By Mark Jensen at ProductCamp Twin Cities 2016
Come learn more about the Internet of Things and the impact it will have on products, services, society and customer interaction. How might this new opportunity impact what you do? We'll discuss that and many other IoT related topics.
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.
DataEd Slides: Getting Data Quality Right – Success StoriesDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is “of sufficient quality.” This program provides a useful framework guiding those approaching Data Quality challenges. Specifically, Data Quality must be approached as an engineering discipline. Data Quality engineering must be approached as a specific ROI-based discipline or it cannot effectively support business strategy. Better understanding of how to “do Data Quality right” allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Program learning objectives include:
• Vivid demonstrations of how chronic business challenges for organizations are often rooted in broader kinds of Data Quality that suggested treatments can address
• Helping you to understand foundational Data Quality concepts, guiding principles, best practices, and an improved approach to Data Quality at your organization
• The basis of a number of specific case studies illustrating the hallmarks and benefits of Data Quality success
Email Marketing Census 2011 PresentationEconsultancy
The fifth annual Email Marketing Industry Census, sponsored by Adestra, is based on the largest UK survey of email marketers.
The census looks at the amount and type of email marketing carried out by organisations, the way that email marketing is conducted, issues affecting the industry and the effectiveness of email compared to other digital marketing channels.
SIM IT Trends Study 2013 - SIMposium SessionLeon Kappelman
Since 1980 the Society for Information Management (SIM) has conducted a survey of its senior IT executive members to gauge trends within the IT industry. SIM's members are among the most accomplished and innovative leaders in IT, so their responses help to benchmark various areas such as major management issues, largest and most worrisome IT investments, sourcing, CIO roles, staffing, spending, and salaries. SIM's IT Trends Study is widely recognized as one of the most representative barometers of the information technology industry. More information at http://www.simnet.org/?ITTrendsStudy.
By Mark Jensen at ProductCamp Twin Cities 2016
Come learn more about the Internet of Things and the impact it will have on products, services, society and customer interaction. How might this new opportunity impact what you do? We'll discuss that and many other IoT related topics.
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.
DataEd Slides: Getting Data Quality Right – Success StoriesDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is “of sufficient quality.” This program provides a useful framework guiding those approaching Data Quality challenges. Specifically, Data Quality must be approached as an engineering discipline. Data Quality engineering must be approached as a specific ROI-based discipline or it cannot effectively support business strategy. Better understanding of how to “do Data Quality right” allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Program learning objectives include:
• Vivid demonstrations of how chronic business challenges for organizations are often rooted in broader kinds of Data Quality that suggested treatments can address
• Helping you to understand foundational Data Quality concepts, guiding principles, best practices, and an improved approach to Data Quality at your organization
• The basis of a number of specific case studies illustrating the hallmarks and benefits of Data Quality success
Email Marketing Census 2011 PresentationEconsultancy
The fifth annual Email Marketing Industry Census, sponsored by Adestra, is based on the largest UK survey of email marketers.
The census looks at the amount and type of email marketing carried out by organisations, the way that email marketing is conducted, issues affecting the industry and the effectiveness of email compared to other digital marketing channels.
SIM IT Trends Study 2013 - SIMposium SessionLeon Kappelman
Since 1980 the Society for Information Management (SIM) has conducted a survey of its senior IT executive members to gauge trends within the IT industry. SIM's members are among the most accomplished and innovative leaders in IT, so their responses help to benchmark various areas such as major management issues, largest and most worrisome IT investments, sourcing, CIO roles, staffing, spending, and salaries. SIM's IT Trends Study is widely recognized as one of the most representative barometers of the information technology industry. More information at http://www.simnet.org/?ITTrendsStudy.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization become. This webinar illustrates Data Modeling as a key activity upon which so much technology depends.
