Customer, client, supplier, internal, private, third-party data…
The amount of data available to real estate companies is truly outstanding! With the push of digitization and the changes Covid demands of real estate, businesses want to put high-quality data into the hands of the people who use it to do their jobs.
That means organizations must build processes that define who owns what data, how it can be used, how to maintain it meaningful to their business, and how to make it consumable by everybody in the organization, not just the data experts.
Data governance is no longer a choice in this data-filled, fast-moving space.
Join us to learn about:
What is Data GovernanceHow to manage, improve and control the quality of the dataHow to empower a common understanding of data through your organization
How to Build Data Governance Programs That Lasts: A Business-First ApproachPrecisely
Data analytics and Artificial Intelligence play an increasingly pivotal role in most modern organizations. To keep those initiatives on track, enterprises must roll out data governance programs to ensure optimal business value. Data governance has become a fundamental element of success, a key to establishing the component of the data integrity framework in any business. The most successful data governance programs use a business-first approach, delivering quick wins and cultivating sustained success throughout the organization. Unfortunately, many organizations neglect to implement such programs until they experience a negative event that highlights the absence of good data governance. That could be a data breach, a breakdown in data quality, or a compliance action that highlights the lack of effective controls. Once that happens, there are several different paths a data governance initiative might take. A typical scenario often plays out this way: The executive team calls for implementation of a company-wide data governance program. The newly-minted data governance team forges ahead, engaging business users throughout the organization and expecting that everyone will be aligned around a common purpose.
Top 4 Priorities in Building Insurance Data Governance Programs That WorkPrecisely
The document discusses building successful data governance programs that take a business-first approach. It recommends linking data governance to business goals, prioritizing critical data that drives key business metrics and outcomes, building engagement across operational, tactical, and strategic levels, and clearing a path for success by removing friction for stakeholders. Taking this approach can accelerate program roll-out by 18-40%, increase reinvestment likelihood by over 75%, and generate 2-7x greater ROI.
Data Governance That Drives the Bottom LinePrecisely
The financial services sector is investing heavily in data governance solutions to find, understand and trust customer data, while also managing compliance risk around an ever-evolving regulatory landscape more effectively.
But do you still find it difficult to get management support for data governance budgets? Do you have the tools you need to determine the “business cost of data” accurately? Can you show the CFO an ROI projection he can count on? Are you able to answer, “Will I see results on the top line or the bottom line?” Are your business line leaders able to identify areas that are losing money due to data problems?
If you answered no to any of these questions, join Precisely in our upcoming webinar that will focus on how Financial Services companies can monetize the return on investment for data governance and how to relate it to business results that every senior leader understands.
Join this on-demand webinar to learn about:
- How to select data initiatives based on corporate goals and strategy
- How to connect the dots from data challenges (quality, availability, accuracy, currency) to specific business metrics around
- How to quantify the data contribution to improving business performance around
- How to leverage metadata and linage to get a 360-degree understanding of your data
- How to evaluate data assets by assigning measures and defining scores.
- How to assign accountability to assets and processes
- How to define and execute the workflows needed to implement corrective actions
- How to highlight the benefits of data governance
Governance as a "painkiller": A Business First Approach to Data GovernancePrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long term.
A Business-first Approach to Building Data Governance ProgramPrecisely
Traditional data governance programs struggle to make the connection between critical policies and processes and its impact on business value and results. This leaves data management and governance practitioners having to continually make the case for data governance to secure business adoption.
Watch this on-demand webinar to learn about the proven methods to identify the data that matters, connect governance policies to business objectives, and quickly deliver value through the life of the program.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption. In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
How to Build Data Governance Programs That Lasts: A Business-First ApproachPrecisely
Data analytics and Artificial Intelligence play an increasingly pivotal role in most modern organizations. To keep those initiatives on track, enterprises must roll out data governance programs to ensure optimal business value. Data governance has become a fundamental element of success, a key to establishing the component of the data integrity framework in any business. The most successful data governance programs use a business-first approach, delivering quick wins and cultivating sustained success throughout the organization. Unfortunately, many organizations neglect to implement such programs until they experience a negative event that highlights the absence of good data governance. That could be a data breach, a breakdown in data quality, or a compliance action that highlights the lack of effective controls. Once that happens, there are several different paths a data governance initiative might take. A typical scenario often plays out this way: The executive team calls for implementation of a company-wide data governance program. The newly-minted data governance team forges ahead, engaging business users throughout the organization and expecting that everyone will be aligned around a common purpose.
Top 4 Priorities in Building Insurance Data Governance Programs That WorkPrecisely
The document discusses building successful data governance programs that take a business-first approach. It recommends linking data governance to business goals, prioritizing critical data that drives key business metrics and outcomes, building engagement across operational, tactical, and strategic levels, and clearing a path for success by removing friction for stakeholders. Taking this approach can accelerate program roll-out by 18-40%, increase reinvestment likelihood by over 75%, and generate 2-7x greater ROI.
Data Governance That Drives the Bottom LinePrecisely
The financial services sector is investing heavily in data governance solutions to find, understand and trust customer data, while also managing compliance risk around an ever-evolving regulatory landscape more effectively.
But do you still find it difficult to get management support for data governance budgets? Do you have the tools you need to determine the “business cost of data” accurately? Can you show the CFO an ROI projection he can count on? Are you able to answer, “Will I see results on the top line or the bottom line?” Are your business line leaders able to identify areas that are losing money due to data problems?
If you answered no to any of these questions, join Precisely in our upcoming webinar that will focus on how Financial Services companies can monetize the return on investment for data governance and how to relate it to business results that every senior leader understands.
