Data analytics-enabled business transformation is no longer a ‘futuristic’ conversation—it is here and now. And if you are still thinking about making investments in this area, it is safe to say that you are behind your competitors. Ninety percent of C-suite responders to the EY-Forbes Insights Data Analytics Survey (India) 2017 affirmed they have already made investments in data analytics, and will continue to do so in the near future.

Data analytics efforts have primarily been focussed on improving customer profitability (whom to cross-sell, what to cross-sell, when to cross-sell, how to cross-sell, how to retain); mitigating risk (improving customer underwriting and collection effectiveness); and improving operations efficiency (supply chain optimization, inventory management, demand forecasting and planning). However, as organizations push the envelope on improving return on investment (RoI) from their analytics initiatives, we are seeing a greater thrust on expanding the breadth of analytics across hitherto-untouched business functions.

Future of HR is less “human" and more “algorithmic": Human resource functions in leading firms are leveraging customized, algorithm-driven resume screening to shortlist candidates for interviews—for instance, on the basis of the likelihood of a candidate spending a long time with the firm. They have also put in place predictive models to identify employees who are getting disengaged with work and are at risk of attrition. Similar efforts are being made in the area of talent management.

Internal auditors harnessing the power of analytics: Auditors are fast moving away from the tried and tested sampling approach to monitor 100% of the transactions using the power of algorithms to identify exceptions or anomalies. They are also putting in place analytics solutions for continuous risk monitoring to further strengthen risk management.

Finance function becomes analytics savvy: Chief financial officers (CFOs) are shining examples across business functions of the innovative usage of data analytics in identifying opportunities for tax saving, faster book closing, and controlling travel and other miscellaneous expenses.

Tech function moving from facilitating analytics to consuming it: Early warning signals for potential application outage have been developed by analysing correlations between multiple metrics on historical application utilization and other outage patterns. Real-time visualization of key system health metrics provides tech management with greater visibility on how critical systems are performing.

However, for all the talk of transformational power of data analytics, there is an undercurrent of scepticism developing among the C-suite on the likely returns on their investments in data analytics. This was reinforced by the findings of the EY-Forbes survey cited above in which we observed an underwhelming 26% of respondents ‘strongly agreeing’ and only 39% of respondents ‘moderately agreeing’ that data analytics has created a noticeable shift in their company’s ability to meet competitive challenges. What is causing this dichotomy where, on the one hand, the C-suite is all gung-ho about investing in data analytics but, on the other hand, there appears to be a disconnect in promise and pay-off of analytics investments?

Organizations at the start of their analytics journey typically look outside for inspirations and hence end up prioritizing analytics solutions that have worked well in their industry.

I see this as a combination of a multitude of factors, the top three being the following:

Data analytics teams functioning as business intelligence (BI) teams in the absence of a strong governance mechanism and clear demarcation of roles: If you’re wondering what’s the big difference between the two, here’s a definition that I find very relevant: business intelligence (BI) is needed to run the business while analytics is needed to change the business. BI is focused on creating operational efficiency by enabling individuals to access data for effectively performing their job functions. Analytics, on the other hand, is about applying advanced statistical techniques to predict what the best decision for the future is. Thus, while both BI and analytics are absolutely critical, organizations need to have strong governance around the roles for these two teams.

Organization focus is skewed towards analytics solution “development" at the risk of losing sight of analytics solution “consumption": I have had multiple informal C-suite conversations wherein the talk of analytics investments revolves around how they are investing in hiring data scientists or purchasing new analytics tools or building up a data lake or building models in machine learning/deep learning. But seldom do I hear the mention of ‘change management’ to ensure that the organization’s processes are being updated to embed analytics models and the business managers are adapting to the newer way of decision-making. The success of an analytics-driven enterprise depends on enabling business managers to understand the ‘art of the possible using analytics’ and investing in training them to help them become better analytics consumers.

What has worked for your competitors might not work for you: Organizations at the start of their analytics journey typically look outside for inspirations and hence end up prioritizing analytics solutions that have worked well in their industry. While a safe approach since these are proven ideas, it is critical to prioritize ideas for analytics solution development for your own business after considering (a) your business priorities, (b) your current state of data architecture and (c) your downstream systems’ capability to embed those analytics solutions for day-to-day decision making. Once prioritized, on the basis of the above three factors, organizations should look at short development cycles instead of waiting for months to launch the perfect solution.

As I look ahead, I see a huge opportunity for businesses to enhance the RoI from analytics investments by focusing on change management and training initiatives to transform their business leaders into “analytics-savvy business leaders", thus helping embed analytics into decision-making. This will help the organizations to also start thinking ‘big’ and ‘disruptive’ as they redirect their analytics solution development efforts from improving core operations to launching entirely new business models.

Jasjeet Singh is a partner with EY and leads the financial services analytics practice in India.