Top 5 benefits of augmented analytics (original) (raw)

Augmented analytics is the use of machine learning and natural language processing to enhance data analytics, data sharing and business intelligence.

The benefits of augmented analytics to companies include better decision-making by business users, wider access to analytics by employees and helping to make companies more agile. As a result, augmented analytics have become the key differentiator for business intelligence platforms, Gartner reports.

1. Augmented analytics helps companies become more agile

Becoming a data-driven organization requires good, clean, data -- and that can be hard to come by.

For example, supply chain organizations can spend months cleaning transactional data because of fragmented data sources, said Amaresh Tripathy, global leader of analytics at Genpact.

"Augmented analytics platforms can clean, merge and transform the data from various enterprise resource planning systems within no time and furnish data quality and profile reports," he said. "Such clean and robust data foundations can accelerate many future digital transformation projects."

Another use case is e-commerce companies that use AI-powered recommendation engines. In the past, this has required trained data scientists to create and manage the models.

"Organizations can generate such recommendations at an accelerated rate by automating standard modeling techniques through augmented analytics platforms," said Tripathy.

That allows "citizen data scientists" -- often, business users with some basic additional training -- to analyze changes in customer purchase patterns, for example. They could then enhance the AI models themselves for more accurate recommendations.

E-commerce platform vendor Sticky.io is one company with a large amount of unexplored data in its data warehouse.

"Leveraging augmented analytics exposes relationships in the data that we would not have thought to bring into our models," said Justin Shoolery, the company's director of data science and analytics.

That results in faster product development.

"We can quickly explore those relationships and pull insights out that greatly accelerate our product development cycle," Shoolery said, adding that his company uses DotData for its augmented analytics capabilities. "We could certainly achieve the same results via traditional modeling approaches, but at the cost of many more hours spent putting together the analysis."

Since augmented analytics is so much faster, he added, Sticky.io can afford to try different modeling techniques and to ask a lot more questions than it would have otherwise.

2. Augmented analytics expands access to analytics

Augmented analytics also helps make the technology accessible to more users, Tripathy said.

"Traditionally, only a small group of business analysts have leveraged business data to generate intelligence reports," he said.

For example, in a company where only financial planning and accounting teams had access to analytics, order management teams would then be able to leverage that data as well, he said, improving customer satisfaction across different delivery channels.

Part of the reason is that the new augmented analytics technologies help make analytics easier to use.

"Most of these augmented features are intuitive add-ons to self-service BI and analytics products," said Doug Henschen, vice president and principal analyst at Constellation Research. "These features augment human capabilities with the power of the computer, using technologies such as machine learning and natural language processing and understanding."

For example, users can write queries in plain English instead of having to write SQL queries, said David Mariani, founder and CTO at AtScale, a business intelligence vendor.

"Natural language queries increase the number of people using data to make decisions," he said.

The flip side of that, natural language generation, helps users understand the results of those queries, he said, by describing results in words or automatically generating appropriate visualizations.

"Natural language generation can offer root cause analysis to help explain the source of shipping delays, for example," he said.

3. Augmented analytics lets users make better, data-driven decisions

According to a 2021 survey of software developers and IT leaders by RevealBI, 41% of companies saw an increase in requests for access to data and analytics. One of the top reasons? To enable users to make data-driven decisions.

Caitlin Randa, manager of EY Technology Consulting at Ernst & Young, said when end users have access to analytics, they can get answers to questions and discover new questions that they didn't know to ask.

"It also allows business users in the enterprise to root cause issues and turn around data-driven solutions to issues in the business, at a fast speed," she said.

4. Augmented analytics speeds up decision-making

When Clay Davis came onboard as vice president for global data and IoT solutions at Tech Data Corporation, it was difficult to find data or get a global view of the business.

He had to pull up Excel spreadsheets and access data in different systems.

Over time, that's all changed, he said.

"We've created a push function, where I no longer have to prepare the data," he said. "I just see the data and drive a business decision out of that. This has saved me a ton of time every month. And not just my time, but our financial analysts' time and our other business leaders' time -- and that's a force multiplier."

Augmented analytics is taking away all those menial tasks that people had to do in order to drive business outcomes, he said.

5. Augmented analytics reduces costs

Instead of having people pouring over data, cleaning the data and getting it into tables for reporting, augmented analytics uses AI and machine learning to automate the entire process, said Dan Simion, VP of AI and analytics at Capgemini.

"This benefits the enterprise because the speed and pace are much faster, and insights are gained in real time," he said.

With decisions made faster, and without the need to hire data scientists, companies see a dramatic reduction in costs, he added.

Companies typically see five to 10 times ROI in the first year, he said.

"Looking long-term, ROI gained in three to five years is anywhere from 30 to 50 times the original investment," he added. "Augmented analytics is allowing companies to move so much faster, and so much more efficiently, that the business continues to benefit the longer it is active and the more broadly it is scaled across the organization, like compound interest."