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Absence of a defined data management career path contributes to data roles being a career “pit stop,” Rob King says. https://lnkd.in/e9TUxUa5

Data Quality Shaming. We've tried, and it doesn't work. Why? Because our customers are tired of data people waving their fingers at them on data quality issues. We've created some massive data quality fatigue, and I wouldn't blame anyone in the C-suite for being tired of hearing it. The fatigue starts when the CEO of a successful business that's hitting all of it's key metrics, including efficiency metrics, is constantly told their data is 'low quality'. If you were that CEO, how would you react? More than likely, you would doubt the assertion - and I wouldn't blame you. Moreover, if you were constantly hearing how bad the data is, and nobody in the data team is able to provide any hard metrics to show how it's negatively affecting your bottom line, in time you would probably just stop listening altogether. Sadly, I think this story plays out for CDOs more often than we admit, and it could help to partially explain why CDO tenures are so short. Don't get me wrong - many data quality challenges are very real. However, when we take a 'sky is falling' approach to communicating the impacts of low quality using sound bites and not hard evidence, or when we synonymize low quality with data transformations, we end up doing more harm than good. Instead, we need to focus on building a culture within our data teams which recognizes that: ✔ data quality is highly variable within organizations *by design*, and supporting a narrative that paints data quality as a binary good vs. bad is a drastic and unproductive distinction. ✔ there is a difference between data quality and data wrangling. Suggesting the work needed to transform operational data into use for analytical purposes is a result of 'low quality data' is entirely misleading. ✔ your business partners have positive intentions and are not out to make your jobs harder. What you call 'low quality data' may be a result of a conscious decision by business users to make a process as efficient as possible. ✔ data in business applications is optimized for operational purposes, not analytical purposes. What you think is bad data may be perfectly fit for purpose - and when you say it's 'low quality', it negatively impacts your customer relationships by making it appear you know nothing about how they operate. ✔ If you're serious about being 'data driven', then show the business the hard data on how improving data quality will drive business outcomes. Until then, you're asking the business to bet on your intuition, which is the exact opposite of being data-driven. If you make these changes, I'm confident you'll have a more productive relationship with your customers and your executives. What do you think? #dataquality #datagovernance #datashaming