PP&A designed and led the data fabric track of a broader brand-perception program, delivered in two phases over five months. In the first phase, the team conducted ten in-depth interviews with chief data officers and heads of data, analytics and data science at large enterprises in North America, Europe, Latin America, Asia and Australia, spanning consumer goods, retail, financial services, energy, life sciences and industrials, and mixing current customers, past customers and organizations that had never considered the client. Discussions covered how digital transformation was driven in each organization, what data fabric meant to them, where they stood on deployment, the vendor landscape and their view of the client and its offering.

An interim report preceded a second phase of ten further interviews with a new set of data and technology leaders across the United States, Europe and Asia-Pacific. This round added the client's next questions: the day-to-day role of the data leader, where they get their information, whether sustainability influences their data and procurement decisions, and how they ranked the five use-case messages the client was preparing to take to market.

The team analyzed every interview against a common framework so findings could be compared across phases, sentiment toward the client tallied rather than left anecdotal, and each positioning recommendation tied to specific evidence.

The two reports gave the client a grounded picture of a market still forming. Data leaders described data fabric as an architecture rather than a product, a multi-year effort mostly in early or mid maturity, and one whose hardest problems were governance, permissioning and cultural change rather than technology. They were building on the cloud infrastructure they already had and turning to best-of-breed specialists for the rest.

The findings on the client's own brand were direct. Only half of the first-phase participants knew it had a data fabric offering at all, and none in any depth. Favorable views were tied to specific long-standing products and a recent acquisition; unfavorable ones to price, complexity and past delivery experience. The second phase showed the same pattern: broad awareness of the company, little association with data fabric, and use-case messages that ranked well when they addressed integration and data silos and poorly when they leaned on AI and machine learning language.

The reports gave the client the evidence to reset its data fabric positioning around the messages that tested best, to hold back sustainability as a lead message, and to plan targeted re-engagement of the enterprises where its brand was recognized but not considered.

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