Business Intelligence (BI) may seem like a product of the digital era. But in fact, it is a pre-IT term indicating – the use of data analysis for devising strategies and making decisions that give businesses a competitive edge. Sounds familiar.
While the inherent meaning has remained the same, over the decades, BI as a set of processes, technologies, and tools has changed a great deal.
The emergence of Augmented Analytics is a case in point. It deftly manages to blend AI elements with traditional Business Intelligence. To gauge the real impact of Augmented Analytics we need to understand what its predecessors brought to the table.
How did Traditional Business Intelligence work?
Traditional Business Intelligence involved analyzing isolated databases to create basic reports. The analysis was performed by specialized data analysts and access to the reports produced was limited to very few. Regular business users did not have access to data. And thus were unable to make data-informed decisions.
How is Self-Service Business Intelligence better?
To make BI more accessible to business users, self-service BI tools were developed. These were equipped with intuitive graphical user interfaces (GUIs).
With these tools, business users get the information they need to make better decisions, with greater ease, and without having to rely a lot on data analysts and IT professionals.
Also, compared to traditional BI systems, these tools are able to process larger volumes, allowing for deeper analyses. Most present-day BI tools and platforms fall under this category.
Need for a new approach to BI
Despite being more insightful and easier-to-use than traditional BI, self-service BI tools do have a few limitations. The process of data preparation, i.e. structuring and cleaning data to facilitate accurate analysis, is for the most part a manual task in self-service BI.
In addition to being cumbersome, this process is also inefficient and highly prone to human errors. Also, the quality of insights provided by self-service BI systems are limited to the type of queries made by business users. If users do not query their BI systems in the right way, they may overlook hidden trends and insights, resulting in missed opportunities or alerts not addressed in time.
Thus, it is important to automate not just data preparation but also parts of the data analysis and insight discovery process. And that can be achieved by powering BI with Artificial Intelligence.
Why is AI-powered BI the need of today?
Let’s begin with a real-world example that highlights this point.
The first indication of the likely spread of Coronavirus.
An AI-driven algorithm was the first to spot COVID-19.
Even before WHO released a statement on 9th January 2020, an artificial intelligence platform called BlueDot alerted its clients on 31st December 2019, about a cluster of pneumonia cases in Wuhan, China.
BlueDot uses Natural Language Processing (NLP) and Machine Learning (ML) to strategically cull data from official statements of public health organizations, global airline ticketing data, livestock health reports and population demographics among others.
The availability of this information within days of its (coronavirus) first occurrence has proven to be very critical in managing its spread from the point of origin, China.
Augmented Analytics, natural next step in BI
Augmented Analytics uses AI algorithms to automate data preparation for analysis by labeling and structuring it. AI also aids analytics by simplifying the insight discovery process. It can highlight noteworthy trends and insights without a specific user query.
How to build Business Agility with Augmented Analytics?
With augmented analytics, businesses can:
1. Perform unbiased data analysis for objective business inputs
2. Identify the root cause of problems with ease
3. Unearth hidden growth opportunities without looking for them
4. Democratize enterprise-wide insights from BI to enhance overall business performance
5. Turn actionable insights into actual efforts
6. Evolve quickly to build an agile enterprise
The global business landscape is evolving at an unprecedented pace. It has become highly unpredictable due to constant changes in market demand, regulations, social trends, customer expectations, and the emergence of contingencies like the COVID-19 pandemic.
Moving forward, the most successful businesses will be the ones able to adapt to such changes smartly and swiftly.
Augmented Analytics proves vital for businesses to detect upcoming trends at the onset, or even predict them before they can happen.
Romil Shah is an AI enthusiast with considerable experience across varied technology domains, primarily Natural Language Generation (NLG), and Blockchain technology. He is passionate about technological innovation and more importantly its real-world applications. His work within the field has brought about constructive conversations and explorations around AI and its extended use in businesses.