Join this on-demand webinar to learn about:
- How to select data initiatives based on corporate goals and strategy
- How to connect the dots from data challenges (quality, availability, accuracy, currency) to specific business metrics around
- How to quantify the data contribution to improving business performance around
- How to leverage metadata and linage to get a 360-degree understanding of your data
- How to evaluate data assets by assigning measures and defining scores.
- How to assign accountability to assets and processes
- How to define and execute the workflows needed to implement corrective actions
- How to highlight the benefits of data governance
Governance as a "painkiller": A Business First Approach to Data GovernancePrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long term.
A Business-first Approach to Building Data Governance ProgramPrecisely
Traditional data governance programs struggle to make the connection between critical policies and processes and its impact on business value and results. This leaves data management and governance practitioners having to continually make the case for data governance to secure business adoption.
Watch this on-demand webinar to learn about the proven methods to identify the data that matters, connect governance policies to business objectives, and quickly deliver value through the life of the program.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption. In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
A business-friendly approach to data governance is imperative to engage all users and accommodate diverse business use cases spanning analytics, operational improvements, and compliance requirements. To increase adoption and collaboration, business and technical data users across your organisation need to have a common, agreed-upon, and documented understanding of which data is most important, what it’s called, and where it’s used.
Watch this on-demand webinar, where we explore the concept of business-first Data Governance, an approach that promotes adoption by the organisation, lays the foundation for data integrity and consistently delivers business value in the long term.
We also look at how Oripharm, one of the dynamic healthcare players in the Nordics and international markets, choose a data governance solution:
• to improve personalisation of products and services
• to achieve accurate and timely credit-risk analysis
• to increase user productivity by improving time-to-insights
• to mitigate risk and facilitate regulatory compliance and reporting
Speakers:
Mikkel Holmgaard - Data Governance Lead, Orifarm
Emily Washington - Sr. Vice President, Product Management, Precisely
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
Four Must-Haves for Successful Data Governance in CPG ManufacturingPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption.
Join us as we explore a lean, business-first data governance approach that connects key initiatives for CPG manufacturers to governance capabilities and quickly delivers business value for the long term.
This webinar shares best practices from data-driven organizations worldwide that have successfully implemented a data governance program. Key topics include:
· Why many data governance initiatives fail to thrive
· Elements of a business-first approach increase the sustainability of your data initiatives
· How to build consensus to increase adoption and collaboration
Data Governance: Business First, Govern AlwayPrecisely
Data Governance requires a strong foundation and commitment to make it work, from understanding a business’ needs first before implementing a future proof governance policy. In this presentation for the CDO/CIO Sit-Down series, Melvin Cheong explores the key components that are necessary to build a sustainable program and a fully functional data integrity framework for long-term engagement within your organisation.
How to Achieve Trusted Data with a Business-First Approach to Data GovernancePrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
Maximize ROI of Insurance Digital Transformation Initiatives with Proven Data...Precisely
Many insurance carriers are transforming the way they do business by deploying new software technologies, migrating data and services to the cloud, and leveraging artificial intelligence (AI) to speed decision-making. Data is at the heart of all these initiatives, and it has a direct impact on success or failure. When that data is integrated into upstream or downstream processes, it can also have a broader impact on the operational, analytical, and compliance needs of the organization. The traditional, and often ad-hoc, tools and processes that organizations employ to support data quality, data integrity, transaction reconciliation, and exception management are often inadequate. They do not provide the speed, technical agility, and intelligence demanded by digital transformation initiatives.
Join us to explore proven methods of how insurance carriers are maximizing ROI and minimizing the time-to-value of digital transformation initiatives by:
• Aligning data governance with organizational and project objectives to reduce implementation effort and duration
• Leveraging automated controls for data quality, including balance and reconciliation of data in motion to avoid operational disruptions and maintain regulatory compliance
• Increasing efficiency and capability through centralized data integrity solution
Information Governance: Reducing Costs and Increasing Customer SatisfactionCapgemini
The document discusses best practices for information governance, including how it can help organizations reduce costs and increase customer satisfaction. It provides an overview of SAP and Capgemini's information governance best practices and addresses common questions clients have around data issues. Information governance is important because data is a key organizational asset, and governance helps ensure consistent, accurate data is available for reporting and decision making. Lack of governance can lead to issues like multiple versions of the truth and inefficient processes. The benefits of effective information governance include reduced costs through improved data management, better decisions from leveraging high-quality data, and increased customer satisfaction.
Optimizing Solution Value– Dynamic Data Quality, Governance, and MDMDATAVERSITY
Data quality used to be a one-dimensional term: Is your data right? As the data landscape has become more complex and sophisticated, data quality has evolved to require a more holistic approach to encompass much more to ensure trust in data. Integrated data quality, governance, and multi-domain data management provides a more robust single view that data-driven companies require to have more complete confidence in critical business decisions.
Join Chuck Kane, VP of Product Management, Precisely, as he shares use case trends that illustrate how innovation in data management continues to evolve. Topics that you will hear addressed:
- Evolving Data Monetization: improve your bottom line with the ability to confidently leverage data as an asset
- Evolving to a Single View of Data: break down silos, and gain a powerful, comprehensive single view of your organization’s data
- Evolving Data on the Move: ensure consistent levels of data quality by monitoring data as it moves throughout the business
- Evolving Operational Value: enrich, fix, and validate data to open the door to new possibilities
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
Data Democratization and AI Drive the Scope for Data GovernancePrecisely
Back by popular demand: join us for a repeat presentation of the June 22, 2022 keynote from Trust 22, How Data Democratization and AI Drive the Scope for Data Governance, with Ken Beutler, Senior Director of Product Management, Precisely, and guest speaker Achim Granzen, Principal Analyst, Forrester.
