The document discusses the challenges clients face with bad customer data, including inconsistent data between systems, lack of data standards and ownership, difficulty retrieving archived data, and high costs of data issues. It provides examples of data quality problems that have cost companies millions or billions of dollars. The document advocates implementing data management and architecture practices to address these challenges and ensure accurate, consistent and secure customer data.
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.
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.
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
Master Data Management: Extracting Value from Your Most Important Intangible ...FindWhitePapers
This SAP Insight explores the importance of master data and the barriers to achieving sound master data, describes the ideal master data management solution, and explains the value and benefits of effective management of master data.
Enterprise Information Management (EIM) in SQL Server 2012Mark Gschwind
These are the slides from my 2013 SQL Saturday presentations in Mountain View and Sacramento. I suggest you view the (newer) videos, as they cover all that material and more. However, here is the session description these slides cover:
A recent survey by Information Week found that data quality is the greatest barrier to BI adoption in enterprises. MDS addresses this challenge with modeling, validation, alerting and security capabilities. In this presentation, you will learn how to use MDS to model your data to ensure correctness, update it with changes from your ERP, and create workflows with notifications. Next you will learn the capabilities of DQS and see how it addresses data standardization, completeness and other challenges. You will then see how to use them together to enable Enterprise Information Management. BI professionals will come away with knowledge on how to use tools that address the greatest risk to success for BI projects - data quality
The Critical Role of Unique IDs in Location Master Data ManagementPrecisely
Learn more about tools that can help you manage and organize your business data in a way that brings context and makes sense.
While software to match addresses is very good and a key component of Location Master Data Management (MDM), there is always at least some degree of error. Just like a fingerprint removes ambiguity about a person’s identity, a unique ID removes questions about a property’s location. It is a must have for linking a location across systems or joining new datasets.
View our on-demand webinar for a discussion around the role unique IDs play in location MDM and see an example of how IDs are being used today.
Unique IDs:
• Remove questions about a property’s location
• Eliminate matching errors
• Reduce processing demands on IT systems
Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
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.
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
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
Kickstart a Data Quality Strategy to Build Trust in DataPrecisely
The success or failure of your data-driven business initiatives relies on your ability to trust your data. But as data volumes grow, it becomes a major challenge to understand, measure, monitor, cleanse, and govern all that data. Join this on-demand session to learn key metrics and steps you can take to kickstart a data quality strategy.
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.
• 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
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
Master Data Management: Extracting Value from Your Most Important Intangible ...FindWhitePapers
This SAP Insight explores the importance of master data and the barriers to achieving sound master data, describes the ideal master data management solution, and explains the value and benefits of effective management of master data.
Enterprise Information Management (EIM) in SQL Server 2012Mark Gschwind
These are the slides from my 2013 SQL Saturday presentations in Mountain View and Sacramento. I suggest you view the (newer) videos, as they cover all that material and more. However, here is the session description these slides cover:
A recent survey by Information Week found that data quality is the greatest barrier to BI adoption in enterprises. MDS addresses this challenge with modeling, validation, alerting and security capabilities. In this presentation, you will learn how to use MDS to model your data to ensure correctness, update it with changes from your ERP, and create workflows with notifications. Next you will learn the capabilities of DQS and see how it addresses data standardization, completeness and other challenges. You will then see how to use them together to enable Enterprise Information Management. BI professionals will come away with knowledge on how to use tools that address the greatest risk to success for BI projects - data quality
The Critical Role of Unique IDs in Location Master Data ManagementPrecisely
Learn more about tools that can help you manage and organize your business data in a way that brings context and makes sense.
While software to match addresses is very good and a key component of Location Master Data Management (MDM), there is always at least some degree of error. Just like a fingerprint removes ambiguity about a person’s identity, a unique ID removes questions about a property’s location. It is a must have for linking a location across systems or joining new datasets.
View our on-demand webinar for a discussion around the role unique IDs play in location MDM and see an example of how IDs are being used today.
Unique IDs:
• Remove questions about a property’s location
• Eliminate matching errors
• Reduce processing demands on IT systems
Data warehousing and business intelligence project reportsonalighai
Developed Data warehouse project with a structured, semi-structured and unstructured sources of data
and generated Business Intelligence reports. Topic for the project was Tobacco products consumption in
America. Studied on which products are more famous among people across and also got to know that
middle school students are the soft targets for the tobacco companies as maximum people start taking
tobacco products at this age.
