As the adage goes, a workman is only as good as his tools. There is no disputing that, but you can never overlook the power of qualification, aptitude, and experience when it comes to data quality. You need to select a team that is acquainted with the high dynamism of the digital world and is up to date with contemporary data management tools and techniques.
The data steward or the data architect or the data leadership / management team should understand both the IT and business aspects of the whole arrangement for a more harmonious strategy. One should understand the objectives of the organization, the type of business in, the demanding market conditions plus the impact of data across these and be conversant with the big picture. Team members, on the other hand, should be selected on merit. You should have the very best individuals in terms of data governance, and data quality initiatives, so your project is planned and implemented with a strong business demands and governance in mind.
Whether you are the team leader or just a member of the team, make sure to be open to new ideas and criticism, as different members have different technical backgrounds and view the project from different angles. Members from the business side are very much likely to disagree with the data specialists, but this will change as the two sides continue meeting and holding talks. This requires a thorough data governance and steering committee that oversees several initiatives such as security, training, compliance, and the importance towards data quality and privacy. Effort spent towards this shall help in reducing the wastage of time that comes with dwelling on nonviable or noncompliant actions and suggestions.
Since a company’s product makes for the primary point of contact between it and its users, there is a need to have all departments and personnel acquainted with the brand’s data quality standards, lest a trivial mistake on one front ruins the whole product. There are hundreds of data source channels that not even the most informed and equipped data leaders can master. An inclusive AI-based data quality platform can enable everyone, including those with little knowledge of data quality, to be visible and be in consensus on data quality and curation processes. Large organizations with numerous employees and several data entry points need to install augmented analytics data quality platform that are easy to ingest data from various sources and facilitate seamless unification of data and improve the overall quality.
Why AI/ML Based Data Quality makes sense?
Impacts of Poor Data Quality
Steps to collect High-Quality Data
Choosing the Right Data Quality Tools
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