Old-world Business Intelligence (BI) software has served us well. However, as data continue to get bigger, flowing at a much faster rate, becoming varied and valuable, traditional BI is not enough to navigate the new normal. According to Statista, IoT-enabled connected devices are projected to increase to 75 billion by the year 2025. Along with the number of IoT devices, the data generated by these connected devices is exploding, with over five quintillion data bytes being generated each day.
In the post-covid phase, there’s been a massive up-gradation of enterprise systems and the ways in which data is captured and stored. 2021 has ushered in a new era of business uncertainty, forcing organisations to be better prepared to handle unexpected changes and continue competitive advantage.
To achieve that, organisations need a more modern approach to exploit the full potential of data — tools and technology that effectively query data, slice and dice it in various ways, and provide compelling insights.
This is where Artificial Intelligence (AI) can come to your rescue. The use of AI and machine learning in BI is helping business enterprises to pull out actionable insights from large and complex datasets and deliver business recommendations easily understood by any business user. Beyond simply informing and prescribing decisions, AI can help businesses exploit the full potential of their data, even train itself to uncover insights to power better choices.
Now, enterprises expect their BI tools to bring in some level of self-learning capabilities, where data itself can help create and refine algorithmic decision-making systems. Salesforce introduced Einstein – an AI-powered system that automates the analysis of data to uncover key trends in an organisation’s marketing and sales process. Microsoft is putting a huge thrust on AI – their Power BI tool incorporates AI to discover actionable insights in data — users can access image recognition and text analytics, create machine learning models, and integrate with Azure Machine Learning.
Meanwhile, business management software firm Domo is combining its capabilities in AI, machine learning, and predictive analytics. Domo customers can extract and analyse data from various sources, including Facebook and Shopify that provides insights on customers, sales volumes and inventory levels.
Making full use of the right data can help with everything from product concept to after-sales service. Over the past few years, there have been rapid changes in the data landscape with the arrival of AI and increasing disruption in the BI landscape.
Earlier, data and analytics helped organisations understand and interpret data from historical events. Simply put, it served as a report of what happened. This was followed by prescriptive analytics, which could predict what could happen in the future. We witnessed the onset of AI in decision-making in prescriptive analytics when from simply forecasting the future, prescriptive analytics started suggesting multiple courses of action along with their possible impact. This helped business leaders to make unbiased, data-driven decisions by not only showcasing options but also providing unambiguous recommendations.
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Real-Time Decision
AI-powered BI help companies to make informed decisions on strategic issues by providing vital information on the current and previous performance of the company as well as future trends, expected demands and customer behaviour.
Operating over 11,000 retail stores, Walmart is using the ML-enabled HANA platform to process its high number of daily transactions in a matter of seconds.
Now, data-enabled decision-making will be AI-powered real-time decision systems. Real-time insights from the rapidly evolving market data can aid business managers in key day-to-day decisions.
Systems will be capable of taking decisions as soon as the supporting data is furnished by leveraging cutting edge data engineering and ML methods to power faster and better enterprise decisions. Machine learning tools in BI like the HANA is expected to reduce the customer’s infrastructure costs and improve operational efficiency.
What’s more, BI tools with NLP-driven chatbots that can analyse data-related queries without the use of complex querying codes are helping businesses develop a culture of analytics, improve adoption of data tools, and make data-driven decisions to grow revenue and improve business performance.
Also Read: Let Augmented Analytics Do Your Heavy Lifting
Democratising data
AI can empower faster business decisions by democratising data within enterprises and help business users make better enterprise decisions. By bringing data and BI out of silos and accessible to non-technical staff, enterprises can make better, real-time decisions to achieve sustainable, long-term competitive advantage.
Also, as AI technology powers the real-time analysis of data, business analysts will be required to focus more on the fundamental skills of data analysis without any programming skills. For example, the machine learning-enabled DataRobot tool is automating predictive modelling and is accessible to users with no skills or experience in machine learning.
According to 2019 business intelligence statistics, over 60 per cent of business executives believe that a well-planned AI strategy can create more data-driven business opportunities. Seventy-two per cent of business leaders consider AI as a major business advantage. Gartner estimates that BI bots enabled with conversational analytics and natural language processing will boost the adoption of business intelligence tools in the workplace. The AI disruption in BI is here to stay and should be embraced by organisations, as it is critical for business growth.
