How do you ensure that data is relevant and of high quality? That is where data governance comes in. Data preparation can consume more than 50% of an analytics/data science team’s time, so governance is undoubtedly key. Another survey found that as much as 30% of time was wasted on non-value-added tasks because of data issues. Successful governance depends on identifying priority data domains and linking them to analytics use cases and transformation efforts. Other important steps include involving stakeholders, automation, data cataloging, and tracking data lineage. However, governance is not one size fits all and needs to be adjusted to meet the organization’s needs. End of the day, data governance typically improves decisions by 20 percent and likely reduces the risk of monetary fines and loss of trust by as much as 40 percent.