Data quality charter vital for lasting business gains

The average annual cost of poor-quality data to organizations is close to $13 million. That's because subpar data triggers multiple issues – from incorrect reporting and regulatory risks to unsound analytics and missed opportunities. 

Simply put, how good your decisions are and how well you thrive depends on the quality of data your business uses. 

So, you cannot afford to struggle with ambiguous ownership, incomplete records, inconsistent information, or duplicate data. However, improving data quality isn't just about resolving individual issues. You need a structured program that is repeatable and aligns technology, processes, and people. 

Let's explore what a data quality program means, why you require a clearly defined program charter, and how to establish a robust foundation for long-term success.   

Decoding Data Quality Programs 

A formal and company-wide initiative, a data quality program helps ensure your data is complete, accurate, timely, consistent, and suitable for intended use. 

With such a program, you stop being reactive to data problems. Rather, you proactively define ownership, standards, and processes for data quality management across teams and systems.  

A data quality program's chief characteristics include: 

• Data quality dimensions that are clearly defined (such as completeness, accuracy, consistency) 
• Assigned data ownership and responsibility 
• Rules that are standardized and checks for data validation 
• Continuous cycles associated with tracking, reporting, and improvement 

A program that is mature considers data as a strategic asset and ensures that quality controls are embedded into everyday operations, digital initiatives, and analytics. 

Data Quality Program Charter: Why It Is Important 

Essential for formally establishing the data quality program, a charter is like a foundational document or map. It defines the aim of the program, the way it will operate, and who will be accountable for its success. Data quality efforts might be inconsistent, fragmented, or unsustainable without a charter. 

So, here's why a charter is important: 

• Benefits 

A well-crafted charter makes sure all stakeholders are on the same page regarding objectives, expectations, and priorities. It formally approves the data quality program, lending it organization-wide visibility and authority. 

Moreover, standardized approaches in the charter minimize double efforts and ad hoc data resolutions. When systems, data volumes, and use cases expand, the data quality program can grow in tune too. 

• Scope and Goals 

The charter outlines the data domains that are included, whether they are financial, customer-centric, operational, or product-specific. It also clearly mentions the systems in scope and business units. And both short-term and long-term goals are defined. This ensures that the data quality program doesn't become unfocused or too broad.   

• Roles and Responsibilities 

An ideal charter defines who is responsible for what with clarity. For instance: 

• Executive sponsors offer funding and oversight 
• Data owners are responsible for particular data domains 
• Data stewards are in charge of data quality rules and resolution of issues 
• Technical teams implement controls and handle monitoring 

• Key Performance Indicators (KPIs) 

The charter decides how program success is measured. Hence, it often includes scorecards for data quality, reduction in errors or rework, improvements in reporting accuracy, and less time for issue resolution. These KPIs help in gauging the program's progress and ensure leadership support is maintained. 

• Return on Investment (ROI)

A well-made charter sheds light on how improving data quality delivers value, like lower operational expenses, better decision-making, reduced compliance risk, and stronger analytics outcomes. Essentially, it connects data quality with ROI, so it's easy to sustain the program and obtain necessary investment. 

Setting up a Data Quality Program: Key Steps 

You need to strike a balance between ambition and practicality when starting with a data quality program. These steps can help: 

1. Identify Data Domains with Maximum Business Impact 

When you focus on crucial domains, the quality program delivers quick wins and helps create momentum. For instance, you can start with customer data used for marketing and billing or financial data that plays a key role in reporting. Or prioritize operational data that drives production decisions or supply chains. 

2. Define Standards for Data Quality 

For every domain, provide clarity on what good data implies. Define rules associated with completeness, validity and accuracy, timeliness, and consistency across systems. Make sure the standards aren't just technical, but also business-driven. 

3. Assign Ownership 

Clear accountability is indispensable to data quality improvement. So, assign data owners to approve standards and fix issues that are escalated. Make sure data stewards keep an eye on quality and coordinate resolutions. And technical teams should execute controls. 

4. Monitor and Implement Controls 

Leverage dashboards and automated tools for data profiling and validation checks to spot problems early on, track trends, and assess performance against KPIs. This will help scale the data quality program across large datasets. 

5. Establish Processes for Issue Management 

Define the processes for reporting, prioritizing, and resolving data issues. Consider including root cause analysis, clear paths for escalation, and feedback loops for the prevention of recurrent problems. Ensure processes are repeatable to foster a continuous improvement culture. 

Core Elements of a Well-Structured Charter Template 

A data quality program charter should ideally check these boxes – alignment, consistency, and clarity. It must also be flexible and adaptable enough to handle evolving situations and encourage collaboration. Hence, a well-structured template is necessary to speed up the adoption of the data quality program. 

Make sure the template includes: 

• Program Details: Name, context, purpose, vision, guiding principles, data domains and systems in scope, benefits expected 
• Controls: Dimensions and standards for data quality, rules and thresholds for validation, mechanisms for monitoring and reporting, verification of the program's effectiveness
• Program Sponsor and Governance: Executive sponsor supporting and funding the program, steering committee that guides the direction of the program, decision-making authority, processes for escalation and approval
• Program Manager: Individual who oversees the daily operations of the program, coordinates activities, and makes sure program goals are satisfied 
• Program Staff Resources: Staff involved directly in the execution of the data quality program and their roles and responsibilities 
• KPIs and Metrics: Precise metrics that help assess the status and direction of the data quality program, baseline measurements, target improvements, reporting frequency 
• Business Impact: Compliance benefits and risk reduction, efficiency gains or cost savings, value to reporting, analytics, and AI initiatives 
• Communication and Change Management: Training plans, approach for stakeholder engagement, continuous improvement cycles 

Get Ready to Manage Data Quality Proactively and Sustainably 

Good data quality is every modern organization's backbone as it drives everything from operational efficiency and strong analytics to informed decision-making, regulatory confidence, and faster innovation. Hence, kick-starting and maintaining an effective and well-defined data quality program is essential. 

For that, you need a comprehensive program charter that fosters focus, clarity, alignment, and proof of value. And a charter template structured expertly ensures that data quality activities complement business goals, roles and responsibilities are transparent, and progresses are measurable. 

All in all, improving data quality is a strategic investment. And over time, you transition from reactive fixes to proactive and sustainable management of data quality.