With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
• Data observability – what is it and how it can complement your data quality strategy
• Why now is the time to incorporate data observability into your DataOps strategy
• How data observability helps prevent data issues from impacting downstream analytics
• How integrated data catalog capabilities allow you to understand the context of alerts.
• Examples of how data observability can be used to prevent real-world issues
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
The Persona-Based Value of Modern Data Governance Precisely
Yes, data governance solutions are now a business imperative. But modern demands are requiring integrated capabilities to discover, understand, profile, and measure data integrity across many different functions across your organization.
This presentation shares four persona-based use cases & demos to illustrate how a single modular, and interoperable solution can optimize collaboration and empower your data teams to deliver data-driven decisions faster and more confidently.
Are you ready for the future of data governance? Check out what will be required:
• Understand data relationships to business objectives, metrics, and request new actions
• Discover new data element alerts to profile and add contextual details to your analysis
• Review needed data quality rules, lineage, and impact and proactively
monitor data changes over time.
• Access & respond to data replication request for more timely results
• Create data quality pipelines and enrich data for more insightful analytics
Advanced Project Data Analytics for Improved Project DeliveryMark Constable
Data Analytics is already beginning to impact how projects are delivered. We can now automate minute taking and capturing actions, we can use Flow to progress chase, Power BI reduces the burden of reporting.
But we are just scratching the surface. It won’t be long before we can leverage the rich dataset of experience to predict what risks are likely to occur, understand which WBS elements will be susceptible to variance, deduce what the optimum resource profile looks like, define a schedule by leveraging data from those projects that have gone before.
The role of a project professional is about to change dramatically. In this webinar we will explore the challenges and opportunities, and how we should respond. It’s a call-to-action for the community to mobilise, help to reshape project delivery and understand the implications for you and your organisation.
Presenter Martin Paver is a Chartered Project Professional, APM Fellow and Chartered Engineer. In December 2017 he established the London Project Data Analytics meetup which has quickly spread across the UK and expanded to 3000+ members. Martin has major project experience including leading a $billion projects with a team of 220 and a multi-billion PMO with a team of 50. He has a detailed grasp of project management and combines this with a broad understanding of recent developments in the field of data science. He is on a mission to ensure that the project management profession readies itself for a transformed future.
Learning outcomes:
- Understand the implications of advanced data analytics on project delivery
- Understand the scope of which functions it is likely to impact
- Help you to develop a strategy for how you engage with it
- Understand how to leverage the benefits and opportunities that will emerge from it
Presenter:
Martin Paver, CEO & Founder, Projecting Success Ltd
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges can often trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from reoccurring.
Learning objectives:
-Help you understand foundational Data Quality concepts for improving Data Quality at your organization
-Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
-Share case studies illustrating the hallmarks and benefits of Data Quality success
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
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
Your AI and ML Projects Are Failing – Key Steps to Get Them Back on TrackPrecisely
With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
• Framing the business problem
• Identifying the “right” data to collect and work with
• Establishing baselines of data quality through data profiling and business rules
• Assessing fitness for purpose for training and evaluating the subsequent models and algorithms
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
The Persona-Based Value of Modern Data Governance Precisely
Yes, data governance solutions are now a business imperative. But modern demands are requiring integrated capabilities to discover, understand, profile, and measure data integrity across many different functions across your organization.
This presentation shares four persona-based use cases & demos to illustrate how a single modular, and interoperable solution can optimize collaboration and empower your data teams to deliver data-driven decisions faster and more confidently.
Are you ready for the future of data governance? Check out what will be required:
• Understand data relationships to business objectives, metrics, and request new actions
• Discover new data element alerts to profile and add contextual details to your analysis
• Review needed data quality rules, lineage, and impact and proactively
monitor data changes over time.
• Access & respond to data replication request for more timely results
• Create data quality pipelines and enrich data for more insightful analytics
Advanced Project Data Analytics for Improved Project DeliveryMark Constable
Data Analytics is already beginning to impact how projects are delivered. We can now automate minute taking and capturing actions, we can use Flow to progress chase, Power BI reduces the burden of reporting.