Understand the challenges with many data governance initiatives today – and how organizations can respond by stepping up their strategies to align for a new scope of data governance. In this presentation you will hear:
• Challenges that still remain in the current state of Data Governance
• How AI and data democratization are impacting data strategies
• The 5 components that will power the impact of data governance
• Recommendations to mature and broaden your data governance capabilities
Data Governance Strategies for Public SectorPrecisely
Many government agencies are on a data-driven transformation journey to elevate data literacy whilst complying with legislative and regulatory policies.
Does your Data Governance strategy meet your regulatory and departmental objectives, and do you have actual and measured objectives that drive your governance? Can you trust your data to make critical decisions that enable policy development, data sharing with other agencies, and drive efficiencies?
Watch this on-demand recording to learn practical approaches your organisation can take to develop a data governance strategy that engages with key stakeholders, meets regulatory requirements, and delivers value for the long-term.
Four Must-Haves for Data Governance in Financial ServicesPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves CDOs and governance leaders having to continually make the case for data governance to secure business adoption.
In this on-demand webinar, we will share the lean, business-first data governance approach that connects key financial services initiatives to governance capabilities and quickly delivers business value for the long-term.
The document discusses the role of a Chief Data Officer in establishing a data governance structure and data quality management program. It notes that currently, data ownership and management is fragmented across different departments with no single party responsible. A CDO would create rules and policies for data governance, establish a data quality team, and ensure standards and accountability for high quality data as a strategic asset. This would help address issues like high costs of poor data quality and system failures due to bad data.
Data Virtualization for Business Consumption (Australia)Denodo
This document discusses data virtualization and its benefits for business users. It summarizes that data virtualization can create a connected data landscape that is easily shared, empower business users with self-service BI tools, develop trusted high quality data, and support flexibility. It notes data virtualization provides a logical data layer that improves decision making, broadens data usage, and offers performant access to integrated data without moving or replicating source systems.
Data Governance: From speed dating to lifelong partnershipPrecisely
Data Governance, like a relationship, requires a strong foundation and commitment to make it work. This presentation explores the main reasons why Data Governance initiatives fail and the key components that are necessary to build a sustainable program with long-term engagement within your organisation. Learn about the Data Integrity Framework, data governance tools, and how to establish a structured decision tree to drive prioritisation.
Real-World Data Governance: Gaining Leadership Support For Data GovernancePrecisely
A commonly used best practice associated with standing up Data Governance programs is that “senior leadership support, sponsor, and understand Data Governance” and the activities and resources associated with governing data. The question becomes, “How do we get leadership’s support when it is possible that they have heard it all before?”
Join Bob Seiner as he covers a topic that is important to organizations gaining leadership support for the first time, and organizations with established Data Governance programs that need to sustain leadership support. Bob will share proven techniques to learn the issues and opportunities that can be addressed through formal governance and the potential introduction of data policy and strategy.
Data governance and data quality are often described as two sides of the same coin. Data governance provides a data framework relevant to business needs, and data quality provides visibility into the health of the data. If you only have a data governance tool, you’re missing half the picture.
Trillium Discovery seamlessly integrates with Collibra for a complete, closed-loop data governance solution. Build your data quality rules in Collibra, and they are automatically passed to Trillium for data quality processing. The data quality results and metrics are then passed back to Collibra – allowing data stewards and business users to see the health of the data right within their Collibra dashboard.
View this webinar on-demand to see how you can leverage this integration in your organization to readily build, apply, and execute business rules based on data governance policies within Collibra.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
A business-friendly approach to data governance is imperative to engage all users and accommodate diverse business use cases spanning analytics, operational improvements, and compliance requirements. To increase adoption and collaboration, business and technical data users across your organisation need to have a common, agreed-upon, and documented understanding of which data is most important, what it’s called, and where it’s used.
Watch this on-demand webinar, where we explore the concept of business-first Data Governance, an approach that promotes adoption by the organisation, lays the foundation for data integrity and consistently delivers business value in the long term.
We also look at how Oripharm, one of the dynamic healthcare players in the Nordics and international markets, choose a data governance solution:
• to improve personalisation of products and services
• to achieve accurate and timely credit-risk analysis
• to increase user productivity by improving time-to-insights
• to mitigate risk and facilitate regulatory compliance and reporting
Speakers:
Mikkel Holmgaard - Data Governance Lead, Orifarm
Emily Washington - Sr. Vice President, Product Management, Precisely
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
Four Must-Haves for Successful Data Governance in CPG ManufacturingPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption.
Join us as we explore a lean, business-first data governance approach that connects key initiatives for CPG manufacturers to governance capabilities and quickly delivers business value for the long term.
This webinar shares best practices from data-driven organizations worldwide that have successfully implemented a data governance program. Key topics include:
· Why many data governance initiatives fail to thrive
· Elements of a business-first approach increase the sustainability of your data initiatives
· How to build consensus to increase adoption and collaboration
Data Governance: Business First, Govern AlwayPrecisely
Data Governance requires a strong foundation and commitment to make it work, from understanding a business’ needs first before implementing a future proof governance policy. In this presentation for the CDO/CIO Sit-Down series, Melvin Cheong explores the key components that are necessary to build a sustainable program and a fully functional data integrity framework for long-term engagement within your organisation.
How to Achieve Trusted Data with a Business-First Approach to Data GovernancePrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
Maximize ROI of Insurance Digital Transformation Initiatives with Proven Data...Precisely
Many insurance carriers are transforming the way they do business by deploying new software technologies, migrating data and services to the cloud, and leveraging artificial intelligence (AI) to speed decision-making. Data is at the heart of all these initiatives, and it has a direct impact on success or failure. When that data is integrated into upstream or downstream processes, it can also have a broader impact on the operational, analytical, and compliance needs of the organization. The traditional, and often ad-hoc, tools and processes that organizations employ to support data quality, data integrity, transaction reconciliation, and exception management are often inadequate. They do not provide the speed, technical agility, and intelligence demanded by digital transformation initiatives.
