Data-Driven Work Cultures: Akhilesh Ayer of WNS Triange On How To Effectively Leverage Data To Take Your Company To The Next Level

An Interview With Pierre Brunelle

It’s very important to focus on quick wins and take use cases where you can show success. This helps build credibility and acceptance and eventually the broader organization would start seeing the benefits of using data analytics for their needs.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Akhilesh Ayer, EVP and Global Head — WNS Triange.

Akhilesh Ayer serves as EVP and Global Head of WNS Triange — WNS’ Data, Analytics and AI practice. WNS Triange enables the data, analytics and digital transformation agenda for global clients. As a part of the management team, Akhilesh is responsible for setting the strategy, managing the P&L, running global operations and providing leadership for the WNS Triange business unit. With over 25 years of experience, Akhilesh has delivered excellence in business expansion, global operations, turnarounds, restructuring, brand positioning, revenue growth and improvement in bottom-line performance. Akhilesh has been part of multiple industry panels and bodies, authored several articles and, blogs, and is a frequent industry speaker.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

Over the years there have been many books I have found fascinating and have had something to learn from each. But “The Goal” by Eliyahu M. Goldratt is a book I particularly like. I read it when I was just about starting and the book has stayed with me because it taught me to look at things in a simple way and focus on outcomes to solve your business problems — a takeaway that has worked well for me.

Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” My work centers on the value of data visualization and data collaboration at all levels of an organization. So, I’m particularly interested in this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

Being data-driven is essentially about two things — technology and culture. An organization needs the technology and processes built around it to gather, store, process and decipher patterns in data to solve business problems. Equally important is its ability to change people’s mindset — from the C-suite to the business users — to base their decisions on data.

Today, taking decisions only based on prior experience and gut feeling is fraught with risks and would lead businesses to lose out to the competition. Also, the recent pandemic has created situations where there are no precedents for us to lean on, so our dependence on historical data has reduced.

For example, global supply chains — heavily relying on a single source may have worked for businesses in the past, but the pandemic has clearly shown the risks involved in such a strategy. Neither past experience nor past data helped them adjust to the level of disruption.

Data-driven decisions are based on insights from information, including real-time data from a complex network of suppliers, manufacturing plants, logistics companies and inventory in their own warehouses. It means moving away from spreadsheets in which people enter data manually — sometimes erroneously — toward dynamic visualization and drill-down options for sharper and more accurate insights on customer demand, risks or production bottlenecks.

According to a global data and analytics survey by Forrester Consulting, commissioned by WNS,

despite the market disruptions caused by the pandemic, 82% of organizations with advanced analytics maturity level saw positive year-over-year revenue growth over the past three years. In comparison, only 9% of beginners experienced revenue growth.

Which companies can most benefit from tools that empower data collaboration?

Data analytics is a capability that levels the playing field for smaller and medium sized companies who want a greater share of the pie. For instance, they can improve the effectiveness of their marketing campaigns and find greater return on investment, as well as burn less cash by weeding out inefficiencies.

It is not a capability that large enterprises can ignore either. Data analytics makes their efforts more fruitful — be it to expand into a new market segment or geography, improve the customer experience and enhance customer loyalty or stay innovative.

Irrespective of the size or industry, data analytics is now a key business asset.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

Advanced analytics including Artificial Intelligence (AI) and Machine Learning (ML) are opening a world of new possibilities across business functions. We can now deploy analytics models to identify and predict risks — people-related such as employees at flight risks, process-related such as inefficiencies adding to revenue leakage or technology-related such as proactive maintenance and breakdown prevention.

Let’s look at the single view of the customer, something data analytics enables us to create. Think of it as a dossier that updates itself each time a customer places an order, browses online or gets in touch with the contact center. With such insights on customer behavior and preferences, businesses can offer them a great experience through personalization. For instance, a customer who is part of a hotel loyalty program is delighted to find that the hotel has given her hypoallergenic bed linen based on her request during a previous stay.

In the Forrester survey, 37% of the organizations called out “improved customer insights and better customer experience delivery” as the topmost benefit of leveraging data and analytics.

Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?

Organizations fall broadly into three categories in terms of maturity of adoption of data, analytics and AI — beginner, intermediate and advanced. Our experience suggests that across the beginner to advanced maturity spectrum the challenges faced to become data-driven are different. The Forrester survey validates our observations. The hurdles for beginners are mostly in defining and articulating a data strategy or what must be the Key Performance Indicators (KPI) to measure success. Those at the intermediate stage find it hard to constantly refine their organizational structure, expertise and analytics processes. Advanced organizations often centralize their data and insights talent at the corporate level, with limited ability to support the specific needs of different lines of business.

It’s critical to get all the five pieces of the puzzle in place to become truly data-driven — strategy, people, practices (methodologies and frameworks), data and cloud platforms. So, in essence, they must first invest in their ability to process vast amounts of data with the right data management, governance and security capabilities, besides having the right talent and strategy. But that’s easier said than done. One efficient approach could be to work with a trusted third-party partner to help them climb up the maturity curve.

Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It to the Next Level”? Please share a story or an example for each.

Change management is an important aspect that many organizations miss, which includes culture change. Data analytics programs must be sponsored at the C-level, with the drive coming right from the CEO. Only then will there be a mindset change of using data-led insights to drive decision-making across the organization.

