It is critical to properly manage data if it’s going to be leveraged for strategic business decisions, operational efficiency, increased profitability and enhanced growth. Mismanaged data leads to data mistrust, and data utilization soon plummets. Data that could be the key to competitive differentiation can soon become wasted opportunity.
But effectively managing data is certainly difficult. As more data is ingested into an organization’s enterprise, it becomes increasingly burdensome to manage. Beyond the data deluge, matters are further complicated by the fact that from the point of creation, data is constantly in motion. As it flows through the data supply chain, it’s being used in myriad ways and frequently transformed. As data continues to move across various systems, processes, and environments, its quality is always at risk.
To realize optimal returns on data assets, it is critical to ensure organizations have a firm understanding of their data from a business perspective and can quantify its quality. When businesses don’t have easy-to-understand, high-quality, readily accessible data, it discourages data usage among business users. If business users do leverage inaccurate, incomplete, or inappropriate data, it may result in flawed and unreliable decision-making. Business decisions based on inaccurate or incomplete data can result in missed opportunities, operational missteps, angry customers, minimized profitability and reputational damage.
By incorporating a comprehensive data governance program into an organization’s data management strategy, however, businesses can ensure the ongoing accuracy and integrity of their data assets. They can also ensure that business users may easily access and understand all data assets, enabling them to simply conduct analysis, and ensuring they choose the right resources for accurate and appropriate results. In these ways, data governance can optimize data management.
Data governance serves many functions within organizations today. It can provide a comprehensive framework for enabling data access, understanding and accountability across an enterprise. It can also provide data quality assurance across the entire data supply chain.
By integrating data quality capabilities into a comprehensive data governance framework, businesses can increase visibility and automation while also improving the usability and reliability of data assets. Integrated capabilities allow for continuous data quality monitoring, and also provide a framework for establishing data quality scores. These scores are valuable for data monetization, and also give business users valuable information regarding the reliability of the available data assets.
Data governance also increases the understanding of data to satisfy the needs of business users within an organization. Educated data consumers who understand what data is available, and exactly what it means, will choose the right resources. By aligning people, processes and technology through data governance, organizations ensure data remains trustworthy, understandable, and accessible, so business users can make accurate and reliable business decisions.
To enact a successful data governance program, organizations need the right technologies with the right capabilities.
Enterprise data governance requires a solution suite with diverse capabilities to perform a variety of functions. Ideally, a data intelligence platform will offer integrated solutions that include data governance capabilities to deliver an all-inclusive view of an organization’s data landscape, from the data available, its owner/steward, lineage and usage, to its associated definitions, synonyms, and business attributes. It should allow business users to easily and quickly define, track, and manage all aspects of their data assets, enabling collaboration and knowledge sharing across the entire enterprise.
In addition, the solution should include data quality capabilities for conducting data integrity checks such as data profiling, consistency, conformity, completeness, timeliness, as well as more advanced quality rules such as reconciliation and transaction tracking. These rules validate the quality of data across the data supply chain to ensure continued trust among business users. Lastly, to automate the regular improvement of data quality rules, the platform should incorporate analytical capabilities that apply machine learning algorithms.
By ensuring that data governance is a foundational component of an organization’s data management strategy, integrating it with other key capabilities, and enabling the right technologies, organizations can establish trust in data across the organization to deliver a competitive business advantage.
If you would like to learn more about how data governance can optimize data management, download the white paper below.
For a deeper dive into this topic, visit our resource center. Here you will find a broad selection of content that represents the compiled wisdom, experience, and advice of our seasoned data experts and thought leaders.