Join us to explore proven methods of how insurance carriers are maximizing ROI and minimizing the time-to-value of digital transformation initiatives by:
• Aligning data governance with organizational and project objectives to reduce implementation effort and duration
• Leveraging automated controls for data quality, including balance and reconciliation of data in motion to avoid operational disruptions and maintain regulatory compliance
• Increasing efficiency and capability through centralized data integrity solution
Information Governance: Reducing Costs and Increasing Customer SatisfactionCapgemini
The document discusses best practices for information governance, including how it can help organizations reduce costs and increase customer satisfaction. It provides an overview of SAP and Capgemini's information governance best practices and addresses common questions clients have around data issues. Information governance is important because data is a key organizational asset, and governance helps ensure consistent, accurate data is available for reporting and decision making. Lack of governance can lead to issues like multiple versions of the truth and inefficient processes. The benefits of effective information governance include reduced costs through improved data management, better decisions from leveraging high-quality data, and increased customer satisfaction.
Optimizing Solution Value– Dynamic Data Quality, Governance, and MDMDATAVERSITY
Data quality used to be a one-dimensional term: Is your data right? As the data landscape has become more complex and sophisticated, data quality has evolved to require a more holistic approach to encompass much more to ensure trust in data. Integrated data quality, governance, and multi-domain data management provides a more robust single view that data-driven companies require to have more complete confidence in critical business decisions.
Join Chuck Kane, VP of Product Management, Precisely, as he shares use case trends that illustrate how innovation in data management continues to evolve. Topics that you will hear addressed:
- Evolving Data Monetization: improve your bottom line with the ability to confidently leverage data as an asset
- Evolving to a Single View of Data: break down silos, and gain a powerful, comprehensive single view of your organization’s data
- Evolving Data on the Move: ensure consistent levels of data quality by monitoring data as it moves throughout the business
- Evolving Operational Value: enrich, fix, and validate data to open the door to new possibilities
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
Data Democratization and AI Drive the Scope for Data GovernancePrecisely
Back by popular demand: join us for a repeat presentation of the June 22, 2022 keynote from Trust 22, How Data Democratization and AI Drive the Scope for Data Governance, with Ken Beutler, Senior Director of Product Management, Precisely, and guest speaker Achim Granzen, Principal Analyst, Forrester.
Understand the challenges with many data governance initiatives today – and how organizations can respond by stepping up their strategies to align for a new scope of data governance. In this presentation you will hear:
• Challenges that still remain in the current state of Data Governance
• How AI and data democratization are impacting data strategies
• The 5 components that will power the impact of data governance
• Recommendations to mature and broaden your data governance capabilities
Data Governance Strategies for Public SectorPrecisely
Many government agencies are on a data-driven transformation journey to elevate data literacy whilst complying with legislative and regulatory policies.
Does your Data Governance strategy meet your regulatory and departmental objectives, and do you have actual and measured objectives that drive your governance? Can you trust your data to make critical decisions that enable policy development, data sharing with other agencies, and drive efficiencies?
Watch this on-demand recording to learn practical approaches your organisation can take to develop a data governance strategy that engages with key stakeholders, meets regulatory requirements, and delivers value for the long-term.
Four Must-Haves for Data Governance in Financial ServicesPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves CDOs and governance leaders having to continually make the case for data governance to secure business adoption.
In this on-demand webinar, we will share the lean, business-first data governance approach that connects key financial services initiatives to governance capabilities and quickly delivers business value for the long-term.
The document discusses the role of a Chief Data Officer in establishing a data governance structure and data quality management program. It notes that currently, data ownership and management is fragmented across different departments with no single party responsible. A CDO would create rules and policies for data governance, establish a data quality team, and ensure standards and accountability for high quality data as a strategic asset. This would help address issues like high costs of poor data quality and system failures due to bad data.
Data Virtualization for Business Consumption (Australia)Denodo
This document discusses data virtualization and its benefits for business users. It summarizes that data virtualization can create a connected data landscape that is easily shared, empower business users with self-service BI tools, develop trusted high quality data, and support flexibility. It notes data virtualization provides a logical data layer that improves decision making, broadens data usage, and offers performant access to integrated data without moving or replicating source systems.
Data Governance: From speed dating to lifelong partnershipPrecisely
Data Governance, like a relationship, requires a strong foundation and commitment to make it work. This presentation explores the main reasons why Data Governance initiatives fail and the key components that are necessary to build a sustainable program with long-term engagement within your organisation. Learn about the Data Integrity Framework, data governance tools, and how to establish a structured decision tree to drive prioritisation.
Real-World Data Governance: Gaining Leadership Support For Data GovernancePrecisely
A commonly used best practice associated with standing up Data Governance programs is that “senior leadership support, sponsor, and understand Data Governance” and the activities and resources associated with governing data. The question becomes, “How do we get leadership’s support when it is possible that they have heard it all before?”
Join Bob Seiner as he covers a topic that is important to organizations gaining leadership support for the first time, and organizations with established Data Governance programs that need to sustain leadership support. Bob will share proven techniques to learn the issues and opportunities that can be addressed through formal governance and the potential introduction of data policy and strategy.
Data governance and data quality are often described as two sides of the same coin. Data governance provides a data framework relevant to business needs, and data quality provides visibility into the health of the data. If you only have a data governance tool, you’re missing half the picture.
Trillium Discovery seamlessly integrates with Collibra for a complete, closed-loop data governance solution. Build your data quality rules in Collibra, and they are automatically passed to Trillium for data quality processing. The data quality results and metrics are then passed back to Collibra – allowing data stewards and business users to see the health of the data right within their Collibra dashboard.
