1. Data Governance
When companies handle data, they must have a governance structure in place to guarantee data access, protection, and quality. Data governance is often viewed as an IT project, with the implementation of a catalog or the adoption of a new tool seen as the end goal. However, CDW experts emphasize that governance is not just about installing software; it’s about activating an organizational capability that requires a change in mindset, behavior, and culture.
Insufficient data governance can also impact data literacy, making it challenging for team members to interpret data accurately for guiding business decisions. To address this, it’s crucial to formalize the team responsible for key decisions regarding data classification, protection levels, and lifecycle management.
2. Data Analysis
Both technical and nontechnical aspects are involved in data analysis, as highlighted by Tableau. In addition to developing statistical and logical skills for data interpretation, employees need to refine their critical-thinking skills to effectively work through problems based on data-driven logic. IBM’s Chief Analytics Officer Tim Humphrey emphasizes the importance of helping individuals understand the value of different insights, especially at scale and across various functional areas.
3. Data Communication
Effective communication of data is essential as businesses aim to break down silos and make data more accessible across departments. Using clear language and avoiding overly technical terms can help ensure that data communication resonates with the intended audience. Establishing a common language for discussing data within the organization is crucial, as noted by Piyanka Jain, president and CEO of data science consulting firm Aryng. This common vernacular is key to fostering a data-driven culture within the company.
