Next Generation Business Intelligence: Customer-Driven Success

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In the customer-driven era, business success depends on how quickly a business can respond to a customer demand. The more that businesses become reliant on real-time outcomes, the more they will seek next-generation (next-gen) BI deployments.

Traditional Business Intelligence (BI) platforms were high-cost and time-intensive applications. The current trend in BI is to move toward “insights-only” platforms, which can instantly respond to dynamic situations.

These modern systems also must meet all applicable regulatory requirements. Thus, the need is for actionable, fully automated BI systems, which can be utilized by mainstream business users without the help of Data Science teams. The Forrester Report on next-gen BI provides a comprehensive overview of what to expect from these interactive systems, and says they have substantially reduced “the time between a great idea and a great outcome.” A survey report on next-gen BI reflects the most popular customer expectations from their future analytics platforms. 

The existing businesses will need a clear BI strategy to move into the next level in analytics. Forbes states that a successful BI strategy will include clear guidelines for every stage of BI, from data collections to actionable insights. If the organizational BI strategy is correctly developed, the organization will have a higher chance of reaping the desired benefits.

The primary goal of the next-generation BI platform is to support the widest user base, which comprises varied types of users with different needs. 2021 Business Intelligence Trends suggests that regulatory compliance will be high on the agenda of most BI vendors.

Next-Gen BI Moves Forward

In a nutshell, Gartner’s 2020 Magic Quadrant for Analytics and BI Platforms describes next-generation BI platforms as those that include the following characteristics:

  • Agile and autonomous platforms
  • Pervasive machine intelligence
  • Machine language- and neuro-linguistic programming-powered
  • Presence of natural language query (NLQ) as a query language
  • Custom embedded analytics and augmented analytics
  • Powerful visualization dashboards and visual analytics
  • Analytics on mobile platforms

A Forbes post indicates that the modern BI platforms are capable of telling stories through the use of “insights,” and technologies like NLQ are particularly useful for users who have no knowledge of a formal query language. The reinvented dashboards and user interfaces will jointly deliver the same data in many forms for different types of users.

The Next-Gen Business Intelligence offering from Infosys symbolizes the collective voice of next-gen BI vendors, who have stressed AI-powered high-performance data platforms, advanced predictive analytics, visual analytics, MDM, and enhanced EPM features. These platform attributes resonate with the larger BI market.

The Most Visible Attributes of Next-Gen BI

To make BI accessible and user-friendly to a widest range of users, the newly designed analytics platforms often display fancy dashboards and user interfaces, which are both visually appealing and powerful. The reporting features of traditional BI have undergone major changes, as discussed in FAQ: Next-generation Business Intelligence Systems.

Another notable attribute of this type of BI is an ever-growing “search” functionality, which will probably empower the future BI user to search and collect data from different sources, including social channels across an organization. These inviting BI interfaces are now visible in airport lounges and retail stores. What is Smart Data Visualization and Can it Make Business Users Smarter? reveals how smart (custom) data-visualization tools can significantly improve the BI outcomes in next-gen applications.

Real-Time Insights Can Only Come from Next-Gen BI

Just as data warehouses have disrupted disconnected data silos across organizations, the next-gen BI applications are disrupting the established business-analytics processes. Today, most businesses, irrespective of size or scale, prefer real-time or near real-time insights to make fast and accurate decisions.

The modern data warehouse is expected to be equipped with “embedded analytics,” which enable users to conduct analytics on live data in businesses processes. The hallmark of these systems is the breaking of silos and analyzing data in the overall business context. Digitalist magazine shares C-Suite perspective on how next-gen BI can help achieve the goals of Embedded Analytics.

User Empowerment: Another Prominent Feature of Next-Gen BI

Self-service analytics have been doing the rounds for a few years now, although full self-service has never been possible given the lack of the right tools in the hands of ordinary business users.

In the next-generation BI phase, solution providers will attempt to blend big data with agile BI within a single framework to enable “systems of insights.” The expectation from these applications is that, apart from handling very high-speed, high-volume, and wide-variety data, the embedded analytics features will deliver instantaneous insights. Tibco’s view of democratization of data considers the new era of analytics and BI.

Data Integration in Next-Gen BI: NLG will Require Expert Data Scientists

Although the overall goal of futuristic BI platforms is to make BI accessible to mainstream BI users with no data science knowledge, data integration is one area where date experts will be needed to work along the citizen data scientists. Automated technologies like deep learning and natural language generation (NLG) will fail if the data has anomalies. This is where data scientists will come in to complete the data integration task. This Gartner article describes where NLG stands on modern BI systems

Impact of Data Science on Future BI Systems

Rather than vanishing from the future business analytics scene, the data scientist will play a pivotal role. Here’s how data science will feature in future BI applications:

  • As advanced ML drives embedded analytics, data scientists may be called in for special-purpose data integration, deep-dive analytics, and visualization tasks.
  • According to Anthony Goldbloom, co-founder and CEO of Kaggle, the centralized data science teams will be replaced by BU-specific data science teams.
  • Many data processing tasks becoming automated, data scientists will concentrate on the data exploration aspects of business analytics, which cannot be accomplished by machine intelligence.
  • BI system automation, rather than replacing data scientists, will work in tandem with the human experts to accomplish complex analytics tasks.

The Future Impact of Data Science on Business Analytics gives a roundup of the above predictions regarding the role of data science in future business analytics.

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