Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. By taking advantage of data, enterprises can shape business decisions, minimize risk for stakeholders, and gain competitive advantage. However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key.
Ensuring data quality and access within an organization, while establishing and maintaining proper governance processes, is a major struggle for many organizations. Here are a few common data management challenges:
1. Regulatory compliance on data use
Whether data protection regulations like GDPR, CCPA, HIPAA, etc. are put in place by governments or a specific industry these data privacy and consent controls go beyond sensitive data to outline how organizations should allow their employees to access enterprise data in general.
2. Proper levels of data protection and data security
Certain data elements are critical for competitive advantage and business differentiation; therefore, those data assets need to be protected against data breaches, ensuring that only authorized users have data access.
3. Data quality
For data to be trusted, it needs to be complete, accurate and well understood. This requires data stewardship and data engineering practices to curate data standards and track data lineage, increasing the value of data. AI and Analytics is only good as the quality of data been used for it.
4. Data silos
A typical organization’s data landscape consists of a large number of data stores across workflows, business processes and business units, including but not limited to data warehouses, data marts, data lakes, ODS, cloud data stores, and CRM databases. Integrating data across this hybrid ecosystem can be time consuming and expensive.
5. The volume of data assets
The number of data assets and data elements that a typical organization stores continues to grow. This extremely large amount of enterprise data – comprising thousands of databases and millions of tables and columns – makes it difficult or impossible for users to find, access and use the data they need.
6. Lack of a common data catalog across data assets
Lack of a common business vocabulary across your organization’s data and the inability to map those categories to existing data leads to inconsistency of business metrics and data analytics in addition to making it difficult for users to easily find and understand the data.
Why you should automate data governance and how a data fabric architecture helps
The challenges outlined above demand a data strategy that includes a governance and privacy framework. Furthermore, to help the framework scale across the enterprise, it needs to be automated.
To help avoid vulnerability and inability to innovate caused by a lack of proper data governance, an architecture that enables the design, implementation and execution of automated governance across the enterprise is needed. This is especially important for organizations that operate in hybrid and multi-cloud environments.
A data fabric is an architectural approach to simplify data access in an organization. This architecture leverages automated governance and privacy to facilitate self-service data consumption. Self-service data consumption is crucial because it improves data users ability to easily find and use the right governed data at the right time regardless of where it resides using foundational data governance technologies such as data cataloging, automated metadata generation, automated governance of data access and lineage, data virtualization, and reporting and auditing.
To learn more about the building blocks of a data governance and privacy framework read our eBook “Data governance and privacy for data leaders”.