Developing data champions

According to recent partnered survey conducted by EXL and Forbes Insights, the gap between businesses that have strong data maturity compared to those who don’t is widening faster than ever. The survey of 200 C-suite leaders found that companies with strong data maturity, referred to as data leaders, were reporting increased customer satisfaction, faster time-to-market, a faster pace of innovation, improved employee experience, and better productivity in comparison to their peers.

Beyond this, it seems the performance gap is widening at an alarming rate. The survey revealed that approximately 46% of leaders were heavy users of cloud services and platforms in comparison to 17% of all others, and far ahead in integrating internal and third-party data, leveraging advanced analytics, and adopting AI and machine learning as well.

A proven methodology to assess data, analytics, and AI maturity

The lack of awareness or inability to measure the maturity of enterprise-level data and analytics functions is hindering the development of strong data leadership and management of AI for enterprises.

The good news is that it’s possible to move from laggard to champion swiftly to drive growth and gain a competitive edge in this data-driven world. EXL has developed a methodology that covers the lifecycle of data in organizations in order to holistically evaluate their data, analytics, and AI maturity. This methodology is operationalized via a comprehensive assessment survey. This assessment evaluates the organizations across the three pillars: organizational focus, data and data management, and analytics and AI.

The three pillars are explained below. They are further divided into ten critical assessment areas within the survey. Each assessment area is measured through a set of questions which are rated on a scale of one-to-five and the output is a maturity index on a scale of one to 100.

Pillar one: Organizational focus

Leadership is critical in setting the vision, strategy, and direction for data, analytics, and AI initiatives within the organization. The first pillar focuses on the organizational culture and leadership’s commitment to becoming data-driven. A data-driven culture requires cross-functional collaboration, breaking down silos and encouraging different teams to share data, insights, and expertise. When employees across the organization embrace a data-driven culture, data literacy and analytical skills improve and the organization becomes more proficient in extracting insights and deriving value from its data assets

Pillar two: Data and data management

The second pillar addresses the foundational aspect of data management that enables effective data utilization by ensuring reliability, accessibility, quality, governance, scalability, and agility. It serves as the primary structure that connects an organization’s data-driven culture with the application of analytics and AI for business success. It broadly focuses on creating comprehensive data coverage and data infrastructure, enhancing data quality, and establishing robust data governance.

Pillar three: Analytics and AI

The third pillar delves into the organization’s capability to derive insights and drive actions from data through the application of analytics and AI across business functions. It unlocks the full potential of the organization’s data assets, creating a multiplier effect to enhance the overall performance. With advanced analytics and AI techniques, organizations can gain deeper insights, use predictive analytics to enhance decisions, optimize processes, personalize experiences, manage risks, and foster innovation.

Driving growth and gaining a competitive edge with data maturity

Our proven methodology is empowering organizations to identify strengths and areas for improvement and helping them to make informed decisions and leverage their data assets effectively to gain a competitive edge in today’s data-centric business landscape. By evaluating our three-pillar assessment areas, businesses can gain valuable insights into their strengths and weaknesses and chart a path toward data-driven excellence.