This document discusses data quality and provides facts about the high costs of poor data quality to businesses and the US economy. It defines data quality as ensuring data is "fit for purpose" by measuring it against its intended uses and dimensions of quality. The document outlines best practices for measuring data quality including profiling data to understand metadata and trends, using statistical process control, master data management to create standardized "gold records", and implementing a data governance program to centrally manage data quality.
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.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
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
DAS Slides: Data Quality Best PracticesDATAVERSITY
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
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
DAS Slides: Data Quality Best PracticesDATAVERSITY
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data stewards are the implementation arm of Data Governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s shared data is reliably interconnected. Whether starting or restarting your Data Stewardship program, success comes from:
- Understanding the cadence/role of foundational data practices supporting organizational operations
- Proving value with tangible ROI
- Improving effectiveness/efficiencies using organization-wide insight
- Comprehending how stewards need to be multifunctional and dexterous, especially at first
- Integrating the role of data debt fighting
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Management PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Management Powerpoint Presentation Slides. We bring to you to the point topic specific slides with apt research and understanding. Putting forth our PPT deck comprises of twenty-seven slides. Our tailor-made Data Management Powerpoint Presentation Slides editable presentation deck assists planners to segment and expound the topic with brevity. The advantageous slides on Data Management Powerpoint Presentation Slides are braced with multiple charts and graphs, overviews, analysis templates agenda slides etc. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
Presentation by Derek Silva from the National Geospatial-Intelligence Agency and Greg Brunner from ESRI for the ESRI Federal GIS Conference, February 8, 2015
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data stewards are the implementation arm of Data Governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s shared data is reliably interconnected. Whether starting or restarting your Data Stewardship program, success comes from:
- Understanding the cadence/role of foundational data practices supporting organizational operations
- Proving value with tangible ROI
- Improving effectiveness/efficiencies using organization-wide insight
- Comprehending how stewards need to be multifunctional and dexterous, especially at first
- Integrating the role of data debt fighting
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
The Business Value of Metadata for Data GovernanceRoland Bullivant
In today’s digital economy, data drives the core processes that deliver profitability and growth - from marketing, to finance, to sales, supply chain, and more. It is also likely that for many large organizations much of their key data is retained in application packages from SAP, Oracle, Microsoft, Salesforce and others. In order to ensure that their foundational data infrastructure runs smoothly, most organizations have adopted a data governance initiative. These typically focus on the people and processes around managing data and information. Without an actionable link to the physical systems that run key business processes, however, governance programs can often lack the ‘teeth’ to effectively implement business change.
Metadata management is a process that can link business processes and drivers with the technical applications that support them. This makes data governance actionable and relevant in today’s fast-paced and results-driven business environment. One of the challenges facing data governance teams however, is the variety in format, accessibility and complexity of metadata across the organization’s systems.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Management PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Management Powerpoint Presentation Slides. We bring to you to the point topic specific slides with apt research and understanding. Putting forth our PPT deck comprises of twenty-seven slides. Our tailor-made Data Management Powerpoint Presentation Slides editable presentation deck assists planners to segment and expound the topic with brevity. The advantageous slides on Data Management Powerpoint Presentation Slides are braced with multiple charts and graphs, overviews, analysis templates agenda slides etc. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
Presentation by Derek Silva from the National Geospatial-Intelligence Agency and Greg Brunner from ESRI for the ESRI Federal GIS Conference, February 8, 2015
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Enterprise Data World Webinars: Master Data Management: Ensuring Value is Del...DATAVERSITY
Now that your organization has decided to move forward with Master Data Management (MDM), how do you make sure that you get the most value from your investment? In this webinar, we will cover the critical success factors of MDM that ensure your master data is used across the enterprise to drive business value. We cover:
· The key processes involved in mastering data
· Data Governance’s role in mastering data
· Leveraging data stewards to make your MDM program efficient
· How to extend MDM from one domain to multiple domains
· Ensuring MDM aligns to business goals and priorities
Anne-Sophie Roessler, International Business Developer, Dataiku - "3 ways to ...Dataconomy Media
Anne-Sophie Roessler, International Business Developer at Dataiku presented "3 ways to Fail your Data Lab Implementation" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
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.
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.
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
Emerging Data Quality Trends for Governing and Analyzing Big DataDATAVERSITY
Business initiatives across industries are applying more data than ever to drive analytics and AI in the quest for new competitive insights. As the volume and variety of data gathered by organizations continues to escalate, both on-premises and in the cloud, traditional methods of Data Quality are transforming to meet this Big Data challenge. This webinar looks at these emerging trends in Data Quality to address Data Governance, entity resolution at scale, AI and machine learning, and establishing Data Quality as a core tenet of data literacy.
