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Q and A

The Evolving Role of Analytics

--Akhilesh Ayer,Head, Research & Analytics Unit; Head of Marketing
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Related To: Solvency II Analytics and Insights Artificial Intelligence Pharma Industry NDC OTAs Analytics-as-a-Service Cloud Services
ABOUT Akhilesh Ayer
Head, Research & Analytics Unit; Head of Marketing
Key Points

  • Organizations are making analytics an integral part of their strategies to achieve broader business goals

  • Productization and Analytics-as-a-Service will drive the next wave of analytics implementations

  • Artificial intelligence, robotics and machine-learning are enabling productization and Analytics-as-a-Service 

Analytics is a key enabler for businesses to achieve their goals, and is now an integral part of larger organizational strategies, says Akhilesh Ayer, Head, Research & Analytics Unit, and Head of Marketing, WNS. While businesses explore the outer limits of analytical capabilities, he adds, emerging trends such as productization and Analytics-as-a-Service offer greater value to clients.

Discussion with Akhilesh Ayer
Q. Do you believe that data, and by extension, analytics has finally got a seat on the board? Disruption is an everyday reality for businesses globally. And it’s not just technology — fast-paced changes in regulations, consumer expectations and markets are disrupting companies too. The realization that companies can no longer rely on past trends to anticipate what comes next has set in. A real-time, data-driven understanding of the world, as it changes around them, has become critical to competing and growing. And analytics is a key enabler for this growth. The emerging CFO, CAO and CDO functions are examples of this need. Analytics is no longer just a supporting tool, it is a part of the larger organizational strategy.
Q. Are businesses getting better at using analytics? Well, there is definite growth in understanding how analytical projects should be structured for better results. For example, recognizing that analytical insights are only as good as the questions posed to the data has resulted in domain experts becoming a core part of such projects. But companies are also realizing that analytics, and the insights it delivers, cannot be a standalone project or process. The adoption of a data-driven, real-time and customer-centric approach often requires re-modeling of the business, starting with buy-in from top management. Eventually, this ensures the alignment of insight and execution, and helps businesses reap the full benefit of the adopted strategy.
Q. Do you see analytics moving beyond the marketing brief to broader business objectives? Yes, the transition is certainly happening. We are seeing companies explore use cases beyond basic customer and market analytics to either enhance business efficiencies or extend customer lifetime values. For example:
  • In the wake of the Solvency II regulation, EU insurers are using analytics to manage underwriting risks, market exposures and conducting geographical analysis of markets. This is done to reduce business complexity, and balance risks and returns better
  • Pharma companies are using analytics and Artificial Intelligence (AI) in a bid to fast-track innovation and drug discovery. These objectives are being met by mapping patient trends to identify target markets, identify gaps in the efficacy and safety of current drugs, and identify test sites where more patients are available
  • The launch of New Distribution Capability or NDC and the overall technological upgrade of all players in the travel and hospitality markets have opened up a whole lot of opportunities while intensifying competition. Online Travel Agents, as well as hotels and airlines, are waking up to the power of social media analytics in conjunction with behavioral and transactional data to understand customer preferences and create differentiators
  • Utilities are increasingly using customer analytics to reduce operational expenses and enhance loyalty. Leading utilities use multi-factor customer analytics to achieve reduced churn, higher customer lifetime value as well as increased up-sell and cross-sell rates
Q. What are the trends that are shaping the analytics market landscape? I just mentioned multi-factor customer analytics. Across domains, companies are realizing the power of integrating analytical data from previously siloed data sets such as basic customer demographic / behavioral, social media, sales and research. Automated and integrated analytics, backed by powerful visualization tools that can monitor brand performances in real time, and across dimensions, are the need of the hour.

Productization, an integrated data analytics platform for example, is a broader emerging trend in the predominantly service-oriented analytics industry. As use cases mature and firm up across businesses, most organizations can benefit from proven solutions that can enable quick adoption of analytics within specific business contexts. This also helps put important data and insights into the hands of decision-makers across functions. Of course, the market has products across the spectrum offering various levels of customization where needed.

Finally, Analytics-as-a-Service is fast becoming a well-established model for the market. A recent research estimates that Analytics-as-a-Service will grow from USD 5.9 Billion in 2015 to USD 22.24 Billion in 2020, at a CAGR of 30.39 percent during the period. The primary drivers for this market are faster and cheaper deployment and ease of use for businesses. Overall, this model offers greater value to clients with reduced operational and process costs, and gives them flexibility in terms of opting in or out of services. It also estimates the number of users.

The main factors enabling both productization as well as Analytics-as-a-Service include the maturing of AI, robotics and machine-learning technologies, as well as cloud hosting services. AI, robotics and machine-learning have reduced the need for human intervention where judgment and cognitive abilities are required. This has not only reduced errors, but improved the overall delivery of analytics.
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