automating with augmented data management enables scientists to redirect their efforts towards a higher-value activity. In fact, predictions suggest that augmented data management can reduce manual data management tasks by as much as 45%.

Understanding augmented data management

In particular, augmented data management will impact data quality, master data management, data integration, database management systems, and metadata management. Furthermore, it will help enable faster, more scalable, and better business decisions. Better still, with the added support of automation, enterprises can enjoy more accurate anomaly detection and correction.

According to Gartner, another added bonus of augmented data management is its capability of converting metadata to “powering dynamic systems” This is in place of it only being used for audit, lineage, and reporting.

Of course, augmented data management will still require a degree of human oversight. However, this makes it all the more powerful; humans, ML, and AI each complement each other and fill the gaps in each other’s shortfalls.

If you’d like to know more about data, why not check out our Tech Chat episode about data breaches?