Data trust pulse survey

Monetizing and trusting data: 2019 is the year to seize the prize

The time to hesitate is over. The race is on: 86% of businesses say 2019 is the year in which they will race to extract value from data. In a sign of determination and optimism, 88% say—in the same PwC pulse survey of 300 executives at US companies with revenues of $500 million or more—that in 2019 they have the potential to pull ahead of their rivals in this race.

What will separate the winners from the rest of the pack? In PwC’s 22nd Annual Global CEO survey, 94% consider data on customer and client preferences/needs as critical or important, but only 15% actually have comprehensive data in this area. Having customer data that feeds new products, new and improved experiences, and new revenue models is how winners will distance themselves from the pack.

There are numerous obstacles to monetizing data: quality, standardization, and security; data-related talent and technology limitations; and, increasingly, regulatory uncertainties.

The solution is data trust - a robust approach that builds organizational trust in data and its uses, while enabling rapid, risk-based decisions about using data to create value.

The data that businesses value most

Consumer data is what companies value most, PwC’s pulse survey reveals. Executives particularly value data on consumer preferences, current and predicted—because of new product and marketing opportunities that data provides. Data can, for example, enable retailers to target promotions and products to individual consumers, as well to deploy AI to game out new go-to-market strategies.

Business customer data is also highly valued, but data on threats to one’s own business is relatively low on the list. That may be understandable for executives focused on monetizing data. But it could also be a missed opportunity to better address business risks that undermine trust in data and its uses.

Value from the data you already have

Over three fourths of survey respondents have crunched the numbers to see how much they can gain simply from using the data that they already have.

These companies see the potential to slash, on average, total annual costs by a third and to increase incremental revenue by over 30%. Add those two numbers together, and the motive for the race to monetize data is clear: it could vastly boost the bottom line.

Many companies are applying artificial intelligence to data in order to monitor manufacturing, increase predictability, reduce delays and defects, and spot maintenance problems before they occur. Entertainment and media firms have turned to their vaults of data to create more personalized content, which generates new revenue.

Two billion dollars annually would be the average revenue boost to Fortune 1000 companies from increasing data’s usability by just 10%, according to research at the University of Texas.

When PwC surveyed companies what will be the most critical way to acquire or create their most valuable type of data, the top answer was they plan to build that data themselves.

That follows from companies’ realization about the value of the data they already have—and the potential magnifying effect of artificial intelligence.

Companies also plan to gather more data with customers’ consent, while, external collaboration—whether through brokers, exchanges, or joint ventures—will continue to provide value.

A healthcare information exchange, for example, accelerates R&D through access to clinical, molecular, and real world data from outside sources: hospitals, clinics, retail screening sites, community healthcare extenders, remote monitoring devices, and more.

Obstacles to monetizing data: they all come down to trust

Why can’t companies monetize data? Again and again, the same six obstacles emerge:

All six obstacles have to do with trust: trust in the data itself, trust in your talent and technology, and trust in your ability to be credible in the eyes of regulators and in the court of public opinion. That’s why all six obstacles have one solution: developing and optimizing your organization's data trust solutions.

  1. Unreliability. Much of a company’s historic data, acquired haphazardly, may lack the detail and demonstrable accuracy needed for use with AI and other advanced automation.
  2. Fast evolving regulations: Compliance challenges are mounting, in multiple jurisdictions. Forty-two percent of US CEOs cited cyber and privacy policy as a trigger for significant changes in their strategy and/or business plans over the next three years. In June 2019, data governance will come up at the Osaka G20 meeting, guaranteeing it further attention.
  3. Information insecurity: From capture through enrichment, maintenance, usage, publication, archiving, and purging, the entire data lifecycle must be secured against both malicious actors and inadvertent violations of privacy rights.
  4. Silos: If different data standards and rules keep your different functions and business lines from sharing data and creating synergies, you won’t maximize its value.
  5. Lack of talent: AI and other automation can do more with less manpower, but you still need scientists and SMEs who know the basics of data science.
  6. Outdated tech: Spreadsheets can only go so far. You need the right technology—often powered by AI—to make sense of your data.

Data Trust: the path to win the race

These six obstacles can cause even the most ambitious to slow down. Without certainty that they can trust their data and data use cases, leaders fear risks—of inaccuracy, non-compliance, insecurity, or simple ineffectiveness—and do far less than they could. They face another risk: falling behind.

Strong data trust solutions also enable you to extract value from data in a way that boosts its reliability. Here’s how to start:

Assess it

Identify where the data is from, who has access to it, what obligations it carries, and how accurate it is. Standardize data formats, labels, and processes across functions and business lines. Deploy new technologies, including AI, as needed.


Strategize it

Build a strategic framework to assess the risks of each data use case, including compliance requirements and the impact on market expectations. With a proven framework for risk-based decisions, you can quickly decide whether and how to monetize data while enhancing trust among all your stakeholders.

Operationalize it

Establish security and privacy protections and update IT systems to comply with regulations as they evolve, adjust security and privacy approaches to the value of data, and ensure ethical data use. Watch for how new technologies and new data-use cases make new kinds of data sensitive: AI can often infer individually personally identifiable information or corporate-impacting strategies from apparently unrelated data points. 

The result

Businesses will get a head start by maximizing data’s power and trustworthiness. Risk officers and business leads will accurately and quickly balance benefits and risks of each use case.

Contact us

Mir Kashifuddin

Partner, PwC US

Jay Cline

US Privacy Leader, Principal, PwC US

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