Three Keys to Remaining Competitive in the Data Analytics Arms Race

Leading organizations have long recognized the importance of having a data-driven analytics-excellence strategy as a competitive differentiator. However, in the context of growing market pressures, effective use of data analytics is arguably now becoming table stakes.

Many companies are operating in an environment of sluggish growth and significantly compressed margins and where the competition is increasingly leaner, smarter and more agile. Regulatory expectations are placing a further strain on revenues and costs, and increasing operational demands. Against this backdrop, it is imperative for organizations to be able to access, serve, and delight valued customers through enhanced customer targeting enabled by advanced analytics.

In this challenging environment, non-traditional dynamic startups are cropping up in multiple sectors and vying for the same "sweet spot" segments pursued by traditional players. In a growingly saturated market, customers are inundated with digital messaging and are highly selective about who they trust, what they respond to, and the level of service they expect. This creates an even stronger need for traditional players to reimagine their processes and even the fundamental value proposition to capture and retain the most valuable customers.

Through our cross-sector study of 80 organizations, we have found that innovators who have successfully deployed data analytics to achieve significant uplift share three characteristics in common.

Paul Mee, Partner, describes the importance of an integrated data analytics operating model.

First, many leading organizations (44% of surveyed organizations) have transitioned towards an integrated data analytics operating model, with a two-tier cross-enterprise arrangement. At the center, a Data Analytics Center of Excellence (CoE) enables enterprise-wide analytics excellence and acts as data broker to source and curate internal and external data. Local analytical teams sit close to business functions and perform analyses using sophisticated best-in-class tools; typically tweaked or refactored by CoE Data Scientists. At a large industrial transportation organization, an enterprise-wide framework was established to drive experimentation and rapid learning in manner where innovation and the associate uplift can be realized in many places across the enterprise.

Second, organizations who have achieved the most notable uplift have invested in more extensive data analytics, moving to the next frontier of data analytics applications. 68% of surveyed organizations are using data analytics beyond marketing, sales and customer targeting, for optimizing operational processes, resource or inventory management, and risk mitigation. For example, a large hotel operator increased annual booking revenues by $40 million through dynamic room price optimization that uses real-time data on local events, weather, seasonality, and inventory levels.

Third, at a number of leading organizations the balance of IT resources and investments deployed has shifted dramatically from more traditional transaction processing, data services, and infrastructure provisioning towards shared, democratized analytics capabilities. As one study participant remarked “our aim is to have high-end analytical capabilities used as commonly in the future as Excel has been in the past”. For one major leading financial service institution, an ambition to enable a more cognitive enterprise has resulted in an aggressive rebasing and outsourcing of IT applications and infrastructure that don’t provide competitive differentiation or customer value-add.


More and more successful organizations are adopting data-driven principles, capabilities, and a growing body of their own analytical talent to remain competitive or to extend their lead in the race. For others, data mastery and the deployment of modern analytical capabilities and advanced methods will be an imperative to keep up.