BE IN THE KNOW BLOG

Where to start with data credibility

March 6, 2012

By Emma Crockett

Left unsupervised, data quality can derail an organization’s workforce analytics efforts. We hear from many organizations that moving to a fact-based decision making culture is of high importance; however, oftentimes the frustration shared by the people tasked with being information providers is that the organization’s decisions-makers will spend more time questioning the data than using it to make decisions. Building credibility in your data is key to shifting the analytics conversation from “Why is this 10?” to “How should we address this?”

Here is where I like to start.

Establish the value of the data. It’s okay to remind the organization that it's not just about good data, but about what a business can do with trusted information from across the organization. Remember that materiality is subjective, so what may be material to a particular decision maker could be immaterial in different circumstances.

For some organizations, just being able to establish accurate headcount and start to analyze basic information on their global workforce could provide huge impact, insights and improvement if they hadn’t been able to do so in a repeatable, consistent and high-quality way in the past. For other organizations, being able to quantify the cost and root cause of turnover in a critical workforce segment might be the most important thing to focus on since so much money is spent sourcing and developing this talent.

Ask your decision makers the following questions:

With this feedback build a matrix of information across a high/medium/low scale for both data materiality and data quality. For example:.

Share these results with your decision makers. Gain buy-in that data quality efforts should be prioritized on information that is both integral to a decision making process and would impact the course of action if the data wasn’t directionally correct. By focusing, firstly, on the value of data as determined by the needs of your customer, there is now a tangible place to establish your data quality efforts.

Don’t get waylaid by people’s perceptions around data accuracy—sometimes data are better than people think! Address the perception of data quality issues but don’t feel like you need to be perfect. Directionally correct metrics are often good enough. And even if the data are sub-par, the only way for them to get better is start to highlight the data and use them; the more people use the data, the more people will be wedded to ensuring their quality and accuracy.

Where have you started in your desire to address data quality in your organization?