How AI Improves BI
The evolution of what business intelligence means for companies has seen a rapid evolution over the years. Earlier we referred to business intelligence as a system of record. Next, business intelligence became a post-mortem way for management executives to understand their business performance at the end of the year or quarter with a view to shaping strategy for the future. In the last couple of decades, the need for data-driven decisions has proliferated every corner of the organization and is more forward-looking and predictive in nature. And with AI coming into the fray, BI is getting a whole new and smarter look.
What is AI?
In simple terms, Artificial Intelligence is the area within computer science that brings human competencies of perception, learning, reasoning and problem-solving into machines. By providing a huge quantum of data and applying algorithmic models to accelerate the learning process, AI can bring nearly match human competence in terms of problem-solving — while providing the high levels of computational scalability of a machine.
The next question to address here is — why are enterprises so interested in AI? There are two broad reasons for this. First, because at a business level, AI can augment and automate human capacity and drive better business outcomes and efficiency. For instance, in the insurance industry, companies are now using Computer Vision (a sub-competency within AI that enables machines to ‘see’ objects) to process insurance claims. Their customers can simply upload the images of their damaged, insured property and leave it to AI to identify and quantify the damage and approve the claim. This leads to a faster turnaround time in claims processing and reduces the costs associated with the claims approval process — all the while providing an improved customer experience.
The second reason why we are seeing a rise in Artificial Intelligence today is simply because we have the means to build AI. Earlier experiments with AI did not lead to anything meaningful because we lacked enough data to teach the algorithms and lacked access to the computational infrastructure required to run these systems. Today, there’s enough data and enough processing power available to run AI — leading to heightened interest, research and adoption.
Today AI is rapidly moving from a few siloed experiments in the enterprise to a much more strategic intervention, fueled by interest from those at the very top of the corporate hierarchy.
The Convergence of AI and BI
Business Intelligence — the use of data, processes, architectures and technology to convert raw data into meaningful information — is an obvious area for an AI-led transformation. The main goal of BI is to streamline the collection, reporting, and analysis of data. The BI tools that are available to you provide a coherent picture of the business. As I mentioned earlier, BI has over the years played a hugely consequential role in how decisions are shaped and their outcomes are measured. Even today, the man purpose of BI is to provide useful information to companies to aid better, purposeful decision-making.
However, with the ever-increasing speed and quantum of data available, BI tools need to take the next step in their evolution in order to remain relevant. With every user expected to make decisions that are driven by evidence and data, we need to improve the accessibility and usability of BI tools to suit non-technical users. At the same time, we need to ensure that the BI tools provide actionable insights from the deluge of data available, rather than creating decision paralysis and fatigue for the users.
This is where AI can meaningfully augment the existing paradigm of BI. With advances in AI, we need to build better business intelligence platforms that can cater to a non-technical user, and guide their decision-making process, rather than hampering it. We need AI and BI to come together and complement each other and help businesses make more informed decisions.
Bringing AI and BI together
Companies are using AI to bring in the next stage of evolution of BI and make it more user-centric, intuitive and proactive. They need AI to reduce the fatigue that non-technical users face in using traditional analytics tools and aid enterprise decision-making. Here are some of the ways we can use AI to improve BI to transform business outcomes and BI adoption.
1. Natural Language Search
The reliance on “data-speak” — the proficiency in technical data querying languages and tools — is one of the severe bottlenecks in the adoption of traditional BI solutions. Within Artificial Intelligence, we now have Natural-Language Processing (NLP techniques) that reduce this overreliance on technical languages and act as a bridge between business users and data systems. NLP allows for converting the queries put by business users into code that can query your data systems and bring answers to the most complex questions.
By allowing users to query their data in natural language text or even voice, the next generation of BI applications can allow users a self-service way to analyze and understand their data and business — leading to the elimination of the barrier created by the need for “data-speak”.
2. Continuous Learning and Personalization
Traditional BI and Analytics dashboards can be extremely hard to read and decode for the non-technical business user. While some data-literate users at every organization feel comfortable, most users are likely to feel overwhelmed by the sheer amount of data and KPIs that are presented in the traditional paradigm of BI.
With AI, we can build BI applications that learn from users and provide a personalized view into the data that they want to see. AI-based systems can capture user feedback as well as interactions to learn how users prefer to consume information and render it accordingly in a way that they understand.
3. Proactive Alerting
I wrote about the data deluge that is engulfing enterprises today. With the quantum of data that is available for analysis, a lot of the highly actionable insights can get lost — leading to a loss for the business. To find that one nugget of actionable insight that really matters is becoming exceedingly tough within traditional BI and Analytics platforms.
Now we can teach machines the criteria within which we need to comb through the data to surface insights. We are seeing great advancements in the area of Machine-Learning driven Anomaly, Outlier, and Early Warning detection — with which we can appraise business users of scenarios in the data and the business which need immediate attention. What’s more, through the capture of user interests we can proliferate alerts in a personalized manner using AI techniques so the right information reaches the right people for their action.
4. Prescriptive Insights
One of the most interesting challenges that AI can solve for BI is to make it more prescriptive. Current BI systems have evolved from descriptive to predictive — from the ability to describe the state of the business to predict what the future state of the business will look like. The next stage of the evolution is prescriptive — where the system actually recommends actions to its users.
This too can be accomplished through artificial intelligence. AI can help assess the impact of multiple actions in a particular scenario and recommend to the users which action is most likely to deliver the best impact. The ability to deliver prescriptive insights can be game-changing for the BI industry.
In conclusion, it is fair to say that AI could be the missing link between business users and current BI tools. With intelligent AI interventions, it is possible to build BI and Analytics software from the ground up that analyze a colossal amount of data, make it easy for users to interact with the data, create a personalized sphere of information and be more proactive in informing users on what they need to know. AI can also help make BI systems much more prescriptive and not just furnish the data but also the underlying factors contributing to it and the best approaches that the user can take to maximize the probability of success.
