What Is CQ And Why Do Companies Need To Care About It With Shivaprasad KT

“It's at the beginning of the journey for CQ. Hopefully it'll find its feet and then in two years from now, you'll have better Google results and CQ.” CQ, a term coined by Ganit CEO, Shivaprasad KT, was something we wanted to learn in our CDO insights series this week. Shiva is the CEO of Ganit, a seasoned leader in data analytics and business strategy with over two decades of experience. Notably, Shiva served as the Managing Director at Impact Analytics and spent eight years as the Regional Head at Mu Sigma Inc., overseeing key client relationships and contributing to organizational strategy. He also has a successful track record as a Consultant at i2 Technologies, where he led a significant enterprise-scale project for a major tech giant in China and India. His career is marked by a commitment to innovation, talent mentoring, and solving complex business challenges. AIM: To begin, could you share the story behind the development of the Consumption Quotient (CQ) concept? Were there any specific instances where you saw companies struggle and realized that there was a gap to be filled? Shivprasad KT - Before I answer the story behind CQ, let me quickly introduce the company. So we are an AI/ML data Science consulting company and we've been around for the last six years, a young company, and co-founded by three people. It's been an amazing journey so far and very gratifying mostly because we've been able to help customers realize the value of analytics and engineering. So CQ is a Consumption Quotient and I'll talk about why Ganit is excited about it and why Ganit is pioneering this concept and what is the need for this, and because we are pioneering it, Google is unable to give that result today. Hopefully in the next couple of years, we will have done enough in CQ that you'll get some Google results. I'll give you a little bit of a background of how CQ came about. So this actually kind of almost talks about why Ganit was founded and why I was thinking about starting a company like Ganit. So, for me, it's been almost two decades in the data science & analytics industry. I've been dabbling with data for decision making for a very long time. Before starting Ganit what I realized was that a lot of the projects that are commissioned on analytics had primarily three reasons why they were commissioned. Number one was that for example I read this article in HBR, about this new trend in analytics, and can we do something about it? Can you build a model or a dashboard? If the genesis happens just based on the article, I think it's a recipe for disaster. The second reason why people want to kind of start a project was because for example my CEO asked me to do this and, if you can do this for me. And the third was, obviously the real business challenge an organization might face, seeing a decline in Consumer customer base in declining sales and they want to find out why is it happening? What can we do in the future? So that's a genuine reason for customers to come to people like us. While the last reason is genuine, the first two reasons were pretty troublesome because if you start with those two premises, what happens is you end up with beautiful looking glamorous dashboards which do nothing for you, but just fill up your table chairs. And you also have a very finely tuned right accuracy print analytics model. But now that is just sitting as a form of a deck or a bespoke tool in some corner in your IT stack. This leads to what I call the Decision Debt which is, either you're making a poor decision or worse you're not making a decision based on this. Some of the solutions that you developed are taken out of context. People take one slide in a bigger deck or a context and they put it into their own deck and then fit their own narrative. All this led to a very staggering statistic which was that about 90% of the analytical solutions that we're getting created were not getting consumed. It's very disheartening for me to see it because clearly, there is money and effort behind it. And the data science engineers are working very hard but the customers are not making good decisions. I thought maybe there's a better way to look at this landscape. Instead of getting excited about just techniques and technology. I thought maybe we should think about the consumption of that solution first and the ability to make a better decision because of that and then get excited about techniques and technology. So I started a company that actually focused on consumption rather than just getting excited about the creation of analytics. AIM: Can you help us with an example on how companies have struggled because of keeping the conception quotient low. And then how to change that? Shivprasad KT - I think it's okay for many people not to understand the concept of consumption quotient today because it's a relatively new idea, and that's what we're trying to educate and pioneer. For example, when you

What Is CQ And Why Do Companies Need To Care About It With Shivaprasad KT

Keep reading. It's free.

Related stories