But we are just scratching the surface. It won’t be long before we can leverage the rich dataset of experience to predict what risks are likely to occur, understand which WBS elements will be susceptible to variance, deduce what the optimum resource profile looks like, define a schedule by leveraging data from those projects that have gone before.
The role of a project professional is about to change dramatically. In this webinar we will explore the challenges and opportunities, and how we should respond. It’s a call-to-action for the community to mobilise, help to reshape project delivery and understand the implications for you and your organisation.
Presenter Martin Paver is a Chartered Project Professional, APM Fellow and Chartered Engineer. In December 2017 he established the London Project Data Analytics meetup which has quickly spread across the UK and expanded to 3000+ members. Martin has major project experience including leading a $billion projects with a team of 220 and a multi-billion PMO with a team of 50. He has a detailed grasp of project management and combines this with a broad understanding of recent developments in the field of data science. He is on a mission to ensure that the project management profession readies itself for a transformed future.
Learning outcomes:
- Understand the implications of advanced data analytics on project delivery
- Understand the scope of which functions it is likely to impact
- Help you to develop a strategy for how you engage with it
- Understand how to leverage the benefits and opportunities that will emerge from it
Presenter:
Martin Paver, CEO & Founder, Projecting Success Ltd
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges can often trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from reoccurring.
Learning objectives:
-Help you understand foundational Data Quality concepts for improving Data Quality at your organization
-Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
-Share case studies illustrating the hallmarks and benefits of Data Quality success
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
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
Your AI and ML Projects Are Failing – Key Steps to Get Them Back on TrackPrecisely
With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
• Framing the business problem
• Identifying the “right” data to collect and work with
• Establishing baselines of data quality through data profiling and business rules
• Assessing fitness for purpose for training and evaluating the subsequent models and algorithms
Objective Benchmarking for Improved Analytics Health and EffectivenessPersonifyMarketing
Achieving a high state of analytics excellence can be a daunting task. It involves mastering progressive stages of data health, technological capability, and staff readiness, all while putting out countless fires and responding to last-minute requests for analysis. Strategic progress can be slow, and charting that progress for the executive team, cumbersome and uncertain.
Join us as Denny Lengkong from Personify Implementation Partner, IntelliData, and Personify's Solution Director, Bill Connell, present a rational framework for understanding analytics health and effectiveness. This webinar will help you learn how to make targeted investments in analytics over time that everyone in your organization will understand.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Most organisations think that they have poor data quality, but don’t know how to measure it or what to do about it. Teams of data scientists, analysts, and ETL developers are either blindly taking a “garbage in -> garbage out” approach, or worse still, “cleansing” data to fit their limited perspectives. DataOps is a systematic approach to measuring data and for planning mitigations for bad data.
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Audit Webinar: Surefire ways to succeed with Data AnalyticsCaseWare IDEA
While the majority of executives and internal audit leaders agree that data analytics is important, according to the 2016 IIA CBOK study, only 40% of respondents are using technology in audit methodology. Why the disconnect?
In this webinar, we identify some of the common challenges associated with starting and continuing to use data analytics in your audit process. Easy-to-implement methods that help expand the use of data analytics and improve your audit coverage are also presented.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
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.
What you till learn:
GOALS - What is the bar for data science teams
PITFALLS - What are common data science struggles
DIAGNOSES - Why so many of our efforts fail to deliver value
RECOMMENDATIONS - How to address these struggles with best practices
Presented by Mac Steele
Director of Product at Domino Data Lab
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Data Scientist Gaby Lio will engage with the audience about project dos and don’ts to ensure your project success. She will then walk through three client use cases to give examples of successful data projects at each stage in the journey to AI adoption.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Then Data Scientist Gaby Lio will engage with the audience about project dos and don’ts and leave you with a checklist to ensure your projects success.
Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Transforming GE Healthcare with Data Platform StrategyDatabricks
Data and Analytics is foundational to the success of GE Healthcare’s digital transformation and market competitiveness. This use case focuses on a heavy platform transformation that GE Healthcare drove in the last year to move from an On prem legacy data platforming strategy to a cloud native and completely services oriented strategy. This was a huge effort for an 18Bn company and executed in the middle of the pandemic. It enables GE Healthcare to leap frog in the enterprise data analytics strategy.
