In the customer-driven era, a business’s success is contingent upon its ability to respond swiftly to a client demand. The more businesses rely on real-time results, the more they will pursue next-generation (next-gen) Business Intelligence (BI) solutions.

Historically, Business Intelligence (BI) solutions were expensive and time-consuming to implement. The current trend in business intelligence is toward “insights-only” solutions that can react rapidly to changing circumstances.

Additionally, these contemporary systems must adhere to all current regulatory requirements. As a result, the need exists for actionable, fully automated business intelligence tools that can be used by non-Data Science teams. According to the Forrester Report on next-generation business intelligence, these interactive technologies have significantly cut “the time between a great concept and a fantastic outcome.” A study report on next-generation business intelligence reveals the most frequently expressed client aspirations for their future analytics systems.

Existing firms will require a well-defined business intelligence strategy to advance to the next level of analytics. According to Forbes, a strong business intelligence strategy will have clear standards for each stage of the process, from data collection to actionable insights. If the organisational business intelligence plan is created correctly, the organisation will have a better chance of achieving the required results.

The fundamental objective of the next-generation business intelligence platform is to accommodate the broadest possible user base, which includes a variety of various sorts of users with varying needs. According to 2021 Business Intelligence Trends, regulatory compliance will be a top priority for the majority of BI vendors.

Next-Generation Business Intelligence Advances

Gartner’s 2020 Magic Quadrant for Analytics and Business Intelligence Platforms defines next-generation business intelligence platforms as those that exhibit the following characteristics:

Platforms that are nimble and autonomous
Machine intelligence that is pervasive
Powered by machine language and neurolinguistic programming
Natural language query (NLQ) is supported as a query language.
Embedded analytics and augmented analytics on a custom basis
Dashboards with visual analytics that are extremely powerful
Analytical applications for mobile platforms
According to a Forbes article, new business intelligence platforms are capable of conveying stories through the use of “insights,” and technologies like NLQ are especially beneficial for consumers who are unfamiliar with a formal query language. The reimagined dashboards and user interfaces will present the same data in a variety of formats for a variety of user types.

Infosys’ Next-Gen Business Intelligence offering embodies the collective voice of next-generation business intelligence vendors, who have emphasised AI-powered high-performance data platforms, advanced predictive analytics, visual analytics, master data management, and enhanced enterprise performance management features. These platform characteristics resonate with the broader business intelligence market.

The Most Visible Features of Next-Generation Business Intelligence

To ensure that business intelligence is accessible and user-friendly to the broadest possible audience, newly built analytics platforms frequently feature visually appealing dashboards and user interfaces. As described in FAQ: Next-generation Business Intelligence Systems, the reporting capabilities of classical BI have experienced significant improvements.

Another distinguishing feature of this sort of business intelligence is an ever-growing “search” capability, which will likely enable future business intelligence users to search for and collect data from a variety of sources, including social channels within an organisation. These attractive business intelligence interfaces are now on display in airport lounges and retail locations. What is Smart Data Visualization, and how can it help business users become more intelligent? demonstrates how intelligent (custom) data visualisation tools may greatly improve business intelligence (BI) outcomes in next-generation applications.

Only Next-Generation Business Intelligence Can Deliver Real-Time Insights

Just as data warehouses have disrupted enterprises’ fragmented data silos, next-generation business intelligence tools are upending existing business analytics methods. Today, most businesses, regardless of their size or scale, seek real-time or near-real-time insights to enable them to make quick and precise decisions.

Modern data warehouses are anticipated to have “embedded analytics,” which enables users to conduct analyses on real data as it flows through business processes. The hallmark of these systems is the dismantling of silos and the analysis of data in the context of the entire organisation. The Digitalist magazine offers a C-Suite viewpoint on how next-generation business intelligence might assist in achieving the aims of embedded analytics.

User Empowerment: Another Prominent Feature of Next-Generation Business Intelligence

Self-service analytics has been around for a few years now, although complete self-service has never been viable due to a lack of appropriate tools in the hands of everyday business users.

Solution vendors will strive to integrate big data and agile BI into a single framework to enable “systems of insights” in the next-generation BI phase. Apart from managing extremely fast, large volumes, and a wide range of data, these applications anticipate that the embedded analytics tools will give instantaneous insights. Tibco’s approach of data democratisation takes into account the new era of analytics and business intelligence.

Data Integration in Next-Generation Business Intelligence: NLG Will Require Skilled Data Scientists

Although the general purpose of futuristic business intelligence platforms is to make business intelligence available to non-data scientists, data integration is one area where data specialists will be required to collaborate with citizen data scientists. If the data contains abnormalities, automated technologies such as deep learning and natural language generation (NLG) would fail. This is where data scientists will step in to assist with the data integration process. This Gartner article discusses the position of NLG in relation to modern business intelligence platforms.

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