Tackling Six Challenges and Failures in Data Governance

Cameron OgdenAugust 2, 2021

Data governance plays a critical role in an organization’s ability to leverage data to make informed decisions. When organizations don’t know where their data came from, how it was collected or have a solid data management plan, data becomes worthless.

If users struggle to understand how their data was ingested, validated, stored, protected and processed, their confidence quickly erodes. This undermines critical business initiatives, leading to operational inefficiencies, bad insights and jeopardizing regulatory compliance.

Data-savvy organizations need to implement a data governance program that integrates data quality and data management. Not only will a formal program align people, processes and technology, helping them to understand data and transform it into an enterprise asset, but it helps users gain data visibility and understanding. As a result, organizations ensure the health of the data ecosystem as information flows and functions across complex data systems. Business users are empowered to quickly find, understand and apply the right data sources for analytics.

However, for organizations to put data governance at the forefront, it requires implementing the correct data “blueprint” and avoiding barriers. Taking a broader look, here are six data governance challenges and obstacles organizations must overcome to achieve success.

Get Data Quality Right: Data governance is about designing policies and processes to improve data trust, and data quality is one method of how to enforce the design. When data is inaccurate, incomplete or not fit-for-use, then it becomes ineffective. Teams not only won’t trust the data but are unlikely to use it. In addition, the information is more vulnerable to security threats and unreliable analysis. By measuring data quality synonymously with data governance effectiveness, organizations can connect the impact of data governance decisions to the impact on data trust.

Importance of Data Transparency: In addition to measuring data quality and implementing data quality rules, organizations suffer from a lack of data transparency into how their information is reported and where it comes from. A data governance program that provides data lineage helps users understand how their data flows through systems and tracks transformations. Organizations must capture and visualize lineage in three dimensions:

  • Business Data Lineage – How data fits the business and the impact to data if the business modifies its use.
  • Technical Data Lineage – Captures data on the physical level such as schemas, tables, columns and how it moves across systems.
  • Process Lineage – Visual lineage diagrams let users understand how data affects underlying business processes and helps users uncover detailed business knowledge and context around data.

Address Data Compliance and Regulatory Risk: Data compliance refers to how organizations store and secure sensitive data, like personal information. Regulatory compliance means that a business must follow state, federal and international laws or regulations relevant to operations. With the increase of new data protection laws like GDPR and CCPA, organizations face expanded responsibilities in meeting compliance needs. If an organization is noncompliant, it may face massive fines and penalties. Organizations need to prioritize data quality and track data lineage to establish an audit trail for data’s lifecycle. By tracking lineage, organizations also show how their information is used, reported and managed from different perspectives.

Form a Data Governance Operating Model for Business Success: Teamwork is essential to accomplishing an organization’s overall goals. And, that teamwork is no different when implementing a data governance program. Teams must deploy an operating model that works across departments and job functions to develop common data definitions and clearly define roles and responsibilities among data owners, users and stewards. The operating model puts the governance organization in motion to collaborate on business-critical use cases. By engaging all relevant parties across the enterprise, organizations help break down department silos, increase collaboration and accountability and work together to make data-driven decisions that drive a business forward.

Take Inventory: As part of data governance, organizations must identify and document the relationships data has to business processes and provide context around data and organizational assets. Without such an inventory, organizations fail to bring clarity and structure to data definitions, synonyms and critical business attributes used to run the business. One of the most important tools an organization must build is a data catalog, which synthesizes all the details about data throughout an enterprise and converts that information into a simple, easy-to-digest format. Data catalogs incorporate information from multiple sources. For example, data dictionaries document data sources and metadata across the enterprise. Business glossaries standardize the terms and definitions an organization uses so everyone is on the same page. The result is a searchable repository of all data assets in an organization.

Organize and Integrate the Right Automation: Businesses can’t solve data issues on their own. It requires the right automation to simplify the process, protect data, help adhere to compliance regulations and realize data’s full potential.

The ideal solution is a data governance platform that integrates data quality, data lineage and data catalog tools to provide users with a comprehensive view of a company’s data landscape. The right platform helps organizations clearly define varying roles and responsibilities among data owners, stewards and business users, providing the organization with a full view of their data landscape.

Interactive data visualization and lineage capabilities help deliver directional and impactful views of data relationships and hierarchies and provides valuable insights into data flows, definitions and responsibilities.

The tool should also include comprehensive data quality capabilities such as quality scoring and ongoing checks for completeness, conformance and validity, and advanced rules that ensure data’s integrity and accuracy. A modern platform will also support and maintain an organization’s data catalog. By autonomously harvesting metadata, lineage and profiling data, companies can derive various lineage perspectives and identify relationships with other business assets. The platform can also measure business impact, provide end-to-end data transparency and help organizations meet compliance requirements.

Does your organization need to streamline data efforts and deliver valuable, trustworthy data across the enterprise? View the brief demo, “Accelerating Data Governance Success Using 3D Lineage” or view the webinar, “General Mills: How to Communicate the Value of Data Governance.”