This document discusses the need for a chief data officer role to oversee organizational data management. It notes that current IT management is not well-suited to leverage data as a strategic asset. Only 1% of organizations achieve data management success due to a lack of professional data management. The requirements dictate a full-time role external to IT to manage data from a function preceding software development. Creating this role could improve performance more than other initiatives. The presentation will provide context on data management and examine why CIOs cannot devote sufficient time or expertise to data, and will explore the ideal relationship between data and IT.
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 Dr. Peter Aiken 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.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/
Leave IT Alone – The Vast Value of Self-ServiceDATAVERSITY
As more and more business roles are expected to be data-driven, the demand for data is growing exponentially. The only way businesses can scale data-driven decision-making is with self-service. But ungoverned self-service access to data doesn't necessarily lead to better decisions. So the critical question for businesses is how to enable analysts and casual business users to self-serve data in a meaningful and trustworthy way. Check out this episode of Deep Dive to find out! Host Eric Kavanagh will share insights about best practices and great ideas in the field of self-service BI. He'll be joined by Kenny Cunanan of Looker, who will explain how practical guard rails can keep users on track, while enabling them to explore data in ways that spark ideas and lead to better decisions.
How Enterprises are Using NoSQL for Mission-Critical ApplicationsDATAVERSITY
NoSQL databases including Couchbase are increasingly being selected as the backend technology for web and mobile apps. Document databases in particular are well suited for a large number of different use cases as an operational datastore.
In this webinar, Perry Krug, Principal Solutions Architect at Couchbase, will give a brief overview of Couchbase Server, a document database and its underlying distributed architecture. In addition, Perry will share how some of the biggest brands in the world use Couchbase, including:
Paypal A scalable NoSQL and big data architecture with real time analytics
Concur A highly available cache solution that supports 1B operations/day
Amadeus A backend data store that supports 1.6B transactions/day
Data-Ed Online: Trends in Data ModelingDATAVERSITY
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
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
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 Dr. Peter Aiken 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.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/
Leave IT Alone – The Vast Value of Self-ServiceDATAVERSITY
As more and more business roles are expected to be data-driven, the demand for data is growing exponentially. The only way businesses can scale data-driven decision-making is with self-service. But ungoverned self-service access to data doesn't necessarily lead to better decisions. So the critical question for businesses is how to enable analysts and casual business users to self-serve data in a meaningful and trustworthy way. Check out this episode of Deep Dive to find out! Host Eric Kavanagh will share insights about best practices and great ideas in the field of self-service BI. He'll be joined by Kenny Cunanan of Looker, who will explain how practical guard rails can keep users on track, while enabling them to explore data in ways that spark ideas and lead to better decisions.
How Enterprises are Using NoSQL for Mission-Critical ApplicationsDATAVERSITY
NoSQL databases including Couchbase are increasingly being selected as the backend technology for web and mobile apps. Document databases in particular are well suited for a large number of different use cases as an operational datastore.
In this webinar, Perry Krug, Principal Solutions Architect at Couchbase, will give a brief overview of Couchbase Server, a document database and its underlying distributed architecture. In addition, Perry will share how some of the biggest brands in the world use Couchbase, including:
Paypal A scalable NoSQL and big data architecture with real time analytics
Concur A highly available cache solution that supports 1B operations/day
Amadeus A backend data store that supports 1.6B transactions/day
Data-Ed Online: Trends in Data ModelingDATAVERSITY
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
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
Data Systems Integration & Business Value PT. 3: Warehousing Data Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
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.
Graph Data Modeling in Four Dimensions – Outline, Differences, Artisanship, A...DATAVERSITY
Graph Data Modeling is, needless to say, good for graph databases. But it can also serve as a general conceptual/logical model. This webinar explains all aspects, starting with an overview, then moving into differences between Graph Data Modeling and “Classic” Data Modeling, best practices of graph modeling, and modeling in agile manners.
