The Importance of Data Health

In today's data-driven landscape, organizations must recognize the critical importance of data health – the extent to which an organization's data effectively supports its business objectives.

Data health is not merely a technical consideration; it is a strategic imperative for organizations that drives informed decision-making, operational efficiency, and enhanced customer experiences.

It is essential for organizations to prioritize and invest in measuring and maintaining the health of their data.

“Assessing the health of your company’s data is crucial. Poor data quality can have a significant impact on your business, resulting in inefficient processes and lost revenue,” wrote AI researcher Vijay Kanade for Spiceworks.

“Bad Data” Has Repercussions on Revenue, Data Trust

Data observability company Monte Carlo says that data engineers spend upwards of 40 percent of their time – or 120 hours per week – dealing with bad data.

“Poor data quality costs companies a tremendous amount of money, impacting over 26 percent of their revenue according to a recent survey by Wakefield Research,” said Monte Carlo in October.

The importance of data health is only growing as Monte Carlo updated its research earlier this month in its “The Annual State of Data Quality Survey” and found that the average percentage of impacted revenue jumped to 31 percent, up from 26 percent in 2022.

“Additionally, an astounding 74 percent reported business stakeholders to identify [poor data quality] issues first, “all or most of the time,” up from 47 percent in 2022,” said Monte Carlo. “These findings suggest data quality remains among the biggest problems facing data teams, with bad data having more severe repercussions on an organization’s revenue and data trust than in years prior.”

Key Elements for Data Health

Big data company Talend defines data health as how well an organization’s data supports its business objectives.

“Data is healthy if it is easily discoverable, understandable, and of value to the people that need to use it, and these characteristics are sustained throughout its lifecycle,” explains Talend. “You’ll know that your organization’s data is healthy when you can prove that it’s valid, complete, and of sufficient quality to produce analytics that decision-makers can feel comfortable relying on for business decisions.”

To establish and maintain data health, organizations should focus on the following key elements:

Data Quality:

• Accuracy: Ensuring that data is free from errors, inconsistencies, and inaccuracies.
  
  
• Completeness: Having all necessary data fields populated to provide a holistic view.
  
  
• Consistency: Ensuring uniformity and standardization across various data sources and systems.
  
  
• Timeliness: Ensuring that data is up to date and relevant for decision-making.
  
  

Data Governance:

• Clear Policies: Establishing comprehensive data governance policies to guide data collection, storage, usage, and access.
  
  
• Data Security: Implementing robust security measures to protect sensitive data from unauthorized access or breaches.
  
  
• Compliance: Ensuring adherence to regulatory requirements governing data privacy and protection.
  
  

Data Integration:

• Seamless Integration: Connecting and consolidating data from disparate sources to provide a unified view.
  
  
• Interoperability: Ensuring that data can be shared and exchanged across different systems and applications.
  
  
• Master Data Management: Maintaining accurate and consistent master data across the organization.
  
  

Talend says it all comes down to data agility, data culture, and data trust for organization-wide data health.

Internal and External Factors Affecting Data Health

Maintaining data health for an organization is a complex task as multiple internal and external factors create a shifting digital landscape.

Some of the factors affecting data health include:

• Hybrid and Cloud-Based Computing: Organizations are being challenged with ensuring seamless data integration across on-premises systems and cloud platforms. The move towards hybrid and cloud-based computing means implementing robust security measures to protect data in all locations.
  
  
• Regulatory Changes Regarding Data: Compliance obligations are increasingly tricky as companies must stay up to date with evolving data protection and privacy regulations to avoid penalties. Ensuring appropriate consent for data collection and processing activities is key with high stakes: Meta (Facebook’s parent company) was fined a record $1.3 billion in May 2023 and ordered to stop sending European user data to the U.S.,  The French data protection authority in December fined Google roughly $57 million for failing to acknowledge how its users’ data is processed, eclipsing Amazon’s $800 million+ fine in 2021 for data protection violations.
  
  
• Data Culture and Processes: Employee awareness and training is crucial as data management involves more than just your company’s data engineers. All employees need to understand the importance of data quality, security, and compliance. Implementing efficient data collection, storage, and management processes needs to be part of an organization's data DNA.
  
