Poor data quality costs businesses significantly through wasted labor, lost productivity and direct financial losses. Rapid changes in circumstances like addresses, phone numbers, jobs and relationships mean that master data changes at a rate of around 2% per month. Siebel's data management solution and new data quality products from Oracle help address these issues through comprehensive data profiling, cleansing, matching and enrichment capabilities. Regular data stewardship is important for ongoing monitoring and governance to maintain high quality master data.
Dynamic Talks: "Implementing data quality automation with open source stack" ...Grid Dynamics
The quality of business decisions, machine learning insights, and executive reports depend on the quality and integrity of the underlying data. There are many ways that data can get corrupted in an analytical data platform from de-synchronization with the system-of-record to defects in data pipelines. We will show how to detect and prevent data corruption with automation, open source tools, and machine learning.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Dynamic Talks: "Implementing data quality automation with open source stack" ...Grid Dynamics
The quality of business decisions, machine learning insights, and executive reports depend on the quality and integrity of the underlying data. There are many ways that data can get corrupted in an analytical data platform from de-synchronization with the system-of-record to defects in data pipelines. We will show how to detect and prevent data corruption with automation, open source tools, and machine learning.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Data Quality: A Raising Data Warehousing ConcernAmin Chowdhury
Characteristics of Data Warehouse
Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Vendor-neutral presentation about the common functionality provided by data profiling tools, which can help automate some of the work needed to begin your preliminary data analysis.
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...DATAVERSITY
There’s a lot of confusion out there about the differences between a data catalog, a data dictionary and a business glossary, and it's not always easy to understand who needs which and why. Join Malcolm Chisholm, Ph.D., President of Data Millennium, and Amichai Fenner, Product Lead at Octopai, as they help decode the mystery. Spoiler alert: one of these enables collaboration across BI and IT, which is it?
MLOps - Getting Machine Learning Into ProductionMichael Pearce
Creating autonomy and self-sufficiency by giving people what they need in order to do the things they need to do! What gets in the way, and how can we overcome those barriers? How do we get started quickly, effectively and safely? We'll come together to look at what MLOps entails, some of the tools available and what common MLOps pipelines look like.
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
The business models across industries around the world are becoming Customer Centric. Recent studies show that “knowing” customers based on internal as well as external data is one of the top priorities of business leaders. On the other hand various surveys also reveal that customers do not mind to share their semi-personal data for the benefit of differentiated service. In that context, the 360 degree view of customer – which was once thought to be a business process, master data management, data integration and data warehouse / business intelligence related problem has now entered into the whole new big world of BIG data including integration with unstructured data sources. Impact of big data on Customer Master Data Management is spread across - from Integration and linkage of unstructured or semi-structured data with structured master data that is maintained within enterprise; to analyze and visualization of the same to generate useful insight about the customers. There are various patterns to handle the challenges across the steps i.e. acquire, link, manage, analyze and distribute the enhanced customer data for differentiated product or services.
CADMEF IMC Academic Roundtable: May 10-11, 2012
DePaul University, Chicago, A Framework to Understand Customer Data Quality in CRM Systems for Financial Services Firms
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Data Quality: A Raising Data Warehousing ConcernAmin Chowdhury
Characteristics of Data Warehouse
Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Vendor-neutral presentation about the common functionality provided by data profiling tools, which can help automate some of the work needed to begin your preliminary data analysis.
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...DATAVERSITY
There’s a lot of confusion out there about the differences between a data catalog, a data dictionary and a business glossary, and it's not always easy to understand who needs which and why. Join Malcolm Chisholm, Ph.D., President of Data Millennium, and Amichai Fenner, Product Lead at Octopai, as they help decode the mystery. Spoiler alert: one of these enables collaboration across BI and IT, which is it?
MLOps - Getting Machine Learning Into ProductionMichael Pearce
Creating autonomy and self-sufficiency by giving people what they need in order to do the things they need to do! What gets in the way, and how can we overcome those barriers? How do we get started quickly, effectively and safely? We'll come together to look at what MLOps entails, some of the tools available and what common MLOps pipelines look like.
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
The business models across industries around the world are becoming Customer Centric. Recent studies show that “knowing” customers based on internal as well as external data is one of the top priorities of business leaders. On the other hand various surveys also reveal that customers do not mind to share their semi-personal data for the benefit of differentiated service. In that context, the 360 degree view of customer – which was once thought to be a business process, master data management, data integration and data warehouse / business intelligence related problem has now entered into the whole new big world of BIG data including integration with unstructured data sources. Impact of big data on Customer Master Data Management is spread across - from Integration and linkage of unstructured or semi-structured data with structured master data that is maintained within enterprise; to analyze and visualization of the same to generate useful insight about the customers. There are various patterns to handle the challenges across the steps i.e. acquire, link, manage, analyze and distribute the enhanced customer data for differentiated product or services.
