Reimagining Data Quality: Key Modern Considerations
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The document discusses modern considerations for data quality management, emphasizing the evolution from reactive to proactive data management approaches. It highlights the varying maturity levels of organizations regarding data quality and the impact of trends such as data democratization, AI, and machine learning on data initiatives in 2023. The conversation is led by experts from Precisely and moderated by a co-founder of the EDM Council, focusing on how companies can enhance their data integrity and decision-making capabilities.
Reimagining Data Quality: Key Modern Considerations
- 1. Reimagining Data Quality: Key Modern Considerations Emily Washington SVP, Product Management Precisely Scott Arnett Sr. Director, Product Management Precisely HEAD SHOT HEAD SHOT A conversation with
- 2. Moderated by Mike Meriton Co-Founder & COO, EDM Council • Joined EDM Council full-time 2015 to lead Industry Engagement • EDM Council Co-Founder & First Chairman (2005-2007) • EDM Council Finance Board Chair (2007-2015) • Former CEO GoldenSource (2002-2015) • Former Executive for D&B Software and Oracle • FinTech Innovation Lab – Executive Mentor (2011 – Present)
- 3. 3 © 2022 EDM Council Inc. Today’s panel Emily Washington SVP, Product Management Precisely Scott Arnett Sr. Director, Product Management Precisely Moderator Mike Meriton Co-Founder & COO EDM Council
- 4. 4 Poll 1: What is the current level of data quality maturity in your organization? • Not Initiated • Early Stage • In Progress • Mature
- 5. Organizational needs are changing… 5 NOW AND THEN Responsive Data Mgmt. / IT teams cleaning data post-entry Operational in use case Focused on supporting business function efficiency and effectiveness On-premises data stores On-prem databases supporting operational systems and BI Proactive Data engineering embedding data quality to build and maintain data pipelines Analytics-driven Focus on analytics, artificial intelligence & machine learning, and decision-intelligence use cases Data cloud Companies now migrating and centralizing data in the cloud
- 6. …and so are data quality needs 6 Manual deployment processes Manually deploy and maintain software and data quality processes Technical SME to manage DQ Dedicated resources to configure and manage data quality Data replication to validate Replicate data within data quality tool to identify data issues Automated deployment processes Automated access to latest features and data quality process deployments Intelligent data quality and usability Leverage semantics, profiles, and observations in a seamless user experience to enable more users Native data quality execution Run data quality natively within environment data is stored NOW AND THEN
- 7. 7 Poll 2: Which of these trends is most impacting your business and related data quality initiatives in 2023? • Rapidly increasing volume and variety of data sources • Data-driven decision-making culture • Artificial Intelligence and Machine Learning applications • Data integration and interoperability • Data Democratization
- 8. Questions?
- 9. The leader in data integrity Our software, data enrichment products and strategic services deliver accuracy, consistency, and context in your data, powering confident decisions. of the Fortune 100 99 countries 100 2,500 employees customers 12,000 Brands you trust, trust us Data leaders partner with us
- 10. Join EDM Council and our membership community of companies… Worldwide Americas, Europe, Africa, Asia, Australia 350+ Member Firms Cross-industry, including Regulators 25,000+ Professionals edmcouncil.org
- 11. FOR MORE INFORMATION: www.precisely.com/contact +1 (877) 700 0970 Thank you!
Editor's Notes
- #5: These organizational trends have a significant impact on how the data quality needs of the business are addressed. We see additional involvement from the business teams not just as a participant but jointly leading the initiatives. Business teams need to ensure that data can be trusted and they are now an active participant in the resolution process. These processes can now become more automated with deployment methods and built-in intelligence. This intelligence can be used to guide users in building the appropriate rules, to perform sophisticated matching processes and by monitoring the data. This observation process can provide proactive alerts to users about potential issues so that the issues can be investigated and resolved before any decisions have been made based on this data. All of this can provide a more seamless user experience by leveraging semantics and metadata as part of the processes. In addition, because companies are migrating and centralizing data in the cloud, they want to ensure data quality validations occur where the data resides, without taking it out of the centralized location to apply data quality rules. This requires data quality processes that can run natively where that data sits.
- #6: It used to be that organizations had data management projects that were led by IT teams and focused primarily on responding to issues. They would target operational use cases and look to improve efficiency and effectiveness. They typically addressed data that resided on premises at the organization. Now we are seeing proactive data engineering with data engineers embedding data quality within data pipelines. There is a huge focus on analytics including artificial intelligence & machine learning use cases. In addition, companies are now migrating and centralizing data in the cloud using cloud data providers such as Snowflake and Databricks.
- #7: These organizational trends have a significant impact on how the data quality needs of the business are addressed. We see additional involvement from the business teams not just as a participant but jointly leading the initiatives. Business teams need to ensure that data can be trusted and they are now an active participant in the resolution process. These processes can now become more automated with deployment methods and built-in intelligence. This intelligence can be used to guide users in building the appropriate rules, to perform sophisticated matching processes and by monitoring the data. This observation process can provide proactive alerts to users about potential issues so that the issues can be investigated and resolved before any decisions have been made based on this data. All of this can provide a more seamless user experience by leveraging semantics and metadata as part of the processes. In addition, because companies are migrating and centralizing data in the cloud, they want to ensure data quality validations occur where the data resides, without taking it out of the centralized location to apply data quality rules. This requires data quality processes that can run natively where that data sits.
- #8: These organizational trends have a significant impact on how the data quality needs of the business are addressed. We see additional involvement from the business teams not just as a participant but jointly leading the initiatives. Business teams need to ensure that data can be trusted and they are now an active participant in the resolution process. These processes can now become more automated with deployment methods and built-in intelligence. This intelligence can be used to guide users in building the appropriate rules, to perform sophisticated matching processes and by monitoring the data. This observation process can provide proactive alerts to users about potential issues so that the issues can be investigated and resolved before any decisions have been made based on this data. All of this can provide a more seamless user experience by leveraging semantics and metadata as part of the processes. In addition, because companies are migrating and centralizing data in the cloud, they want to ensure data quality validations occur where the data resides, without taking it out of the centralized location to apply data quality rules. This requires data quality processes that can run natively where that data sits.