  
• Data Spigot Has No Off Switch: The speed and flow of data are on the rise, and it appears the information spigot will not shut off anytime soon. In fact, the amount of data created daily around the world is hard to fathom – with estimates in the range of 2.5 quintillion bytes of data being created and consumed each day. A quintillion, by the way, is a million trillion. By some estimates, the world created 94 zettabytes of data last year – meaning some 90 percent of all data ever created in the world’s history was created in the last two years. Organizations must find a way to harness this overwhelming flow of data.
  
  

The Advantages of Good Data Health

As we said before, maintaining good data health offers numerous advantages for an organization, including:

• Informed Decision-Making: Reliable and accurate data enables data-driven decision-making, leading to better business outcomes.
  
  
• Enhanced Operational Efficiency: Access to high-quality data streamlines processes, reduces errors, and improves overall operational efficiency.
  
  
• Improved Customer Experiences: Reliable data enables organizations to understand customer preferences and deliver personalized experiences.
  
  
• Effective Risk Management: Good data health supports risk assessment and management, allowing organizations to identify and mitigate potential risks.
  
  

Talend says that companies that can manage their data health can better take advantage of initiatives such as enabling analytics, modernizing cloud and data, establishing data excellence, and accelerating operational data.

“Every initiative has an associated business outcome: increased revenue, reduced costs, or mitigated risk,” says Talend.

The Metrics of Measuring Data Health

Just as individuals monitor their personal health by using measurements such as blood pressure readings, body weight measurement, and cholesterol numbers, organizations must measure their data health by using a set of metrics.

A good starting point is the “Six Core Data Quality Dimensions” as defined by the Data Management Association of the UK:

• Accuracy: Assessing the level of correctness and precision in data records. Definition: The degree to which data correctly describes the "real world" object or event being described.
  
  
• Completeness: Examining the presence of all required data fields. Definition: The proportion of stored data against the potential of "100 percent complete"
  
  
• Consistency: Ensuring uniformity and conformity across data sources. Definition: The absence of difference, when comparing two or more representations of a thing against a definition.
  
  
• Timeliness: Measuring the freshness and relevance of data for decision-making. Definition: The degree to which data represent reality from the required point in time.
  
  
• Uniqueness: Ensuring that data is not being duplicated in the system. Definition: No thing will be recorded more than once based upon how that thing is identified.
  
  
• Validity: Evaluating the compliance of data with predefined rules and constraints. Definition: Data is valid if it conforms to the syntax (format, type, range) of its definition.

We can add to this base with additional metrics to measure data health such as:

• Integrity: Checking for data integrity, ensuring that data remains intact and unaltered.
  
  
• Relevance: Assessing the alignment of data with business objectives and needs.
  
  
• Accessibility: Evaluating the ease of accessing and retrieving data when required.
  
  
• Retention: Examining the appropriate storage and retention of data based on legal and regulatory requirements.
  
  
• Security: Measuring the effectiveness of data security measures to protect against unauthorized access or breaches.
  
  
• Compliance: Assessing adherence to data governance policies and procedures.

Periodic readings of these 12 measurements can provide a complete picture of your organization's data health.

Consequences of Ignoring Data Health

Given the importance of data health, you would think it would be a top priority, but Talend’s data health surveys have found that less than half of executives are certain that their company even uses data quality standards.

Even more shocking, a third of executives said there were no documented standards in place, yet 95 percent of those surveyed saw a need for universal, cross-industry data quality standards.

Neglecting data health can have significant negative consequences for organizations, such as:

• Poor Decision-Making: Inaccurate or incomplete data can lead to misguided decisions, hindering business growth and performance.
  
  
• Operational Inefficiencies: Data inconsistencies and errors can result in process inefficiencies, delays, and increased costs.
  
  
• Compliance Risks: Ignoring data health can expose organizations to non-compliance with data protection regulations, leading to legal and reputational risks.
  
  
• Customer Dissatisfaction: Inaccurate or outdated customer data can result in poor customer experiences and damaged relationships.
  
  
• Missed Opportunities: Inability to leverage data for insights and innovation may lead to missed opportunities for growth and competitive advantage.

In a digital-first world, data health has emerged as a fundamental requirement for organizations striving for success and sustainability.