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.
Understanding, Planning and Achieving
Data Quality in Your Organization
by Joe Caserta, President of Caserta Concepts
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
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.
Understanding, Planning and Achieving
Data Quality in Your Organization
by Joe Caserta, President of Caserta Concepts
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
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 quality - The True Big Data ChallengeStefan Kühn
Data Quality is one of the most-overlooked key aspect in any Big Data project or approach. This talk adresses the problem from various perspectives, discusses the main challenges and identifies possible solutions.
Data Quality: The Data Science struggle nobody mentions - Data Science MeetUp...University of Twente
Presentation about data quality at the second Data Science MeetUp Twente https://www.meetup.com/Data-Meetup-Twente/events/241545781/ on "Responsible Data Analytics", 7 Sep 2017.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
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
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)Denny Lee
These are the slides on differential privacy case studies I had presented at the MindSwap on Privacy Technology, October 19–20, 2007. Center for Computational Thinking, Carnegie Mellon, Pittsburgh, PA.
Effectiveness of Data Analytics and Big Data in United States Presidential Elections, Polls, Voting and Campaigns. U.S. presidential elections are the most talked about topic now a days. Who will win race? Donald Trump or Hillary Clinton ? This presentation gives an insight on how people can utilize the data analytics approaches to achieve specific goals and get insight to the target users.
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs, creating a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
Employing Machine learning techniques and building models, better approaches and preventive ways can thus be introduced to lower mortality rates & costs
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
Data quality - The True Big Data ChallengeStefan Kühn
Data Quality is one of the most-overlooked key aspect in any Big Data project or approach. This talk adresses the problem from various perspectives, discusses the main challenges and identifies possible solutions.
Data Quality: The Data Science struggle nobody mentions - Data Science MeetUp...University of Twente
Presentation about data quality at the second Data Science MeetUp Twente https://www.meetup.com/Data-Meetup-Twente/events/241545781/ on "Responsible Data Analytics", 7 Sep 2017.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
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
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)Denny Lee
These are the slides on differential privacy case studies I had presented at the MindSwap on Privacy Technology, October 19–20, 2007. Center for Computational Thinking, Carnegie Mellon, Pittsburgh, PA.
Effectiveness of Data Analytics and Big Data in United States Presidential Elections, Polls, Voting and Campaigns. U.S. presidential elections are the most talked about topic now a days. Who will win race? Donald Trump or Hillary Clinton ? This presentation gives an insight on how people can utilize the data analytics approaches to achieve specific goals and get insight to the target users.
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
Large amounts of antibiotics used for human therapy result in the selection of pathogenic bacteria resistant to multiple drugs, creating a burden on medical care in hospitals, especially for patients admitted to intensive care units (ICU).
Employing Machine learning techniques and building models, better approaches and preventive ways can thus be introduced to lower mortality rates & costs
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
Data is an integral and invaluable asset of any organization to pursue the vision of value-creation for customers. Given its significance, efficient management of organizational data can be a potential differentiator between thriving and failing in today’s competitive world. Leverage CRMIT’s Marketing Utilization Services for CRM, including
1. Data Utilization - Data sampling, data de-duplication, data scrubbing & data cleansing.
2. Data enrichment - Data profiling and data segmentation
3. Campaign management – E-campaigns, lead nurturing and lead scoring
4. Analytics - Analysis to Insights
Uncover Untold Stories in Your Data: A Deep Dive on Data ProfilingJosiah Renaudin
How well do you know your data? Organizations are discovering the value in their data—as evidence of what they have done and a clue to how they can improve the bottom line. With the increase in analytics, it is no secret that there are more eyes on the data. And analyzing data can give valuable insight into patterns that drive efficiencies or errors. It is important to use this information and make sure it is being used correctly. However, excavating the data is not always as simple as it seems. Catherine Cruz Agosto and Shauna Ayers are your guides as they define data profiling and its importance, delve into different strategies you can use, and discuss how to get the most out of your data. Come and learn useful tools and strategies you can take back to get to know and better use your data.
Bitcoin, Transaction Fees and The Cost of Poor QualityRSky215
For decades, transaction fees have been an unavoidable cost of doing business for merchants. This document seeks to explore The Cost of Poor Quality and the implications Bitcoin has to eliminate these costs and dramatically increase profit margins.