In short, graph data models are:
- Fast to deliver
- Flexible enough to allow agile schema evolution as you go
- Intuitive to read, also for business users
- Richer in semantics than ERD diagrams
- Easily mappable to many contemporary types of platforms, not only graph
- Derivable from many legacy models such as UML, etc.
- Well suited for knowledge graphs, of course
Do not miss the train! Get started on Graph Data Modeling by participating in this webinar!
The first step towards understanding data assets impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
• Understanding how to leverage metadata practices in support of business strategy
• Discussing foundational metadata concepts
• Exploring guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
IT leaders from across North America were invited to share their viewpoint and perspective on delivering Agile IT. The study reflects the responses and trends related to their ability to deliver on business demands and readiness of existing technology to support those needs. We aggregated the results into following major themes: Strategy vs Reality, Agility & Technology Readiness, and Culture, Structure & People.
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.
DataEd Slides: Expressing Data Improvements as Business OutcomesDATAVERSITY
Join us and learn how you can better align your Data Management projects with business objectives to justify funding and gain management approval. Failure to successfully monetize Data Management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand that data practices are the root causes of many business problems, they become more willing to make the required investments. However, we need to also approach them. The No. 1 reason that data programs fail to deliver is that they do not set or measure specific objectives that are meaningful to management. While there are opportunities to assist at the project level, data improvements are better able to be leveraged at the organization level. An improvable, dedicated data program can only be achieved by repeated application of data practices in service of specific business objectives. Data improvements typically do not maintain an ROI calculation. ROIs expressed in terms that board/executive management cares about deeply ensure data program viability. Improving organizational execution of specific data practice improvements must lead directly to specific improvements in organizational KPIs. While organizations may not be currently practiced in this ability, it is quite easy to learn. This presentation uses a number of specific examples calculating the business impact of data improvements. Program learning objectives include:
• Coming to grips with the state of practice
• Understanding the need for a comparable baseline measure
• Seeing application in a number of contexts
Data Prep - A Key Ingredient for Cloud-based AnalyticsDATAVERSITY
Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.
DataEd Slides: Leveraging Data Management TechnologiesDATAVERSITY
Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of technologies that can be used to increase the productivity of Data Management efforts. The goal is to invest in as little infrastructure as possible while still achieving business/program objectives. This program’s learning objectives include:
• Understanding technology considerations
• Appreciating the overview of data technologies and then specifically
• CASE technologies
• Repositories
• Profiling/discovery tools
• Data Quality engineering tools
• Appreciating the complete Data Quality life cycle
Data-Ed Online: Emerging Trends in Data JobsDATAVERSITY
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
Slides: How Automating Data Lineage Improves BI PerformanceDATAVERSITY
BI landscapes are becoming increasingly complex with the surge in adoption of cloud technologies. Your BI group may have one foot in legacy systems and the other in more modern cloud-based systems, and this alone makes managing and understanding your data virtually impossible.
From needing to understand the impact of a change in a source system from the ETL through to reporting, to finding the source of a reporting error that an end-user questioned you on, to quickly responding to auditors’ demands – these recurring daily BI tasks and more turn into weeks-long projects.
Join us for our upcoming webinar where you’ll learn:
• How to enable your BI group to fix problems sooner for quicker access to accurate data
• The advantages of moving from manual to automated data lineage
• Use cases for BI and analytics groups in a variety of industries, including finance and insurance
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing Data Strategy concepts often goes underappreciated, especially the multifaceted nature of the challenges that need to be met. 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.
In this webinar, we will discuss these barriers—the titular “Seven Deadly Data Sins”—and in the process will also:
- Elaborate on 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 and alternative approaches
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
Learn how to launch your data governance program, by answering three questions:
- What does my data mean: collect and manage business definitions and relations, taxonomies and classifications, business rules and ontologies;
- How can I involve all stakeholders: engage them across business units and geographies, with stewards, data owners, … in a guiding workflow;
- How do I operationalize data governance: link MDM, DQ and BI to the business, use business-driven semantic modelling, achieve end-to end traceabilitiy. During this session we will use examples from different verticals: Finance, Government, Utilities,… .
