Augmented analytics: Democratizing data analysis

Source: GCN

Augmented analytics is the next step  in the evolution of analytics, according to research firm Gartner. Calling it the “third major wave for data and analytics capabilities,” Gartner describes augmented analytics as drawing on machine learning, natural-language processing and generation, text mining and automated data processing to find actionable patterns and remove bias.

It uses automation to design algorithms to detect schemas of interest and find metadata.  By automating much of the data preparation along with the pattern recognition and operational functions, users are able to “interact with the data in a way that’s not so labor-intensive or so specialized,” said Rick Howard, a research vice president at the company.

Augmented analytics differs from traditional business intelligence (BI) and analytics tools in that it does not require predefined data models. It automatically correlates relevant data by investigating metrics at every level by finding drivers and root causes. By automatically creating intelligent visualizations,  augmented analytics can deliver real-time insights, according to Naga Avinash, a research analyst at Frost and Sullivan’s TechVision.

Today, only about 32% of employees at an average public- or private-sector organization interact with data through self-service and data discovery tools or BI platforms, which means much of data preparation, cleaning and cataloging work is still done manually.

“You really want to move that number up to 100%, and augmented analytics is going to make it much easier to prepare that data and find those patterns and just make it usable,” Howard said.

Although augmented analytics is in its early stages — Howard says use of it in any real way is two to five years off — some organizations are already moving toward it. In October 2018, an office of the National Institutes of Health issued a request for information on augmented analytics platforms, “seeking to identify, test and select a platform in order to provide easy to use, advanced analytic capabilities to the NIH extramural program, review and evaluation staff.”

Additionally, Avinash pointed to Colorado’s Smart Data Initiative, which includes a six-week program that brings together public-, private- and nonprofit-sector data analysts to understand the “relevant public datasets and develop data-based, and analytical insights for Colorado’s key topic areas such as [the] opioid crisis, water supply, and smart cities.”

“Government organizations can apply augmented analytics to open data by collaborating with industry partners having broader experience in analytics and emerging augmented analytics tools and methodologies,” he added. “For example, by applying augmented analytics to weather data and other geospatial data, governments can generate insights and take better decisions, forecast [storm] damage and prevent [it], if possible.”

By 2020 — when more than 40% of data science tasks will be automated — augmented analytics will be a leading driver of procurements of analytics and BI products, data science and machine learning platforms and embedded analytics, according to Gartner. Technological advancements and adoption of augmented analytics will help drive what Frost and Sullivan calls the Smart Data market toward $31.5 billion by 2022.

A main challenge to implementing augmented analytics is today’s workforce. Employees lack the skillsets needed for these emerging technologies, and agencies aren’t investing in training and hiring practices that can help them take advantage of these tools, Howard said.

Government is not yet “what we would call an insight-driven organization, [delivering] insight-driven policy and practice,” he said. “Augmented analytics can really [help] them with the vast amount of information in their possession today.”

The effort would be worth it, though, Avinash said. “I consider this as the next generation intelligence tool that enhances the business value by drawing hidden insights from any kind of data,” he said.


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