View this webinar on-demand to see how you can leverage this integration in your organization to readily build, apply, and execute business rules based on data governance policies within Collibra.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Similar to What is Data Governance and why it’s crucial for PropTech (20)
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
AI-Ready Data - The Key to Transforming Projects into Production.pptxPrecisely
Moving AI projects from the laboratory to production requires careful consideration of data preparation. Join us for a fireside chat where industry experts, including Antonio Cotroneo (Director, Product Marketing, Precisely) and Sanjeev Mohan (Principal, SanjMo), will discuss the crucial role of AI-ready data in achieving success in AI projects. Gain essential insights and considerations to ensure your AI solutions are built on a solid foundation of accurate, consistent, and context-rich data. Explore practical insights and learn how data integrity drives innovation and competitive advantage. Transform your approach to AI with a focus on data readiness.
Building a Multi-Layered Defense for Your IBM i SecurityPrecisely
In today's challenging security environment, new vulnerabilities emerge daily, leaving even patched systems exposed. While IBM works tirelessly to release fixes as they discover vulnerabilities, bad actors are constantly innovating. Don't settle for reactive defense – secure your IT with a layered approach!
This holistic strategy builds multiple security walls, making it far harder for attackers to breach your defenses. Even if a certain vulnerability is exploited, one of the controls could stop the attack or at least delay it until you can take action.
Join us for this webcast to hear about:
• How security risks continue to evolve and change
• The importance of keeping all your systems patched an up-to-date
• A multi-layered approach to network, system object and data security
Navigating the Cloud: Best Practices for Successful MigrationPrecisely
In today's digital landscape, migrating workloads and applications to the cloud has become imperative for businesses seeking scalability, flexibility, and efficiency. However, executing a seamless transition requires strategic planning and careful execution. Join us as we delve into the insightful insights around cloud migration, where we will explore three key topics:
i. Considerations to take when planning for cloud migration
ii. Best practices for successfully migrating to the cloud
iii. Real-world customer stories
Unlocking the Power of Your IBM i and Z Security Data with Google ChroniclePrecisely
In today's ever-evolving threat landscape, any siloed systems, or data leave organizations vulnerable. This is especially true when mission-critical systems like IBM i and IBM Z mainframes are not included in your security planning. Valuable security data from these systems often remains isolated, hindering your ability to detect and respond to threats effectively.
Ironstream and bridge this gap for IBM systems by integrating the important security data from these mission-critical systems into Google Chronicle where it can be seen, analyzed and correlated with the data from other enterprise systems Here's what you'll learn:
• The unique challenges of securing IBM i and Z mainframes
• Why traditional security tools fall short for mainframe data
• The power of Google Chronicle for unified security intelligence
• How to gain comprehensive visibility into your entire IT ecosystem
• Real-world use cases for integrating IBM i and Z security data with Google Chronicle
Join us for this webcast to hear about:
• The unique challenges of securing IBM i and IBM Z systems
• Real-world use cases for integrating IBM i and IBM Z security data with Google Chronicle
• Combining Ironstream and Google Chronicle to deliver faster threat detection, investigation, and response times
Unlocking the Potential of the Cloud for IBM Power SystemsPrecisely
Are you considering leveraging the cloud alongside your existing IBM AIX and IBM I systems infrastructure? There are likely benefits to be realized in scalability, flexibility and even cost.
However, to realize these benefits, you need to be aware of the challenges and opportunities that come with integrating your IBM Power Systems in the cloud. These challenges range from data synchronization to testing to planning for fallback in the event of problems.
Join us for this webcast to hear about:
• Seamless migration strategies
• Best practices for operating in the cloud
• Benefits of cloud-based HA/DR for IBM AIX and IBM i
Crucial Considerations for AI-ready Data.pdfPrecisely
This document discusses the importance of ensuring data is ready for AI applications. It notes that while most businesses invest in AI, only 4% of organizations say their data is truly AI-ready. It identifies several issues that can arise from using bad data for AI, including bias, poor performance, and inaccurate predictions. The document advocates for establishing strong data governance, quality practices, and integration capabilities to address issues like completeness, validity, and bias. It provides examples of how two companies leveraged these approaches to enhance their AI and machine learning models. The document emphasizes that achieving trusted AI requires a focus on data integrity throughout the data journey from generation to activation.
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
This document discusses how to empower businesses through worry-free data processing. Key steps include collecting and organizing relevant business data, developing efficient processes for analyzing and interpreting the data, and using insights from the data to help businesses make better decisions and improve their operations in a sustainable way over time.
It can be challenging display and share capacity data that is meaningful to end users. There is an overabundance of data points related to capacity, and the summarization of this data is difficult to construct and display.
You are already spending time and money to handle the critical need to manage systems capacity, performance and estimate future needs. Are you it spending wisely? Are you getting the level of results from your investment that you really need? Can you prove it?
The good news is that the return on investment of implementing capacity management and capacity planning is most definitely positive and provable, both in terms of tangible monetary value and in some less tangible but no-less-valuable benefits.
Join us for this webinar and learn:
• Top Trends in Capacity Management
• Common customer pain points
• Ways to demonstrate these benefits to your company
Automate Studio Training: Materials Maintenance Tips for Efficiency and Ease ...Precisely
Ready to improve efficiency, provide easy to use data automations and take materials master (MM) data maintenance to the next level?
Find out how during our Automate Studio training on March 28 – led by Sigrid Kok, Principal Sales Engineer, and Isra Azam, Sales Engineer, at Precisely.
This session’s for you if you want to discover the best approaches for creating, extending or maintaining different types of materials, as well as automating the tricky parts of these processes that slow you down.
Greater control over your Automate Studio business processes means bigger, better results. We’ll show you how to enable your business users to interact with SAP from Microsoft Office and other familiar platforms – resulting in more efficient SAP data management, along with improved data integrity and accuracy.
