AI and BI: Powering the Next-Generation of Analytics

December 8, 2019

AI and BI

Business Intelligence has had an impactful history as traditional BI originally appeared in the 1960s as a system of sharing information across organizations. In the 1980s, it further developed alongside computer models for decision-making and turning data into insights before becoming a specific offering from BI teams with IT-reliant service solutions. In today’s vast data producing environment, modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.

Business intelligence software are rapidly developing as them becomes compulsive for many organizations. Currently, a number of leading organizations are leveraging GPU parallel processing technology to infuse AI into their BI applications, and this strategy will quickly define the next generation of business analytics. Adding AI into BI is the most impactful way to speed up data insight. Establishing an integrated AI+BI database, an insight engine means an organization can shift from an analytics position that looks back to the one that looks forward.

There are several use cases where businesses combine AI and BI for next-gen analytical insight engines that utilize both in-memory storage and GPU processing. For instance, retailers are transforming supply chain management as they can now feed and assess streaming data from suppliers and shippers against real-time inventory data from retail operations.


AI is at the Core of Next-Gen Analytics

Augmented intelligence, machine learning and natural language processing (NLP) have become key parts of business intelligence platforms. However, as analytics platforms have developed, AI for BI still hasn’t progressed to the point where analytics tools can truly free up humans from the tedious tasks associated with data analysis, as well as where data analysis is part of everyday applications instead of a stand-alone application unto itself.

Today, enterprises are entering into a new era governed by data. AI, specifically, is increasingly evolving as a key driver that shapes business processes and BI decision making on a daily basis. From small to large enterprises, all are leveraging AI to enhance the efficiency of business processes and deliver smarter, more specialized customer experiences.


Why There is a Need for AI-Powered BI Systems?

The explosion of new big data sources like smartphones, tablets and the Internet of Things (IoT) devices compel businesses to no longer be oppressed by massive amounts of static reports created by BI software systems. They need more actionable insights. This leads AI-driven BI systems that can transform business data into simple, precise, real-time narratives and reports.

Avoiding data overload – Data nowadays is growing at an unprecedented rate and can easily choke the companies’ business operations. When a company has data blasting its BI platform from different sources, this is where AI-powered BI tools come in. It helps to analyze all the data and deliver tailor-made insights. Thus, investing in AI-powered business intelligence software can assist companies to break down data into manageable insights.

Delivering Insights in Real-Time – The growth of big data in the market makes it hard to make strategic decisions on time. But AI leaps in recent years power BI tools to offer dashboards that provide alerts and business insights to managers for key decision making.

Easing the Talent Shortage – There is a huge shortage of professionals with data analytical skills worldwide and in the United States also, there is an acute shortage of nearly 1.5 million analysts. So, employing data experts in every department within an organization has become vital. However, practicing AI-powered BI software can bring tremendous changes in businesses, keeping them competitive in the tech-driven business environment.

In the years to come, AI-infused BI tools will go beyond surfacing insights. They will propose ways to address or fix issues, run simulations to optimize processes, make new performance targets based on predictions, and take action automatically.