Overcome these six data consumption barriers to become a more data-driven organization

Posted by Marbenz Antonio on June 23, 2022

Data-Driven Marketing: 6 Steps to Success | CircleBack

By seeing data as a crucial asset that gives insights for better and more informed decision-making, implementing the correct data strategy supports innovation and excellent business outcomes. Enterprises may shape business decisions, reduce the risk for stakeholders, and gain a competitive advantage by leveraging data. However, a foundational step toward becoming a data-driven business requires trusted, widely available, and easily accessible data for internal users; hence, a strong data governance program is important.

Many organizations struggle to ensure data quality and access within their organizations while also developing and maintaining proper governance mechanisms. Here are a few examples of common data management issues:

  1. Regulatory compliance on data useWhether put in place by governments or specific industries, data protection standards such as GDPR, CCPA, HIPAA, and others go beyond sensitive data to detail how firms should allow their employees to access enterprise data in general.
  2. Proper levels of data protection and data securityCertain data pieces are a real competitive advantage and business uniqueness; as a result, those data assets must be protected against data breaches, with only authorized individuals having data access.
  3. Data quality
    Data must be thorough, accurate, and well understood to be trusted. This necessitates data stewardship and data engineering processes to manage data standards and track data history, hence enhancing data value. AI and analytics are only as good as the data utilized to power them.
  4. Data silosA typical organization’s data landscape includes many data stores spread across workflows, business processes, and business units, such as data warehouses, data marts, data lakes, ODS, cloud data stores, and CRM databases. Data integration across this hybrid environment can be time-consuming and costly.
  5. The volume of data assetsThe quantity of data assets and data components stored by the average firm continues to expand. This massive amount of enterprise data, which consists of thousands of databases and millions of tables and columns, make it difficult or impossible for users to identify, access and use the information they require.
  6. Lack of a common data catalog across data assetsThe absence of a standard business vocabulary throughout your organization’s data, as well as the inability to connect those categories to current data, leads to inconsistency in business metrics and data analytics, as well as making it difficult for users to discover and comprehend the data.

Why you should automate data governance and how a data fabric architecture helps

The aforementioned difficulties necessitate a data strategy that includes governance and privacy structure. Furthermore, the framework must be automated to scale across the organization.

An architecture that facilitates the design, development, and execution of automated governance across the company is required to help avoid vulnerability and inability to innovate caused by a lack of adequate data governance. This is especially true for businesses that operate in hybrid and multi-cloud settings.

A data fabric is an architectural technique that is used to ease data access in an organization. To enable self-service data consumption, this architecture makes use of automated governance and privacy. Self-service data consumption is important because it improves data users’ ability to find and use the right governed data at the right time, no matter where it resides, by utilizing foundational data governance technologies such as data cataloging, automated metadata generation, automated governance of data access and lineage, data virtualization, and reporting and auditing.


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