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
More Related Content
Similar to Keeping the Pulse of Your Data: Why You Need Data Observability to Improve Data Quality
Objective Benchmarking for Improved Analytics Health and EffectivenessPersonifyMarketing
Achieving a high state of analytics excellence can be a daunting task. It involves mastering progressive stages of data health, technological capability, and staff readiness, all while putting out countless fires and responding to last-minute requests for analysis. Strategic progress can be slow, and charting that progress for the executive team, cumbersome and uncertain.
Join us as Denny Lengkong from Personify Implementation Partner, IntelliData, and Personify's Solution Director, Bill Connell, present a rational framework for understanding analytics health and effectiveness. This webinar will help you learn how to make targeted investments in analytics over time that everyone in your organization will understand.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Most organisations think that they have poor data quality, but don’t know how to measure it or what to do about it. Teams of data scientists, analysts, and ETL developers are either blindly taking a “garbage in -> garbage out” approach, or worse still, “cleansing” data to fit their limited perspectives. DataOps is a systematic approach to measuring data and for planning mitigations for bad data.
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Audit Webinar: Surefire ways to succeed with Data AnalyticsCaseWare IDEA
While the majority of executives and internal audit leaders agree that data analytics is important, according to the 2016 IIA CBOK study, only 40% of respondents are using technology in audit methodology. Why the disconnect?
In this webinar, we identify some of the common challenges associated with starting and continuing to use data analytics in your audit process. Easy-to-implement methods that help expand the use of data analytics and improve your audit coverage are also presented.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
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.
What you till learn:
GOALS - What is the bar for data science teams
PITFALLS - What are common data science struggles
DIAGNOSES - Why so many of our efforts fail to deliver value
RECOMMENDATIONS - How to address these struggles with best practices
Presented by Mac Steele
Director of Product at Domino Data Lab
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Data Scientist Gaby Lio will engage with the audience about project dos and don’ts to ensure your project success. She will then walk through three client use cases to give examples of successful data projects at each stage in the journey to AI adoption.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Then Data Scientist Gaby Lio will engage with the audience about project dos and don’ts and leave you with a checklist to ensure your projects success.
Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Transforming GE Healthcare with Data Platform StrategyDatabricks
Data and Analytics is foundational to the success of GE Healthcare’s digital transformation and market competitiveness. This use case focuses on a heavy platform transformation that GE Healthcare drove in the last year to move from an On prem legacy data platforming strategy to a cloud native and completely services oriented strategy. This was a huge effort for an 18Bn company and executed in the middle of the pandemic. It enables GE Healthcare to leap frog in the enterprise data analytics strategy.
Similar to Keeping the Pulse of Your Data: Why You Need Data Observability to Improve Data Quality (20)
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
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.
In dieser Präsentation diskutieren wir, welche Tools aus unserer Sicht dabei helfen, die Transformation zu SAP S/4HANA optimal zu gestalten. Aber wir blicken auch nach vorne!
In unserem Beitrag fokussieren wir uns nicht nur auf kurzfristige Lösungen, sondern es geht auch um das Thema „Nachhaltigkeit“. Um Investitionen für die Zukunft.
Dazu gehören Entwicklungen, die die SAP Welt nachhaltig verändern werden.
Wir betrachten zukünftige Technologien, wie KI oder Machine Learning, die dazu beitragen, datenintensive SAP Prozesse zu optimieren, die Datenqualität zu verbessern, manuelle Prozesse zu reduzieren und Mitarbeiter zu entlasten.
