Business intelligence: Trends you should know for 2020

sandeep p

As acting on data becomes central to the business, how could tools develop in the new year?

Data— and intelligent use of it— is becoming integral for business success today, and that’s seen a rapid rise in the availability of business intelligence (BI) tools.

With the ability to react to business insights becoming more of a competitive differentiator, the business intelligence tool landscape is booming— bringing with it new opportunities for advanced insights as capabilities grow and data endpoints continue to proliferate.

With that in mind, below we look ahead at how the business intelligence sector looks set to develop in 2020 and beyond.

 

More integrated BI systems 


Not only will business intelligence systems continue to see greater adoption, but they will become more tightly embedded into businesses’ existing systems. 

Integrated BI systems will enable a seamless experience for users when gleaning valuable insights from data. Presently, various software providers are working toward this increased integration and application program interface (API) accelerates the development. 

At the same time, integration allows third-party service providers to not only offer BI tools but also run BI applications through other means. With embedded BI systems in organizations, users can navigate data on their own and react to data without exiting the company’s BI platform.

In the end, interconnected systems facilitate the sharing of data across organizations and streamline decision-making. 

 

Machine learning fuels data insights 


Besides that, BI software can benefit from the power of machine learning (ML) and become more intuitive. 

The development of BI systems to be more predictive will lead to the expansion of labeling data content more effectively and provide insights according to the context of the proposition. For example, an ML-enabled BI tool will be able to tailor its response to suit the inquiry of a user. 

What’s driving the development of BI tools to accurately ‘understand’ a user’s questions is—Natural Language Processing (NLP). 

NLP trains BI models to comprehend a range of languages, seeking patterns in the complexity of the human language and present only the most relevant content at the end. This can allow BI software to make an educated guess about data inquiries based on past trends and patterns, presenting more reliable and relevant content. 

 

Data “Proactivity” 


ML-driven BI systems may gradually innovate to be self-sufficient and active in presenting significant data through experience. In this sense, users are passively provided with insights in the form of reports or on dashboards as BI systems proactively showcase prominent data in visualization or at an advanced level, present direct answers in notifications. 

At this point, companies gain an advantage when real-time data are displayed in visual dashboards designs, engaging templates, and sophisticated graphs or tables. These presentations are not just appealing to the eye but also yield new perspectives during discussions.

The concept of “data proactivity” can be attributed to the integration of BI software with a user’s existing system, which allows for easier access to company data leading to better representation of business insights. 

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