NEW DELHI : Dr. Athina Kanioura, Chief Analytics Officer and Global Lead for Applied Intelligence, Accenture is responsible for sales and customer analytics globally. She has grown Accenture’s Applied Intelligence and analytics team to nearly 35,000 people and focuses on helping clients drive business transformation by using AI, data and analytics. On a recent visit to India, she spoke on how to develop a successful data strategy and the challenges faced by companies. Edited excerpts:

What are some of the challenges faced by companies in translating data into actionable insights?

One is that companies prefer to invest in the infrastructure to ensure everything is located in one place, everything is orchestrated the right way, and then tackle a business problem. And then there is the other school of thought that I don't need to have everything perfect in the platform. I need to first identify the business problem, I need to solve for that, even if it's sub-optimal to start with, but at least I can take decisions faster. I'm a proponent of the second model. We always try to tell our clients that if you spend too much effort in creating the perfect technology, you will lose the touch with the consumers because consumers are becoming more and more impatient. They cannot wait for you to solve the problem in 18 months, when you have finished the infrastructure, you expect them to solve the problem. Now, they expect it the next day. Then there is a huge amount of challenge in terms of the mindset. At the end of the day, the ultimate consumers of any analytics or data are the people in the company that are working for it. And asking someone who has been working for the past 15-20 years doing a certain type of work to adopt a different way of working is a challenge.

And unless it is a top to down endorsement that I want to become data driven, I want to be AI powered, it's very hard for the middle layer of the employees to inherently change the way they are working. And so we have seen companies that have really kind of made that mindshift, really succeed. Also companies need execute on the ground. So what is the execution process you have to put in place? What are the change management processes, what is the training of the people, what is the upskilling of the people, what are the learning boards you have to put in place to ensure that the people have all the information they need to be able to use those capabilities.

You mentioned that Accenture is investing in data science. What's the kind of investment you are making in India?

We typically invest approximately 5% of all our revenues in research. India has been our jewel, both from a global perspective and local perspective. Our India business is shown as an example for the rest of the Asia Pacific world, so we are aggressively hiring for this space.

If I were to look at the talent strategy we have in India, we have strong relationship with top universities. We sponsor master’s program and PhD programs in the country. We also encourage our people to work in academia or take a sabbatical. We have had a recent example of a senior manager who took some time off and wrote a book around machine learning. This is part of our culture.

For me, the proudest moment was several years ago when India was thinking about the maternity policy. We were the first to introduce the six months maternity leave. We applied intelligence, did the analysis on the impact of the short maternity period on our female employees, analyzed the returning ratios and used analytics to be able to look at the root causes of why women didn't return back to the workforce. This for me is the power of analytics. We were able to use Analytics in a country that inherently for us is very strategic.

Globally, also we have made the commitment as a company that 50% of our employees should be female employees. So, I want to incentivize my women to stay and progress in the company. And for me, it's part of our duty as a data and analytics organization to also inform and form good policy making.

So how do companies build a successful data strategy?

Companies need to have a clear understanding of what are the strategic priorities of the company, so they need to start about thinking where do they want to be as a company in the next three years? That will then determine not just their business strategy, but also their analytics and data strategy. Second, they need to identify what are the roadblocks they need to take into account to be able to define a better data strategy. It could be technology roadblocks, it could be business roadblocks, it could be process or assets roadblocks.

What is the role of new technology in Data Analytics?

It is critical, there is no way that we could do what we do now without the new technologies. Technology is critical in two ways. One, it enables our data scientists to do things much faster and automate a lot of the processes that they have designed, and two, technology allows them to work on the things that are important. When you automate a lot of the processes, data scientists can focus on the interpretation process. So, he or she becomes more of a business validator and help us having a deep understanding of the technology, not one technology, all the technologies, because none of our clients are using one technology. And so technology is important. We use it to the advantage of our clients, but it's equally very complex for our clients to navigate. And therefore, they need our help to simplify how they can invest information.

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