Werfen Sie mit uns einen Blick in die Zukunft und gestalten Sie die digitale Transformation in Ihrem Unternehmen mit.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Search and Society: Reimagining Information Access for Radical Futures
Keeping the Pulse of Your Data: Why You Need Data Observability to Improve Data Quality
1. Keeping the Pulse
of Your Data:
Why You Need Data
Observability to
Improve Data Quality
2. Housekeeping
Webinar Audio
• Today’s webcast audio is streamed through your
computer speakers
• If you need technical assistance with the web interface
or audio, please reach out to us using the Q&A box
Questions Welcome
• Submit your questions at any time during the presentation
using the Q&A box. If we don't get to your question, we will
follow-up via email
Recording and slides
• This webinar is being recorded. You will receive an email
following the webinar with a link to the recording and slides
2
4. Agenda
• Introduction to data observability
• How data observability works
• Use case examples & demonstration
• Q&A
4
5. 47%
of newly created
data records have at
least one critical error
68%
of organizations say
disparate data negatively
impacts their organization
84%
of CEOs say that they are
concerned about the integrity of the
data they are making decisions on
Data integrity is a business imperative
6. Introduction to Data
Observability
• Data downtime disrupts critical data
pipelines and processes that power
downstream analytics and operations
• Lack of visibility around health of data
reduces confidence in business decisions
• Traditional manual methods do not scale,
are error-prone, and are resource intensive
6
7. • “W. Edwards Deming The Father of Quality Management” started the
observability concept 100 years ago
• Observability is a key foundational concept of SPC, Lean, Six Sigma and
any process dependent on building quality into repetitive tasks
• Using statistical methods to control complex processes to ensure quality
data products over time
What is Data Observability?
7
IDC; Phil Goodwin and Stewart Bond, “IDC Market Glance: DataOps, 2Q21” (June 2021)
Gartner, Hype Cycle for Data Management, 2022, Melody Chien, Ankush Jain, Robert Thanaraj, June 30, 2022
8. Why Now?
8
• Businesses are more data-driven
than ever
• Problematic events are infrequent
but can be catastrophic
• User’s data expertise has evolved
along with expectations to do
more with it
• Data proliferation and technology
diversification
• AI has evolved to support the
complexity of the problem
10. QA is done at the
time of development
Random issues are
surfaced
Users find and
report defects
10
10
Typical Data Products and Pipelines
Traditionally, the quality of a data product or pipeline is ensured during the
development process and not throughout the operational lifecycle.
Data Product(s)
X
Data Source #1
?
Data Source #2
?
Data Source #3
?
Data Source #4
?
Create and/or
Source The Data
Transform
Data
Enrich / Blend /
Merge Data
Publish an
Expose Data
P
r
o
c
e
s
s
11. 11
11
Data Pipelines with Data Observability
Data Observability tools the performance of data products and processes in order to
detect significant variations before they result in the creation of erroneous work product in reports,
analytics, insights and outcomes.
Data Source #1 Data Source #2 Data Source #3
!
Data Source #4
Create and/or
Source The Data
Transform
Data
Enrich / Blend /
Merge Data
Publish an
Expose Data
P
r
o
c
e
s
s
Issues identified and resolved prior to final product
O
b
s
e
r
v
e
Data Product(s)
14. Data Observability and Data Quality
15
Rules
Metadata
• Alerts and dashboards for overall data health
trending and threshold analysis
• Anomaly detection based on volume, freshness,
distribution and schema metadata
• Predictive analysis simulating human intelligence
to identify potential adverse data integrity events
“Observability is the missing piece today to give our data stewards access
to data discovery insights without having to go to IT for queries or reports”
- Jean-Paul Otte, CDO, Degroof Petercam
15. • Provides a single, searchable inventory of data
assets
• Allows technical users to easily search, explore,
understand, and collaborate on critical data assets
• Visualizes relationships, lineage, and business
impact of your data
• Supports the sharing of knowledge, comments, and
surveys
• Enables data stewards to monitor, audit, certify,
and track data across its lifecycle through
integrated data governance
THE IMPORTANCE OF AN INTEGRATED DATA CATALOG
16. Demonstration
17
• Alerts and Alerts Management –
Volume, Data drifts, Schema drifts
etc
• Integrated Data Catalog
• How to create and
configure Observers
• Self-served Data Discovery using
Profiling
17. Demonstration Recap
22
• Alerts and Alerts Management –
Volume, Data drifts, Schema drifts
etc
• Integrated Data Catalog
• How to create and
configure Observers
• Self-served Data Discovery using
Profiling
18. of your data with continuous measuring and monitoring
associated with erroneous analytics that impact business decisions
when outliers and anomalies are identified
to solve operational issues and the cost of adverse events
1
2
3
4
when issues occur by understanding the cause
5
Data Observability benefits
20. The modular, interoperable Precisely Data
Integrity Suite contains everything you need
to deliver accurate, consistent, contextual
data to your business - wherever and
whenever it’s needed.
