Even though data analytics has been around for some time now, data analytics and insights are still widely misunderstood concepts among many business professionals. I frequently listen to business owners bemoan their lack of “data-driven insights”, scoffing at the scarcity of actionable information derived from their analytics platform. Forrester Research interestingly notes that “Business satisfaction with analytics output fell by 20% between 2014 and 2015.” The common thread in this sentiment seems to be an assumption that analytics platforms would do all the work, with next-to-zero input from business users.
But while analytics does bring people closer to those crucial “aha!” moments, gleaning insights from the resultant output is very much the responsibility of business users who interact with your data. For the sake of clarity, we’ll discuss both data analytics and insights within the context of how it can help a fictitious retail bank improve its customer services.
In This Post
- Data analytics acts as the vehicle toward insight
- Insights are achieved when you start interacting with your data
- The challenge is in how fast you can make data insights work for you
- With multiple data sources and 100’s of variables, it is humanly impossible to find these data insights
- About the Author
Data analytics acts as the vehicle toward insight
Data analytics involves the tools, technology, processes and people required to make correlations between data points, identify trends, patterns and predict outcomes. It lays the foundation for businesses to start asking the right questions that lead to the holy grail of big data: insights.
If a retail bank wants to improve on its customer services at brick-and-mortar branches, it could collect data on customer sentiment through a host of contact points. Subsequent analysis of this data will reveal information about the bank’s customers, the services they prefer, frequency of services used, overall brand sentiment and so on. While extremely valuable, these outputs are only the starting block for a deeper journey into how the bank can optimize its services to cater to the needs of its diverse market.
So, while analytics clearly facilitates a deeper understanding of customer demographics and sentiment, it will be up to the bank to scrutinize their findings in a way that will lead to consequential insights.
Insights are achieved when you start interacting with your data
Using the resultant data outputs, the retail bank could ask questions like, “Which customer segment has the highest demand for digital banking services?” or “What percentage of our customers prefer visiting brick-and-mortar branches over self-service apps?” Answers to questions like these are what result in data insights. This, in turn, allows the bank to find more personalized solutions to customer problems while simultaneously addressing the needs of the larger customer base.
Even deeper insights are achieved by combining key questions and subjecting them to your data analytics platform. So, while data analytics does well to set the context for a particular situation, gaining truly meaningful insights largely depends on the interaction that occurs between the business and its data analytics platform.
The challenge is in how fast you can make data insights work for you
The problem most companies seem to have is turning data analytics into valuable insights within a timeframe that makes them applicable to a given scenario. With customers becoming increasingly fickle, and the competition more aggressive, the need for fast insights for a true competitive edge has never been more urgent. For most companies, this will remain a key challenge and makes the need for increasingly advanced, predictive and real-time analytics a serious one for businesses who want a true edge over their peers.
With multiple data sources and 100’s of variables, it is humanly impossible to find these data insights
The other challenge is – while it is possible to find relations between different variables in a smaller dataset, the process of finding insights when there are 100’s of variables across multiple datasets is humanly impossible. It is about time we leverage machine intelligence to derive these insights.
At Tellius, we’ve built a platform that is designed to bridge the gap between analytics and insights by placing business users at the center of their data. Through unique and powerful search-driven analytics, we’re helping businesses gain valuable insights in near real-time. To learn more about how we’re ushering in a new era in data analytics and insights, contact us.
About the Author
Ajay Khanna is CEO and Founder of Tellius with vision to re-define data intelligence by combining power of search with predictive analytics. Ajay has background with building and growing successful innovative startups. He is a passionate Tech innovator with experience in building new technologies and disruptive business models.