Introduction to Augmented Analytics

In the world of business intelligence, augmented analytics solutions have been acquiring traction. In practical terms, augmented analytics is intended to facilitate more growth and help produce more revenue.

Augmented analytics automates insights by invoking a mixture of machine learning and natural language generation.

When it comes to Gartner’s description, there are three key elements that businesses should know:

• Machine learning
• Natural-language generation
• Automating insights

Machine Learning

Machine Learning is a part of artificial intelligence that “is based on algorithms that can understand from data without relying on rules-based programming.”

Put another way, machine learning programs can adapt to various uses without being explicitly programmed to do so.

In practice, this implies machines process a ton of data until they receive really good at accomplishing tasks much in the same way people learn and become more skilled when they get more experience in a subject or field.

But augmented analytics is more than only machine learning. Natural-language generation really carries this technology to the next level.

Natural-Language Generation

Natural-language generation (NLG) suggests the process that interprets a machine’s findings into words and phrases that humans can know.

Particularly, NLG focuses on the product of data analysis. When a system detects that sales are down in a specific category, NLG allows the system to tell you, directly: “Sales in Category A declined by 30 percent.”

NLG is a necessary companion to machine learning because it allows the normal, non-technical person to learn what’s occurring in your data.

It’s not only about communicating data trends efficiently; it’s about transforming intangible algorithms into something human, so business users can internalize and use the insights they’re getting.

With natural language search, users can ask those follow-up questions immediately(though the degree to which augmented analytics platforms can sustain “why” questions varies greatly). Plus, augmented analytics solutions can allow users to drill down into the specifics of their insights to, for instance, gain more detailed information about every category. This leads us to the third important aspect of augmented analytics: automating insights.

Automating Insights

Data-driven insights manage business strategy.

The incorporation of machine learning and NLG enables businesses to automate the labor-intensive method of analyzing data and communicating significant findings to business people.

These automated insights can then be leveraged to evaluate your performance and overall brand health, recognize growth pockets and opportunities, and determine a holistic knowledge of how your brands compare to the marketplace. All of these factors add to a solid business approach.

Ultimately, this automation leads to insights that are pushed by algorithms that would otherwise need a vital investment of time and energy from technical members of your team.

As such, augmented analytics democratizes information, so data scientists and analysts aren’t the simply people on your team who can make understanding the results.

Benefits

Augmented analytics allows business users to act on the insights they gain, freeing up data scientists to concentrate on much more complex queries.

Augmented analytics can profit businesses in a myriad of ways, so let’s discuss specifics:

• Deeper data analysis. Augmented analytics do the hefty lifting for you. By analyzing exhaustive data mixtures, augmented analytics can recognize which portions are truly impacting your output.
• Faster results. Augmented analytics enables business users to receive answers to their questions immediately, in a matter of seconds. No longer will they have to wait for data analysts to satisfy the gap.
• Better utilization of resources. When your data analysts aren’t bogged down with problems, they have more time to concentrate on deeper research that machines can’t yet support, contributing more bang for your buck.
• Actionable insights. Finally, augmented analytics interpret the data analysis process so you can obtain important insights into your data that can tell your business strategies.
• Compare Top Business Analytics Software Leaders

These advantages are the basis of a solid business strategy that discusses the ever-changing needs of customers and shifts in the market.

Plus, they increase efficiency. Without a long, drawn-out method to get business questions acknowledged, your team can work with more up-to-date and related data. Quick, agile analytics are increasingly driving revenue. They can play an important role in fringing out the competition and, finally, propelling your business forward.

Still Confused regarding Augmented Analytics, Connect with experts at TestUnity, and resolve your doubts.[/vc_column_text][/vc_column][/vc_row]

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