25
21. 7 strong modules deliver exceptional value
Data
Integration
Data
Observability
Data
Governance
Data
Quality
Geo
Addressing
Spatial
Analytics
Data
Enrichment
Break down
data silos
by quickly
building
modern data
pipelines that
drive
innovation
Proactively
uncover data
anomalies and
take action
before they
become costly
downstream
issues
Manage data
policy and
processes with
greater insight
into your data’s
meaning,
lineage, and
impact
Deliver data
that’s accurate,
consistent, and
fit for purpose
across
operational
and analytical
systems
Verify,
standardize,
cleanse, and
geocode
addresses to
unlock valuable
context for more
informed
decision making
Derive and
visualize spatial
relationships
hidden in your
data to reveal
critical context
for better
decisions
Enrich your
business data
with expertly
curated datasets
containing
thousands of
attributes for
faster, confident
decisions
Welcome to our session today, I want to thank you all for joining us and let you know how excited we are to be with you today to talk about data observability and how it can help to improve your data quality.
Just a bit of housekeeping before we get started. If you have any questions today please put them in the Q&A box. This session is being recorded and you will receive an email following the webinar with a link to the recording and slides.
I’d like to introduce your speakers for today’s session. My name is Julie Skeen. I am a Sr. Product Marketing Manager at Precisely responsible for Data Quality and Observability. And with me I have my colleague Shalaish. …
The plan for our session today is to share some introductory information about data observability. We will then discuss how data observability works and show you some use case examples in action. We will allow time at the end to answer questions.
Let’s jump in…
Here you see a few stats from Forbes, the Harvard Business Review, and Precisely’s own Data Trends Survey.
Looking at these - When two-thirds of organizations say siloed data negatively impacts their data initiatives and almost half of newly created data records have at least one critical error, it is no wonder that 84% of CEOs doubt the integrity of the data on which they make decisions!
So….let’s learn how data observability can help.
There are a number of business challenges that occur that can be improved by using a data observability solution. See if any of these sound familiar.
Something goes wrong within the data pipeline that impacts downstream operations or analytics. You might experience this as an email from IT saying that your BI tool is unavailable.
Do you ever experience a lack of confidence in decision making based on the data in your BI tool or advanced analytics processes?
Does your team find that writing scripts or other manual methods that were used in the past to look for operational data issues no longer scale as data volumes increase?
If any of these challenges resonate, then your organization can benefit from a data observability solution.
So what is data observability?
Observability itself is not a new concept. It started over 100 years ago and is a key concept in many process methodologies and is used in industries such as manufacturing as well as software development. What is newer is applying these concepts to data.
Data Observability ensures the reliability of your processes and analytics by alerting you to potential data integrity events. It answers the question, “Is my data ready to be used?”
And by the term “used” we mean anywhere a business depends on the data being accurate. This obviously means a lot of things to a lot of different people.
If you are dependent on a BI report, you may ask, is the data that is feeding my reports correct?
If you are a data engineer, moving data through pipelines, you may want to know if the data is being transferred correctly.
If you are a data scientist building advanced data science models, you want to know if the models are reflective of recent data changes or need to be retrained.
Take for example a simple process where the finance dept makes business decisions based on a daily report of online orders. If for some reason the data feeding the report from the source systems is incorrect, the insights and outcomes based on the report will be flawed, with obvious negative downstream impacts.
If this all sounds similar to data quality that’s because it is another way to ensure quality of your data. We will talk more about how data observability relates to traditional data quality in a few minutes.
First we want to talk about why data observability is more important now than ever
We all know businesses are using data for more purposes, and ultimately becoming more dependent on it.
Think about if you were driving a car while looking at your phone. Your primary goal is to get from point A to B safely, but if you are constantly inundated with distractions and you are trying to look at your phone while driving, this can cause you to drive off the road or collide with another vehicle. The same applies to data in your business. Your objective is to run your business, but if you are constantly distracted worrying about data issues that MIGHT happen then you’ll drive off the road. Similar to the gauges in your car you want only relevant alerts to help you drive your business successfully.