This 90-minute session will be filled with a variety of topics, including:
real world approaches for creating multiple types of materials, balancing flexibility and power with simplicity and ease of use
tips on material creation, including
downloading the generated material number
using formulas to format prior to upload, such as capitalization or zero padding to make it easy to get the data right the first time
conditionally require fields based on other field entries
using LOV for fields that are free form entry for standard values
tips on modifying alternate units of measure, building from scratch using GUI scripting
modify multiple language descriptions, build from scratch using a standard BAPI
make end-to-end MM process flows more of a reality with features including APIs and predictive AI
Through these topics, you’ll gain plenty of actionable takeaways that you can start implementing right away – including how to:
improve your data integrity and accuracy
make scripts flexible and usable for automation users
seamlessly handle both simple and complex parts of material master
interact with SAP from both business user and script developers’ perspectives
easily upload and download data between SAP and Excel – and how to format the data before upload using simple formulas
You’ll leave this session feeling ready and empowered to save time, boost efficiency, and change the way you work.
Automate Studio reduces your dependency on technical resources to help you create automation scenarios – and our team of experts is here to make sure you get the most out of our solution throughout the journey.
Questions? Sigrid & Isra will be ready to answer them during a live Q&A at the end of the session.
Who should attend:
Attendees who will get the most out of this session are Automate Studio developers and runners familiar with SAP MM. Knowledge of Automate Studio script creation is nice to have, but not required.
Leveraging Mainframe Data in Near Real Time to Unleash Innovation With Cloud:...Precisely
Join us for an insightful roundtable discussion featuring experts from AWS, Confluent, and Precisely as they delve into the complexities and opportunities of migrating mainframe data to the cloud.
In this engaging webinar, participants will learn about the various considerations, strategies, and customer challenges associated with replicating mainframe data to cloud environments.
Our panelists will share practical insights, real-world experiences, and best practices to help organizations successfully navigate this transformative journey.
Whether you're considering migrating and modernizing your mainframe applications to cloud, or augmenting mainframe-based applications with data replication to cloud, this roundtable will provide valuable perspectives and insights to maximize the benefits of migrating mainframe data to the cloud.
Join us on March 27 to gain a deeper understanding of the opportunities and challenges in this evolving landscape.
Data Innovation Summit: Data Integrity TrendsPrecisely
Data integrity remains an evolving process of discovery, identification, and resolution. With an all-time low in public confidence on data being used for decision-making, attention has gradually shifted to data quality and data integration across multiple systems and frameworks. Data integrity becomes a focal point again for companies to make strategic moves in a world facing an evolving economy.
Key takeaways:
· How to build a data-driven culture within your organization
· Tips to engage with key stakeholders in your business and examples from other businesses around the world
· How to establish and maintain a business-first approach to data governance
· A summary of the findings from a recent survey of global data executives by Drexel University's LeBow College of Business
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
Artificial Intelligence (AI) has become a strategic imperative in a rapidly evolving business landscape. However, the rush to embrace AI comes with risks, as illustrated by instances of AI-generated content with fake citations and potentially dangerous recommendations. The critical factor underpinning trustworthy AI is data integrity, ensuring data is accurate, consistent, and full of rich context.
Attend our upcoming webinar, "AI You Can Trust: Ensuring Success with Data Integrity," as we explore organizational challenges in maintaining data integrity for AI applications and real-world use cases showcasing the transformative impact of high-integrity data on AI success.
During this panel discussion, we'll highlight everything from personalized recommendations and AI-powered workflows to machine learning applications and innovative AI assistants.
Key Topics:
AI Use Cases with Data Integrity: Discover how data integrity shapes the success of AI applications through six compelling use cases.
Solving AI Challenges: Uncover practical solutions to common AI challenges such as bias, unreliable results, lack of contextual relevance, and inadequate data security.
Three Considerations of Data Integrity for AI: Learn the essential pillars—complete, trusted, and contextual—that underpin data integrity for AI success.
Precisely and AWS Partnership: Explore how the collaboration between Precisely and Amazon Web Services (AWS) addresses these challenges and empowers organizations to achieve AI-ready data.
Join our panelists to unlock the full potential of AI by starting your data integrity journey today. Trust in AI begins with trusted data – let's future-proof your AI together.
Less Bias. More Accurate. Relevant Outcomes.
Optimisez la fonction financière en automatisant vos processus SAPPrecisely
La fonction finance est au cœur du succès de l’entreprise, et doit aussi évoluer pour faire face aux enjeux d’aujourd’hui : aller plus vite, traiter plus d’informations et assurer une qualité des données sans faille.
Nous vous proposons de découvrir ensemble comment répondre à ces défis, notamment les points suivants :
Gérer les référentiels comptables et financiers, comptes comptables, clients, fournisseurs, centres de couts, centres de profits…Accélérer les clôtures et permettre de passer les écritures comptables nécessaires, de lancer les rapports adéquats et d’extraire les informations en temps réelOrganiser les taches en les affectant de manière ordonnancée à leurs responsables ou en les lançant automatiquement et les suivre de manière granulaire
Notre webinaire sera l’occasion d’évoquer et d’illustrer cette palette de capacités disponibles pour des utilisateurs métier sans code ou avec peu de code et nous vous espérons nombreux.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Building Production Ready Search Pipelines with Spark and Milvus
What is Data Governance and why it’s crucial for PropTech
1. Data Governance
Carmen Adame| Product Marketing, PropTech
Ruslan Sultanov | Product Marketing
Empowering PropTech through Data
2. Why Data
Governance?
• The real estate market is undergoing significant and
exciting change – driven by technology and data.
• The amount of data from multiple sources can be
overwhelming.
• Companies in a transformational journey of digital data
and technology depend on the accuracy, relevancy,
and completeness of their data.
• The risk of having data that provides no business value
is too high.