Sean Kandel - Data profiling: Assessing the overall content and quality of a ...huguk
The task of “data profiling”—assessing the overall content and quality of a data set—is a core aspect of the analytic experience. Traditionally, profiling was a fairly cut-and-dried task: load the raw numbers into a stat package, run some basic descriptive statistics, and report the output in a summary file or perhaps a simple data visualization. However, data volumes can be so large today that traditional tools and methods for computing descriptive statistics become intractable; even with scalable infrastructure like Hadoop, aggressive optimization and statistical approximation techniques must be used. In this talk Sean will cover technical challenges in keeping data profiling agile in the Big Data era. He will discuss both research results and real-world best practices used by analysts in the field, including methods for sampling, summarizing and sketching data, and the pros and cons of using these various approaches.
Sean is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.
Presentation by Peter Fontaine, CBO's Assistant Director for Budget Analysis, to a Global Network of Parliamentary Budget Offices Community Meeting Sponsored by the World Bank Institute
Exposing Your Hidden Costs of PerformanceJuran Global
An ongoing challenge today for most organizations is to do more with less. Many companies are spending more time with their customers, while customers are demanding competitive pricing on products and services. As the planned profit margin erodes, the question management is posing to their staff is, "How do we maintain our margins and still meet the customers’ demands?" Basically, how do we leave less dollars on the table? The answer is Cost of Poor Quality (COPQ) analysis, which can be used to identify and reduce operational wastes while maintaining margins.
Dr. Joseph DeFeo, Chairman and CEO of Juran Global, shares:
* Typical misconceptions about quality.
* How COPQ affects the bottom line.
* How to identify the "tip of the iceberg."
* The costs hidden in the bottom of the iceberg.
* How to estimate costs using total resources and unit costs.
Data visualization has enabled us to compress data and express them visually in many interesting new ways. It is often cited that we are trying to tell stories through them. Is that really the case? How can we ensure that the audience is able to retain, recall and retell our data-driven stories.
Using examples and videos learning from different storytelling mediums, I walk through why stories are important and what we can learn about how stories work from these mediums. I then detail out a framework built on the See the data | Show the Visual | Tell the Story | Engage the Audience paradigm to convert the data in to a data-visual-story.
This slide deck was used in Bangalore Meetup - Crafting Visual Stories with Data - in March 2014 @ InMobi's Bangalore Office.
Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies.
Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, current data profiling techniques hardly scale beyond what can only be called small data. Finally, more and more data beyond the traditional relational databases are being created and beg to be profiled. The talk proposes new research directions and challenges, including interactive and incremental profiling and profiling heterogeneous and non-relational data.
Speaker: Felix Naumann studied mathematics, economy, and computer sciences at the University of Technology in Berlin. After receiving his diploma (MA) in 1997 he joined the graduate school "Distributed Information Systems" at Humboldt University of Berlin. He completed his PhD thesis on "Quality-driven Query Answering" in 2000. In 2001 and 2002 he worked at the IBM Almaden Research Center on topics around data integration. From 2003 - 2006 he was assistant professor for information integration at the Humboldt-University of Berlin. Since then he holds the chair for information systems at the Hasso Plattner Institute at the University of Potsdam in Germany.
Privacy in AI/ML Systems: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we protect the privacy of users when building large-scale AI based systems? How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of privacy-preserving AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Understanding human information
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
Thomas H. Davenport, best-selling co-author of Competing on Analytics and Analytics at Work, and President's Distinguished Professor at Babson College presented at the Premier Business Leadership Series 2010 http://www.sas.com/theserieshk
Davenport will present straightforward, practical advice from his new book Analytics at Work: Smarter Decisions, Better Results, including laying out a plan of action for deploying and succeeding with business analytics inside your company.
Learn how to:
- Use an analytics approach to run your business.
Put the right assets in place and deploy them most effectively.
- Launch an analytics initiative.
- Sustain an analytics focus over time.
- Evaluate your organisation's current analytical capabilities.
- Use analytics to make better decisions.
Week2day2 communicating data for impactNishant Kumar
This presentation is based on an article “ Data is Worthless if You Don’t Communicate It ” by Thomas H. Davenport published under Harvard Business review in year 2013 .
Why does telling a story with your data matters Explain the impo.docxfranknwest27899
Why does telling a story with your data matters? Explain the importance of accurate data in today's business environment.