These data issues are more relevant today for a variety of reasons, but they can be grouped into 2 main categories: data proliferation and technology diversification.
By Data Proliferation I simply mean, there is more data – a lot more data. A Forbes Study estimates we’re creating 2.5 quintillion/exabytes of data every day, and this data is spanning a variety of locations such as cloud, on-prem, and hybrid cloud not to mention the movement of data across all these locations.
By Technology Diversification I mean pivotal business transformation initiatives empowered by amazing next generation tech that represents a rethinking of established legacy systems. These efforts almost always span a diversity of vendors, applications, and technologies such as streaming, IOT and AI/ML.
Data consumers, users and producers cannot take their hands off the wheel to validate that the data is ready for use as they need to stay focused on steering the business.
Data observability enables business value not only when fast insights allow for quick decisions, but also when the data being used for insights is trusted.
It’s one thing to identify data issues, but more importantly data issues need to be corrected before the data is used in making decisions.
Data issues will happen. No system or process is perfect. Proactively addressing issues prevents them from impacting the business.
As you see in this picture, Data Observability shines a light on your potential data issues with passive user interaction.
This captures the essence of Data Observability and just how simple it is to shine light on a potential problem and change course verses having to salvage the wreckage.
If there is one takeaway from this overview, please remember: Data Observability is proactive and intended to improve data reliability and reduce the data downtime.
Using a variety of techniques, Data Observability surfaces issues in source systems before they become significant. We’re going to show you a few examples of those techniques today based on volume and data drift detection methods that answer questions such as:
Is my data ready to use?
Do I have all my data?
Do I have the right data?
And with data proliferation and technology diversification at play, reactive methods simply do not scale, are error-prone and resource intensive.
As the adage goes, an ounce of prevention is worth a pound of cure.
The process of managing the data life cycle and data journey and monitoring an enterprise has become incredibly sophisticated and complex. It is not out of the ordinary to see thousands of pipelines and transformations spanning hundreds of data sources. Data quality is often validated at the final delivered stage.
Comparing this to a traditional manufacturing process, it's the equivalent of ensuring the quality of the finished product with a post-manufacturing inspection of the product. And as you can imagine, this process is incredibly costly from both time and a risk perspective. The same concept applies to your typical data products such as analytics, reports, applications and any pipelines or processes driving an outcome. For a data pipeline it means a stakeholder is finding the issue and reporting it to the creator of the analytics. Again, it means having to go back to an earlier stage after the production is thought to be complete.
Contrast that process with what it looks like when you add data observability. Data observability enables the user to visualize the data process and see deviations from the typical patterns. What that means in this example is you see a typical data process that spans multiple data sources and transformations. This is a simple view, but in reality, there could be hundreds of different transformations spanning many different data sources and as it moves through the pipeline, it is observed at each stage, ensuring the entire process is stable.
Data source #3,is applying enrichment as well as blending and merging of data. You can see that there’s some sort of anomaly that has the potential to jeopardize the final data product. Catching it early in this stage allows the appropriate resource to be notified, and the issue assessed and resolved before the data is made available for consumption. Many studies have been published validating the cost savings of finding issues earlier in the lifecycle. This cost savings can be significant. The early resolution eliminates wasted time and resources in latter stages of the pipeline, not to mention the risk of negative business outcomes. It’s critical that data issues are discovered and remediated before business decisions are made based on inaccurate analytics.
How does data observability work? It is really broken down into three main sets of capabilities. The first is discovering the data that you want to observe and collecting information about those assets through a variety of techniques and tools.
The second component performs the analysis to identify any adverse data integrity events. The analysis can get quite sophisticated. It often implements modern AI and ML methods to crunch massive amounts of metadata and related information,
Finally, is action - and that's bringing those alerts and insights into the forefront for both manual and automated resolution and is essentially the step to do something about the data issues that have been found.