4. Data Governance
Data governance is everything you do to ensure data is secure,
private, accurate, available, and usable. It includes the actions people
must take, the processes they must follow, and the technology that
supports them throughout the data life cycle.
5. Benefits of Data Governance
5
Manage risk &
Enhance Compliance
Understand exposure of
sensitive data, security
breaches and avoid risks
associated with non-
compliance
Establish trust. Understand
customer trends, suppliers,
and partners.
Enhanced customer care
Access data needed to
improve products and
services and seize
opportunities for new
revenues
Better decision
making
5
7. Good Data - Good Owners
Data Governance is the convergence of People,
Processes, and Technology. It is about consensus
building, ownership, and overcoming barriers. It not
only aligns IT and business functions to leverage the
benefits of data, but also defines data ownership
and policies, decision rights, and escalation
procedures.
Presentation name
7
8. Focusing on what matters (Critical Data)
Data
Selection of data maintained at the system
level (tables and fields)
Information
Information required to run the business
and conduct daily operations
KPIs / Performance Measures / Analytics
Measuring process effectiveness & enabling
sound business decisions
Actionable Insights & Business Value
Strategic enterprise and organizational
business value drivers
CRITICAL DATA
9. Not all data is created equal
95% of
business
results
5% results
Data Governance
programs that
prioritize critical
data have 5x faster
time-to-value
~5% critical data
95% all other data
10. Mapping data governance business value
Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities
Improve
personalization of
customer
engagement and
Brand recognition
Marketing
Sales
Finance
• Increase NPS by 5%
• 17%+ repeat
customer purchases
• 11% reduced churn
• Establish a common
view of trusted
customer data
assets
• Data Catalog
• Data Lineage
• Approval
Workflow
• Data Quality rules
Increase sales
and revenue
through faster
speed-to-market
Marketing
R&D
Finance
• $15M+ top-line
revenue
• 25% increased
deployment speed
• Establish stage
gates, rules,
policies, and quality
measures from
Ideation through
Commercialization
• DQ rules
• Business process
monitoring
• Data quality
metrics
Increase user
productivity by
improving time-
to-insights
Business Analytics
IT
Data Office
• Improve decision-
accuracy by 22%
• Reduce time-to-
insight by 45%
• Launch data
literacy campaign
across business
data SMEs
• Data lineage
• Data Catalog
• Automated
workflow
Reduce supply
chain costs
associated with
errors in orders
Vendor Management
Finance
Supply Chain
• Reduce COGS
by 4%
• Improve OTIF
by 15%
• Establish common
semantics view
across order
fulfillment data
• Impact analysis
• DQ rules
• Business process
monitoring
11. Pain Killer Vs Vitamin
Goal DG Objective DG Capabilities
Improve
personalization of
customer
engagement and
services
• Establish trusted view
of customer data
assets
• Data Catalog
• Data Lineage
• Approval Workflow
• Data Quality rules
Increase sales
through faster
speed-to-market
• Establish stage gates,
rules, policies, and
quality measures for
Commercialization
process
• DQ rules
• Business process
monitoring
• Data quality metrics
Increase user
productivity by
improving time-to-
insights
• Launch data literacy
campaign across
business data SMEs
• Data lineage
• Data Catalog
• Automated workflow
Reduce supply
chain costs
associated with
errors in orders
• Establish common
semantics view
across order
fulfillment data
• Impact analysis
Centralized collection
of customer data
elements used for
marketing and
promotion
Data profile providing
additional context on
volume, counts,
location, and contents
Data lineage flow of
upstream/downstream
relationships
Impact analysis to
business processes,
metrics, and analytics
Approved governance
ownership indicating
data is certified for
access and use
Automated approval
workflow to grant
access to data at
source
Data integrity metrics
to indicate data that is
accurate, consistent,
and trusted
Quality monitoring to
trigger notifications
below acceptable
values
P A I N K I L L E R
“ M u s t H a v e s ”
V I T A M I N
“ B o n u s ”
13. Data governance is no longer a choice in
this data-filled, fast-moving space.
14. Takeaways
• Data Governance helps put
quality data in the hands of the
people who use it
• Enables you to make faster
decisions, improves customer
engagement and avoid risks
• Data Governance helps you
achieve business goals and
drive value faster
Without effective data governance, data inconsistencies in different systems across an organization might not get resolved. For example, customer names may be listed differently in sales, logistics and customer service systems. That could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence (BI), enterprise reporting and analytics applications. In addition, data errors might not be identified and fixed, further affecting BI and analytics accuracy.
Poor data governance can also hamper regulatory compliance initiatives. That could cause problems for companies that need to comply with the increasing number of data privacy and protection laws, such as the European Union's GDPR and the California Consumer Privacy Act (CCPA). An enterprise data governance program typically includes the development of common data definitions and standard data formats that are applied in all business systems, boosting data consistency for both business and compliance uses.
To sum things up, successful Data Governance determines who owns the data, how and by whom data is created and updated, and who arbitrates decisions when disagreements or needs arise.
Now, how do we understand what is our critical data? Well, if you start from the base of this pyramid as just kind of a working visual or an idea that might represent 100% of all the data that we have in the organization and almost every organization, regardless of the industry, the 100% of the data that that organization has, the only use about 40% to run the business. So the 40% of the information that's used to run the business could be information that is about our key customers, or are important suppliers or vendors, or are most profitable products or services, or a charts of accounts?
It's about 10% of that 40% subset. So of 100% of all the data, 40% of it is the information that's used to run the business, only about 10% of that 40% is then used for insights and KPIs and critical processes in the organization. And at the very top of the pyramid, about 5% is a critical data.
In that 5% of the critical data, again is data that's used across our three silos. So take a look at actionable insights and business value across risk mitigation across analytics and insights and across running the business more effectively from an operations perspective.
