8 actions to improve data and analytics and fuel business growth

4 minute read 25 Mar 2022
Authors
Louise C. Keely

EY-Parthenon Principal, Strategy and Transactions, Ernst & Young LLP

Strategist. Data scientist. Economist. Focused on consumer demand and channels.

Traci Gusher

EY Americas Data and Analytics Leader

Uses advanced analytics and artificial intelligence to bring value to clients’ challenging issues and opportunities. Experienced triathlete. Passionate about animal rescue and cancer research.

4 minute read 25 Mar 2022

Separately resourced data and analytics functions can translate information into insights that drive corporate strategy and long-term value.

The secret to business transformation may be buried in the avalanche of data that companies gather. Strong, separately resourced data and analytics functions can translate information into insights that drive corporate strategy and long-term value. 

“If you’re not building an operating model around data and analytics, you are falling behind your competition,” Louise Keely, EY-Parthenon Principal at Ernst & Young LLP, says.

Elevating analytics to gain a competitive edge

Most companies use some form of analytics to examine customer, distribution and production information, as well as other aspects of the business. Few, however, capture the full potential of analytics as a transformational platform where:

  • Analytics are available on demand and can be dynamically utilized to make real-time business decisions
  • Integrated strategic, operational and financial forecasting and planning is enabled by internal and external data with advanced analytics and machine learning driving predictions
  • The organization makes a long-term investment in artificial intelligence (AI) that includes leveraging an ecosystem of partners in both technology and data providers

How to accelerate the transformation journey

Building the right capabilities and operating model to maximize the value of data and analytics functions can take some time. These leading practices can help them through the journey:

  1. Create a target operating model that separates data and analytics functions as distinct but closely connected organizational groups. Too often we hear “data analytics” or “data and analytics” as a single concept.
  2. Treat data as a strategic function with capabilities, from data strategy and governance through enablement and distribution. Make sure the data is high quality, carefully governed and nurtured.
  3. Build an external data strategy and data supply chain that joins external and internal data for more accurate predictive analytical models.
  4. Integrate new insights into existing systems and processes, rather than creating entirely separate user interfaces or unique reporting.
  5. Prioritize use cases for data and analytics based on strategic value, complexity and process impact readiness.
  6. Support the data and analytics teams with other corporate functions, including IT for technology and engineering, human resources for talent, and legal for data privacy and other regulatory compliance.  
  7. Utilize analytics in conjunction with traditional KPIs in order to support the company’s long-term value strategy.
  8. Partner with or acquire organizations to help provide capabilities when building them internally is too time consuming or expensive.

Case study example

A global human resources consulting firm was exploring ways to derive value from their data and sought to invest in building out a data and analytics strategy. EY professionals helped the firm determine high-priority use cases for analytics, recommended data sources to support them, enabled integrated data stores with both internal and external data and executed analytics pilots to get started. The pilots reduced key issues on initial use cases and the organization has a roadmap to leverage analytics to drive 12% in EBITDA growth.

What the destination looks like

Well-developed data and analytics functions can provide measurable results across industries. Examples include:

  • Improvements in near-term business performance, including those related to manufacturing optimization, predictive inventory management, and distribution scenario modeling in industries such as life sciences, automotive and oil and gas
  • Reduction in costs related to customer support by leveraging conversational AI and voice analytics across financial services, consumer products, retail and healthcare
  • A revenue stream of its own, such as when retailers sell data to third-party advertisers — including, first and foremost, their product suppliers
  • An insightful guide to long-term business decisions, such as new businesses to enter, M&A opportunities and portfolio optimization across industries

“Analytics is the closest thing CEOs will ever have to a crystal ball. But you need dedicated resources across the business and good data that you can rely on,” says Traci Gusher, EY Americas Data and Analytics Leader.

Summary

To maximize the value of data analytics, CEOs can follow leading practices to build the right operating model and capabilities. Data — both internal and external — should be of high quality, carefully governed and nurtured. Using AI and machine learning can improve predictive insights based on this data. Having separate but closely coordinated data and analytics functions can improve business value.

About this article

Authors
Louise C. Keely

EY-Parthenon Principal, Strategy and Transactions, Ernst & Young LLP

Strategist. Data scientist. Economist. Focused on consumer demand and channels.

Traci Gusher

EY Americas Data and Analytics Leader

Uses advanced analytics and artificial intelligence to bring value to clients’ challenging issues and opportunities. Experienced triathlete. Passionate about animal rescue and cancer research.