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
Data-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Takeaways:
•Our profession is advancing its knowledge and has a wide-spread basis for partnerships
•New industry assessment standard is based on successful CMM/CMMI foundation
•Clear need for data strategy
•A clear and unambiguous call for participation
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers – the titular “Seven Deadly Data Sins” – and in the process will also:
• Elaborate upon the three critical factors that lead to strategy failure
• Demonstrate a two-stage Data Strategy implementation process
• Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions
SharePoint 2013 - Why, How and What? - Session #SPCon13Roland Driesen
SharePoint 2013 offers even better equipment than SharePoint 2010 did and has good reviews, a very positive vibe, and an appealing look and feel. But, how do you implement and use this great technology? Or, being cynical: WHY even bother? This session is about rephrasing your thoughts on Collaboration and/or Enterprise Social. Based on the concept of “Start with WHY” Roland will show you the real benefits to leverage the technology adoption lifecycle across your organization (innovators > early adopters > early majority > late majority) and why not to focus on the laggards. With the four stages model of user adoption Roland will give you practical advice on HOW to support this process including the Getting Things Done method with SharePoint to also appeal to the "not-so-social-media-savvy". The WHAT of SharePoint will be presented by co-presenters during the two days. This session is a summary of the 3G implementation methodology; a blend of great thinkers (Carl Gustav Jung, Simon Sinek, Michael Sampson, David Allen) and proven methods (Getting Things Done, Insights Discovery) combined with great technology (SharePoint 2013) to successfully motivate at least 84% of any population in the real usage of your SharePoint platform.
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
A Data Integration Case Study - Avoid Creating a “Franken-Beast”DATAVERSITY
Why should people store many kinds of data in one place? Why not put the many kinds in many places? Everyone who works with data and databases today acknowledges that we are all dealing with many different forms of data, requiring new tools and management approaches. This trend has been termed “Polyglot Persistence” by Martin Fowler, in his now-famous 2011 blog post. Enterprise data and even single-system data today often span all of the following: structured data sources, binaries, text data, and semantic RDF triples.
Yet the implementation of systems that manage poly-structured data is still dangerously complex and risky. In this webinar, Dr. Damon Feldman from MarkLogic will recount a true story of complex data integration gone wrong. Dr. Feldman will review the approaches and pitfalls associated with this issue, one he’s dubbed “Avoiding the Franken-Beast.” You’ll hear about a real-world project that grew too complex, and you’ll learn how to avoid this same situation of creating your own data “Franken-Beast.”
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
How Social and the Cloud Impact Your Governance StrategyChristian Buckley
As organizations look to expand their SharePoint on premises footprint using social, mobile, or cloud based platforms and services, some governance considerations. Presented originally at SharePoint Saturday Virginia Beach 2013 (#SPSVB)
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization become. This webinar illustrates Data Modeling as a key activity upon which so much technology depends.
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
Data-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Takeaways:
•Our profession is advancing its knowledge and has a wide-spread basis for partnerships
•New industry assessment standard is based on successful CMM/CMMI foundation
•Clear need for data strategy
•A clear and unambiguous call for participation
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers – the titular “Seven Deadly Data Sins” – and in the process will also:
• Elaborate upon the three critical factors that lead to strategy failure
• Demonstrate a two-stage Data Strategy implementation process
• Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions
SharePoint 2013 - Why, How and What? - Session #SPCon13Roland Driesen
SharePoint 2013 offers even better equipment than SharePoint 2010 did and has good reviews, a very positive vibe, and an appealing look and feel. But, how do you implement and use this great technology? Or, being cynical: WHY even bother? This session is about rephrasing your thoughts on Collaboration and/or Enterprise Social. Based on the concept of “Start with WHY” Roland will show you the real benefits to leverage the technology adoption lifecycle across your organization (innovators > early adopters > early majority > late majority) and why not to focus on the laggards. With the four stages model of user adoption Roland will give you practical advice on HOW to support this process including the Getting Things Done method with SharePoint to also appeal to the "not-so-social-media-savvy". The WHAT of SharePoint will be presented by co-presenters during the two days. This session is a summary of the 3G implementation methodology; a blend of great thinkers (Carl Gustav Jung, Simon Sinek, Michael Sampson, David Allen) and proven methods (Getting Things Done, Insights Discovery) combined with great technology (SharePoint 2013) to successfully motivate at least 84% of any population in the real usage of your SharePoint platform.
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
A Data Integration Case Study - Avoid Creating a “Franken-Beast”DATAVERSITY
Why should people store many kinds of data in one place? Why not put the many kinds in many places? Everyone who works with data and databases today acknowledges that we are all dealing with many different forms of data, requiring new tools and management approaches. This trend has been termed “Polyglot Persistence” by Martin Fowler, in his now-famous 2011 blog post. Enterprise data and even single-system data today often span all of the following: structured data sources, binaries, text data, and semantic RDF triples.
