Adastra’s Data Quality Methodology 

Adastra’s approach to delivering Data Quality allows you to prioritize data sets and accelerate time-to-value for your organization. Download our guide for a step-by-step walkthrough of Adastra’s Data Quality Methodology. 

With emerging advanced uses like Machine Learning, organizations that collect large volumes of data undoubtedly have an advantage. However, this also comes with the immense responsibility of ensuring data quality within all data sets, as the quality of insights directly correlates with the quality of data. 

Today, Data Quality is measurable and requires that the data be checked for completeness, conformity, consistency, validity, accuracy, timeliness/relevance, duplication and integrity. 

Data Governance is the process of establishing trusted, quality data that is validated, unified and fit for purpose. Typically, an enterprise-wide Data Governance framework leverages a top-down approach, selling the concept internally, defining the framework, identifying data domains, standards and processes, and preparing the data for analytics. This approach requires a lot of preparation and time before any value can be extracted. 

However, all data sets are not equally critical or used as frequently in high value projects or analytics. Keeping this in mind, Adastra’s approach of choice to Data Governance is a bottom-up one, starting with targeted data sets at project-level and then scaling across the organization. Adastra has developed a five-step cyclical process to help organizations analyze and optimize their data quality:  

  1. Understanding data assets: This foundational step involves recognizing what data is being captured and identify what is missing. This includes activities such as data classification, metadata collection and data profiling.   
  2. Measuring Data Quality: This involves establishing requirements and data quality rules for measurement, as well as considering the impact that data quality has on your business and its operations.  
  3. Monitoring and reporting Data Quality: Next, a controlled process is created to monitor and report data quality with an eye on the necessary steps to be taken. This includes looking for trends in the data quality and asking critical questions like what needs immediate action and proactive steps that can be taken to improve data quality.   
  4. Improving Data Quality: The data quality improvement plan includes setting up of rules and processes for monitoring, analysis, data cleansing and validation.  
  5. Scaling towards Master Data Management: Once the data quality meets the recommended standards, the data must undergo consolidation and matching. This involves creating a clean record that can be uniquely identified and referenced by other data.  

While this approach follows all the steps of the enterprise-wide approach, resequencing of the steps significantly accelerates the time to value for your organization. 

By taking an iterative approach, analyzing and improving data quality within high value use cases, organizations will quantify their issues while cleansing data that is core to their organization. Throughout the process, Adastra further refines and optimizes the Data Governance framework, roles, responsibilities, processes and standards based on experience to ensure data quality excellence. 

This Data Quality Methodology Guide explores Adastra’s best practices and outlines a step-by-step methodology for instilling data quality within targeted data sets, with an aim of accelerating value across your organization. 

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