M76 Analytics – Analysing for the Better Future | The Enterprise World
M76 Analytics

M76 Analytics – Analysing for the Better Future

M76 Analytics is a niche business algorithm company providing applicable analytics support for the direct consumption of Senior Business Leaders.

Mr. Jai Mrug, the founder of M76 Analytics is a known election analyst, and psephologist in India, since 1998. He has worked on several aspects of data, more often than not, taking up numeric challenges that would represent deep data mining and metric quantification. Even before the AI boom began, he had a parallel career in Data Analytics, while pursuing his mainstream career in Enterprise Solutions. It was the autumn of 2013. An offer to carry out analytics offshore, as a contractor, for an American e-commerce start-up, laid the foundation of a more sustained and organized approach to data.

Incubation by the business incubator of IIT Bombay, Mumbai, bought into perspective the need to create a platformed approach to Solving Business Problems with data. One of the major motivations to build a productized approach was a statement from the Harvard Business Review, October 2013.

“Scores of start-ups and some incumbents are exploring the possibility of using predictive technology and big data analytics. Only a limited number of consulting jobs can currently be productized, but that will change as consultants develop new intellectual property.

New IP leads to new tool kits and frameworks, which in turn lead to further automation and technology products. We expect that as artificial intelligence and big data capabilities improve, the pace of productization (Of Consulting Services) will increase”

Since then M76 Analytics (Gururaya Solutions Private Limited) has worked with several marquee customers such as Times Now, a very large AMC, Thomas Cook India, amongst others, working on solving Data Specific problems with them. In a conversation with Mr. Jai Mrug, he shared the journey of M76 Analytics and how the company is transforming the various sectors across the globe.

  • What were the initial challenges you faced?

The initial challenges were twofold. First, socializing the customer into the business use case. Most customers invariably felt that they had value to add to their enterprise goal through analytics. However they did not know what the exact use case was to be, that is they did not know where to start. The first step to that was cognizance of where the existing tools failed them. That was a sort of catharsis we had to jointly work through with our customers. The outcome was a refined need of the business statement.

The second challenge was grooming the necessary talent to work in the organization. We realized that having experienced people is rather less important, than having people with imaginative capabilities. The ability to think out of the box, approach solutions from first principles, while not having the baggage of experience. Eventually, solutions architected out of first principles brought about robustness, scalability and configurability, while also providing business value to the customers.

  • Which was that point that triggered the growth of the company?

Once a couple of solutions from first principles were deployed, it was possible to generate a framework of the data techniques used for solution building. The exact relationship between Machine Learning and Artificial Intelligence was then understood. The Data techniques could then be neatly delineated as Machine Learning techniques, that eventually led to Artificial Intelligence, and the learning and recommendation rules could be codified generically. The ability to generalize the entire solution approach across different verticals and business solution areas was a major boost to the entire philosophy of productization of ML and AI algorithms. Once basic ML and AI techniques were platformed, the attention moved immediately to creating the necessary visualization for the same. There was a eureka sense, that the product was now ready for prime time.

  • What is the reason behind your company’s long-standing success?

Reflecting, we were able to arrive at niche algorithmic solutions, based on persistence to solve customer problems from first principles. Arriving at a business solution to the customer’s problem, without compromise, with a continuous fine-tuning of the data model, as well as fine-tuning of the product architecture.

Another pull through was the ability to stand even in the face of obtaining just a few smaller projects. Sometimes the initial ticket sizes were invariably small. The key was grasping customer problems, and persisting to solve them.

Persistence was needed not just on the ticket size of the engagements, but also on their duration, and how collaborative those journeys were. Often associations need us to do long journeys with the customers, almost evolving them, and evolving with them, to start with journeys of data transformation, mining, actually understanding how machine learning reflects into the business contexts, and what metric based interpretations of the learning, would the customer consider as Artificial Intelligence. All these need to be delineated and annotated through collaborative customer journeys.

There is no strength without suffering and no success without sacrifice.

Sri Aurobindo

We learned that there were no shortcuts to avoid these journeys and at the same time, that these journeys were the most robust foundation of our success.

  • What are the products/services the company focuses on? How are your services different from those in the market?