Yet the implementation of systems that manage poly-structured data is still dangerously complex and risky. In this webinar, Dr. Damon Feldman from MarkLogic will recount a true story of complex data integration gone wrong. Dr. Feldman will review the approaches and pitfalls associated with this issue, one he’s dubbed “Avoiding the Franken-Beast.” You’ll hear about a real-world project that grew too complex, and you’ll learn how to avoid this same situation of creating your own data “Franken-Beast.”
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
How Social and the Cloud Impact Your Governance StrategyChristian Buckley
As organizations look to expand their SharePoint on premises footprint using social, mobile, or cloud based platforms and services, some governance considerations. Presented originally at SharePoint Saturday Virginia Beach 2013 (#SPSVB)
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
How does the Modern Data Stack enable collaboration for data teams? Collaboration works like a flywheel that harnesses the collective energy of a data team and directs it towards new opportunities and innovation. Outstanding achievements emerge when teams collaborate to integrate and leverage their strengths towards a common goal. We’ll walk through some of the approaches that successful teams employ at Amazon, AWS, and Netflix to succeed on these fronts. We’ll also walk through what we called the Data Collaboration Stack, from DataOps to MLOps.
[DSC DACH 23] The building blocks of a successful data strategy - Mario Meir-...DataScienceConferenc1
Executives talk about data and want their companies to become data driven. But a lot of companies fail, which is due to various reasons. Becoming data driven is a cultural transformation in a company, not a technical implementation. In this talk, we will look at potential risks and how to implement a data culture.
Learn about the emerging field of big data and advanced quantitative models and how the Rady School's MS in Business Analytics program is designed to solve important business problems.
Data modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
For over four decades, IT strategy has been about the alignment of technology with the needs of the “customer,” be it an organization, business, end user, or device. The most important part of system acquisition is deciding what to build or buy, as it is better to deliver no solution at all than it is to deliver the wrong solution. But there are two distinct dimensions to getting requirements and ensuring that they, and the IT solution that results, not only aligns with the business as it is, but is built in such a way that it can sustain that alignment in a cost-effective and time-efficient manner. Specifically, (1) narrow requirements, which focus on the short-term needs for specific parts, functions, or processes of the business; and, (2) broad requirements, which focus on a comprehensive, enterprise-wide approach with holistic and longer-range objectives like simplicity, suppleness, and total cost of ownership. We typically call these “Systems Analysis and Design” and “Enterprise Architecture” respectively. Ideally, organizations should be able to do both well, and effectively balance the inevitable tradeoffs between them. Sadly, in the vast majority of organizations, that is not yet the case.
Professor Kappelman will present the results of a ground-breaking study from the Society for Information Management (SIM) Enterprise Architecture Working Group that developed and validated measures for these two distinct types of requirements capabilities. Findings include:
• Empirical validation that there is, in fact, a difference between requirement capabilities in a narrow or individual system context (i.e., Systems Analysis and Design within the bounds of a specific development project), and requirements capabilities in a broad or enterprise context (i.e., Enterprise Architecture regarding how those individual systems fit together in an enterprise-wide strategic design).
• Strong evidence that requirements capabilities overall are immature, with narrow activities more mature than the corresponding broad enterprise capabilities.
• Solid evidence, based on fifteen years of studies, that software development capabilities are generally maturing, but are still fairly immature.
This research provides requirements engineers, software designers, software developers, and other IT practitioners with tools to assess their own requirements engineering and software development capabilities. and compare them with those of their peers. Suggestions for improvements are made.
DAS Slides: Self-Service Reporting and Data Prep – Benefits & RisksDATAVERSITY
As more organizations see the value of becoming data-driven, an increasing number of business stakeholders want to become more actively involved in the reporting and preparation of critical business data. Tools and technologies have evolved to support this desire, and the ability to manage and analyze vast amounts of disparate data has become more accessible than ever before. With this increased visibility and usage of data, the need for data quality, metadata context, lineage and audit, and other core fundamental best practices is greater than ever.
How can an effective architecture & governance model be created that supports both business agility, as well as long-term sustainability and risk reduction? Where do these responsibilities lie between business and IT stakeholders? Join our panel of experts as they discuss the latest best practices, architectures, and tools that support self-service reporting and data prep to maximize benefits while at the same time reducing risk.
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Towards a productive Linked Data environment within Enterprises
1. Towards a productive Linked Data environment
within Enterprises
Andreas Both
2019-05-22, Leipziger Semantic Web Tag (LSWT 2019)
2. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Motivation for the talk
• share experience and insights
• provide options of actions
• help to overcome common misconceptions
Disclaimer
Imagesource:pixabay.com/illustrations/signs-right-wrong-good-bad-1172209/–License:PixabayLicense.
