What the Fudge are Data Contracts?

Introduction

In today's data-driven economy, data contracts are an essential topic for organizations across industries. The definition of data contracts may vary depending on the person defining them - a business person may have a different perspective than a technical expert. The reality of data contracts differs based on the purposes of data usage and data sharing between parties. However, there is a common framework for understanding data contracts.

It's particularly exciting for the data community that the industry has realized the importance of data when it comes to leading innovation, research development, delivering personalized customer experience, building products that generate value for consumers, and keeping the competition at bay. Retail and other companies such as 亚马逊 , 谷歌 , 沃尔玛 , Flipkart , and the founders of OpenAI have realized the value aspect of data and have been at the helm of using data to drive business decisions. It is safe to say that almost all organizations across industries now see data as a strategic asset to survive in current market conditions. The number of businesses undertaking the data digital transformation journey has only increased in the last 12 months. With such unprecedented speed, the future with a data-driven economy brings vast opportunities for innovation, including a generational shift in how humans consume and act on information. Newer generations are exposed to insights never before available to earlier generations, and this is where the pivot is happening.

Key Drivers for Data Contract

The concept of data contracts is not necessarily new; organizations have considered this idea before, and we are only now seeing actionable steps to ensure data assets are properly managed. Key drivers for data contracts can be categorized using value metrics such as Revenue, Cost, and Risk (RCR):

1. Revenue:?A key driver for data contracts as it focuses on using data to monetize insights. One example of this is leveraging customer data to personalize products and increase sales, revenue, and service offerings. Uber is an excellent example of using data to improve its business, as it uses fleet data to match drivers with shorter travel times, leading to increased revenue, higher earnings for drivers, and satisfied customers. The insurance industry is also leveraging data to drive revenue, such as building confidence with agents by ensuring accurate and consistent compensation for selling insurance bundles, leading to more incentive to write additional bundles.
2. Cost:?A dimension of data that is associated with reducing the cost of user productivity. For instance, it could include mundane tasks such as a data analyst spending time finding the right data to generate a business performance report for executives to make a business decision, or a data scientist looking for the right data to perform predictive analytics for better customer experience. Another example could be the cost associated with complying with growing regulations. Especially industries that are heavily regulated are required to comply with standards for safeguarding and protecting personal information; if not done correctly, it could result in hefty fines. Manufacturing is one of many examples where data insights on product demand, availability of materials, supplier readiness, and more could significantly reduce the cost associated with supply chain processes.?
3. Risk:? The dimension of data associated with managing risk. It involves ensuring data visibility to identify where sensitive or confidential data is stored and used within the organization. This includes performing internal audits to ensure compliance with security and privacy regulations and preventing data leaks or breaches. For most organizations, the data analytics function is the largest consumer of data, making it a hot spot for significant risk exposure. This is especially true if data analytics users don't have standardized processes for understanding usage guidelines, the context of data, and its ethical use for business activities. Companies need to prevent misuse of data by employees and business processes that may compromise business trade secrets, customer privacy, and anti-trust regulations by using data without understanding security parameters.

Actors involved with Data Contracts

Every data contract involves two parties:

1. Data Owner: The authority on data is responsible for managing and approving access, safeguarding ethical use, data sharing, and data protection. While most organizations don't necessarily have one formal role dedicated to the "data owner," there exist a few variations of data owners, such as data steward, data governance manager, line of business leader, head of customer domain, product domain, etc. The data owner role helps organizations understand and track who is using what data for what purposes.
2. Data Consumer: Users or actors with varying data consumption needs to perform business activities. For example, a data analyst or data scientist needs data to build business intelligence reports, perform predictive analytics tasks such as testing the effectiveness of algorithms or models. A marketing leader needs customer data to run a marketing campaign (e.g., personalized ads campaign, etc.), and so on. In recent years, as the volume of data continues to grow, organizations are experiencing a massive rise in data consumers who need self-service access to trusted data. Data consumers are at the core of a data culture. Without appropriate levels of data consumer literacy, organizations are at a huge disadvantage and cannot achieve a data-driven status. In short, data consumers are users who need data to perform business activities.

Depending on the size of the organization, data owners and consumers may be present in different nested layers and hierarchies of the company.

Definition of Data Contracts

Data contracts typically refer to formal agreements between two parties, software components, or services that define how data should be exchanged. They are synonymous with data sharing agreements, which are nothing but agreements between two parties that govern how a particular data asset will be shared, including data definition, ownership, usage guidelines, consent, 3rd party sharing, cross-system and API transfers, quality standards, privacy guidelines, expiration date, data sovereignty, limitations, and violations.

Conclusion

In summary, as more organizations lead with data to drive business outcomes, data contracts are crucial in guiding its employees, 3rd party partners, business processes, stakeholder decision-making, and data management activities to manage data as an enterprise asset and, more importantly, achieve data-driven status, wherein data storage, processing, and access are secure and compliant with regulations, and provide the right balance between driving innovation and managing risk associated with data misuse. Lastly, data contracts provide visibility and empower users to prioritize key data assets, which can be enriched with trust attributes and unlock unprecedented value internally and externally to an organization.

Don't leave the management of your valuable data assets to chance. Start implementing data contracts today to ensure your organization is properly managing, safeguarding, and monetizing your data.

Not sure where to start? Check out the "4 Key Tenets to Thriving as a Chief Data Officer" article to learn how to solve data challenges to benefit your business and help you stay ahead of the competition.