Many organizations are undergoing a significant shift as they actively explore new options for differentiation and creating new revenue streams. This may mean monetizing shareable data, acquiring entities that hold key data points and establishing partnerships that give organizations a deeper understanding of their customers.

Organizations view this as an area of accelerated growth and a source of revenue as the value of data exchange continues to rise and companies are increasingly buying data.

The monetization of data assets and the consumption of third-party data have become critical parts of leading organizations’ business strategies as digital transformation increases the amount of data generated and a growing number of companies seek to combine data with their existing offerings to add value and provide personalized experiences to their customers. As a result, the Data-as-a-Service (DaaS) market is poised for growth as spending is expected to continue to rise and most organizations look to increase their list of third-party data providers. This trend will increase the importance of data marketplaces and lead to the consolidation of DaaS providers.

Data-as-a-Service (DaaS): A service provider that enables data access on demand to users regardless of their geographic location. Also called DaaS, data services are similar to Software as a Service (SaaS) in that the information is stored in the cloud and is accessible by a wide range of systems and devices.

As these trends and new business models take shape, it’s also important to think carefully about what data is being shared, how it’s being shared and the context in which it will be used. Along with the emergence of new business models is the rise of big data and artificial intelligence technologies that require different approaches to data governance and how data will be managed in the future.

89% of businesses are facing challenges managing data

- 2019 Global data management research - Experian  

Data trust creates a competitive advantage

Part of the careful consideration that needs to go into these new models is the need to earn consumers’ trust by giving them assurance of privacy, enhanced transparency, consent-driven data sharing and ethical use of their data. Today, consumers find themselves in a world where almost everything they do either is being or can be recorded. While this might be a welcoming thought to some parts of an organization, it’s fast becoming a concern to many others. In recent times, it’s not hard to find a breaking story about the misuse of data relating to a consumer that resulted in not only large fines but something even greater: reputational risk.

And while some might argue that in a certain number of these cases, the concern that brands will lose significant support of their customers; the reality instead, seems to be proving otherwise. But what if organizations thought about this scenario differently? What if consumers realize they’re getting the same service from two competitive brands with one seemingly small difference: only one has a framework that makes their customers feel their information is protected, treated with respect and used to their benefit.

Data trust is long past being a great idea. In order to position themselves for the future, many organizations are quickly realizing that building an actionable framework for data trust is crucial. So the question today isn’t a matter of what to do or when to do it but of how to go about building that framework.

Only 25% of customers believe their data is handled responsibly

- PwC US Survey, 2017  

How to build a data-trusted organization

Leading organizations will need to develop an internal culture based on a clear organizational purpose and objectives for building data trust. This will require alignment of all internal and external stakeholders, a data strategy reinforced by data ethics code, a data and data-use governance and management framework that supports data capabilities.

Organizations will also need a value realization framework that tracks the business benefits of intangible elements, such as data trust and ethics, in a sustainable manner. In the near future, there is a strong possibility for adding data as a strategic asset to external reporting and adding it to the corporate social responsibility agenda. Key elements of building a data-trusted organization include:

Data strategy

A data strategy should define clear roles and responsibilities for executing it and managing and mitigating increased data risks. Where possible, consider the creation of a chief data officer role assigned to someone responsible for coordinating these activities across the organization. Align key data stakeholders to principles that support the strategy. The strategy should integrate key governance structures, policies, standards and procedures for privacy, data use and data security governance.

Data use ethical framework

Implementing a data ethics framework with oversight by a board subcommittee. Align the framework to both public and regulatory policy requirements. The data ethics framework will ensure the use of data to benefit both consumers and the organization, resulting in a fair exchange; a culture of continuous improvement and data that’s fit for use; insights that are sustainable over time; transparent and inclusive data sharing and use; and multi-stakeholder benefit and risk assessments of data use from all interested parties.

Data-use governance

A data-use governance framework should address who governs decisions on permitted uses of data. It should include an appropriate governance structure, escalation and decision-making paths, roles and responsibilities and data principles, policies and rules to help organizations make effective decisions on data acquisition, processing, storage, access, sharing and, most importantly, use. Consider standardizing and integrating data governance practices across the organization and explore process automation to reduce cost.

Data management

More mature data management capabilities will increase the organization’s ability to innovate, monetize data and implement new business models with agility while protecting customers’ and partners’ data.

Customer experience design and testing

Embed data design principles, such as data privacy and integrity considerations, in mapping out customer experiences. Where applicable, conduct market testing to evaluate different alternatives or customer consultations to inform decisions or design. Where informed by advanced analytics models, consider risks such as bias, interpretability and explainability.

Data trust culture and capabilities

Addressing organizational culture and capability challenges will go a long way in developing data-use governance that aligns to the organization’s ethical framework. A concerted effort is required to change organizational culture to support data trust and minimize data-related risks. Other challenges include the need for stakeholder alignment and leadership, as there can be a conflict of interest between stakeholders that can inhibit the adoption of a fair exchange of data and data ethics.

By designing a framework that incorporates the six key considerations above, organizations are not only able to manage their regulatory responsibilities around data and data use but they can also set a robust foundation for enhanced customer experiences, improved operations and dynamic business and revenue growth. It’s all about moving organizational culture away from a restricted mindset that focuses on limitations toward one of growth that seeks to innovate and proactively respond to changes in the market.