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For many organizations, it is often a challenge to quantify the impact of data-related issues and convert them into dollars. I think it is one of the reasons why there is still a lack of focus on the data as an asset – it is too hard to measure the economic impact.
In this article, I want to propose a simple approach to help you identify where value is being created or lost in your organization’s data world. I find it very helpful to think about a firm’s data landscape in terms of positive and negative forces.
Data only creates value when being used. So any forces that make it easier, faster and cheaper to use and re-use data can be regarded as positive. Anything that makes it harder, impossible, or more expensive can be viewed as a negative force.
Positive forces are the ones adding value to data, and conversely negative forces eradicate value or hinder value creation.
The general rule of thumb is to assume that data from any process or system can (and likely will) have a downstream use. This should, of course, consider the regulatory, ethics, and security landscape that applies to a particular organization.
Let us look at some examples.
Positive data force examples:
- All operational and engineering teams agree on using the same customer identification system.
- An executive mandates clean up and standardization of terminology in their department.
- A data engineer creates a well-documented data model that brings together data from several sources. The model makes it easy to access, understand, and consume the data.
- Managers of several departments decide to adjust their operational processes to use a single data source for customer analytics.
- A data management professional creates a curated catalogue of relevant external data sets and makes it available throughout the organizations’ data teams.
- A developer builds an API to expose data and metadata of a legacy system.
- Software engineering leadership team enforces company-wide adoption of commonly used reference data.
- Architecture team mandates a systems integrator company to use the well-established sources for master data for onboarding of a new SaaS solution.
- Grass-roots data champion movement decides to use a standardized approach to data modeling all across the company.
Negative data force examples:
- Development team performing data migration from a legacy system loses some meaningful information in the process.
- New employee changes the terminology used in the data products his team publishes, without coordinating it with the consumers.
- A data scientist makes a wrong conclusion about the correlation of metrics. They communicate it to the executive team without validation. The execs proceed with a strategy based on the newly discovered “fact”.
- IT Ops department refuses to share technical data with the marketing team, arguing that it will not be useful.
- Engineering team changes the semantics of a field in a core database without proper coordination with other teams and resulting in a company-wide reporting fallout.
- Chief architect holds on to a legacy system architecture, restricting data availability for machine learning workloads and resulting in exponential data management cost increase.
- A data engineer publishes a data set without documentation, assuming that the users will be familiar with the data.
As you can see, positive forces make it easier, faster and/or cheaper to use data, while negative data forces are the opposite.
“Force” is a fairly general term, and I use it deliberately, as we can apply it to architectures, org structures, engineering practices, business processes, policies, mindsets and anything else. Positive and negative data forces exist on all levels of an organization.
Whatever you handle, you can ask yourself – how does my area contribute from the point of view of my organization’s most valuable asset – data? Fundamentally, is it a good force?
And if you want to take it a step further, ask yourself whether your organization has a culture that consciously cultivates positive data forces and reverses negative ones.
What do you see as negative and positive data forces in your org? Share your examples in the comments.
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