The future of Business Intelligence

The next wave of analytics and BI tools, Augmented Analytics will feel noticeably different than the current era’s tools. Augmented analytics integrate AI elements into the analytics and BI process to help users prepare their data, discover new insights, and easily share them with everyone in the organization. This new paradigm will feel different because augmented analytics’ subtle integration of artificial intelligence and natural language processing elements will change the user experience across the entire BI process. Data ingestion, insight discovery, understanding correlations in data, and interacting with the platform will all become more streamlined and powerful than their modern counterparts in a self-service paradigm that really is self-service.

What Is Augmented Analytics?

According to Gartner, in their October 2018 research “Augmented Analytics Is the Future of Data and Analytics,” “Augmented analytics uses machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery and sharing. It also automates data science and ML model development, management and deployment”. The bottom line on augmented analytics is that artificial intelligence will change everything about the analytics and business intelligence process, simplifying or eliminating some steps and radically changing and improving others. Before we dig in to all the specific ways augmented analytics will change the analysis and business intelligence process, let’s look at the evolution of business intelligence.

Companies better position themselves to succeed as they learn to use insights from data to drive improvements both internally and for their customers. For firms looking for ways to maintain the competitive advantage that being insights driven has afforded them, the solution may lie in augmented analytics. Powered by machine learning and a cognitive user interface (UI), augmented analytics can help all decision makers uncover insights faster, understand root causes, and present what is likely to happen in the future, all while reducing human bias. This approach, the next step of business analytics evolution, can ensure that all non-IT, non-data professionals have access to the right information at the right time to influence the best possible outcomes.

Business Users Are Not Confident In Their Ability To Fully Leverage Analytics

Authoring new analytics content largely depends on IT and data professionals, in part because business users lack the data literacy, analytics skills, and tools needed to be more self-sufficient with analytics. For business users familiar with older-generation analytical tools, knowing where to find the right data sets, curating and integrating data from multiple sources, and building new dashboards and reports remains a significant challenge. They also feel their IT colleagues are not hearing them. Indeed, IT and non-IT employees have differing views on the self-service analytics challenge. Just 29% of IT pros consider a lack of user confidence with analytics tools to be a challenge, compared to 40% of non-IT users. This disconnect makes it difficult for business users to get the most out of analytics content without ongoing support from IT or data teams.

Source: A commissioned study conducted by Forrester Consulting on behalf of SAP, February 2020