CADMEF IMC Academic Roundtable: May 10-11, 2012
DePaul University, Chicago, A Framework to Understand Customer Data Quality in CRM Systems for Financial Services Firms
There are many very good power quality data analysis tools, but they are often vendor specific, and designed as an engineering desktop tool. This presentation is an introduction to a 'fleet view' dashboard built in the open source software (OSS) space. It is web based, built on an open source data layer that provides automated analytics, and can accept data from any standard COMTRADE or PQDIF formatted event file.
A brief introduction to Data Quality rule development and implementation covering:
- What are Data Quality Rules.
- Examples of Data Quality Rules.
- What are the benefits of rules.
- How can I create my own rules?
- What alternate approaches are there to building my own rules?
The presentation also includes a very brief overview of our Data Quality Rule services. For more information on this please contact us.
Data-Ed Online: Engineering Solutions to Data Quality ChallengesData Blueprint
This webinar originally aired on Tuesday, October 9th, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
This presentation provides guidance to organizations considering or preparing for data quality initiatives. We will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality can be engineered provides a useful framework in which to develop an organizational approach. This in turn will allow organizations to more quickly identify data problems caused by structural issues versus practice-oriented defects. Participants will also learn the importance of practicing data quality engineering quantification.
Response Rates Impact Data Quality, But not How you Might ThinkStephanie Eckman
delivered at World Bank, part of Development Data Group Learning Series
Washington DC, 2016-03-07
Response rates do not always provide an accurate depiction of data quality. Research based on a large multi-country survey indicate that when interviewers play a substantial role in sample selection, interviewer manipulation may artificially generate high response rates. For example, when using the random walk selection technique, interviewers should select every kth household, but they have substantial leeway in deciding which household is the kth one, and may preferentially select those where someone is home. Or, when rostering a household to select a random respondent, interviewers may leave off household members who are seldom at home. If many interviewers engage is such behaviors, a high response rate may in fact be the result of biased sample selection and therefore indicate low data quality.
There are two lessons from these findings. First, response rates should not be used as the sole or primary proxy for data quality. Second, whenever possible, interviewers’ role in sample selection should be minimized. The talk concludes with a review of alternative sampling methods that take advantage of geospatial data such as satellite photos, drone imagery and handheld GPS devices. The ideal sampling techniques are ones that minimize interviewer discretion and allow for verification of interviewer performance.
Do you lose precious time due to data quality problems?
Do you need to integrate data from multiples sources and provide an integrated view of your customer or product attributes to other systems?
SQL Server 2016 Data Quality and Master Data Services can help you.
DAMA Webinar - Big and Little Data QualityDATAVERSITY
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
Data Quality Management (DQM) impacts a number of key business drivers, ranging from regulatory
compliances, to customer satisfaction, to building new business models. Quality is one of the key functions
under Data Governance, as unverified/unqualified data has little value to the organization. One of the leading
global research and advisory firm estimates that an average Fortune 500 enterprise loses about $9.7mn
annually over data quality issues. Although the true intangible cost of poor data is much higher, the sad truth
is that data quality has not been paid the attention it deserves.
Big Data - it's the big buzz. But is it dead on arrival?
In this presentation Daragh O Brien looks at the history of information management, the challenges of data quality and governance, and the implications for big data...
Big Data and Analytics in your Organisation talk.pdfPaul Laughlin
My presentation to the ILA Digital Community Showcase event at the Vale Hotel in Glamorgan on 7 Oct 2022. Introducing the topic of Digital Transformation to healthcare leaders in Wales.
Unlock the Power of Handwriting Recognition to Optimize Your Business ProcessesZia Consulting
While OCR technology has evolved substantially, it still struggles with handwriting recognition. However, modern machine learning offers new tools to solve long-standing challenges. New software solutions can now optimize complex business processes by turning even hand-filled paper documents into business-ready data automatically.
Learn how leading insurance, financial services, and healthcare companies are leveraging new technology to automate processing of their paper-to-digital operations (including handwritten forms). You’ll hear how New York Life and five of the top 10 U.S. insurers have gained efficiencies and enabled critical business improvements with a 99.9% accuracy rate.
Suresh Menon, Vice President, Product Management - Information Quality Solutions at Informatica, shares how to master your data and your business from the 2015 Informatica Government Summit.