We offer a Business Strategy Workspace. These workspaces use Decision Support Systems as the foundation and build on them in the larger context of inferences and interactivity required for a Decision Support System. Since these sort of Decision Support Systems themselves are new to the market, we necessarily offer them in a man + machine model. So we do not just offer our customers the entire Decision Support System, we also offer them the services around it.

We offer wing to wing product features starting from basic high productivity automation or data transformations that can build executive decision support to high-end data learned solutions, that not just offer predictive solutions, but also recommend optimal ways to carry out business.

These features are embedded into a framework that is highly customizable and configurable. Our services are around deploying the framework, and then offering the customer turnkey solutions by managing the platform for them. That way we do the entire nine yards for the customer from envisioning a solution on our platform, to having them distraction-free day-to-day management of their business, while we manage the platform for them.

  • How do you decide to take the company a step further in terms of your products/services?

We are working on several adapters and data receivers, which will not only extend the catchment area of data for our customer solutions, but also increase the richness of views that the customers can have regarding their data. These solutions also help us go to the next level in terms of developing not just business optimal solutions, but creating solutions that can fully implement production cost control, sales channel profitability, and enterprise cash flow.

  • Is there any new addition to the list of products/services? Anything exciting you would like to share?

We have developed our election simulators, which we are likely to launch soon on national television. We are also building huge demographic learners, which can be used on mobility data to produce rich insights for public service providers, right from hospitals to malls. We believe there is a huge potential for the B2C segment, especially those in the space of hospitality and travel to utilize these data sets and generate meaningful insights for business.

  • How has technology transformed the traditional way of business?

Our services have offered huge productivity benefits to businesses from the word go. Not just that we have also been generating direct cash savings for the businesses of our customers. These are directly measurable benefits, either in the form of saved sales incentives, saved transportation costs, or a reduction in monthly frauds. In every engagement, we offer direct quantifiable benefits. 

Persistence was needed not just on the ticket size of the engagements, but also on their duration, and how collaborative those journeys were.

  • Can you please brief us about your professional experience?

I graduated in the year 1996, with a degree in Mechanical Engineering from IIT Bombay. Subsequently, I worked for three years in Reliance Industries, with a core shop floor responsibility. I returned to my alma mater to pursue my post-graduation in Management, with a specialized focus on Operations Management,

Since 2001, i.e. after my post-graduation I ran a dual career, one in election research, and the other in Enterprise Software. On the software side, I spend close to 12 years working in giants such as Wipro and Patni (now Capgemini). On the election research side, I worked for more than a decade as the retainer consultant to Times Now, the leading English News Channel in India.

M76 Analytics (Gururaya Solutions Private Limited) started as a small team contracting off analytic research from the United States in 2013. A year and a half later in 2015, the company was incubated by SINE, the business incubator of IIT Bombay, Mumbai, to build a business-centric data platform.

The founder also parallel co-founded a pure election focussed polling company, called VMR (Votersmood Research), which works purely in the space of election research.

  • What are the key achievements of your entrepreneurial journey?

The entire organization so far has been run on a self-funded model, while seeking a temporary seed fund assistance from the business incubator of IIT Bombay, which was subsequently returned.

Since mobilizing talent initially was a challenge given the scarce resources, the company had to always work with young graduates fresh from college. Soon the talent was groomed in-house, and the challenge of expensive lateral talent was met with sufficient bandwidth, from those who took to their first job with great passion. This was in a way an adventure in substitution of experience with process and passion. It has now started paying off.

The company has filed two patents last year with the USPTO, in the space of Machine Learning and Artificial Intelligence. It is now on the way to productizing those patents.

  • Is there any special experience with your clients you would like to highlight?

One of the initial prototypes that we carried out for our customers, was for a large Asset Management Company. We worked through a lot of innovative metrics to help them obtain the right root cause for a certain business phenomenon they were encountering. When the CFO saw those, he just exclaimed “you have opened our eyes”.

Another Eureka moment in our customer journeys, was when demonstrating a fraud management solution to large forex and travel operator, the CEO took such a great liking to the solution, that we immediately got referred to two other potential opportunities within the organization.

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