This is the second part of our “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the top 10 Data & Analytics 2019 trends identified by Gartner. If you haven’t yet seen the first post on Augmented Analytics check it out here.
In this post, we’ll cover another up-and-coming data & analytics technology that could help businesses save massive amounts of time when it comes to making their data ready-for-use, Augmented Data Management.
Definition
Augmented data management uses machine learning and AI to make enterprise data management disciplines, such as data quality and integration, metadata management, master data management, and database management systems, “self-configuring and self-tuning,”
Gartner / TechTarget
Whereas Augmented Analytics leverages artificial intelligence (AI) in the form of machine learning (ML) and natural language processing (NLP) to drive business insights, the primary focus of augmented data management is to significantly cut down the amount of time companies, and their data teams, have to spend on the required manual work necessary to make data ready for “consumption”.
Why It Matters
It’s no surprise that companies that can rapidly prepare their data and use it for analysis have the ability to impactful business decisions ahead of their competition. It’s the not-so-secret weapon a truly modern company can leverage. It makes you wonder why not more companies are jumping on such initiatives around their business data. The answer is simple. Data management and processing are difficult, expensive, and time-intensive tasks. This problem is one of the, if not the, most significant bottlenecks to data becoming the lifeblood of an enterprise, or in other words to companies achieving full “Enterprise Consciousness”.
Nowadays, most high-level insight driven from data is garnered after multiple rounds of cleaning and processing are performed by various data team members. Even a 5% reduction across the board as it relates to these processes could have the potential of inching the floodgates open towards more companies becoming real-time data platform organizations.
The promise of Augmented Data Management is a massive reduction in the time needed to process data through automated machine-processes leveraging AI and ML. Armed with an Augmented Data Management solution, businesses will be able to have machine-backed automated data labeling, tagging as well as powerful features like the automated detection of semantics and taxonomy. Furthermore, Augmented Data Management will arm data scientists and analysts with powerful tools as it pertains to automatic data alerts as well as creating suggestions for solutions to the aforementioned errors.
At its apex, Augmented Data Management will have the potential to reduce manual data management work by 45% as per Gartner.
Applications
There are no outstanding applications that currently fulfill the goals of Augmented Data Management. The best thing you can leverage today are systems that enable and accelerate data management. These are useful applications for this purpose.
Conclusion
In the next 5 to 10 years companies that can truly leverage their data at a fast-pace (ie: real-time) will be the ones that will have a competitive advantage. The opportunities that Augmented Data Management (and it’s twin Augmented Analytics) will provide are going to enable more and more organizations to truly build their “data fabric” into something that is a consistent, sustainable, and powerful source of business drive.
Anant
Interested in learning more about Data & Analytics? Our practice is currently focusing on technologies such as Spark, Cassandra (check out this post on Cassandra vs Databases), Graph, Kafka and DataStax Enterprise and the following are our go-to places for our Data & Analytics brief.
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