Second, identify the right data monetization opportunities. Start with easy wins that will inspire confidence and form the base for launching bigger, more complex and enterprise-wide initiatives. For instance, an insurance company wants to transform the claims process using analytical models. Pick one area with maximum potential for quick results such as a loss severity model or a claims litigation model, instead of transforming the entire process. Insurance companies can realize quick benefits by identifying missed recovery opportunities and predicting recovery opportunities proactively for new claims entering the system. Deploying the recovery models at FNOL helps in controlling the recovery leakages and with improved recovery rates as well as reduced cycle times due to early identification of such cases. This will help to better establish credibility and generate business buy-in.

Third, know what problems you want to solve with data. Start with the business problem and collect data to feed your model that will find answers, rather than just collecting data without any stated purpose and then wondering what to do with it. A leading fitness product company wanted to improve its customer activations. We executed a data modernization strategy involving migration and integration of existing unified CRM data warehouse to enterprise cloud. The project involved thorough data profiling and cleaning, ETL automation, data reconciliation, ensuring robust data security through encryption especially for PII data, addressing incompatibility issues between databases, and so on. All of this was encompassed by data audit and governance to ensure data integrity. The modernization move enabled the client to achieve higher customer activations and better visibility into customer lifecycle behaviors, and faster time- to-market.

The fourth lever is technology maturity. Organizations must invest in advanced data management solutions hosted on the cloud, without which the downstream ability to process big data and act on it quickly will be lost.

Fifth, work with experts who have the experience of working with multiple use cases in your industry, so you do not have to re-invent the wheel and face the uphill task on your own.

The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

As I’ve mentioned in my earlier answers, culture change must start at the top. C-suite involvement is important in formulating a business strategy for data usage, monetization and impact. The articulation of the strategy from the top will direct implementation.

Second, it’s very important to focus on quick wins and take use cases where you can show success. This helps build credibility and acceptance and eventually the broader organization would start seeing the benefits of using data analytics for their needs.

Third, ensure that the data analytics teams start working on use cases that have business buy-in. This ensures there is support and sponsorship from the end-users and they champion the cause of the data analytics program.

And finally, it’s crucial to communicate widely and repeatedly about the success of data and analytics initiatives, the benefits of adopting them and the risks if not adopted. Only then will data be treated as an asset.

While the points mentioned above are absolutely critical to help drive the culture, implementing data and analytics practice will involve broader changes and the organization will need to navigate through them one step at a time. It involves new investments, embracing a different way of doing things, readiness to experiment and engaging with multiple stakeholders across the organization with specific roles but ones that need collaboration. For instance, the company will need new policies that are in line with current regulations on data privacy and security.

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

We know that the move toward digital is an irreversible trend and data is an irreplaceable asset. The Covid-19 pandemic has enriched our sources of data and presented us with an opportunity to better understand customer behavior and prepare ourselves better for regulatory changes, geopolitical tensions and black swan events. With the external environment expected to remain volatile, the need for data-driven insights for businesses will further intensify.

Organizations that want to remain adaptive will start embedding analytics in every business function — from improving the customer experience to eliminating operational inefficiencies and reducing risks.

The WNS-Forrester survey shows that 63% of decision-makers recognize that data and analytics are strategic enablers and 68% expect an increase in organizational spending on data and analytics in the next 12 months. They also see a larger role for third-party service providers in the near term.

Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you?

I’m excited about WNS Triange, our recently repositioned data, analytics and AI practice. WNS Triange co-creates Intelligent Enterprises for our clients by helping them manage intelligence as an asset and transform those assets into insights across the value chain. We help them share those insights throughout the enterprise more effectively for enhanced overall performance, and thus effectively manage critical imperatives.

WNS is investing heavily in incubating next-generation services and platforms with cloud, ML and AI technologies. Triange is built on the three core pillars of Triange Consult, Triange NxT and Triange Center of Excellence (CoE).

Triange Consult, the consulting arm, sets the right foundation for organizations’ data, analytics and AI needs. From the initial gap analysis and change management to governing standards and processes leading to an implementation roadmap, it helps companies define their journey to an insight-driven enterprise.

Triange NxT is an analytics-led platform suite that provides readily deployable assets and solutions by combining intelligent cloud-enabled data, analytics and AI capabilities, along with our partnerships with all major cloud providers and niche start-ups.

Triange CoE drives the end-to-end execution of industry-specific analytics programs, powered by domain expertise, global delivery capabilities, functional knowledge and technology best practices.

How can our readers further follow your work?

You can follow me on LinkedIn.

About WNS Triange

WNS Triange (formerly WNS Research and Analytics practice) powers business growth and innovation for 120+ global companies with data, analytics and Artificial Intelligence (AI). Driven by a specialized team of over 4000 analysts, data scientists and domain experts, WNS Triange helps translate data into actionable insights for impactful decision-making. Built on the pillars of consulting (Triange Consult), future-ready platforms (Triange Nxt) and domain and technology (Triange CoE), WNS Triange seamlessly blends strategy, industry-specific nuances, AI and Machine Learning (ML) operations, and intelligent cloud platforms.

Driving a futuristic edge are WNS Triange’s modular cloud-based platforms and solutions leveraging advanced AI and ML to provide end-to-end integration and processing of data to actionable insights. WNS Triange leverages the combined strength of WNS’ domain expertise, co-creation labs, strategic partnerships and outcome-based engagement models.

About The Interviewer: Pierre Brunelle is co-CEO and Chief Product Officer (CPO) of Noteable, the collaborative notebook platform that enables teams to use and visualize data, together. Prior to Noteable, Brunelle led Amazon’s internal and SageMaker notebook initiatives. Pierre holds an MS in Building Engineering and an MRes in Decision Sciences and Risk Management.