From Compliance to Customer 360: Winning with Data Quality & Data GovernancePrecisely
Winning football teams will dominate opponents both defensively and offensively. Similarly, the most successful businesses will best utilize enterprise data for effective “defense” (e.g., compliance, such as GDPR and CCAR) as well as “offense” (increased customer engagement and revenue).
View our on-demand webcast and discover how integrated data quality and data governance tools help you confidently achieve regulatory compliance, as well as revenue-building initiatives requiring a 360-degree view of your customers.
Data management experts Ian Rowlands, Product Marketing Manager of ASG and Harald Smith, Director, Product Management of Trillium Software discusses how Trillium Software for data quality, integrated with ASG’s Enterprise Data Intelligence solution, helps you pinpoint where data quality impacts your business, ensuring your enterprise data can be trusted to drive regulatory compliance as well as better business decisions.
View this on-demand webcast to learn how to:
• Improve data quality by leveraging data lineage maps
• Gain insight into where data quality gaps may exist, which may impact regulatory compliance and customer engagement initiatives
• Understand how changes may affect critical data elements and data quality
Too often data myths lead to inefficient processes in CRM, broken systems and/or paralysis analysis. You don’t need to wield the hammer of Thor to make progress with your data management strategy, you just have to separate fact from fable.
On this recorded webinar, Donato Diorio (@idonato) and Michael Farrington (@michaelforce), two experts in CRM and marketing automation technology, dispel several common data myths providing tangible and actionable advice to ensure good fortune for all who rely on your data.
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.
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
Artificial Intelligence Expert Session Webinar ibi
Tom Redman of Data Quality Solutions and Information Builders' CMO Michael Corcoran share the latest on artificial intelligence trends in this webinar.
Improve customer experience with a customer intelligence platformDavid Corrigan
A short presentation on the real problem with customer experience - the status quo. The issue is the link between your data and analytics strategy. What's required is context and intelligence to make data analytics-ready. Customer intelligence platforms are designed to do that - to produce an intelligent customer 360 for analytic and operational use cases. Better customer data is the foundation for improving the customer experience.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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
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.
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.
Key Trends Shaping the Future of Infrastructure.pdf
Sound Data Quality for CRM
1. Sound Customer Data Quality for CRM Manoj Tahiliani, Senior Manager, Customer Hub Strategy
2. The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
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7. Poor Data Quality is the #1 enemy of CRM Solutions Out of Date Rapid changes in a dynamic society: marriages, divorces, births, deaths, moves Garbage Typos, misspellings, transposed numbers, etc. Fraud Purposeful misrepresentation of data: identity theft, wrong information (bankruptcies, occupation, education, etc) Missed Opportunities Information that we do not know about (customer relationships, up-sells, cross-sells)
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9. Example of Customer Data Quality Issue A Simple Customer Table Sample Name Address City State Zip Phone Email Bob Williams 36 Jones Avenue Newton MA 02106 617 555 000 [email_address] Robert Williams 36 Jones Av. MA 02106 617555000 Burkes, Mike and Ilda 38 Jones av. Nweton MA 02106 617-532-9550 [email_address] Jason Bourne, Bourne & Cie. 76 East 51 st Newton MA 617-536-5480 6175541329 … … … … … … … Mis-fielded data Matching Records Typos Mixed business and contact names Multiple Names Non Standard formats Missing Data
17. Data Quality Functionality in a Glance Profiling Cleansing Matching Enrichment Understand data status, deduce patterns Tel# is null 30% LName + FName (Asian Countries); FN+MN+PN+LN (Latin); Addr = #, street, city, state, zip, country; St, Str = Street (ENU/DEU); Spot and correct data errors; transform to std format/phrase Identify and eliminate duplicates Haidong Song = 宋海东 = Attach additional attributes and categorizations Haidong Song: “single, 1 child, Summit Estate, DoNot Mail” Functionality Customer Data example Comprehensive data quality Feature Batch and Real-time
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19. New Data Quality Products Matching Engine Data Quality Matching Server Data Quality Cleansing Server Administration UI / Rules Editor Improved performance 18 Languages 52 Languages Address Standardization Module 240 Languages support Data Quality Profiling Profiling Console & Engine Old Offering (SSA) New Offering
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24. Oracle Data Quality Matching Server Siebel UCM / CRM Application Object Manager User Interface Data Admin Oracle DQ Matching Server Loader & Utilities Rule Manager Key & Search Strategies Match Purposes Search Server Update Synchronizer Console Server Console Administrative Clients Population Override Mgr Edit Rule Wizard Indexes Rules Base