People ask me all the time. This is a common concern. They say, You know, Ross, we have all of this data. How are we gonna govern it all? The data is moving so quickly, we have new stakeholders that have new needs every day. How do we get our hands around the data that in a way that is scalable and allows us to adapt over the long term?The fact that matter is that the majority of your data doesn't need to be governed. It might have utility, it might be useful, but it doesn't mean that it necessarily needs to be governed.
So if we prioritize effectively, we can provide 5X faster time to value than if we were to focus on you know the 20 or 50 or you know even greater percentage of data that we have in the organization. And this is I think a key point in the way that we think about having access to the data that's in organization versus what we should govern and what we should manage and what we should look after from a data quality standpoint because only about 5% of the critical data that we have ultimately ends up driving about 95% of the the business results in our organization.
So if you take a look to the left here, you might notice some goals that are fairly common. Might be familiar to your own organization, such as improving how personalized are products and services are for our customers, making them more contextual for our customers, or increasing sales and revenue by getting the market faster and beating our competition or increasing our user productivity for data consumers by helping them help self service to the data that exists in the organization. These might be goals as a part of the digitization or data transformation initiative.
Each of these goals have their own organizational stakeholders, teams, regional stakeholders, and then also expected outcomes that get measured in the form of dashboards and scorecards and KPIs and analytics. We need to make it really crystal clear how our data governance objectives tie back to these organizational initiatives and stakeholders, and the reason is because people don't have time to make the connection themselves. They're already focused on their day-to-day work. They're already spending nights and weekends trying to get their business goal achieved or their particular project completed and so we don't want to put the extra step to make people think we want to make data governance relevant to the things that they're already bought into as opposed to asking them to think outside of their goal or initiative and to start to adopt our terminology and explain to them what our rules and policies and things like that.
So governance immediately becomes important if it serves their initiatives and their needs versus the other way around. It also gives us insight into the capabilities and the tools that we need to provide to the organization. And oftentimes, one of the things that we see is organizations will go out and they'll just kind of buy the regular tooling that they think is the standard tooling that they need for data governance program without really thinking about where the key capabilities and the context of their business goals.
And this comes to the second point, which is thinking about the solutions and the capabilities from a product, but also from a process perspective.
And IN the terms of painkillers and vitamins, or the must haves versus the bonus or or the nice to have capabilities?
You know, if you're thinking about improving your overall health and well being, you're not gonna be really focused on going to the gym if you have some sort of ailment or pain. So if you have a headache or a stomach ache, or if your knee hurts or your shoulder hurts, you're not going to be really focused on going to the gym that day. You're going to be focused on getting some rest and recovering until you feel better. And oftentimes, partitioning and in prioritizing these capabilities helps. When we think about what are the capabilities that are must haves to get the core business goal or initiative off the ground.
So as an example for thinking about the personalization of customer goods and services with customer data, we might want to establish a foundation initially of centralizing a collection of really critical customer data elements that we usually use for marketing and promotion activities. Then we would establish a data lineage flow from source to target so that we can see how that information moves upstream and downstream.
Then, once we understand what is the critical data and the affected systems within that upstream and downstream process, we would define a governance process that would establish ownership for indicating what data is certified for access and for use. And then we can overlay data quality checks on top that can indicate if the data is accurate, consistent and trusted. If we do those things from a foundational perspective, it's already a big win for the organization, but then we can come over top and overlay vitamins or or bonus capabilities so to speak in the form of providing additional data profiles, contextual insights or impact analysis that show how the customer data also impacts our business processes. Or we can automate our approval workflows or we could establish data monitoring or machine learning that will help us gain insights into why the data quality in some areas is more trustworthy than other areas that we can establish and understand those patterns.
Some final stats to bring it home. In our experience, because we identified the goals that are most important to us, identified the critical data that impacts those goals – we want to communicate this with Value Metrics around our BUSINESS FIRST APPROACH methodology that our stakeholders care about:
Our customers accelerate data governance program roll out by 18-40%
Our customers generate as much as 7X greater ROI with better governed data
And over 75% of our customers expanded or reinvested in their data governance programs when they realized the benefits of a business first approach
The objective is to deliver value to the business via data. And for that data to mean something,
it needs to be consumable by everybody in the organization, not just the data experts.
Today, more and more businesses want to put high-quality data into the hands of the people
who need it to do their jobs. That means organizations must build processes that define who
owns what data and how it can be used.
The take away of all this is that we really need to link data governance program initiatives to the higher level business goals, to the stakeholders, the business outcomes and then back into the capabilities that we need to deliver from a data governance perspective. This really helps us create the initial business case. It makes it clear for our stakeholders why the governance program is going to be essential, but also more importantly, how it will help to accelerate them to their expected result or the outcome that they're looking for.
We can also prioritize painkillers and vitamins, so to speak, or the nice to haves and the must haves to protect and grow the business and ultimately launch the the program forward. So as a first step, we want to link the data governance program to the overarching business goals and initiatives of the organization.
Companies no longer simply talk about being “data-driven;” they are actively implementing practices and processes. For BI projects to be effective, business users need to be able to find relevant data. Data governance is key to making data to actionable across the organization. Data governance is all about understanding, trusting and accessing quality data.
And being a data governance expert requires passion for people, processes and technology.
Data governance is more than simple tools. It is about getting data into the hands of the people on the front lines, and putting the data to work.
So that concludes our session for today. I appreciate everyone listening and hope that you learned something new. Ross and I are happy to answer any questions or respond to any comments in the chat. So we'll go ahead and open it up for questions and comments at this point.
Seed Questions
Who in an organization usually initiates a data governance initiative?What is the most common reason Telcos initiate a data governance program
How large is the typical data governance team within an organization?
We have a data catalog – what’s the difference between a data catalog and data governance?