Improving the Data Stewardship Experience (DSX): Productive Motivational Strategies for Data Governance
ABSTRACT: Data Stewardship Experience strategies can meet several needs and remove the stigma of data governance as a rigid and bureaucratic gatekeeping discipline.
In my previous blog, I discussed the importance and challenges of developing a robust organizational architecture for data governance (a.k.a. "governance architecture"). In this blog, I will provide tips to improve the motivational dimension of data governance architecture, specifically, how to create a productive and rewarding experience for data stewards. How can we help these folks?
Data Stewardship Experience: What and Why
As products and services become more digitized and data-driven, enterprises embrace the concept of the data marketplace to manage and consume data assets. Because data consumers are in essence customers, we can apply the lessons from the Customer Experience (CX) discipline to data stewardship to improve data products and services. With this in mind, this description of an “experience economy” by B. Joseph Pine II and James H. Gilmore offers useful context:
"An experience is as real an offering as any service and product. The transition from selling services to selling experiences will be no easier than the last great shift from the industrial to the service economy. Unless companies want to be in a commoditized business, they will be compelled to upgrade their offerings to the next stage of economic value."
Data Stewardship Experience, or DSX, seeks to improve the productivity of the data stewards as they face the challenges—including a lack of incentives and over-reliance on heroics—defined in my last blog. To improve their experience, DSX needs to optimize the cognitive, affective, social, and behavioral stimulants during data handling and usage activities.
DSX goes farther than the traditional customer experience. While traditional CX focuses on the consumer, DSX must support a data marketplace that operates as a multi-sided platform (a la eBay, Amazon, and Uber) in which both the managers and consumers of data play the role of steward. A DSX initiative should enhance the experience on both sides.
Treating data like a product takes a step in the right direction. The "data as a product" architecture patterns and methods (e.g., data mesh, data fabric, and DataOps) are quickly gaining acceptance as leading practices. However, most enterprises do not have a formal market and incentive system to promote seamless exchange among data producers and consumers.
Hence, DSX is crucial to stimulate cognitive evaluation, instigate memorable involvement, and inspire altruistic action. It also can amplify one's self-expression and values through interactions with others in the enterprise data marketplace.
DSX Categories of Leading Practices
Below is a sampling of leading practices that enhance DSX, categorized by their appeal to personal growth, community, societal contribution, and disruptive innovation goals. These categories address several levels of cognitive, social, and psychological elements in Maslow's hierarchy of needs. They can motivate professionals to become productive data stewards and remove the stigma of data governance as a rigid and bureaucratic gatekeeping discipline over time.
Figure 1: DSX and Maslow's Hierarchy of Needs
Let’s explore the four categories of DSX: personal growth, community, societal contribution, and innovation.
Professionals appreciate being able to grow and develop self-mastery. A clear career path, challenging projects, and rotation opportunities are keys to attracting high-potential talent and retaining a resilient workforce. These are essential success factors for the dynamic, ambiguous, and demanding field of data stewardship.
The European Open Science Cloud (EOSC) Association was founded with a mission to accelerate the creation of new knowledge, inspire education, spur innovation, and promote accessibility and transparency. As part of that, the EOSC Data stewardship curricula and career paths Task Force develops roles, core activities, specializations, competency profiles, and levels of training for data stewards.
Equifax's Data and Analytics division offers solutions to help businesses and consumers minimize financial risk and better manage their financial health. It offers a Rotational Program for admitted data professionals (producers or consumers) to rotate through 3 to 4 different areas over two years. They can work with data quality, governance, and product validation projects while receiving accelerated, immersive, and in-depth learning about Equifax products, customers, and business.
Some organizations lean on social motivators – social nudges, support groups, and professional networking – to invigorate participation and retention of data stewards and sustain accountability and excellence in data governance. Components include data literacy training & apprenticeship, best practice sharing, and forums to enhance communication, coordination, and cooperation across domains and industries.
T-Mobile builds data-driven culture through its Data Intelligence Program. For example, its EDGe (Enterprise Data Governance) Portal serves multiple communication and support channels. The channels include a stewardship council, enterprise-wide demos, office hours, Slack, newsletter, catalog usability testing groups, and a social mixer called "Data and Doritos."
The EDM Council is a cross-industry community dedicated to elevating data management as a business and operational priority. Started in 2005 by a group of banks and data management vendors, today it has 300+ worldwide members, including other industries. As part of its contributions, EDM has produced DCAM (Data Management Capability Assessment Model) and FIBO (Financial Industry Business Ontology), a standard data model for the banking industry.
One of the strongest motivators for contemporary professionals is the opportunity to leave a lasting legacy of service and charity to fellow humanity and the planet. For that reason, some world-class enterprises rely on and invest in data governance as part of their Corporate Social Responsibility (CSR) or Environmental Social Governance (ESG) programs.
PepsiCo's ESG Data Governance program seeks to maintain data integrity while producing consistent, comparable, and reliable ESG disclosures. To that end, a dedicated team within PepsiCo's Sustainability Office manages the data governance structure underpinning each sustainability goal while driving teams' accountability. In addition, they work closely with partners across the company to gather information according to an established methodology and governance process.
As a global data-driven logistics solutions provider, the Maersk Data Ethics program emphasizes transparency, respect, security, and innovation in managing data storage and use, to avoid abuse and privacy infringement. Maersk's data ethics policy covers the human right to privacy, the use of artificial intelligence, and confidential data handling. Their cross-functionally anchored governance ensures technologies and data are used appropriately while staying compliant with regulations and adhering to high ethical standards.
Effective data governance serves as a foundation to enable metadata-driven automation and control enforcement on data and analytics platforms. Data stewardship can unlock the exponential value of metadata by operating in a synergistic, results-driven, and inclusive manner to capitalize on the collective ingenuity of the enterprise. In turn, their teams become excited to contribute to data stewardship initiatives. Some data offices demonstrate commitment to disrupting through innovation.
The Humana Data Exchange (HDX) is an interoperability platform accelerating innovation in healthcare with products like the API Explorer and Synthetic Data. It provides well-governed, secure, and anonymized data assets to software developers, data scientists, and product team stakeholders in the healthcare industry for free to promote innovative collaboration. The API formats conform to FHIR, a standard for information exchange of health data. In addition, the data dictionary provides sample datasets containing up to 100 synthetically-derived individual records to the public.
- Bayer AG's "Data as an Asset" program analyzed available data catalogs on the market. They found none complying with their data-centricity requirements and FAIR and Linked data principles. As a result, they decided to create Corporate Linked Data (COLID), a technical solution for corporate environments that provides a metadata repository for corporate assets based on semantic models. The COLID software is used across all Bayer divisions and is now an open-source project.
Figure 2: Summary of DSX Leading Practices and Examples
Modern enterprises aspiring to thrive on information assets and the data marketplace need solid data governance based on highly productive data stewardship. With effective DSX strategies, your data stewards can feel rewarded and valued, less likely to experience burn-out, and more strategically aligned via strong partnerships across technology and business teams. The four strategic categories of DSX strategy include community, personal growth, societal contribution, and innovation. But DSX strategy must also take into account the structure, size, and complexity of the enterprise, as well as risks and regulatory expectations. The next blog in this series will offer a framework for evaluating these factors.