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3. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Motivation for the talk
A talk about “Linked Data towards action” ...
isn’t this like 5 years ago?
Positive
• improved tools/toolchains
• more open data sets
• many new vocabularies
• in general: increased
understanding of Linked
Data engineering processes
Negative
• many companies still
struggle on taking advantage
of linked enterprise data
• applying the Linked Data
paradigm still no common
approach
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4. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Spotlights
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9. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Recapitulation
Main goal
Web of (linked) documents → Web of Linked Data
⇒ make the (data) world a better place!
Core Ideas of Linked Data
1. identify things using URIs/IRIs
2. use URIs/IRIs to refer to data
3. provide semantics for data, make semantics accessible
4. interlink data stores
→ break established data-access approach in enterprises
⇒ a huge change in understanding and behaving is expected from
teams/organisations!
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10. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
About change management, it is.
While changing the information system paradigm ...
• change of project goals and metrics
• change of project execution
• change of required skills
• change of technology stack
• change the common understanding of “finished”
• change of pattern (team communication, software design, iteration slicing, . . . )
• change the power balance (long-term vs. short-term, visual vs. backend, . . . )
• . . .
→ change management is a very important issue
Image“Yoda”byPaulHudsonislicensedunderCCBY2.0
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11. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Change Management
Incomplete questions to initialize and drive a change process:
• What might cause misconceptions?
• What will make the change stick?
• What might block the change?
Observation
Mostly about changing the behavior of individuals!
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12. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Experienced Situations
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13. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Experienced Situations
Feedback: “Linked Data technologies solve a strategic problem”
• is enabler for additional value chain
• requires changing of paradigms with long-term effects
Observations and Consequences
hard for companies to invest enough into strategic approaches
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15. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Experienced Situations
Feedback: “Linked Data is yet another ETL data integration process”
• data needs to be transformed
• for good experience a common approach is to copy data into a
triplestore
Observations and Consequences
classification of Linked Data projects similar to ETL projects
(well-know in enterprises for 20+ years)
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17. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Experienced Situations
Feedback: “The whole movement seems to follow a formal
approach.”
• ontologies provide sound theoretical foundations
• during data modelling we require precise statements
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19. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Success Factors
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20. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Success Factors
Find matching metaphors
• less: reuse of common linked data terminology
• more: domain specific, precisely matching naming
→ positive impact: easier for customers to adopt your thinking and
better expectation management
Example: “data silos”
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25. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Success Factors
Join a community of developers (product development)
• less: disconnection from product development
• more: advantages of JSON-LD, openAPI extension, . . .
→ positive impact: bottom-up movement starting
Derived research questions
improve integration into software engineering process
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26. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Success Factors
Agile Approach
• less: long-term planning, high ramp-up costs, top-down approach
• more: agility and iterations
→ increased transparency and faster value transfer
Derived research questions
tools for change management of ontologies
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27. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Success Factors
Community Ontology Management Approach
• less: centralized approach, gate-keeper
• more: community approach, community management
→ increased commitment in development teams, increased quality
Derived research questions
improve tooling for community management, co-evolution,
self-services, . . .
Image“bouncers2”byCharlesLeBlancislicensedunderCCBY-SA2.0
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28. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Success Factors
Pragmatic Approach
• less: theoretical, formal, meta-level discussions
• more: pareto-optimal actions
→ positive impact: first impact quickly achieved
Derived research questions
improve method and tool capabilities w.r.t. uncertainty
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29. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Conclusions and Take Away
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30. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Conclusions
• 3 unwanted situation
◦ w.r.t. strategic importance
◦ w.r.t. ETL processes
◦ w.r.t. formalisms
• 5 action items which might be success factors
◦ metaphors
◦ community of developers (community of practice)
◦ adopt agile methodology
◦ community focus
◦ pragmatic approach
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31. LSWT 2019: Towards a productive Linked Data environment within Enterprises Andreas Both
Take Away
• take on classification of challenges you might be confronted while
working in the field of Linked Data
• insights into some common challenges during the execution of
Linked Data projects
• possible action items during the execution of a Linked Data driven
project
→ towards a productive Linked Data environment within
enterprises
Prof. Dr. Andreas Both
andreas.both@hs-anhalt.de
Anhalt University of Applied Sciences
linkedin.com/in/andreas-both-9426722
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