- A “data democracy” can be defined as an organization that is conducive to making money from data.
- Successful data monetization depends on "purple people" born of collaboration between domain experts and data experts.
- "Purple people” are essential to the diffusion of innovation.
We call an organization that is conducive to making money from data a “data democracy.” In a data democracy, people of all kinds are inspired to engage in data monetization. They are rewarded for questioning the status quo, sharing ideas, adopting novel practices, changing habits, and contributing to organizational goals. They believe that data is valuable, is essential, and plays a role in the organization’s success.
It takes a lot of effort to get the average employee ready and willing to participate in the data monetization movement. Part of the challenge is rooted in the old problem of data versus domain knowledge. Domain experts (accountants, marketers, nurses, civil servants, factory workers, sales associates — anyone with expertise in a part of an organization) and data experts (analysts, data scientists, dashboard designers, database administrators) each have something important to offer to a data monetization initiative.
For example, to fix a product defect, you need a product manager to interpret the problem and a software developer to write the code. But before coding can start, the developer must understand the problem and the product manager must recognize the potential of data assets and data monetization capabilities. It’s tricky to come to a common problem understanding, using the same language, and to agree on the optimal use of these data monetization resources. Turf battles, skill gaps, and politics get in the way. Nevertheless, leaders of data democracies actively manage through these hurdles and design their organizations for success.
In short, your organization won’t become a data democracy organically. Data and domain experts must be motivated to learn from each other. Without a deep understanding of the organization’s needs, data experts will be hard pressed to develop the most useful data monetization capabilities and the most reusable data assets. Shared knowledge — more data savvy among domain experts and more organization savvy among data experts — is the key to valuable innovation as well as the diffusion of those innovations — scaling and reusing them. Innovation and the diffusion of innovation are achievable in data democracies.
Imagine that all the “data” people in an organization were colored red and all the “domain” people were colored blue. As these red and blue people regularly interact, share what they know, and learn from each other, their knowledge blends and they become less red or blue and more purple. They develop a shared grasp of data in their particular organizational context. A data democracy is populated by purple people!
Organizational design is commonly thought of as the way in which workflows, authority relationships, and social ties are organized within the organization. In the case of data democracy, workflows, authority relationships, and social ties are configured into structures that blend red and blue people. The blending occurs by virtue of data-domain connections: organizational structures linking data experts and domain experts that facilitate knowledge exchange and learning.
Dr. Ida A. Someh studied how relationships between analytics groups and business-domain groups can be configured to facilitate knowledge integration in data-driven organizational initiatives. She found five common data-domain connections: embedded experts, multidisciplinary teams, shared services, social networks, and advisory services. These five connections are different means of knowledge sharing — creating purple people — crucial to both innovation and the diffusion of innovations across the organization. They work differently, and they work together. Think of these connections as tools in your organizational design toolkit, the data democracy special edition toolkit. Organizations can use any and all of the five connecting structures; ideally, organizations should support enough structures to yield as much of a data democracy as they need.
The connections facilitate two-way collaboration, conversations, and learning. They build on and help consolidate any knowledge gained in formal training experiences. For example, if a domain expert takes a statistics course, a data expert can help to apply that new skill to a particular problem. If a data expert takes a course in marketing, a domain expert from marketing can help contextualize concepts from the course to the specific organization. The connections make it easier for domain experts to become aware of, access, and use data assets and data monetization capabilities.