Data is an important arsenal for companies to grow and progress in their respective fields. However, huge amounts of data may be useless for companies that do not have an effective data analytics and management system in place. Data analytics in itself is an all-encompassing field that is witnessing rapid advancement.
Analytics India Magazine interviewed Akhilesh Ayer, the Executive Vice President and Head for WNS’ Research and Analytics (R&A) practice. Ayer shares with us the learnings about data and business processes that he has gained through two decades of his professional journey.
Akhilesh Ayer: What makes WNS stand apart is that we excel in both industry expertise and functional domain knowledge. That is how we approach the building of our tech stack as well – we create domain-centric solutions built on futuristic AI and ML technologies developed on Intelligent Cloud. We have a consulting-led, proprietary framework-driven approach to solving business problems such as supply chain analytics, fraud modelling, claims and underwriting solutions, pricing, customer propensity to buy, credit risk, etc., using client-preferred technologies in both next-gen Big Data or legacy data environments.
Our flexible approach to delivery means that we offer our solutions both on-premise or on scalable cloud-enabled solutions (Microsoft Azure, AWS, GCP). Our teams are equipped to work on any kind of technologies and techniques across the spectrum, including Big Data technologies (Apache, Hadoop, Spark, MongoDB, Cassandra), Visualization (Tableau, Qlik, Power BI), AI/ML/Data Science programming/coding languages (R, Python, PySpark, SQL, SAS, R Shiny), Cloud (AWS, Azure, GCP), to name a few.
Modularity is another strong suit of the WNS tech stack. Given the vast number of industries and functions within them that we work with, we can offer these solutions as plug-and-play services for clients, thanks to this modularity. We are constantly looking to move to next-gen technologies to better our stack and strengthen our proprietary solutions.
We also have an ecosystem of niche partnerships to supplement our proprietary offerings in the latest technologies, including data lineage, governance and domain-driven data partnerships.
We have also created WNS Co-Creation Labs – there is one in Pune, another in New York, a third in London, and a fourth coming up in Australia. At the labs, we work with customers to reimagine their existing business problems, especially on digital platforms. We leverage design-thinking principles to co-create solutions using data, analytics, AI and ML. Using the elements of ‘play’, with intuitive workflows and designed as immersive spaces, these labs serve as, if I can use the term, ‘time machines’ that offer clients a peek into how their futures will be transformed with the solutions we co-create today.
We also have a host of productised ML/Big Data offerings. SKENSE is a cognitive data capturing and contextualisation platform. Unified Analytics Platform is an end-to-end industry analytics platform using domain-specific AI algorithms and hosted on Intelligent Cloud. Then there is SOCIOSEER, an AI-driven solution for social media analytics and monitoring. SENTINEL is an AI-led trendspotting tool. And these are just a few examples. We have also developed 25+ digital accelerators, including a cutting-edge AI workbench and an AI-led data catalogue that reduces time-to-insights for our clients on specific business use cases.
Akhilesh Ayer: Digital foundations are increasingly becoming the bedrock of all organisations, irrespective of their size. Large, medium, and small companies are using the power of analytics and AI to accelerate their own decision-making, optimise costs, or create differentiated products and offerings.
Interestingly, while data is being used to enhance the quality of a certain offering or to optimise processes, the insight or the data itself has monetary value that companies can resell and repackage with their partners. For instance, the underwriting of an insurance policy needs to be based on the insured’s past data as well as industry data in general. With data and analytics, one is actually able to effectively look at both and make decisions that are both faster and have a much more nuanced analysis. While the advantage of making sound decisions based on statistical principles has always been understood and valued, with data analytics, decisions today have become real-time and are enriched with data that comes from both traditional and non-traditional sources.
In the post-pandemic world, our clients have been forced to reshape the way they engage with the end customers. This digital disruption is driving the push towards embedding analytics into all business areas to enable digital transformation – to improve business productivity and foster a data-driven decision-making culture more than ever.
Akhilesh Ayer: It is phenomenal and fascinating to watch how data is driving business decisions in real-time – even for traditional business – enabling adjustments to supply chains, offering better visibility, and aiding dynamic pricing. There are quite a few challenges as well.
