Artificial intelligence and machine learning: Transforming business intelligence, one insight at a time

Business intelligence (BI) has been important for business leaders in making decisions for a long time. BI software analyses data and presents actionable information for businesses to make decisions. Traditional BI, in its descriptive nature, largely focused on transforming raw data from data warehouses like relational databases into meaningful and useful information. As technology kept evolving, the diverse nuances of business intelligence have transitioned from being an IT solution to self-serve analytics. With the advent of artificial intelligence and machine learning, these functions have moved the needle further much beyond a decision support tool.

Tale of transition

With the explosion of data in the digital era, the need of the hour is to be able to process huge volumes of various types of data and translate them to visual narratives. Gartner Predicts that by 2021, 75% of prebuilt reports will be replaced or augmented by automated insights. These industry insights require highly evolved BI software to communicate the message to the business.

The wave of self-service BI will develop further with Artificial Intelligence (AI) complementing BI and making it smarter. Advancements in Natural Language Processing (NLP) enable end-users to seek answers to business questions constructed in basic language instead of complex query language programming. This would considerably democratize the value to data by giving more power to non-technical end-users.

These advances also enable organizations to make better use of their fresh operational data to drive immediate actions. With advanced algorithms crunching data and providing insights, decision-makers can now focus on validating machine-generated insights instead of testing a hypothesis. It goes without saying that this is dependent on how AI develops trust with business on its offerings.

Defining the next

Two key trends to focus on in the years to come are data governance and the story-telling aspect of BI. Data governance is crucial for enterprises, largely with the General Data Protection Regulation (GDPR) in effect in the European Union and potential implementation of similar regulations in other parts of the world. It is also important to focus on data quality as poor quality can impact confidence on generated insights. A comprehensive BI data governance plan that embeds procedures, roles, and rules of general data governance, within the BI environment is mandatory for businesses to get the most optimal results.

With the evolution of systems, the ease of sharing data, dashboards and applications should become seamless. A systematic governance plan would facilitate the right level of access to people across data, dashboards and so on in ensuring that users make accurate decisions while being compliant with internal policies or external regulations. In this context, a collaborative model between IT and business would be key.

Storytelling is the second component to communicate the insights effectively to business leaders. It will not be enough only to provide numbers and graphs, but an interactive experience will be required to arrive at meaningful takeaways for businesses and their stakeholders. As we enter this new age of BI, we need to realize the importance of building designs that customers want. Design thinking and human-centric designs are critical to explain complex insights in a consumable manner. It will make artificial intelligence more human driven with empathy and user-centric research.

BI helps businesses and organisations grow much beyond just driving profits. If you look at the Indian market for business intelligence, let’s consider Maruti Suzuki as an example. They have leveraged Qlik to analyse and derive business insights in order to accelerate operational productivity. As a result, Maruti was able to streamline its sales and distribution management with the solution from Qlik. In addition, it also enabled Maruti to effectively track supplier and dealer performance, handle tough after-sales, in managing opportunities strategically while enhancing the whole procedure of spare parts management.

Consider the homegrown example of YES Bank. Initially, the organization faced a slowdown in employee communication internally and wanted business intelligence solutioneering to play a key role in solving it. The gaps in communication between the employees resulted in delayed targets, reduced morale and weak workflow. YES Bank implanted a new business intelligence system and this single step enabled them to store the entire data in a warehouse and automatically create reports from a user dashboard. The system ensured that only authorized members could access specific data. The BI tools in addition, eased the process to review the actions which are required to be executed, so that any problems which arise can be addressed effectively.

In conclusion, BI has come a long way from what it was in the 1990s. In today’s world, it is important to consider BI as an integrated ecosystem with key roles played by advanced big data technologies, quality and governance systems, artificial intelligence algorithms and custom visualization applications. So, the time of data-driven business Intelligence conveyed as Interesting stories is finally here, to make tangible business impacts.