What Does Data Quality Technology Want?

During a recent Radiolab podcast, Kevin Kelly, author of the book What Technology Wants, used the analogy of how a flower leans toward sunlight because it “wants” the sunlight, to describe what the interweaving web of evolving technical innovations (what he refers to as the super-organi

During a recent Radiolab podcast, Kevin Kelly, author of the book What Technology Wants, used the analogy of how a flower leans toward sunlight because it “wants” the sunlight, to describe what the interweaving web of evolving technical innovations (what he refers to as the super-organism of technology) is leaning toward—in other words, what technology wants.

The other Radiolab guest was Steven Johnson, author of the book Where Good Ideas Come From, who somewhat dispelled the traditional notion of the eureka effect by explaining that the evolution of ideas, like all evolution, stumbles its way toward the next good idea, which inevitably leads to a significant breakthrough, such as what happens with innovations in technology.

Listening to this thought-provoking podcast made me ponder the question: What does data quality technology want?

In a previous post, I used the term OOBE-DQ to refer to the out-of-box-experience (OOBE) provided by data quality (DQ) tools, which usually becomes a debate between “ease of use” and “powerful functionality” after you ignore the Magic Beans sales pitch that guarantees you the data quality tool is both remarkably easy to use and incredibly powerful.

The data quality market continues to evolve away from esoteric technical tools and stumble its way toward the next good idea, which is business-empowering suites providing robust functionality with increasingly role-based user interfaces, which are tailored to the specific needs of different users.  Of course, many vendors would love to claim sole responsibility for what they would call significant innovations in data quality technology, instead of what are simply by-products of an evolving market.

The deployment of data quality functionality within and across organizations also continues to evolve, as data cleansing activities are being complemented by real-time defect prevention services used to greatly minimize poor data quality at the multiple points of origin within the enterprise data ecosystem.

However, viewpoints about the role of data quality technology generally remain split between two opposing perspectives:

1. Technology enables a data quality process, but doesn’t obviate the need for people (e.g., data stewards) to remain actively involved and be held accountable for maintaining the quality of data.
2. Technology automates a data quality process, and a well-designed and properly implemented technical solution obviates the need for people to be actively involved after its implementation.

Do you think that continuing advancements and innovations in data quality technology will obviate the need for people to be actively involved in data quality processes?  In the future, will we have high quality data because our technology essentially wants it and therefore leans our organizations toward high quality data? 

Please feel free to post a comment below and explain your vote or simply share your opinions and experiences.