3 best practices for improving and maintaining data quality
To excel in business today, organizations are increasingly relying on insights generated by data analysis. And they realize that insights are only as good as the data they come from. More data does not mean better insights. It’s not the quantity, but the quality of data that impacts the value of insights.
KPMG’s 2016 Global CEO Outlook highlights that 84 percent of CEOs are concerned about the quality of the data they’re basing decisions on. As business decisions are very much affected by the quality of data, what they need is a reliable data foundation that always assures high data quality.
Today's economy is completely data-driven. Customers generate enormous amounts of data at every point of interaction. Businesses use enterprise-wide systems and third-party tools that again push more data. The volume, variety, and velocity of data available to companies increases by the hour. What matters in the end is how this data is used effectively.
Data helps businesses profile their customers, communicate well with them, and present them the right products at the right time. Data helps improve business operations and make them efficient. Moreover, companies always explore ways to leverage data to generate more revenue and increase their profitability.
Data today can indeed help businesses stand out from the crowd. But only when that data is reliable, updated, and of high quality.
Why Data Quality Matters
Data quality is the degree to which completeness, validity, accuracy, consistency, availability and timeliness of data fulfills requirements appropriate for a specific use. In short, a measure of how fit the data is for the intended use.
But it's never easy. Data quality is one of the biggest challenges every company faces. The high volume and complexity of data collected across multiple sources creates an enormous task for companies when they want to manage data quality.
Issues in data quality directly affect downstream processes, revenue, compliance, and analytics; while missed opportunities and higher operational costs are also often attributed to poor quality of data. Managing the quality of data is imperative for complete data governance as well as regulatory compliance.
Quality data ensures confidence in the analysis and generated insights. If companies want to lead, they need to be one step ahead of the competition, and that can be achieved only with reliable, actionable insights.
What Impacts Data Quality
Poor data quality makes extensive impact on business including wrong product delivery, off the mark forecasts, inadequate planning, rework, poor customer experience and loss of reputation.
Most of the factors affecting data quality are the defining elements such as accuracy, completeness and consistency. In the case of healthcare services, for example, inaccurate patient information and health records lead to adverse health outcomes. For retail business, inconsistency in the customer contact details not only creates delivery issues and customer complaints but also misses marketing opportunities.
For all data, validity is always crucial. If data is not validated against the defined parameters such as format, range, and source, it is as good as absent. Depending on the urgency and critical nature of the operations, other factors specific to industries may become equally important.
For example, sales, marketing, and support teams must have clean and updated customer data to provide uniform customer experience and establish consistent, relevant, and compliant communication.
Finally, with no ambiguity, overlap or duplication, reliability of data across all sources is absolutely essential for high data quality.
Managing Data Quality
Recognizing and strategizing data quality management is crucial for uniform data usage across the enterprise. This can be a resource-intensive challenge, especially as data grows faster than what can be managed with traditional tools.
Management of data quality improves significantly by defining and implementing a quality framework. An iterative approach delivers better results when streamlined with modern techniques such as efficient workflows, collaborative data curation and use of the machine learning technology.
Workflows for data change management
Being data-driven is essential in the modern business environment. The key factor in maintaining data quality for any data-driven enterprise is process management that offers structured data maintenance with full tracking and traceability. Workflow processes for data change and deletion requests that can be triggered by field service users or external parties like customers or suppliers keeps data up to date.
When change requests go through efficient review and approval workflows, a comprehensive record can be maintained for changes in data. Accounting for completeness, accuracy, and consistency, the change management workflows assure high data quality, with the added advantage of comparing changes at the attribute level.
Data quality management is an enterprise-wide continuous process with several stakeholders. They need full visibility into the status of change requests, for ensuring that only updated data is used in analysis, insights, and related forecasts.
As a continuous stream of new data flows into systems, collaborative efforts can guarantee high quality, clean, and consistent data. Data attribute-level discussion threads and voting capabilities, available within the end user business applications enable users to flag and validate potential data discrepancies. Facilitating all the stakeholders for their contribution towards data curation and the underlying quality governance program builds a collaborative quality culture.
Machine learning for data quality improvement
Speed of data quality management must match the speed of data generation to maintain clean, reliable data. While this outcome is difficult to achieve with traditional methods, modern technology offers a better solution. Machine learning can be leveraged to monitor, score, and improve the data quality to stay ahead of data challenges.
The key areas for integrating machine learning include:
- Understanding and quantifying data quality
- Better data matching and deduplication
- Machine learning-assisted data enrichment
Machine learning capabilities in data management systems continuously improve data quality, to ensure that all operational and customer-facing teams always work with accurate and consistent data.
In a Nutshell
Data quality indicates how fit the data is for the intended use, with quantified factors that include completeness, validity, accuracy, consistency, availability and timeliness.
Data quality directly affects operational efficiency, customer experience, revenue opportunities, compliance, analysis reliability, precision of insights, and business decision confidence.
Organizations can reduce the risk and increase the trust in data quality management by employing these best practices:
- Define fundamental data quality strategy and implement the quality management framework
- Identify the factors affecting data quality, quantify them, and measure
- Standardize the processes and deploy workflows for change management
- Facilitate collaborative data curation
- Integrate modern technologies such as machine learning to eliminate possible lags and to build on iterative improvements.