Augmented Analytics Growth Affects Traditional Business Intelligence Process

by July 30, 2019

Augmented Analytics

Augmented analytics tools, natural language processing (NLP), and graph analytics are the top trends to look over this year. Augmented analytics is a new technology category coined by Gartner in 2017, and it comprises machine learning, NLP and other artificial intelligence functionality into business intelligence (BI) software to assist citizen data scientists and business users to more quickly and accurately locate germane data, distill patterns of information, model the data for analysis and elucidate BI insights.

Through its embedded components, such as AI, machine learning and NLP, augmented analytics lessens the manual work of traditional BI process to expeditiously and precisely position and assess pertinent data.


Affecting BI Vendors

Business intelligence tools have come a long way from basic performance monitoring to key performance indicator reporting, helping organizations to advance their decision-making process and social collaboration. It amasses and processes a huge volume of unstructured data to explore meaningful trends and recognize new business opportunities. The BI software offers advanced analytics to a wide range of users from line-of-business workers to data scientists.

But now the promise of augmented analytics tools will make BI more easy to get to non-technical business experts. However, the challenges top BI vendors face is that their BI platforms may be displaced by these automatic systems that proactively surface actionable insights more efficiently.

According to the Co-founder and head of R&D at, a conversational analytics service, Micha Breakstone, the top BI players realize that augmented analytics holds both a promise and a threat for them. As a result, they are focused on capitalizing the potentials upside by simplifying interfaces and dropping friction of highly technical interfaces and proprietary languages. Simultaneously, vendors in BI are working to curtail risks by both offering deeper analysis capabilities for data scientists and designing their tools to be more insight-driven.

Developments in AI and machine learning technologies beyond traditional BI tools enable businesses to search for significant insights like envisaging the customers’ value and their preferences that can support in improving marketing efforts. But there are some limitations – finding the talent, time and resources – required to create high-quality analytical models for augmented BI analytics tools to run.

Companies should familiarize with the ways augmented analytics tools complement BI before rolling out a major initiative. “It’s important to avoid the temptation to try to build the perfect solution from day one. Starting with a simple model, it’s possible to prove business value, learn and iterate. Those early models become key inputs for future models,” Sewook Wee, Director of data science and analytics at real estate site Trulia LLC, said.

As per Gartner, augmented analytics tools are the next wave of disruption in the data and analytics market. Data leaders must think and adopt the tools as the platform capabilities continue to mature. By 2020, augmented analytics will become a leading driver to purchase BI and analytics systems, along with data science and machine learning platforms and embedded analytics tools.

Augmented analytics tools will be more utilized within the next few years to help analysts to link data and better prepare data matches for analysis, as these kinds of tools are already available in the market place. Those include Salesforce Einstein, IBM Watson Analytics and ThoughtSpot.