Polestar Solutions on Why Data is Not Just for Today but for a Better Tomorrow
Tell us about Polestar Solutions’ journey, and the opportunities and challenges you have faced along the way.
It was sometime in 2011 that I started to have frequent conversations with my co-founders — Amit and Ajay about the need for data-driven solutions, the challenges businesses were facing and the existing gap in the market to help businesses realize more of their data. Since founding Polestar Solutions, we have had an incredible journey and have come across some amazing opportunities along the way. Apart from our other focus geographies, India is a huge focus market for us. Currently, India is going through a wave of data analytics, digital transformation and digitization which many corporates and government sectors are incorporating; this is a great opportunity for us to partner for data ingestion and interpretation. 10 years later, we have built a great team and have partnered with top league product platforms, have 5 offices (including 1 in the US), and are honored to serve our clients across new age startups, Fortune 500 companies, conglomerates, as well as in the government sector.
Our journey has come with its own set of challenges which any young high growth organization may face. The first 2-3 years were the survival period for us, like it is with any bootstrapped services organization. You have to stay afloat. The next 4-5 years was a period of consolidation; as we built deeper competencies based on what we wanted to do, the expertise we wanted to develop and the type of organizations we wanted to associate with. Right now, we are in the acceleration phase — we have built the right team and have garnered good expertise as well as partnerships. We will keep innovating, and improving what we do and will continue to leapfrog into the future.
How important has analytics become for organizations? How would you describe the way Polestar Solutions helps companies in their digital transformation journey?
When we started off people were talking about data and diagrammatic representation for digital transformation and analytics which used to appear as a peripheral application. Now 10 years later, when we look at how organizations are seeing digital transformation — data is at the core of everything. Today, the biggest difference is in the thought process — what type of data and outcomes businesses want to capture which can actually help them stay competitive and provide insights into their business.
Organizations have become aware that data is not just for today but helps prepare you for tomorrow.
We help businesses right from understanding the data which might be getting captured anywhere in the enterprise as well as the gaps — which is a game-changer. When we start working with an organization we understand what they want to achieve, their challenges at the time and create a data backbone in order to ease decision-making across personas in the organization. This allows us to provide insights into hidden values emerging across functions. This creates a common layer for the organization to tap into for all their decision-making needs, actionable insights, as well as enable cultural change. By enabling organizations to bring in a cultural change we take them from decisions based on gut-feel to being backed by data.
How can companies build successful internal analytics and AI-ML practices?
There is no single ingredient, however, this needs to be solved from a multi-dimensional perspective if you want to make it successful. It is as much about a cultural shift as it is the implementation and adoption of digital platforms. In order to build a successful internal analytics and AI-ML practice, enterprises need to align with their business objectives and customer expectations and then find the right partner-of-choice to help them introduce a culture of innovation and challenge the status quo. With digital transformation being the end-goal for many forward-thinking organizations today, it is important to not just look at new technologies in order to stand out but to also set clear business objectives and align with key business stakeholders.
You recently raised an undisclosed round of funding in January, and entered the US market. What is the roadmap ahead for Polestar Solutions?
Our recent fundraise was driven by our significant revenue growth, strong traction and competitive advantage we have consistently established over consecutive years. The fundraise will help us deploy funds to fuel our growth in our existing markets — North Americas, Asia Pacific, ANZ, and the UK. Currently, we are looking at strengthening our teams in the US and are hiring across senior industry and business leadership positions.
We have been investing more towards actionable and smart insights, and expanding our industry-specific analytics assets and frameworks so that we can provide better tailored solutions to our clients. We also want to move higher into the value chain by focusing on analytics strategy engagement. Enterprise Performance Management is also one of our key focus areas, as we see a growing reliance of companies on technology-led innovation to streamline their processes and plans. In this regard, we have partnered with leading technology OEMs to be in a position to deliver the best and most well-suited technology solutions to the end users.
How different is it to navigate the US market as opposed to India?
In the past decade, we have seen an explosive growth, adoption and implementation of data analytics in India due to a bunch of factors right from internet adoption to understanding the need for a data-first approach. However, while data analytics continues to grow as one of the hottest trends in India, there is still a dearth of skilled workforce and high implementation costs which continues to pose a challenge. The other challenge here is that we still continue to face a catch-22 situation between embracing technology and legacy traditions/ practices.
The US market, on the other hand, is a more mature geography in terms of automation and digitization — a lot of the regular data problems which we are solving for Indian enterprises, are already in the process of being solved in the US. Blending technology, industry and functional expertise is also another area where the US is playing a big role. The US is a hyper competitive and high opportunity geography, so one has to think about how you’re going to navigate that market and increase your visibility as a service provider when you plan to enter it. The US continues to maintain its dominance which is also attributed to greater customer satisfaction, last-mile delivery of insights and a huge focus on ML-driven smart insights.
Nearly 70% of analytics projects struggle to prove their worth. Why do you think that occurs, and what can enterprises do to prove successful with their analytics journeys?
I think what’s most important is strong management and having a clear business objective. Most of the analytics implementations come on a need-basis where there is little emphasis on the organizational roadmap. It is important for the vision to tie up with what you are trying to achieve with your analytics projects and this should percolate into how each function works. Additionally, there needs to be equal commitment from an organization’s side in terms of quality time and bandwidth and from the technology partner’s side for implementation. No partner can do it on their own — the enterprises’ support is needed in order to bring about a change.
We also need to understand that change is not accepted immediately, however, there will be a period in an organization’s journey where the old ways of working are challenged. There should be a clear path to figure out how the organization should function, and who to partner with in order to navigate everything from adoption, management insights, business objectives, KPIs, data ingestion, final delivery of insights, to cultural change.
We are seeing a steady growth in adoption of cognitive analytics and AI. What is your take on the maturity level of enterprises globally for these futuristic initiatives?
For organizations looking to adopt cognitive analytics and AI — it is a complete journey and the most important part of the journey is data. Even before the data landscape is figured out, many enterprises want to plunge into data analytics initiatives — it’s like putting the cart before the horse. Speaking of maturity levels of enterprises, it varies from geographies and industries; like I mentioned earlier, there are certain geographies where digital transformation is still underway and the data landscape is evolving whereas there are others like the US where the market is quite mature. Certain industries too such as Pharma and BFSI have matured on the analytics curve — there are many organizations which have a strong data management framework and unified data layer and they are moving towards smart intelligence and consumption systems through cognitive analytics and AI.