Let’s look a little deeper at the key capability of data observability analysis – anomaly detection. It might not be obvious when we talk about the analysis components of data observability that the underpinning of this capability set is extensive intelligence powering those insights. Outlier detection for identifying anomalies has been proven to be an effective technique in many use cases and is an integral part of data observability.
Here you can see a few typical patterns of anomaly detection used in data observability. This is just scratching the surface of AI and ML methods used to determine outliers. If you are familiar with these types of methods you will see this includes a variety of methods such as random noise, step changes, trends and others, many which are very complex. If you aren’t familiar with those specifics, the main takeaway here is that there is extensive Artificial Intelligence & Machine Learning that is supporting the anomaly detection so that, while you can build specific rules in data observability – you don’t have to because the system will learn what to expect from your data and will alert you when anything appears outside the norm.
Another key capability set in data observability is the action step. This is where you can see visually see alerts that have occurred on your data pipelines and those alerts can be proactively pushed out via notifications. Here you can see an example of a volume alert and the related assets that are impacted by this alert.
---
When we look at Data Observability and traditional Data Quality we consider them as complimentary capabilities, and there is some overlap. Data Observability may be under the same umbrella as data quality or it might be owned with DataOps. Both focus on the use of metadata AND the traditional Data Quality dimensions of accuracy, completeness, conformity. Both benefit from integrations with the data catalog and are critical for any data governance initiatives.
The biggest difference is how each capability set performs this evaluation. Data Observability emphasizes the identification of anomalies and outliers in data based on patterns over time. Think of this as is similar to human inference – how you or I would look at a data trend line and draw a conclusion - verses the static, predefined rules you find as a part of most data quality tools.
While we, at Precisely, offer both Data Observability and Data Quality as distinct capabilities sets, we also make sure that both sets of functionality complement each other to ensure customers get the most possible value from the solutions.
The other capability that is critical to data observability is an integrated data catalog. There are number of benefits that we see from having a data catalog integrated with data observability:
The catalog provides a single, searchable inventory of data assets and sllows technical users to easily search, explore, and understand the data
It also allows users to visualize relationships, lineage, and business impact to the data and enables collaboration through a variety of mechanisms
It also enables data stewards to monitor, audit, certify, and track data across its lifecycle
-----
Now we want to show you some examples of situations that happen when things don’t go as planned with data pipelines and give you a view into how data observability helps to address each of these scenarios.
Shalaish
OK. And with that, I'm going to hand over to my colleague Shalaish who is going to show this in action.
Shalaish
Shalaish
Shalaish
Shalaish
Thanks Shalaish.
Now that you’ve heard about what data observability is and seen how it can apply to specific use cases I want to review the benefits of data observability. Many of these may be apparent to you from what you’ve seen.
First is understanding data health – as the system continuously measures and monitors what is happening you can utilize dashboards to understand the health across your data landscape.
Data observability can reduce the risks associated with erroneous business intelligence and advanced analytics that have the potential to impact a variety of business decisions
Proactive alerts are provided when the intelligence determines there is an anomaly or outlier and that notification is shown both visually and pushed to the appropriate users
Data Observability also enables you to reduce the time to solve operational issues and reduce the cost of potentially adverse events
And finally, it allows you to quicky remediate the issues, and integrated data quality capabilities further expedite this process.
Before I close out, I want to mention that the product you saw today is Precisely’s Data Observability solution which is part of Precisely’s Data Integrity Suite.
The Precisely Data Integrity Suite is modular, interoperable and contains everything you need to deliver accurate, consistent, contextual data to your business.
The Precisely data integrity suite is set of seven interoperable modules that enable your business to build trust in your data.
The suite has been built so you can start wherever you are in your data integrity journey. This means that the modules are designed to be implemented either together or stand alone, with best-in-class capabilities. For example, you can start with data observability and layer in other modules over time. Here you can see a brief view of the modules of the Precisely Data Integrity Suite.
Now we will address a few questions before we finish up for today. Shalaish, the first question is
1. How does Data Observability work with other applications?
The next question is:
2. What type of user would use Data Observability?
Those are all the questions we have time for today. If we did not get to your question we will follow up with you via email.
Thank you for joining us today. If you would like to learn more about data observability we have a provided a resource for your. Thank you for your time and attention.