Data Stewardship and Governance in a Connected World

Editor’s note: This is Part 4 of a five-part series exploring the critical role of data in a digital supply chain transformation.

You can read Part 1 by clicking on this link
You can read Part 2 by clicking on this link.
You can read Part 3 by clicking on this link.
You can read Part 5 by clicking on this link.

All businesses today are operating with ever-increasing amounts of data. Leading enterprises use this data to provide advantage to themselves and their customers. But there is a need for business leaders to add to their focus an emerging management imperative, demonstrating values-based data stewardship and governance.

Data-driven enterprises gather and manage volumes of data. Increasingly customers, and society, are judging your brand on your stewardship of this data. Good stewardship requires well thought out data governance to understand the data in your possession and control how the data is accessed and used. In the connected world we live in, this data comes from both internal and external networks in ways that can challenge corporate policies once believed to be reasonable and responsible. For example, attitudes and norms around the world about “data ownership” in commercial activities are being recast. Subjects of the data vs holders of the data are asserting providence.

In previous articles in this series, we discussed that the Supply Chain is no longer a back-office function and needs to be customer facing and data driven. We also wrote extensively about new leadership skills that are required in part 3 of this series, but it is important to note, once again, that only 37% of executives report that their companies have become data driven. To become data enterprises, companies need to elevate data and AI literacy of all their leaders and professionals. This need is acute if companies want to be afforded the freedom to operate their business in innovative and flexible ways.

In a data-driven company, an enterprise data model is a necessity as it enables a corporation to understand what data is required to run their business and who “owns” each data set. But that is by no means a comprehensive inventory of the data being used or available within the corporation. As Artificial Intelligence and Machine Learning is increasingly being integrated into decision making, those models are not always well inventoried, and the data inputs and outputs are frequently part of a black box. Add to that the exchange of data with your suppliers to gain transparency across your supply chain network and data governance becomes challenging indeed.

Consequently, there is a pressing need to establish data values and data policies that enable both external trust and internal efficiency. Linked to that, there is a management need to understand the data you have and who inside your organization has the responsibility to demonstrate responsible handling and use of the data. However, data today no longer stops at the enterprise boundary. It is shared amongst customers, retailers, suppliers, and even competitors. Business leaders must understand how your data is being used beyond the enterprise walls, who has access and how access is being controlled. For example, is your supplier free to share your data with your competitor. Just like other areas of your business, you need to understand and control both benefits and risks.

Some of the key questions you should consider:
• Do you have a data inventory internally and know what data you access externally?
• Do you have an Enterprise Data Model where data owners are identified, and data integrity and privacy are a priority?
• How is your data being shared outside the enterprise, who has access and how is access controlled? What data is being accessed by your company in external networks and how is data integrity assured?
• Have you updated your supplier contracts, compliance audits and RFPs to include your “data values”?
• As a good data steward, what are your responsibilities and how do you build internal and external trust?
• What controls do you have in place to ensure that only authorized data is shared?
• And most importantly, how do you ensure that data is used in ways that create value, earns trust, and builds a positive brand image amongst your many stakeholders?

To understand how brand image is affected by data trust, just consider Facebook and American Express. Both are excellent data citizens, have robust data policies and an organization to monitor and access risk. Yet their brand perception for data trust is very different.

There is an implied data contract between the corporation and its supplier network and customers. What is that contract with your business and how will data integrity and privacy be maintained?

Many corporations are aggressively stepping up to data governance challenges and we are seeing the emergence of organizational structures designed to address the issues. For example, Mastercard has a Chief Data Officer, American Express has named a Chief Privacy Counsel and Data Strategy Leader and Walmart has a Chief Counsel, Digital Citizenship. They all recognize the tight linkage between data stewardship, brand, and privacy.

Recognizing these complex and leading-edge challenges, in 2019, The Center for Global Enterprise began conversations with CEOs across industry about supply chain related items such as:

• Due Diligence Framework for Responsible Data Enterprises: The framework would provide guidance on, for example, aspects of data risk, rights and control, algorithmic safety, regulatory impact, and aspects of ESG that bear on data and AI-centric business models.
• Customer Data, Consent & Trust: Companies are increasingly seeking first-party data. This requires consumer consent. Yet there is no clear understanding of what consumers value in giving consent, the best way for companies to seek and gain consent and the overall value exchange between consumers and the value companies seek to provide them.
• Predictive Supply Chains: Increasingly, supply chains are becoming powerful sources of data, which can power predictive models to help companies more accurately forecast supply and demand, reposition elements of the supply chain to strengthen resiliency and provide a more precise understanding of market dynamics. There is an opportunity to facilitate best practice sharing in data, AI, and business process design to enable predictive supply chains.

These items and other work will enable supply chains and their leaders to become increasingly data driven and transparent. Data governance and stewardship are critical to the future success of any business and complex data management challenges must be addressed. Like many fundamental changes, those that understand what it means to be data-driven and a good data steward will be best positioned to succeed.

About the author: Christopher G. Caine is President of the Center for Global Enterprise, a New York-based non-profit organization dedicated to the study of the contemporary corporation in the era of global economic integration. He is also President & CEO of Mercator XXI, a professional services firm helping clients engage the global economy.

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