Reimagining Data Quality: Key Modern Considerations

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According to a recent Gartner survey, 62% of respondents consider data quality the top priority in data and analytics initiatives but only 27% of practitioners completely trust data. Do these statistics align with how you feel about your organization’s data? If so, how can you boost confidence in data, and ensure you can trust it for fast, confident decision-making? It’s time to take away the doubts about your data and reimagine data quality. After all, the quantity of data we create and manage continues to explode, with no signs of slowing down. Join us for this fireside chat as we explore how technology advancements are requiring us to reimagine data quality. We’ll cover the many benefits of this modern approach, including how these advances enable you to: Harness the explosion of artificial intelligence and machine learning to move faster and gain the biggest ROIImprove data quality efficiencies with hybrid cloud operationsEmpower new users to onboard and ramp up initiatives fasterBreak down data silos with a modern data catalog and easily access the datasets most relevant to your needs – so you can get to analysis and reporting faster

Technology

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

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
© 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
Poll 1: What is the current level
of data quality maturity in your
organization?
• Not Initiated
• Early Stage
• In Progress
• Mature

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

…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
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

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

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

FOR MORE INFORMATION:
www.precisely.com/contact
+1 (877) 700 0970
Thank you!

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

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In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

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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

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.
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.
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.
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.

Reimagining Data Quality: Key Modern Considerations

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Editor's Notes