By Prangya Pandab - January 18, 2022 3 Mins Read
The role of data analytics and business intelligence (BI) has dramatically evolved in recent years, with most enterprises now relying on self-service analytics and dashboards to give insights to a large number of business users and analysts. The dashboard’s capacity to visualize data for users has democratized data and provided everyone with digestible insights.
Even though BI dashboards democratized data analytics, they have now become out-dated for modern workflows, and they can’t keep up with the quantity and complexity of today’s data demand. As a result, there is an insights gap between data experts who can evaluate big data and analysts and business users who are looking for intelligence-driven decisions.
A regional sales team, for example, requires a dashboard to compare fourth-quarter sales to previous year’s data. If they discover an issue in a particular state, they’ll need a new dashboard to figure out what’s causing the discrepancy. It may be necessary to mine pertinent factors such as demographics and purchasing behavior in this scenario. With each new variable, a new dashboard is added to the library, making it more difficult to discover solutions. It is the analyst’s responsibility to interact with numerous dashboards in order to find key insights while also performing manual analysis on these insights.
Another issue that many businesses confront is converting data analytics into actionable insights in a timely manner. While identifying trends and correlations across multiple factors in a smaller dataset is doable, discovering insights across hundreds of variables and diverse datasets becomes considerably more challenging. And by the time opportunities are discovered, it may be too late, resulting in loss of customers as well revenue.
Users are forced to manually assess findings since dashboards lack intelligent reasoning, which opens the door to time loss and human error. This is an out-dated method of finding information. There are better ways for analysts and business users to acquire practical insights without relying significantly on data scientists.
Artificial intelligence (AI) can not only understand intent but also sift through trillions of results and offer meaningful information in a way that makes sense to the user, thanks to developments like natural language processing (NLP).
Ultimately, enterprises should invest in solutions that can augment BI and analytics in order to provide more intelligent insights. To reduce the insights gap, technologies like NLP can augment traditional analytics and answer challenging questions like “how” and “why.”
These tools, when combined with advances in machine learning and artificial intelligence, can give insights in real time, rather than hours or days after a query is initiated. Businesses can move beyond dashboards and stimulate creative decision-making throughout the entire organization by leveraging augmented analytics and making them as accessible as chatting to a smartphone.
Organizations are currently creating so much data that they are unable to optimise its value. Those seeking insights can use traditional BI tools to find the lowest common denominator. Legacy business intelligence technologies are inefficient, causing businesses to waste time and money on data science expertise that could be better spent on proactively solving other issues.
BI Dashboards alone are insufficient to provide analysts and users with the information they need to spot new opportunities. If a company is having trouble getting the most out of its data, current technologies that combine BI with AI and machine learning can help them get more out of it.
Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
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