Evolving the data analytics operating model

Trends driving data analytics operating models

Fifty-nine percent of respondents to Deloitte’s Global CIO Survey identified artificial intelligence and machine learning (AI/ML) as their organization’s top investment area. Cloud-based data platforms and analytics-as-a-service have made it easier and more cost-efficient to access analytics technologies through flexible consumption models. Let’s take a closer look at the trends driving organizations’ increasing need for advanced analytics and a new data analytics organizational structure:

• Data growth and proliferation: Rapid digitization of processes, the physical-digital-physical loop of data, and digital exhaust from intelligent products are creating high volumes of siloed data. Combing through it enables companies to develop more targeted products and services, enhance feature sets, offer rich customer service, and more.
• Cloud and flexible consumption: Cloud computing is emerging as a force multiplier in the data and analytics space to create more opportunities for the enterprise. Shifting from on-premises models to flexible consumption models in almost all cloud technology use cases can enable a growth agenda, improve business agility, and increase scalability.
• Future of work: Accelerating connectivity and increasingly powerful cognitive tools are changing the nature and future of work. Already, many technology teams are moving from traditional project operating models to ones that are more outcome-centric, focusing on delivering value rather than services.

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Moving toward an enhanced data analytics organizational structure

Harnessing the emerging trends of data growth and proliferation, cloud and flexible consumption, and future of work can generate rapid changes in an organizations’ structure, operations, and processes. This also enables organizations to transition toward an analytics-as-a-service model—by shifting from a product- to a services-based model that includes strategic services, functional services, support services, shared services, and infrastructure services.

The data analytics operating model should be coupled with a governance structure that spans business and IT and is focused on:

• Centralizing strategy, governance, and technology
• Optimizing use of analytics talent
• Monitoring data proliferation caused by business spinning up new cloud-based analytical tools and processes
• Alleviating cloud data and data-in-motion security concerns

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New data analytics operating model, big benefits

According to Deloitte’s Global CIO Survey, organizations are using digital technologies and capabilities to transform business operations (69 percent) and drive top-line growth through improved customer experiences.¹ Cloud-based data platforms, coupled with an analytics-as-a-service operating model, can support these objectives by delivering a host of benefits across the enterprise.

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The bottom line

The boundary between business and technology is blurring, accelerating organizations’ move toward a new data analytics operating model through cloud adoption and an evolving business-IT construct. This model is a key foundational element to help their organizations harness emerging trends, develop actionable insights, and deliver results and value more quickly to business and IT stakeholders.

As with any major change, the transition to the data analytics organizational structure of the future requires a shared vision among key leaders, early identification and engagement of the right sponsors, and setting bold yet achievable short- and long-term goals.

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Endnote

¹ Deloitte, Manifesting legacy: 2018 Global CIO Survey, August 2018