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    Using Qualitative Data Valuation for Strategic Business Improvement

    Understanding how data relates to, impacts, or adds value to the business model is top-of-mind for almost every data practitioner these days. But quantifying the return on investment into data is a challenge in even the simplest of businesses.

    To solve this problem, one must be able to visualize how data relates to an organization’s underlying business processes — through a qualitative data valuation process that examines the different systems, inputs, products, outputs and customers involved.

    However, “wrapping your head around” such complex, abstract concepts can be very difficult. While you can easily create flow charts to provide a top-level overview of a given process, if your goal is to make continuous, fundamental changes, you will need to take another approach. In short, you need a way to account for all the nitty-gritty details that go into a given process, and how all of those details impact your business performance. This is where identifying and leveraging your conceptual metadata can make a tangible impact on your business.

    Metadata is data that is used to describe other data. For people of a certain age, a classic analogy is an old-fashioned library card catalog. Every book in a library contains associated metadata, such as title, name of author, publisher, and so forth. When properly collected, metadata provides critical pieces of information that you can use to find what you’re looking for — helping you to conduct in-depth research and analysis.

    I’ve spent the better part of a decade exploring the various ways organizations can gather and analyze conceptual metadata to make improvements in data governance, data quality, advanced analytics, and other aspects of enterprise data management. What I’ve found is that when you treat the individual steps in a business process as a form of conceptual metadata, you can create a dynamic model that allows for ongoing, real-time feedback. This model allows you to see the impact of different types of data, making it easier for you to continuously re-evaluate and refresh your actual business processes.

    Getting started

    The best place place to start is by getting clear about the various relationships that exist between your data and your processes. Generally speaking, the visualization model you develop will help you define, understand and explore the relationships between your data, your people, and your technology.

    In some of our qualitative data valuation engagements, my company helps organizations gain qualitative insights into specific business processes by understanding which parties are involved, what specific data they use in the process and what exactly they do with that data (that is, whether they create, modify or simply read it). To do so, we conduct conceptual metadata acquisition through a process we call ‘Controlled Chaos.’

    Our strategy is to identify as many believable experts as possible who are associated with, and who care about, a given data element. These people can be experts of all kinds, from different parts of the organization. What we have found works best is to start by gathering stakeholders from the broader data community in a room (either a physical room or e-room) and through a structured agenda, collaboratively collect all of the unique viewpoints, perspectives and thoughts about the data element in real-time. (By the way, while the gathering of conceptual metadata is something we offer as a service, it’s certainly a process that any organization can do on its own as well.)

    Once the rich conceptual metadata that helps to describe how your organization’s business processes and data intertwine is captured, you can begin analyzing and visualizing those relationships.

    Finding trends, identifying problems

    For illustrative purposes, let’s say an organization has been having problems getting an accurate count of customers, and some feel that part of the problem is how data is managed during the sales process. The organization conducts a session with stakeholders, and gathers conceptual metadata about the key steps in the sales process, who’s involved, what data they use and how they use it.

    Now I can create a vizualization (Figure 1) that offers a first pass at the somewhat complex series of relationships between people, process steps and data. At this level, nothing in particular stands out as an issue.

    Fig. 1


    However, when we go a level deeper, we can begin to gain some real insights. In Figure 2, we’ve isolated just those relationships that result in business data elements being created or changed. This provides a powerful insight into any constraints that data may be causing on the business process.

    Fig. 2


    In this admittedly simple example, we see a classic data governance challenge: there’s one data element (called ‘Customer Status’) that is populated or changed in multiple business processes. Why should this be a concern? In our experience, it’s quite problematic if each of these process steps has its own organizational stakeholders who each use the same field for different purposes. Having a mechanism to visualize this abstract problem is a very powerful step in driving real improvement to your organization’s most valuable data.

    A visual analysis of this form of metadata enables you to quickly identify problematic parts of the organization’s processes that are not optimized, are overly reliant on tribal knowledge, or that need to be re-engineered from the ground up.

    Knowing the limitations

    Keep in mind, this is what I would characterize as a qualitative data valuation insight into a problem that can be fixed rather easily. But there can be, and probably are, many other such process related issues throughout an organization — and many of them are subtler or more complex — and their negative impact can be just as harmful or worse. Fortunately, there is a far more robust, quantitative methodology one can apply.


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