Data Governance is the Missing Link between Data and Business Process

In my career, which has spanned both sides of buying and selling IT, one thing hasn’t changed: Data people and business process people don’t talk to each other. Business process people assume that data simply exists and care little about how it is “managed”, while data people focus more on the bits and bytes stored in repositories than how the bits and bytes are created and consumed. With data and process sitting on different layers on enterprise architecture diagrams, the prevailing view is that everything will work as long as the interfaces are well defined.

In reality, data and business process are far more intertwined. In today’s service-dominated economy, most business processes operate on data rather than physical goods: They consume data, perform a set of tasks, and produce data that feed into the next set of processes downstream.  In other words, data is consumed by business processes as input, and data is produced by business processes as output.

These relationships lead to a couple of important observations:

  1. Business processes should be responsible for the quality of the data they produce. Ironically, data people don’t produce data and shouldn’t be held responsible! The right solution to chronic data quality problems is to either modify the business processes that produce the data, or put additional process controls in place to improve the quality of the data output.
  2. Business processes suffer a great deal from bad data. Wrong or missing input data leads to rework and manual intervention, ultimately driving up cost per transaction. Business efficiency depends on good data.

To improve efficiency, every core business process should have a codified set of policies that define the requirements for input data as well as accountability for output data. Getting these policies defined and communicated is the first missing link between data and business processes.

In addition to the producer and consumer relationships described above, data and business processes are linked in another important way. The act of managing data assets needs to be instituted as a set of business processes in their own right. As an analogy, managing human resources, another key enterprise-level asset, requires a strong set of processes. Data assets should be treated with the same rigor. When a piece of data fails to meet validation rules, who should be notified? What are the steps for correcting it? What’s the escalation path? These tasks have to be automated using workflow that enables a broad range of business people to actively manage data assets.

These missing links, data policies and a new set of business processes for data management, are what data governance is all about. In all the chatter on data governance, business process has been sorely missing. Too many vendors talk about data governance simply as another use case for existing IT-centric tools without addressing the links between data and business processes. A true data governance solution needs to bridge these two realms that have been apart for too long. In my next blog, I’ll explore how to do this in practice.

What do you think? Do you agree?

2 replies
  1. Steve Pappas
    Steve Pappas says:


    I think you are on the right track, but I’d take it a step further.

    In my opinion, when embarking down the path of Data Governance, one has to assess the data, the business process and the human process. Suffice it to say unless all three are addressed it will create a black hole effect. In that I mean automating business process data feeds, queues with rigid rules may help the data flowing but not help the human up-front at the data genesis point.

    In my experience, the front-line folks can have all the business rules built and controlling the flow of their experience, but they too often need to navigate a myriad of applications to do their day to day job, so tightening up an apps’ rules drivers will not cover all of the apps a knowledge worker needs to work in. It should also be noted that the pressure of time-based SLAs drive those front-line workers like customer service agents, call center agents, as well as others.

    Given the pressures of their environments it naturally creates a difficult atmosphere to affect organic governance due to the fact that they do not have guidance as well as governance. Guidance is the ride along, “how-to” approach to keeping workers on-track within each task and process, by watching over the many apps they are in and knowing their role to display the exact steps of the process in a simple and concise manner that requires no training and no integration. The knowledge workers merely have the real-time guidance system side-by-side all of the other things they are doing on the desktop. Non-Invasive Governance.

    Governance also needs to be thought of as complexity, compliance and change governance to address the many types to be encountered by the knowledge worker.

    This multi-pronged method lowers risk and increases compliance, especially in highly regulated industries.

  2. Winston Chen
    Winston Chen says:

    Steve, Thanks for your comments.
    Yes, I agree. An important part of data management, and a frequently overlooked part, is the behavior of what you call “front-line” workers. The people who represent the real world in bit-and-bytes and create data. And the people who consume information and take action upon it. Application rules play a role, but they can only address a subset of data problems. A comprehensive approach to data governance needs to address human behavior as well as system logic.

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