According to the post, which can be read here, the Data Wedge is the relationship between what data IS, what data DOES and what data MEANS.
The post was inspired by a recent Trillium Software webinar on Gaining Clarity and Confidence in Your Data. Customers were polled to identify which type of user is most interested in accessing data traceability and data quality indicators.
Surprisingly: “Business Users” came out on top, with “Reporting and Analytics team” closely behind. “Technical Users” on the other hand cam in next to last.
Does this mean that ETL tools that provide technical lineage to technical users are less important to successful data governance and data quality than tools that target providing these metrics to the business and reporting teams? Who needs this information and in what format was the theme of Collibra’s most popular posts about the new approach to delivering business friendly data lineage diagrams.
Trillium Software’s, Mark Pierce looks at the difference between how business and technical users think of data to define the data wedge:
Technical users excel in understanding what data IS, the coding and integration, the 0s and 1s, managing and provision of infrastructure, as well as how specific data supports mission critical organizational applications. (Amongst countless other strategic tasks, often thankless).
Business users understand what that specific data DOES, and excel in using the data made available to them within applications. Not only at an organization macro level, but how it directly impacts their critic departmental processes and performance.
It is only when the two are brought together that we as an organisation understand what data MEANS.
By chance, this perspective was raised in a chat started with John O’Gorman (and others) on LinkedIn last week.
Following a post t I shared about how Data Quality and Staffing issues still plague analytics efforts, John commented:
“There is a deeper disconnect here, I think; one that simply manifests itself as symptomatic data quality and a skills gaps. The relationship between Information, Language and Data is one that needs to be more fully described than it is now. The return on all the investment in technology is unacceptably low and the evidence that something is just not working has never been more clear.”
John talks about semantics – or the meaning of data. He says the disconnect between business and IT can be linked to the assumption that the MEANING of data is assumed to by each to be the other’s responsibility. Meaning falls through the cracks and “business doesn’t have the tools to do anything about it.”
John is partially right.
Technology that targets IT users (such as ETL tools) focus on defining what data IS.
New tools, like Collibra, are designed around semantic models that target business users to bring the context of what data DOES and what data MEANS.
Business and reporting users should have a larger role to play in selecting technology that supports their needs, rather than being forced to make do with the outputs from the ETL or other tech solutions.