Why Augmented Analytics Is the Future of Data Analysis

If data scientists have learned anything over the last few years, it’s that the faster you can gather and process vital data, and the deeper you can go on the analytics, the more impactful your insights will be. That’s why the field of augmented analytics (AA) is so hot today. AA represents the use of enabling technologies such as machine learning (ML) and artificial intelligence (AI) to drive better data preparation, insight generation and insights explanation to “augment” how data scientists explore and exploit data, according to Gartner

What makes AA so compelling to data analysts and data scientists is its ability to enable faster and deeper access to insights derived from huge data sets. The intelligence it generates uncovers hidden patterns and deviations in data that human data analysts might miss, and also removes potential human bias as recommendations or conclusions are generated.   

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The Benefits of Augmented Analytics

Augmented analytics enhances the analytical process across all facets of the data lifecycle, from data prep to analysis and insight delivery. The business benefits can be summarized as follows:

  1. Delivers Faster Data Value: Combining AI and ML with data science brings about more robust data prep, faster visualization of data, and more speedy insights (which in the end enables better productivity). AI algorithms replace the manual processing of data, provide automatic associations of different data sources, and help clean up the data. 
  2. Surfaces Buried Insights: Typically, data analysts begin with a hypothesis around the insights they want to investigate and work the data to get to a conclusion. But with AA, AI algorithms do the heavy lifting, providing contextual recommendations that users may not have even considered. AI uncovers correlations and outliers between datasets that can help create the most astute discoveries. 
  3. Builds Trust in Data: Machine learning algorithms have the power to track user interactions with data. Every interaction provides clues that help generate more relevant suggestions and insights for the user. That in turn builds trust in the data and analysis by the individual user and facilitates greater adoption of AA in the organization. 
  4. Grows Data Literacy: Augmented analytics makes data more accessible to a larger group of stakeholders, allowing them to ask questions of the data along the way and get more value from both structured and unstructured datasets. When everyday workers can get insights quickly, it improves data literacy across the organization and gives users confidence in their data-based conclusions.  

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Augmented Analytics Use Cases

The Forbes Business Development Council recently outlined several compelling use cases for AA, all of which carry important lessons for companies to grow the value of the data they analyze. 

Profiling the Customer Base

With AA, data analysts can evaluate massive customer datasets and systematically generate accurate customer profiles that help segment the most valuable purchasers. Digging into customers’ purchasing history and projecting sales interactions on prospects of a similar granular segment, for example, helps create better forecasting so sales teams are prepared to meet demand. 

Predicting Important Market Trends

It’s never easy to spot trends in a given market, but augmented analytics makes it possible to sort through large amounts of public data to identify potential market trends before they happen. Machine learning analytics could, for example, examine social media activity, public discussion boards, and product reviews to spot a growing need for a certain technology like blockchain for IoT solutions, so that a company could create relevant services to meet the growing new demand.

Creating Proactive Recommendations

Sales teams and e-commerce marketers can use augmented analytics to evaluate customer requests, or see how even a vague need (customers don’t always know exactly what they are looking for) can map to a viable solution. Even failed deals can be mapped onto customer personas to create new recommendations and increase future deal closure. 

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The Growing Role of AI in Data Analytics

There’s little doubt that AI and machine learning can help companies make huge strides with the way they process and analyze data. Unfortunately, 76 percent of c-level executives currently say they struggle to scale their AI investments in spite of the fact that they know how critical it is for their growth. The great equalizer for these companies will be ensuring the skillsets of their data scientists and data analysts include AI and machine learning technologies.

AI engineers can be trained to create practical applications using a wide range of intelligent agents, including knowledge-based systems and agent decision-making functions that power data analysis. And machine learning experts can excel in supervised and unsupervised learning, mathematical and heuristic techniques, and hands-on modeling to develop machine learning algorithms that drive augmented analytics. By building new skill sets, any company can ride the future wave of augmented analytics. 

About the Author

Stuart RauchStuart Rauch

Stuart Rauch is a 25-year product marketing veteran and president of ContentBox Marketing Inc. He has run marketing organizations at several enterprise software companies, including NetSuite, Oracle, PeopleSoft, EVault and Secure Computing. Stuart is a specialist in content development and brings a unique blend of creativity, linguistic acumen and product knowledge to his clients in the technology space.

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