Data and analytics are revolutionizing the way enterprises operate by improving business performance, increasing customer retention rates, boosting revenue and so much more. Regardless of industry, data is the heart and soul of every business, and managing data is increasingly crucial as organizations strategize for competitive advantage and long-term growth.
Yet, data only yields value if it is accurate and easily understood and leveraged by business users. When data quality is poor, business users can base decisions on faulty insights, which frequently leads to misguided business strategies and negative outcomes. Business users will quickly lose confidence in their data and question their data sources. As a result, businesses can suffer from a cascade of consequences such as lower data utilization, customer satisfaction concerns and most importantly, lack of compelling analytical insights.
The fact is, with organizations increasingly leveraging raw data from multiple internal and external sources, often of unknown quality, a proliferation of business problems can occur because of mismanaged and misunderstood data.
Managing the Data Deluge
Data must be managed appropriately to integrate it across all areas of the business effectively. Properly managing data also ensures it remains reliable and accurate as it moves across the complex data supply chain. However, managing data today is complicated. It requires a highly developed data management strategy with the right policies and processes in place to ensure that data is effectively and appropriately leveraged.
To develop a deep understanding of data and to quantify the value of it to support strategic business decisions, organizations must set guidelines for data management, data access, use and decision-making.
Extracting information from data requires a complete data governance program that provides business users with readily accessible data and allows them to analyze it to solve business problems quickly. A comprehensive data governance program should ensure that business users are aware of the quality and lineage of data, while also establishing accountability and assigning ownership. This ensures appropriate use of the right data for the right purpose, and the resources to support the business users and answer their data questions.
Leveraging Data Governance to Incorporate Analytics Across the Organization
Data governance is about coordinating people, processes, and technology to leverage data as an enterprise asset. At the center of data governance is data understanding. Data understanding is about ensuring an organization’s data is both accessible and used properly to maximize its value. Business users won’t use data they can’t find or don’t understand. By establishing a business glossary and data dictionaries, tracking data lineage and creating uniform policies and procedures, organizations develop a clear understanding of data responsibilities and standards to increase data utilization among business users.
Business users also need to trust the integrity of their data if they are going to utilize it for analytical insights. As data travels through the data supply chain, it is subject to new processes, uses, and transformations, all of which impact data quality. By scoring and monitoring data quality as part of a comprehensive data governance framework, organizations can prevent the downstream pollution.
Data governance is an ongoing initiative. The process will continue to evolve and improve over time. Once the data governance model matures, organizations can layer machine learning algorithms continuously to strengthen organizational data quality. By performing analytics in concert with data quality business rules, organizations substantially enhance the efficiency and effectiveness of their data integrity checks. However, organizations will need the right solution suite for data governance success.
Establishing a Prosperous Data Governance Model for Managing Data
Establishing a data governance model that focuses on an enterprise-wide understanding of data assets requires a modern solution suite with integrated capabilities for data governance, data quality, and analytics. The solution suite should automatically bring together data across the enterprise and organize it in a meaningful way for any user to interact with it.
The solution suite should clearly define data ownership and processes reinforce accountability and provide for smooth issue escalation and resolution. With transparency into the data landscape, business users can easily define data and associated business terms, track data lineage, and manage all aspects of their data, enabling collaboration, knowledge-sharing, and user empowerment, creating transparency across the enterprise.
If you would like to learn more about incorporating analytics with data governance, download the data sheet below.
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