79 tagged or highlighted with various visualisation techniques. And good data quality software will add workflow techniques, such as notifications or triggers, for timely remediation of data quality issues as they arise. Reacting to problems after they happen remains very costly and companies who are reactive, instead of proactive, regarding data issues will continue to suffer from questionable decisions and missed opportunities. Too often data quality is viewed only through the lens of an assessment, as a sort of necessary evil similar to a security or financial audit. But the value truly lies in continuous improvement. Data quality should be a cycle: the assessment runs regularly – or even better, continuously – automation is refined all the time, and new actions are taken at the source, before bad data enters the system. As with any governance process, data quality improvement is a balance between tools, processes and people. Putting humans in the loop – people who are experts on the data but not experts on data quality – requires a highly specialised workflow and user experience that few products are able to provide. Talend is leading the way here, with tools including the Trust Score formula, Data Inventory and Data Stewardship to enable the collaborative curation of data with human-generated metadata, such as ratings and tagging. As in medicine, we may never have a perfect picture of all the factors that affect our data health. But by establishing a culture of continuous improvement, backed by people equipped with the best tools and software available for data quality, we can protect ourselves from the biggest and most common risks. And if we embed quality functionality into the data lifecycle before it enters the pipeline, we can make data health a way of life. Krishna Tammana is chief technology officer at Talend
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