The business environment is data-rich, organisations are collecting vast amounts of information daily. But simply amassing data is not enough – it’s the health of that data that determines whether it delivers value. Unhealthy data can lead to poor decisions, inefficiencies, and missed opportunities. The question then becomes: how do you ensure your data is healthy, trustworthy, and primed for delivering measurable business value?
The answer lies in a structured approach to data health. The BREATH framework offers a comprehensive solution for assessing, maintaining, and leveraging data health to drive business outcomes. Here’s why data health matters and how BREATH can transform your approach to data.
What Is Data Health and Why Does It Matter?
Healthy data is accurate, consistent, timely, and relevant. It forms the foundation of reliable insights and confident decision-making. When data health is compromised, businesses face risks like regulatory non-compliance, operational inefficiencies, and reputational damage.
Data health matters because it directly impacts:
- Decision Quality: Clean, reliable data leads to actionable and accurate insights.
- Operational Efficiency: Healthy data reduces the time spent cleaning or reconciling datasets.
- Compliance and Trust: Adhering to privacy regulations and maintaining stakeholder confidence requires robust data governance.
Introducing the BREATH Framework
The BREATH framework—an acronym for Baseline, Refine, Evaluate, Automate, Track, and Harmonise—provides a structured methodology for managing data health throughout its lifecycle. By focusing on these six pillars, businesses can ensure their data is a consistent source of measurable value.
1. Baseline: Define Your Starting Point
Understand where your data stands today. Assess its quality, integrity, and alignment with business goals. This step lays the groundwork for identifying gaps and setting priorities.
Example: Identify missing fields or duplicate records in your CRM system to create a data improvement roadmap.
2. Refine: Clean and Standardise Data
Refining data involves removing errors, inconsistencies, and redundancies. Standardisation ensures that data across sources follows the same structure and rules.
Example: Standardising customer addresses in a retail database enables more accurate segmentation for marketing campaigns.
3. Evaluate: Measure Data Quality
Establish KPIs for data health and measure against them regularly. Key metrics might include completeness, accuracy, and timeliness.
Example: A financial institution tracks the accuracy of credit risk models to ensure they reflect current market conditions.
4. Automate: Implement Continuous Improvement
Automating data quality checks reduces manual effort and ensures ongoing health. Tools like automated data validation scripts or real-time error detection systems play a key role.
Example: A SaaS provider uses automated scripts to flag incomplete onboarding data, ensuring smooth customer setups.
5. Track: Monitor Data Over Time
Healthy data today doesn’t guarantee health tomorrow. Continuous monitoring helps identify emerging issues early.
Example: Monitoring data flows in a supply chain system helps a manufacturer spot discrepancies in vendor delivery records before they escalate.
6. Harmonise: Ensure Alignment Across Systems
Harmonising data involves integrating datasets from multiple sources to create a unified view. This ensures consistency and accuracy in decision-making.
Example: Consolidating sales data from various regions allows a global company to create accurate revenue forecasts.
How BREATH Uncovers Business Value
By following the BREATH framework, businesses can move beyond reactive fixes to proactive management of their data assets. The result? Data becomes a reliable resource for driving business value. Here’s how:
- Enhanced Decision-Making: Accurate, clean data enables leaders to make informed decisions confidently.
- Operational Excellence: Automated checks and harmonised datasets reduce inefficiencies, saving time and resources.
- Risk Mitigation: Consistent monitoring ensures compliance with data regulations, protecting against costly breaches or fines.
A Case in Point: BREATH in Action
A fintech company struggled with unreliable customer data, leading to inefficiencies in onboarding and compliance processes. By implementing the BREATH framework, they:
- Established a baseline of their data quality, revealing 30% of records with critical errors.
- Refined their data by standardising customer fields and removing duplicates.
- Automated checks that flagged inconsistencies during new customer onboarding.
- Tracked data improvements over six months, reducing onboarding errors by 50%.
This approach not only streamlined their operations but also boosted customer satisfaction and compliance readiness.
Conclusion: Healthy Data Equals Measurable Value
Data health isn’t just a technical concern – it’s a business imperative. With the BREATH framework, organisations can ensure their data is accurate, trustworthy, and ready to deliver measurable value. By investing in data health today, businesses can uncover insights, improve efficiency, and gain a competitive edge.