How This Mumbai-Based Google-for-Analytics Automates Data to Insights Generation
Intending to transform business monitoring, Nimesh Mehta set up a cognitive business analytics platform, ‘Rockmetric’. The idea was to automate the ‘data to insights’ journey for business teams and enable enterprises to deliver ad-hoc visual analytics, augmented insights and root-cause analysis without expanding support teams. In an exclusive interaction with Analytics India Magazine, Nimesh, founder and CEO of Rockmetric, shared several insights on the idea behind Rockmetric, how it leverages AI and ML, building intuition in AI models, its future plans and much more. AIM: What inspired you to build Rockmetric? Nimesh: Having been associated with management consulting for a long time, I was well aware that while the world’s information is at your fingertips, senior management struggles a lot to monitor their teams for several reasons. One, the data stack is very complex. Second, the human intervention required in preparing data is also very tedious. The broad idea then was to build an architecture that would give senior leaders complete visibility of the organisation and could be an ally for senior CXOs to make the team more productive. So, that’s where the idea of building Rockmetric, a Google-like search for analytics, came up. AIM: How do you interpret Rockmetric as “Google for analytics”? Nimesh: Google enables you to search data more effectively and lets you learn about new things. The most important thing that Google does is scan through all your data and proactively keep prompting you in terms of what’s next. Our broad idea is to be that kind of ally to a business user—where once they use us—they can ask us questions anytime and get results anytime. If they want to explore more about the business, they can do it without any constraint, and the system can proactively support them to figure out what’s breaking and what’s going well. AIM: How did you identify the problem that Rockmetric essentially addresses? Nimesh:  We tried to understand three things. First, if there’s an industry where everything is there, the problem is solved. Second, if the incumbents are decades old and there’s no innovation. And third, if the customers are unhappy. So if such a triad exists, it is like a perfect space for disruption. Business reporting was one such area. Everyone had moved on to advanced analytics and big data. But, reporting as a problem was still underserved. People were still using decade-old tools, like excel spreadsheets and manual analysis. Moreover, although people were using visualisation tools, they were not enough. So, we put reporting on steroids with powerful functionalities like natural language search, ad hoc data exploration, and automated insights and enable this on very large data tables. For example, Rockmetric enables business users to analyse datasets as large as 250 GB, unlike usual cases of less than a GB. AIM: Could you mention some of the use cases that Rockmetric addresses? Nimesh: We essentially target high-value analytics use cases like portfolio monitoring in financial services, customer insights, risk and fraud investigation, customer segmentation and procurement. These are the areas where a business user needs to generate insights and get alerts and exception reports without learning sequel or Python. AIM: Does catering to a wide variety of clients make generating insights from different kinds of data challenging? If yes, how do you deal with that? Nimesh: We have a wide variety of clients with different datasets and scenarios. However, our platform doesn’t try to deliver domain instances. Instead, it automates the data pipeline, generation of insights and the ability of customers to extract insights from their data. While configuring insights suited to a particular business, we collaborate with business leaders. Moreover, once the diagnostic insights are generated, we delegate the decision-making aspect to the business users because that’s where their domain expertise lies. So now, the analytics team works on complex models and the business team gets direct access to diagnostic insights generated from the data with the help of tools. It is then that business users generate outcomes from that data. This is unlike the earlier case where the domain user would go to an analyst with no domain expertise to decipher the data. AIM: Rockmetric is a low-code business analytics tool. Why did you choose to opt for no-code? Nimesh: The broad idea was if a business leader, for example, a sales CEO, has an analytically sound business team that wants to analyse a larger dataset that isn’t possible with MS Access or Excel, the business team is not going to learn SQL or Python. In such a scenario, Rockmetric comes in. We have a system where through natural language in English, one can ask the question and the machine automatically converts it into an SQL query and delivers an outcome automatically. Our focus has largely been on how a non-technical business user who is very savvy with analytics can analyse large data sets without learning to code. AIM: How do you integrate AI and ML into your tech stack? Nimesh: We do not sell AI and ML to our customers. We provide them with a solution and thus simplify the problem. That’s where AI gets used. For example, in the case of natural language search, AI is used to try and figure out what the query is and to generate insight; a ranking algorithm is applied to deliver an accurate output based on the user query; a recommendation engine model is utilised to deliver automated insights to the customer. So, there are various stages where AI is used. The overall idea is to make the architecture intelligent such that people with moderate to low technical skills can operate and deliver business outcomes. AIM: How have you built ‘intuition’ within the AI model? Nimesh: Three things are considered to account for intuition in the AI model. First is the search itself. The second is the product of UI or UX. And, the third is the kind of insights that are delivered. So, when a senior leader looks at a trending chart and wants to figure out why the sales dip day by day, the biggest challenge is to train analysts to deliver something relevant. At Rockmetric, we have taken the brightest minds from our customers and our team and then trained the machine to deliver relevant insights automatically. Thus, if an organisation is using it, it doesn’t have to train people to deliver relevant output. The machine gives an output once it is trained for the first time. AIM: Are there any new products in the pipeline? Nimesh: We have two more offerings coming up. The first is ‘Rockmetric Flash’, a light product offering where companies can deliver secure reporting to field force dealers or external customers. The second one is a small, low-code module to automate the creation of the data mart process. This will enable data teams to provide a decentralised data mart to businesses. With this, Rockmetric will become a much more comprehensive platform rather than a small utility tool. AIM: Do you think if a data regulation law comes in, it will adversely impact Rockmetric’s business operations? Nimesh: No. In fact, if regulation comes in, I think it will be great for Rockmetric. We are one of the few extremely secure tools designed for BFSI, containerised and portable across any cloud platform. Moreover, it can work on-premises as well. There are a lot of platforms in the market which are only on the cloud and can lock you on a cloud provider. Thus, most companies will have to ensure that they don’t get locked in with a specific cloud provider or have a backup option to operate on-prem in case of a war or disaster. Rockmetric is an ideal choice in such circumstances. The stricter the data controls, the better the growth for Rockmetric. AIM: How do you envision the future of Rockmetric? Nimesh: Our future plans are twofold. One is that we want to be the most trusted business monitoring partner and hope that senior leaders and all top organisations will monetise business monitoring, business reviews and providing secure access to the end users. We want to transform the market from only creating dashboards and reports to actively monitoring their business. Secondly, we want to be a comprehensive, price-reliable analytics platform that provides a secure environment where business users don’t get locked into any single cloud provider.
The focus has largely been on how a non-technical business user analyses large data sets without learning to code.
The focus has largely been on how a non-technical business user analyses large data sets without learning to code.
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["Go", "TPU", "AI", "ML", "RAG", "Python", "Aim", "analytics", "SQL", "R"]
["AI", "ML", "analytics", "Aim", "RAG", "TPU", "Python", "R", "SQL", "Go"]
Zinnia loves writing and it is this love that has brought her to the field of tech journalism.