Tools used: SSMS, SSIS, SSAS, SSRS, R-Studio, Power BI, Excel
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.
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
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
Kickstart a Data Quality Strategy to Build Trust in DataPrecisely
The success or failure of your data-driven business initiatives relies on your ability to trust your data. But as data volumes grow, it becomes a major challenge to understand, measure, monitor, cleanse, and govern all that data. Join this on-demand session to learn key metrics and steps you can take to kickstart a data quality strategy.
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.
• 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
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...
Data Stewardship is an approach to Data Governance that formalises accountability for managing information resources on behalf of others and for the best interests of the organization
Data Stewardship consists of the people, organisation, and processes to ensure that the appropriately designated stewards are responsible for the governed data.
This presentation contains our view on how data can be Strategically managed and stewarded in an organization, and the categories where rules can be applied to facilitate that process.
Beyond Firefighting: A Leaders Guide to Proactive Data Quality ManagementHarley Capewell
Tired of trying to fight data quality issues in a sporadic,
reactive fashion? This white paper by data quality expert
Dylan Jones draws on his many years of experience
helping organisations adopt a more proactive, holistic
view of data quality management.
Boosting Cybersecurity with Data Governance (peer reviewed)Guy Pearce
Data Governance has a significant role to play in information security, with special data classes beyond the regular four cyber classes (public, confidential, classified and restricted) being useful in helping the organization identify whether sensitive data was exposed in a breach.
A successful data governance capability requires a strategy to align regulatory drivers and technology enhancement initiatives with business needs and objectives, taking into account the organizational, technological and cultural changes that will need to take place.
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.
data collection, data integration, data management, data modeling.pptxSourabhkumar729579
it contains presentation of data collection, data integration, data management, data modeling.
it is made by sourabh kumar student of MCA from central university of haryana
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
An effective data management solution can help businesses achieve best business practices and quality customer service responses. It helps make the process easier and faster.
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.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
‘Metriken für ein ROI-basiertes Datenqualitätsmanagement’ Dr. Mathias KlierDataValueTalk
‘Metriken für ein ROI-basiertes Datenqualitätsmanagement’
Dr. Mathias Klier, Leopold-Franzens-University of Innsbruck/School of Management Information Systems
Rudy Moenaert - What Do I Know About My Customers - Human InferenceDataValueTalk
Who is my customer, how does he behave? Where is he? Is my customer really who he says he is? Correct customer knowledge and up-to-date data that are of good quality is essential to companies. Especially when the economic outlook is not very positive.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
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Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
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3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
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1. What’s the price of bad customer data ? Breakfast Session 15 September 2009 Hotel Sofitel Brussels
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6. The threats (1/2) « Data Quality problems cost U.S. businesses more than $600 billion a year. » (TDWI) « NASA lost its $125M Mars Climate Orbiter because one group of engineers used kilograms and meters, while another used pounds and feet. The error caused the spacecraft to fly too close to the Martian surface where it either burned up or broke up as it swung around the planet. » « Rogue trader lost $691 Million due to lack of data governance. » « Average costs per data mart are $1.5M to $2.0M and a data mart consolidation initiative can reduce costs by 50%. » (META Group) « Master data problems leads to $250M law suit of a large investment bank. » Client Challenges
7. The threats (2/2) « Overcharging customers due to master data issues lands a large utility in a $1B law suit. » « For the U.S. grocery industry, up to 1 percent of net revenue lost, and one in 2,000 of sales lost because the item was out of stock, were attributable to bad master data. » (Gartner) « Retailers and manufacturers can reduce their current supply chain costs by 1 to 3 percent depending upon their current state. » (GCI) « Inaccurate and time consuming product information exchanges between CPG trading partners caused an estimated $25M - $50M in extra costs across the supply chain. » (AT Kearney Study) « 30% of all operational errors are due to poor information quality. » (Reuters) Client Challenges
