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Linking Analytics Maturity to Business Performance

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Chief Operating Officer at WNSa leading business process management (BPM) company

Organizations today operate in an environment where they must manage shocks and shifts at a pace and severity they have not encountered before. Leaders should keep their ear to the ground at all times and cover as many blind spots as possible. They know that data-driven decisions enable them to do so, but many organizations have yet to acquire these capabilities.

It’s a classic case of what we know in management language as the ‘know-do gap’. The term, coined many years ago by Stanford University professors Jeffrey Pfeffer and Robert Sutton, reflects the realities of data and analytics capabilities in organizations across industries.

Gaps remain between intention and action

A recent global data and analytics questionnaire by Forrester Consulting, commissioned by WNS, shows that 63% of decision makers recognize that data and analytics are strategic factors. However, only 37% have prioritized investment in building these capabilities. Although this gap is visible in all geographic areas, it is more pronounced in the US (30%) than in other regions. Of the six industry sectors surveyed, the largest gap was reported in manufacturing and materials organizations (40%), followed by retail (37%), life sciences (26%) and insurance (25%).

The strategic divergence we see in various industries stems from the lack of sufficient focus on any or all of the key competencies below when building a data-driven business.

  • A well-articulated data strategy with C-level commitment and budgets.
  • The availability of people who have the expertise to manage a variety of roles, starting with a chief data officer or a chief analytics officer.
  • Processes to build a strong foundation, such as data privacy, security, and governance.
  • Easy access to data across the enterprise that is clean, unified and ready for analysis.
  • The necessary technology for agility and scalability.

We believe the pandemic has further exacerbated the existing gap as organizations focused on business recovery with short-term initiatives to drive revenue growth. But there are also other reasons. Excelling in all five competencies is complex and organizations find that they have limited expertise in achieving success, regardless of their maturity level.

Towards high analytics maturity

The WNS-Forrester survey shows what organizations at different maturity levels struggle with.

Many organizations just embarking on their data and analytics journey find it difficult to define a data usage strategy. Some questions they struggle with: How are we going to use the data? How do we make money with it? What will be the key performance indicators (KPIs) to measure the success of data analytics initiatives?

Those with an intermediate maturity level have a strategy, but must constantly refine their structure and processes. Their challenges are usually at the operational level: what business problems do we solve? How do we achieve the ROI? How do we get business buy-in for an initiative?

High analytics maturity is about building a robust strategy, a top-down approach to creating a data-driven culture in the organization, having the right talent, and being agile in how use cases are prioritized for maximum business value. At the technology level, mature organizations have scalable data management platforms that can process data in large volumes and deliver repeated success.

Being high on the maturity curve translates into a direct, positive impact. McKinsey reports: that companies that are “outperformers” tend to excel in a number of key areas. These outperformers experience 15%-25% growth in earnings before interest, taxes, depreciation and amortization (EBITDA), which would otherwise have taken several years.

Lessons from Analytics Implementations

The road to analytics success is undoubtedly a smooth one. Organizations often make the mistake of focusing on technology and ignoring other aspects.

An important factor is the C-level buy-in. Since data-driven decision-making marks a shift in an organization’s culture, without a top-down approach, it is doomed to fail. When the CEO demonstrates confidence in analytics, it helps accelerate the mindset shift down to business users.

To ensure buy-in, it is also important to achieve quick wins and communicate them effectively throughout the organization. Therefore, the team needs to know how to prioritize use cases that will quickly deliver success.

Take, for example, a transformation process for insurance claims. The company wants to know which claims it should speed up, which claims it should send for investigation, or who should handle which type of claims. An analysis model can be built for each question. But that’s not a sensible approach. Prioritize a use case based on ease of implementation, implementation costs, and the ability to easily measure impact. Once success is established, create a roadmap for other potential areas of impact.

Another critical factor is to know what problems need to be solved. Organizations sometimes make the mistake of first collecting data and then thinking about problems that the data can solve. Instead, the problems to be solved should form the basis for the data they want to collect. So start with a business problem and then turn it into an analysis problem.

Businesses will do well to partner with trusted third-party service providers who offer best practices and lessons from their own experience. By doing this, they know what choices to make along the way and how to use early profits to draw a roadmap for repeated success across the enterprise.


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