1. Data Governance
When businesses work with data, they need a governance structure to ensure the access, protection and quality of that data. Data governance is often treated like an IT project, with the deployment of a catalog or the adoption of a new tool considered the end of the story. But, CDW experts note, “governance isn’t a piece of software you install. It’s an organizational capability you activate. That activation requires a shift in mindset, behavior and culture.”
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Weak data governance also affects data literacy: If the quality of the data is in question, how can team members correctly interpret it to guide business decisions? You can’t manage what you can’t measure. Formalize the team in charge of key choices around data classification, protection levels and lifecycle management, for example.
2. Data Analysis
There are technical and nontechnical aspects to this, Tableau highlights. On the technical side, there are statistical and logical skills that can be developed to interpret and evaluate data. On the nontechnical side is the refinement of critical-thinking skills: How can understanding data help employees work through problems supported by logic?
“Sometimes, you need to help people appreciate the value that different types of insights can bring, especially at scale and outside of individual functional areas and domains,” IBM Chief Analytics Officer Tim Humphrey says in an IBM report.
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3. Data Communication
As a business breaks down silos and makes data more accessible throughout its departments, it’s important to use clear language and avoid overly technical words to make sure communication about data connects with the right audience.
“Establish a common way of talking about data throughout the organization,” says Piyanka Jain, president and CEO of data science consulting firm Aryng, in an MIT Sloan School of Management article. She notes that if data analysts use acronyms that not all marketers understand, they may not be conveying their insights in the best way.
“Establishing that common vernacular is very important to establishing a culture of data,” Jain says.