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13. DM&A Capability Definitions How we respond Data Security is the processes and technology to protect data from unauthorized access, viewing, modification or deletion whether the intent is accidental, intentional, or malicious. Data Security initiatives should be in concert with enterprise-wide Security efforts including physical security, network security and technology security. Data Security Data Quality is the ability of data to satisfy the stated business, system, and technical requirements of an organization. Data Quality is typically measure in terms of completeness, timeliness, accuracy, consistency, relevance, and integrity. Data Quality Master Data is the fundamental business data in an enterprise. Master Data is typically long-lived and used across multiple applications. Master data can also be considered the language of doing business – the business objects and classifications that describe overall business information. Well-managed Master Data typically consists of hundreds of categories including customers, products, suppliers, key performance indicators, etc. Metadata is structured information about data or, simply, “data about data”. Master Data & Metadata Data Architecture is the processes, systems and human organizations required to store, access, move and organize data. Data Architecture Data Structure is how data is organized in a specific enterprise. The Data Structure includes multiple levels of an enterprise ranging from overall corporate data models down to the level of an individual system. Data Structure Data Governance is how an enterprise oversees its data assets. Governance includes the rules, policies, procedures, roles and responsibilities that guide overall management of an enterprise’s data. Governance provides the guidance to ensure that data is accurate & consistent, complete, available, and secure. Data Governance
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19. The threats (1/2) « Data Quality problems cost U.S. businesses more than $600 billion a year. » (TDWI) « NASA lost its $125M Mars Climate Orbiter because one group of engineers used kilograms and meters, while another used pounds and feet. The error caused the spacecraft to fly too close to the Martian surface where it either burned up or broke up as it swung around the planet. » « Rogue trader lost $691 Million due to lack of data governance. » « Average costs per data mart are $1.5M to $2.0M and a data mart consolidation initiative can reduce costs by 50%. » (META Group) « Master data problems leads to $250M law suit of a large investment bank. » How we respond Data Quality Data Governance Data Governance Data Architecture Master Data & Metadata
20. The threats (2/2) « Overcharging customers due to master data issues lands a large utility in a $1B law suit. » « For the U.S. grocery industry, up to 1 percent of net revenue lost, and one in 2,000 of sales lost because the item was out of stock, were attributable to bad master data. » (Gartner) « Retailers and manufacturers can reduce their current supply chain costs by 1 to 3 percent depending upon their current state. » (GCI) « Inaccurate and time consuming product information exchanges between CPG trading partners caused an estimated $25M - $50M in extra costs across the supply chain. » (AT Kearney Study) « 30% of all operational errors are due to poor information quality. » (Reuters) How we respond Master Data & Metadata Master Data & Metadata Master Data & Metadata Master Data & Metadata Data Quality
21. Data Governance Definitions DM&A Definitions Data Ownership is the responsibility for the creation of the data, and the enforcement of enterprise business rules. Data Owners usually refers to the business owners of Master/Business Data. Data Ownership Data Stewardship is the accountability for the management of data assets. Data Stewards do not own the data; instead they are the caretakers of the enterprise data assets. The Data Stewards ensure the quality, accuracy and security of the data. Data Stewardship Data Governance is how an enterprise manages its data assets. Governance includes the rules, policies, procedures, roles and responsibilities that guide overall management of an enterprise’s data. Governance provides the guidance to ensure that data is accurate & consistent, complete, available, and secure. Data Governance Data Standards are the precise criteria, specifications, and rules for the definition, creation, storage and usage of data within an organization. Data Standards include basic items like naming conventions, number of characters, and value ranges. Data Standards may also dictate specific quality measures, retention rules, and backup frequency. Data Standards Data Policies are the high-level and/or detailed rules and procedures that an enterprise utilizes to manage its data assets. Data Policies might include adherence of data to business rules, enforcing authentication and access rights to data, compliance with laws and regulations, and protection of data assets. Data Policies
22. Data Structure Definitions DM&A Definitions Data Taxonomy is the classification of data within an enterprise. An alternate definition is that Data Taxonomy is the terminology used within an enterprise when looking at its data. Data Taxonomy applies to both structured and unstructured data. The Data Taxonomy could be the product catalog including components and part numbers (structured data) and it could be the classification or grouping of documents (unstructured data). Data Taxonomy Data Modeling is the creation of Data Models that capture business requirements and present them in a structured way. Data Modeling enables an enterprise to communicate its data entities, attributes, and relationships, support system development and maintenance projects, and underlay most enterprise data initiatives. Data Modeling is generally done at both the Enterprise and Business Unit levels. Data Modeling Data Structure is how data is organized in a specific enterprise. The Data Structure includes multiple levels of an enterprise ranging from overall corporate data models down to the level of an individual system. Data Structure