Data Storytelling: What It Is, Why It Matters
Telling a compelling story with your data helps you get your point across effectively. Here are four tips to keep your data from getting lost in translation.
8 Non-Tech Skills IT Pros Need To Succeed
(Click image for larger view and slideshow.)
Organizations can do a lot more with their data if they understand it better than they do. While businesses continue to invest dollars in business intelligence (BI) and analytics tools, they aren't necessarily getting the information they need to improve business decision-making.
Data visualizations
help by transforming complex information into something easier to understand. However, two people can interpret the same data visualization differently. Notably, data visualizations tend to answer "what" questions, but they don't tend to explain the "why," or provide other contextual information. Data storytelling does exactly that.
"Data storytelling weaves data and visualizations into a narrative tailored to a specific audience in order to convey credibility in the analytical approach, confidence in the results, and a compelling set of insights that is actionable to the audience." said Ryan Fuller, general manager at Microsoft and former CEO and cofounder of enterprise analytics company VoloMetrix, in an interview. "The narrative is the key vehicle to convey insights, and the visualizations are important proof points to back up the narrative."
Executives, managers, and employees have always told stories as part of their everyday work experience, but they are increasingly being required to use data to support their points of view, claims, and recommendations. The danger, of course, is data can be tortured into saying almost anything.
"One of the biggest mistakes is trying to fit the data to the story, which often results in a jumbled narrative that doesn't arrive at a compelling conclusion," said Francois Ajenstat, VP of product development at BI and analytics solution provider
Tableau
, in an interview. "Always start with the data, then build your story around it, rather than vice versa."
After speaking with experts in data science and analytics, we've developed the following four tips to help guide your data storytelling.
1. General Storytelling Rules Apply
Effective data storytelling is a lot like storytelling generally. The data story should have a beginning, a middle, and an end. It should also include a thesis (or a hypothesis), supporting facts (data), a logical structure, and a compelling presentation. Yet, all too often, those responsible for analyzing data are unable to present it in a way that's meaningful to the audience.
"A common mistake is spending too much time on the technical aspect or methodology and not providing much creativity in pointing out how the data can help the business," said David Liebskind, VP of anal.
How Data as a Service can make your campaigns more successful - Dun and Brads...B2B Marketing
Many organisations have invested in technology to help with their lead generation programmes. However, this often has not delivered the anticipated benefits and as a result, a high proportion of marketing generated leads are not worked by sales, wasting valuable budget. Sales and marketing alignment seems as far away as ever. New Data as a Service (DaaS) based solutions allow you to integrate real time external data, directly into your marketing and sales automation systems to transform your campaigns, access insights not previously available and generate the right leads for your business. In this session you will learn:
What DaaS is and how it works
How to generate the right leads to drive sales and marketing alignment
Know all about your inbound leads, in an instant
Tailor your conversations to accelerate the sales cycle
How you can link social media data into your CRM
Presentation by Bob Sutor at the International Association of Privacy Professionals in Washington, DC USA, on March 6, 2014. This short presentation was meant to stimulate ideas that would then be complemented by discussions about privacy policies as it relates to Big Data, and in that sense is not complete regarding all aspects of privacy that come from the issues discussed.
4 Critical Requirements for Building Truly Intelligent AI ModelsInnodata, Inc
Did you know 85% of AI projects will fail because of a lack of training data?
Before investing time and money in machine learning, discover 4 critical requirements every company needs to employ in order to build effective machine learning applications and bring intelligence to artificial intelligence.
HPE IDOL Technical Overview - july 2016Andrey Karpov
Search and Analytics Platform for Text and Rich Media
Open Innovation is transforming everything
Connected people, apps and things generating massive data in many forms
How do you bridge the gap between data and outcomes?
Augmented Intelligence power apps for competitive advantage
Machine Learning at the Service of Business Augmented Intelligence
HPE Big Data Advanced Analytics Software Solutions
Strong information and weak information
HPE IDOL: Natural Language Processing (NLP) engine
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
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
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
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.
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.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
"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.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Adventures in Data Profiling
1. Jim Harris
Blogger‐in‐Chief
www.ocdqblog.com
Jim Harris
Digitally signed by Jim Harris
DN: cn=Jim Harris, o=Obsessive-Compulsive Data Quality (OCDQ), ou, email=jim.harris@ocdqblog.
com, c=US
Date: 2010.03.04 10:55:20 -06'00'