Prioritisation – More often than not, companies are aware of the importance of data but do not know how to prioritise their data investments to get optimal ROI. Spending huge amounts of money on creating large data infrastructure is not the only solution.
Scalability – Second comes the problem of scaling. Everyone wants to do it. But in many cases, they do not know how to do it. And if they do, then they are probably unaware of the factors that are affecting the quality of data – it could be poor data governance or poor data taxonomy. In several instances, lack of senior management alignment, talent shortage, and general lack of prioritisation when using data analytics affect scaling significantly.
Quality of Data – That is as far as the use of data analytics is concerned. When it comes to data analytics itself, there are a few challenges. The first being the quality of data itself – while there are a lot of tools and technologies that have evolved to make sense of data, if you don’t organise your underlying data ecosystem, then it will only help so much.
Talent – Another major challenge for many developed markets like the US and Europe is a huge talent shortage in data analytics.
Business Context – Finally, today, there is a lot of focus on underlying technologies used for data analytics. But there needs to be a lot of appreciation of the actual business and the business problem that the company is trying to solve through data.
Akhilesh Ayer: The pandemic has definitely forced companies to sit up and pay attention to data analytics. Even those for whom this wasn’t a priority before have jumped onto the digital bandwagon. Consumers in a post-pandemic era are engaging in ways they have never before; they are increasingly looking at differentiated experiences, and companies will have to factor that into their strategies going forward.
So investments in data will increasingly start receiving senior management attention, the right kind of budgets and focus across departments and functions within an organisation. Data analytics will become a core differentiator for a lot of organisations. That, in my opinion, is a big shift. Consequently, there will also be a demand for seasoned analytics professionals. It will also result in demand for more innovative and scalable technologies and talent that is good at predictive analytics, AI, ML, Big Data – the whole shebang.
One thing that will change, though, is the way data analytics is perceived from the business point of view. Earlier, it was very method and tool focused – that is required too – but now, it will also need to be understood from the ‘relevance to the business’ perspective. So, while data professionals grow as domain experts, they will also need to develop as functional experts because it is this combination of domain knowledge and functional knowledge, along with computing skills and the ability to visualise the analysis to tell a cohesive data-driven story, which will bring real value for businesses.
Akhilesh Ayer: To say that there is still a lot of potential to further deploy and leverage data analytics would perhaps be a better way to answer this question.
Two areas companies should focus on in the coming 18-24 months are
a) setting up an augmented data governance function and
b) enabling data-driven transformation leveraging the power of intelligent cloud.
When enterprises move data to the cloud, they also modernise their data platforms. Intelligent cloud and AI today offer opportunities to companies to leapfrog the competition and accelerate their data analytics journey. There are also breakthroughs in cloud-led, out-of-the-box integrations that enable AI and ML innovations using open-source tools and technologies.
Function wise, I would say even mature industries that have adopted analytics relatively early have far greater potential now to harness the power of AI, whether it is for underwriting and pricing in insurance, or supply chain management and customer intelligence in retail and CPG, or credit and risk functions in banking.
Akhilesh Ayer: The challenge remains as to who would take ownership of the action initiated by unsupervised algorithms. I think we are still at the initial stages of that maturity path. The more trained the algorithms, the more the variety of data fed – there will be fewer concerns. Also, in what areas algorithms should be deployed or not is another grey area. But both organisations and regulatory bodies need to evolve the right framework and governance mechanisms constantly. Consented data is just the input. Clearly, calling out the boundary conditions of the output and application area is also equally important.
Akhilesh Ayer: I don’t think there is just one answer. They often fail at different points along the data value chain. Many evolved companies have kind of sorted out the upstream issues but are now challenged to articulate business value through the right use cases. But companies that are still in the early stages of their data analytics journey are struggling with foundational problems. Across the board, there are three common challenges facing companies – 1) data, analytics and AI strategy, its sponsorship and funding, 2) talent availability and capability leverage and 3) creating a sustainable data-driven culture and ensuring data, analytics and AI, form the core DNA of the organisation.
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