Data is the currency of modern decision-making — yet when it’s flawed, the consequences can ripple through an organization like a domino effect. Poor-quality data undermines critical decisions, creates operational inefficiencies, and can even tarnish your brand’s reputation. Recent studies reveal that a staggering 70% of organizations struggle to trust their data, with data quality being the primary culprit.
Compounding the issue, data downtime — the period when data issues prevent teams from accessing reliable insights — has surged, with a 166% increase in resolution time for data quality problems in 2023.
For executives, this is no longer an IT issue — it’s a business-critical imperative. In 2023 alone, 31% of revenue losses were directly tied to data quality issues. A well-executed data quality strategy shifts this dynamic by tackling data problems proactively instead of reactively. Through clearly defined frameworks, governance policies, and automated processes, businesses ensure their data remains clean, actionable, and has a strategic advantage.
In this blog, we’ll guide you through the key justifications for prioritizing data quality, the positioning of data as a product, and actionable steps to craft a foolproof data quality plan that aligns with today’s complex business demands.
Why every business needs a data quality strategy
What makes a data quality strategy indispensable? Beyond being a technical necessity, it serves as a safeguard against risk and a catalyst for innovation.
1. Reducing operational risks and costs
Imagine an e-commerce business sending packages to incorrect addresses due to flawed customer data. Not only does this inflate shipping costs, but it also damages customer trust. High-quality data eliminates these costly errors by ensuring accuracy upfront.
By investing in data quality processes, organizations reduce rework, mitigate avoidable delays, and align operations toward long-term cost savings. It’s not an expense — it’s an investment in efficiency.
2. Supporting data-driven decision-making
Inaccurate datasets can derail even the best-intentioned analytics teams, leading to misguided business strategies. Reliable, high-quality data builds confidence across all levels of leadership, enabling executives to act decisively using dashboards, forecasts, and analytics they can trust.
3. Enhancing the customer experience
Your customers expect personalized, seamless interactions with your brand. However, bad data — such as outdated contact information or duplicate records — creates friction and disrupts the customer journey.
For example, Chipotle faced challenges with inconsistent store location and menu data, which impacted customer interactions and operational efficiency. By implementing a centralized data repository with Semarchy, they streamlined their data management, ensuring accurate, reliable information across all locations. As a result, they enhanced the customer experience while achieving scalability on a global level.
4. Staying ahead in regulatory compliance
For industries bound by regulations, poor data quality is not just inconvenient — it’s an expensive liability. Fines, compliance violations, or rejected filings can set back even the most established organizations. Clean, reliable data ensures adherence to regulatory requirements and protects your business from penalties.
5. Enabling AI and machine learning
AI success is a direct reflection of the quality of its training data. Feed an AI application an unreliable dataset, and you get unreliable outputs. By ensuring your organization works with AI-ready data, you accelerate innovation, improve decision-making, and enable AI models to drive real business value instead of creating new challenges.
Treat data like a product: a new mindset
To revolutionize how you approach data quality, think of data as a product — a deliverable that evolves and provides measurable value over time.
1. It’s a core deliverable
Like any product, data isn’t a by-product of your operations — it’s a core business asset designed to deliver value, whether through improved decision-making or streamlined processes.
2. Defined ownership
“Data product owners” are crucial for maintaining data usability and relevance. These assigned individuals ensure accountability while aligning datasets with business needs.
3. Reliable pipelines matter
Just as great products arrive on time and in perfect condition, data pipelines must maintain consistent quality and reliability. Breakdowns here erode stakeholder trust.
4. The lifecycle approach
From creation to deletion, every piece of data should follow a lifecycle akin to product development — where testing, delivery, and refinement are non-negotiable.
This mindset ensures data remains not only a high-value input but also a reliable output that informs your organization’s most important decisions.
Seven key steps to building your data quality roadmap
Developing a comprehensive data quality strategy doesn’t have to be daunting. Follow these steps to create a structure that’s actionable, sustainable, and objective-aligned:
1. Audit existing data and infrastructure
Start by evaluating your current data health. Use tools like data profiling to identify inconsistencies, duplicates, or gaps within your datasets. Establishing your Master Data Management (MDM) platform at this stage is crucial, as this will help you organize your data ready for the next implementation steps and ensure accurate and successful results.
2. Define clear objectives
Set measurable benchmarks for data quality dimensions such as accuracy, consistency, and completeness. For instance, aim for 98% accuracy in critical customer records over six months as a guide for your work efforts.
3. Establish governance frameworks
Assign key roles — such as data stewards — who oversee ongoing data quality maintenance. Make sure you carefully document your data quality policies and ensure team accountability for adhering to governance protocols.
4. Leverage automation
Data quality tools, such as those available in the Semarchy Data Platform, can detect inaccuracies, validate fields, and monitor compliance in real time. This reduces manual intervention while maintaining adherence to your quality standards.
5. Clean up historical data
Legacy systems and inactive datasets often hide outdated or inaccurate information. Apply standardized data cleansing techniques to bring older data in line with today’s expectations and remove any data that’s no longer needed to free up space and avoid unnecessary work.
6. Build a culture of data ownership
Educate your teams about the importance of data quality, and delegate specific roles for ongoing data governance. A shared sense of responsibility ensures organizational alignment toward this goal.
7. Monitor and refine continuously
Your data ecosystem will evolve, and so will your strategy. Once your strategy is in motion, implementing reliable data quality reporting is key to measuring success. Metrics such as ‘completeness rates’ or ‘accuracy percentages’ give teams actionable insights into progress and areas requiring focus.
The competitive advantage of data quality
A robust data quality strategy goes beyond technical fixes — it reflects a cultural shift. It unites teams, provokes informed decision-making, and elevates the business.
By treating data as a product and choosing the right frameworks, you position quality at the intersection of strategy and execution. The results? Reduced risks, improved customer trust, and a tangible ROI you can trace across the enterprise.
At Semarchy, we’re here to guide your data quality journey. Ready to transform your data into a trusted asset? Contact us today to explore our Data Platform for MDM, Data Intelligence and Data Integration.
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