October 20, 2014

On Transparency, Data, and Trust

Transparency in data handling practices is becoming a clear and valuable trend as organizations begin to realize the competitive advantage available to them as a result of developing that capability. The importance of transparent data dealings are particularly relevant for mission- and values-based organizations that depend in large part on deep engagement and trusting relationships with their partners, constituents, and members.

There is a clear motivation for this trend. It has a basis in the human motivations around doing good works, aligning values with actions, and experiencing the results of those benefits across the board. Developing an organizational capability around data handling transparency is one major contributor to realizing those benefits.

There are some clear and well-known drivers of this trend: consumers, constituents, and members are becoming more and more savvy. They are beginning to understand that the data they generate has both economic and social value. Data breaches are increasing in frequency, size, and public awareness. Target, Home Depot, eBay, and Sony are just a few of the most recent commercial examples. We are all at risk, and people are beginning to realize that. By developing proactive, transparent, and explicit data handling policies and practices — and by communicating them regularly! — organizations can begin to experience the deeper engagement and brand advocacy that transparency can provide.

Two current, real-world examples of organizations that are well down the road of developing this capability are Umpqua Bank and Exact Target. Umpqua Bank, based in Portland, Oregon, offers a very proactive communication in the form of a short, one-page, easily digestible format of their data handling practices. Exact Target, a digital marketing company, strongly advocates for fair and ethical data handling practices promoted from onboarding of new hires from day one and given the name “Being Orange.”

But, it’s not always easy.

What makes getting to transparency in data handling a challenge? There are well-known and obvious constraints:

  • Ethics is hard: it’s a highly abstract topic that has very real world consequences
  • You’re steering a big ship that will take some time to change course: your existing workload is not going away, and you have to figure out how to develop a transparent data handling practice in addition to your daily duties — a workload balancing question that’s not always easy to answer
  • There is a lack of a common vocabulary for exploring data ethics topics
  • Legislation and regulation (compliance) requirements lag far behind technology innovation

There are numerous emerging organizations addressing many of these issues. They can provide guidance, inspiration, and emerging best practices to help inform your own initiatives. The Data & Society Institute, Project VRM, Federal Trade Commission Guidelines, the Office of Science and Technology Policy, etc.

To start exploring this inside your own organization, a close, hard, and detailed look at your organizational values is a great place to begin. Additionally, identifying what your actual data handling practices are will help you understand where there might be gaps between your organizational values and your existing data handling practices. This is a non-trivial process and needs to be executed with explicit intention and vigor. Consider launching a formal initiative to identify, explore, and develop an action plan to execute on the activities necessary to develop this organizational capability.

There can be many challenges to getting to transparency in your data handling practices:

  • Organizational culture/dynamics
  • Existing workload
  • Historical precedent
  • Legal/compliance requirements
  • Existing privacy policies

To begin, ask opening challenge questions:

  • What do (and don’t) people know about the data we’re capturing and using?
  • What kind of relationship would they have with us if they did know?
  • How can we align our values with our data handling practices, such that their experience aligns with those values?

One approach to resolving these kinds of constraints is to utilize a “workshop” format to get the right people in the same room to collaborate and co-create the policy and other statements and operational processes necessary to develop transparency around data handling practices.

The goal is to build transparency into policy design, development, and execution. A collaboratively developed set of policies that integrates transparent behavior and communication supports organizational values at the operational level.

There are well-known and demonstrated benefits to developing transparency:

  • Innovation: Organizations that adopt more transparent data handling practices are able to evolve their products and services more rapidly—thereby being more nimble and agile in responding to changes in their constituents’ or members’ needs
  • Adoption: Organizations that have deeper, more trusting relationships find it easier for those innovations to be adopted. In your own case, you’re more likely to adopt a new product or service from a company you trust than one you don’t
  • Loyalty: The longevity of trusting relationships is maintained over time by being consistent, explicit, and transparent about your data handling practices

There are manifold benefits to developing a transparent data handling capability. It’s not always easy but, in the long run, the benefits far outweigh the challenges.

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Kord Davis
Kord Davis, Co-Founder of Signal Workshop, is a digital strategist, author, speaker, and facilitator. Working at the intersection of people and data, Kord brings an academic background in philosophy and 20+ years of technology experience to the field. A highly experienced practitioner of design and visual thinking, Kord brings the power of collaboration to the exploration of how design and technology can help people and organizations work more effectively. You can find Kord on Twitter @kordindex.
Interest Categories: Data, Operations, Policy
Tags: Data, Operations