23. Data Architecture Definitions DM&A Definitions Data Storage is the physical storage of data on an enterprise’s (or outsourcer’s) hardware. Data Storage Data Access is the various mechanism used to view, add, change, or delete data. Data Access includes transactional, analytical, and archival systems. Data Access Data Migration is the automated movement or migration of enterprise data such as from a transactional data base to a specific data store. Data Migration is sometimes defined to also include the migration of data from transactional systems to data archives. Data Migration Data Architecture is the processes, systems and human organizations required to store, access, move and organize data. Data Architecture Data Retirement is the removal of data from Data Storage. Data Retirement is not simply the deletion of data. Data Retirement is a process that may include long-term retention of key information and historical data for future analysis or reuse. Data Retirement must adhere to Local and National laws especially as it relates to Data Privacy. In some circumstances, data may be unretired such as a transaction with a former customer. Data Retirement Data Archiving is the storage of an enterprise’s data on a secondary storage medium. Data is archived to minimize the cost of online data storage. Depending on the archiving process and technology, archived data can be accessed in near real-time or only after an extended period. Data Archiving
24. Master & Meta Data Definitions DM&A Definitions Metadata is structured information about data or, simply, “data about data”. Metadata DM&A considers Reference Data to be a form of Master Data. Reference Data can sometimes be defined as code/decode data or external coded information. Reference Data Master Data is the fundamental business data in an enterprise. Master Data is typically long-lived and used across multiple applications. Master data can also be considered the language of doing business – the business objects and classifications that describe overall business information. Well-managed Master Data typically consists of hundreds of categories including customers, products, suppliers, key performance indicators, etc. Master Data Metadata Management is the tools and processes used to manage Metadata. Typically there are three types of Metadata that is managed: 1) Business metadata; 2) Technical metadata; 3) Operational metadata. Metadata Management is used to define, create, update, migrate, and disseminate metadata throughout an enterprise. Metadata Management DM&A Considers Reference Data Management to be synonymous with Master Data Management. Reference Data Management Master Data Management (MDM) is the collection of processes and technology that ensures that Master Data is coordinated across the enterprise. MDM provides a unified Master Data service that provides accurate, consistent and complete Master Data across the enterprise and to business partners. Master Data Management
25. Data Quality Definitions DM&A Definitions Data Monitoring is the automated and/or manual processes used to continuously evaluate the condition of an enterprise’s data. Information obtained from Data Monitoring activities is used to plan and focus data improvement initiatives. Data Monitoring Data Compliance is the ongoing processes to ensure adherence of data to both enterprise business rules, and, especially, to legal and regulatory requirements. Data Compliance includes 4 items: Controls, Audit, Regulatory Compliance & Legal Compliance. Data Compliance Data Traceability is the tracking of the lifecycle of data to determine and demonstrate all changes and access to the data. Data Traceability helps an enterprise demonstrate transparency, compliance and adherence to regulation. Data Traceability along with Data Compliance can be considered part of a Data Audit process. Data Traceability Data Cleansing is the process of detecting and correcting erroneous data and data anomalies both within and across systems. Data Cleansing can take place in both real-time as data is entered or afterwards as part of a Data Cleansing initiative. Data Cleansing Data Profiling is the systematic analysis of data to gather actionable and measurable information about its quality. Information gathered from Data Profiling activities is used to assess the overall health of the data and determine the direction of Data Quality initiatives. Data Profiling Data Quality is the ability of data to satisfy the stated business, system, and technical requirements of an organization. Data Quality is typically measured in terms of completeness, timeliness, accuracy, consistency, relevance, and integrity. Data Quality
26. Data Security Definitions DM&A Definitions Data Retention defines the policies and rules that an enterprise utilizes to keep data online, in archives, and in backups. Data is generally retained for regulatory and legal reasons as well as for historical analysis or Business Intelligence. Data Retention Data Privacy is the legal right and expectation of confidentiality in the collection and sharing of data. Data Privacy is an evolving area with numerous local and national laws. Data Privacy is also known as Data Protection. Data Privacy Data Security is the processes and technology to protect data from unauthorized access, viewing, modification or deletion whether the intent is accidental, intentional, or malicious. Data Security initiatives should be in concert with enterprise-wide Security efforts including physical security, network security and technology security. Data Security