[Strata NYC 2019] Turning big data into knowledge: Managing metadata and data...Kaan Onuk
Discover how Uber thinks about building big data knowledge platforms to allow teams to discover, manage, and govern entities. Explore how to build an extensible metadata management platform and infrastructure to democratize data at Uber's scale
Compliance issues can impact organizations in many ways. For medical device companies, this can be in the form of the FDA’s unique device identification (UDI) requirements. These requirements, a result of the passage of The FDA Amendments Act of 2007, stipulate that most medical devices carry a unique device identifier.
A webinar addressing how enterprise data management enables UDI compliance was presented live on May 23, 2013 in a joint session with Kelle O’Neal of First San Francisco Partners and Ross Hart of Riversand Technologies.
During the presentation, the following areas were discussed:
- The FDA legislation and the impact it will have on your organization
- Current UDI data challenges and benefits
- How enterprise information management and PIM support UDI
- How to get a UDI program started
- How to ensure a successful UDI program
These are the slides used in Kelle's portion of the presentation.
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.
10 Reasons Why the Quality of Data is Important for HotelsRevnomixSolutions
Revenue Management Systems, AI, & automation have immense potential to boost revenue for hotels but the efficiency heavily depends on the quality of data. Visit https://www.revnomix.com/why-the-quality-of-data-is-important-for-hotels/ to know more.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
We live in an increasingly data driven world, but without a real deep understanding of the ethical delimmas around it. In this presentation, we'll look at some recent ethical problems that have cropped up and discuss what can be done to address them
SIM RTP Meeting - So Who's Using Open Source Anyway?Alex Meadows
Open Source has been around for several decades now, but there is still a bit of mystery around what makes open source work and concern about using it in the enterprise. Open Source technologies are being widely used in many industries, including analytics, software development, social media, data center management, and more.
The discussion will be moderated by Julie Batchelor and panelists include:
* Todd Lewis, Open Source evangelist
* Jason Hibbets, Open Source Community Manager
* Jim Salter, Co-Owner and Chief Technology Officer at Openoid, LLC
* Alex Meadows, data scientist
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
Building next generation data warehousesAlex Meadows
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
How Linked Data Can Speed Information DiscoveryAlex Meadows
Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.
Triple stores are finally seeing mainstream use, but what exactly is all this talk about linked data? In this deck, we discuss what the semantic web is and how to map your relational data sets into a triple store database using open source software.
Slides used for a presentation to introduce the field of business analytics. Covers what BA is, how it is a part of business intelligence, and what areas make up BA.
Big Data has been around long enough that there are some common issues that occur whenever an organization tries to implement and integrate it into their ecosystem. This presentation covers some of those pitfalls, which also impact traditional data warehouses/business intelligence ecosystems
"Big Data" is big business, but what does it really mean? How will big data impact industries and consumers? This slide deck goes through some of the high level details of the market and how it is revolutionizing the world.
Providing value to the customer is one of the biggest challenges for any team to succeed in, let alone BI teams. Agile allows for moving into a faster delivery mode by slowing down to speed up. In this presentation, we cover tips for setting up an Agile practice, common pitfalls to avoid, and why Agile is just now taking off in the BI space.
2. Data Quality Facts
● Cost of poor data quality in US - $600 Billion
● Poor Data/Lack of visibility cited as #1 reason for
project cost overruns
● Poor data quality costs the US Economy $3.1 Trillion a
year
● Implementing data quality best practices boosts
revenue by 66%
● Median Fortune 1000 company could increase
revenue by $2.01 Billion if they improved usability of
data by 10%
Source: http://www.webmastat.com/blog/2012/09/07/7-facts-about-data-quality/
3. What is Data Quality?
Measuring data to determine if it is
“fit for purpose”
4. Fit For Purpose?
● “Bad” data is a myth!
● Two Questions
● What is the data used for?
● What can be measured to make sure it meets
the need?
● Application use vs. Reporting/Analysis
5. Data Quality Dimensions
● Consistency ● Accuracy
● Correctness ● Objectivity
● Timeliness ● Conciseness
● Precision ● Usefulness
● Unamiguous ● Usability
● Completeness ● Relevance
● Reliability ● Amount of data
Source: Data Quality Fundamentals, The Data Warehousing Institute
6. Measuring Data Quality
● Profiling – understanding metadata
● Point in time shows what data looks like now
● Automating shows trends
– Alert to new/potential issues as they happen
– Potentially fix issues in near real time
– Six Sigma Principals
8. Data Profiling Analysis
● Duplication ● Character Set
● Pattern matching ● Reference Data
● Boolean/String/Numb Matching
er ● Value Distribution
● Date Gap ● Inter-Data Set
● Date/time Comparisons
● Day of Week
9. Master Data Management
● Create a gold standard for data
● Distribute data so that all sources are uniform
● Names
● Addresses
● Phone Numbers
● Products
● Can hook into third party sources
10. Data Governance Program
● Central authority for data quality control
● Applies information collected from data
profiling, MDM, etc. Uniformly across the
business
● Communication channels between business
and IT groups