The Cost of Poor Data Governance, and 4 Practical Steps to Strengthen Yours

From overspending on data to security breaches and stalled growth, the symptoms of poor data governance are costly. Here’s how to get it right.

What do the following events share in common below?

• Through a data audit, a large multinational corporation discovers it has been paying a data broker for the same data three times for years when it only needed to pay once.

• A start-up discovers it lacks a key piece of data before it can monetize its insights with clients.  

• A junior IT manager, looking to fund their drug habit, is caught by law enforcement in an attempt to sell PII data from their retail employer.

• A consumer promotion firm is unable to grow until it remedies a myriad of data quality issues.

Answer = Lack of strong data governance.

Or below events?

• The CFO of an e-commerce company is able to prevent duplicate purchases and save costs on redundant subscriptions, allowing them to improve profitability.

• The director of marketing at a telecommunications company is able to leverage clean, reliable customer data to accomplish personalized marketing campaigns and improve customer retention rates.

Answer = Effects of good data governance.

AI Generated

Data governance refers to the overall management and control of an organization's data assets. It encompasses the processes, policies, and standards that ensure the quality, availability, integrity, and security of data across an organization.

The goal of data governance is to establish a framework that defines how data is collected, stored, processed, and used within an organization. It provides guidelines for data management, establishes accountability for data-related decisions, and ensures compliance with relevant regulations and industry standards.

Key components of data governance include:

• Data Policies: Documented guidelines and rules that outline how data should be managed, including data quality standards, data classification, data access controls, and data retention policies.

• Data Stewardship: The assignment of responsibility and accountability for data management to individuals or teams within the organization. Data stewards are responsible for data quality, data integrity, and ensuring compliance with data policies.

• Data Architecture: The design and organization of data assets within the organization, including data models, data dictionaries, and data flows. Data architecture provides a blueprint for how data is structured, stored, and accessed.

• Data Quality Management: Processes and activities aimed at ensuring the accuracy, completeness, consistency, and timeliness of data. This includes data profiling, data cleansing, data validation, and ongoing monitoring of data quality.

• Data Security and Privacy: Measures and controls to protect sensitive data from unauthorized access, breaches, or misuse. Data governance includes defining access controls, encryption methods, data masking, and ensuring compliance with privacy regulations such as GDPR or CCPA.

• Data Compliance and Regulatory Requirements: Ensuring that the organization's data practices align with relevant legal and regulatory requirements. This includes data protection laws, industry-specific regulations, and internal policies.

• Data Lifecycle Management: The management of data throughout its lifecycle, from creation to deletion or archival. It involves defining processes for data capture, storage, retention, archival, and disposal.

Effective data governance helps organizations achieve several benefits, including improved data quality, increased trust in data, enhanced decision-making, reduced data-related risks, and better compliance with regulations. It also facilitates data sharing and collaboration across different departments and ensures that data is treated as a valuable corporate asset.

To achieve strong data governance for your organization, keep these four things in mind:

1. Assemble the right team.  Keep under ten members, ideally fewer.  Ensure representation across the organization from IT to Finance to Marketing to Legal to C-Level executives.  Establish the lead, meet on a regular basis, set and track objectives and alter the team as required over time.

2. Secure the right data.  Constantly challenge the data you hold across all elements of 1st, 2nd and 3rd party elements.  Jettison what you no longer require, test before you pay for data and ensure an ROI exists for any investment in data. 

3. Ensure the data is right.  Be skeptical with all data.  Develop and document QA procedures, put a stoplight chart against key strategic data elements and evolve your QA procedures over time.  Constantly check the quality of your 1st party data.  Streamline data procedures with your 2nd party data sources.  Demand thresholds from your 3rd party providers and always assess alternatives.

4. Adhere to and maintain processes.  From data security to ever-evolving privacy laws, document, publish and train your team on best practices.  Minimize who has access to PII data, when it is used and who is authorized to access it and how.

5. Bonus Step: Communicate, communicate and communicate some more.  Humans innately don’t like rules and guardrails. If Data leadership has a seat at the executive table and the necessary influence, data governance can be implemented relatively hassle free. However if that’s not the case, then you need to invest time in articulating the benefits of good data governance to the organization and each key department. Weave in an element of “what is in it for me” for other leaders.

For an organization to get the most value from their data investment, it is vital that you establish a strong data governance and TAP Analytics can help you get there.  TAP Analytics has a heritage of assisting its clients in improving the data governance of their operations.  If you’d like to learn how we may be able to help your organization, get in touch with us as hello@thetapconsultancy.com.