The toll that poor data quality takes on an enterprise’s data lifecycle can be measured in many ways. For starters, there is the expense in terms of loss of time, loss of productivity, or money spent to identify and rectify inaccuracies. Another measurement to consider is how data is understood when conveyed across roles, which might relate to varying attributes of the data. Also, there are many missed opportunities for reusability and automation when organizations do not build out competencies to invalidate, capture, and remediate data quality issues.
Investments in data quality can help cut costs and accelerate time to value from your business data. More importantly, organizations can then focus on advancing the automation of data management and operationalizing analytics.
Here are three methods to improve data quality and optimize the data refinement process.
Data accessibility is essential not only to drive collaboration between technical and non-technical users but also to ensure that user knowledge and experience is integrated into the data refining process. Data quality challenges may require more business context on the data’s application and usage, making the way an application provides data access that much more critical.
Improving data access and providing a broad array of techniques can help speed time to value tremendously. Access to data profiles gives a visual and statistical perspective to identify gaps across attribute values, histograms to identify the frequency of occurrence of values and validation of data uniqueness, or lack thereof, in environments and formats that do not support constraints.
By giving access to business users and data stewards to categorize and apply data quality rules, organizations can build out competencies in identifying and implementing high-value data. Data engineers can then leverage business subjects, data categories, and tags along with historical rule associations to automate data quality on ingestion of new sources.
If data access lends to discovering and understanding the quality of the data through statistical and visual interpretation, then remediation is operationalizing the techniques from those learnings to automate cleansing, moving and removing data from the system.
Without a remediation process, data systems appear frozen while bad data is found and removed before business applications and reports can access data. Reports are delayed or worse, key partitions of trusted data are rebuilt in duplicate where mixed data quality may inadvertently wind up.
By having a data quality validation process in place, bad data is safely separated from high-quality data, while workstreams can be automated to fix data deficiencies, leading to trusted data to support all lines of business and speed time to insights.
Validation and remediation of data paves the road to production of master reference entities. Though the sources, formats, or structure of data may differ, mastering provides consistency to the data and capability to synchronize data (old and new) to the dimension.
Imagine having validated contact information from social applications in one source and ratified customer information from a transactional system in another source. Both serve separate purposes to support the lines of business, but uniting them through mastery gives a complete and unique view of each customer across lines of business.
Production of consistent and synchronized entities leads to quicker and more definitive business decisions from the next best offer to the prevention of customer churn.
Organizations that have mastered the access and understanding of data have promoted multiple paths for its users, skilled and unskilled, to reliably and securely use data for better business outcomes. Implementing data remediation ensures that comprehension of business information standards and usage are integrated into the data lifecycle. Trust is essential in this data system, as it is built on collaboration across roles and the automation of processes in support of data accuracy, ultimately defined by the organization as a whole.
How are you building trust in your data?
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