We discuss their main drivers for starting a Data Governance initiative, as well as their pragmatic approach in moving from gradual roll out to support and sustain their Data Governance program.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
DataEd Slides: Data Management vs. Data StrategyDATAVERSITY
Organizations across most industries make some attempt to utilize Data Management and data strategies. While most organizations have both concepts implemented, they must understand their required interoperability to fully achieve their goals.
Learning Objectives
• Gaining a good understanding of both important topics
• Understanding that data only operates at a very intricate, specifically dependent, intent and what this means
• Understand state-of-the-practice
• Coordination is key, requiring necessary but insufficient interdependencies and sequencing
• Practice makes perfect
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
2016 Building Bridges - Need for a Data Management StrategyBrad Bronsch
An institution’s success is either hampered or realized by the the lack of or availability of accurate, timely information. Without a comprehensive, enterprise approach to data management, it’s difficult to meet this need.
Social media and relationship development for salesEconsultancy
Econsultancy Director Peter Abraham's presentation on the topic of social media and relationship development for sales. (originally presented at Chicago Booth University School of Business)
The Chief Data Officer's Agenda: What a CDO Needs to Know about Data QualityDATAVERSITY
Data is the ultimate cross-enterprise asset. Data and information flow throughout an organization and require both business and IT expertise to manage them effectively. The same data is available for multiple users, unlike physical products that once sold to a buyer cannot be resold to the next customer who walks through the door or places an online order. These properties create unique challenges for the CDO chartered to oversee the data management organization. The purpose of any data management work is to ensure high quality, trusted data and information, yet data quality is a profession within itself. Join us to learn what a CDO needs to know about data quality:
The relationship between data quality, governance, and other data management functions
Options for structuring within your organization
The difference between data quality programs and projects
What a CDO can do to help both data quality programs and projects succeed
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Data-Ed: Unlocking business value through data modeling and data architecture...Data Blueprint
When asked why they are architecting data, many in the practice answer: "Because that is what must be done." However, a better approach to this question is to speak in terms that are understood in the executive suite – business results! All of our organizations are faced with various organizational challenges that require analysis. Building new systems is just one example. This webinar describes the use of data architecting as a basic analysis method (one of many that good analysts should keep in their “toolbox"). I will demonstrate various uses of data architecting to inform, clarify, understand, and resolve aspects of a variety of business problems. As opposed to showing how to architect data, I 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.
Learning Objectives:
Understanding how to contribute to organizational challenges beyond traditional data architecting
Realizing the fundamental difference between "definition" and "purpose"
Guiding analyses through data analysis
Using data modeling in conjunction with architecture/engineering techniques
Understanding foundational data architecture concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data architecting in support of business strategy
Data Systems Integration & Business Value PT. 3: Warehousing Data Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
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.
Graph Data Modeling in Four Dimensions – Outline, Differences, Artisanship, A...DATAVERSITY
Graph Data Modeling is, needless to say, good for graph databases. But it can also serve as a general conceptual/logical model. This webinar explains all aspects, starting with an overview, then moving into differences between Graph Data Modeling and “Classic” Data Modeling, best practices of graph modeling, and modeling in agile manners.
In short, graph data models are:
- Fast to deliver
- Flexible enough to allow agile schema evolution as you go
- Intuitive to read, also for business users
- Richer in semantics than ERD diagrams
- Easily mappable to many contemporary types of platforms, not only graph
- Derivable from many legacy models such as UML, etc.
- Well suited for knowledge graphs, of course
Do not miss the train! Get started on Graph Data Modeling by participating in this webinar!
The first step towards understanding data assets impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
• Understanding how to leverage metadata practices in support of business strategy
• Discussing foundational metadata concepts
• Exploring guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
IT leaders from across North America were invited to share their viewpoint and perspective on delivering Agile IT. The study reflects the responses and trends related to their ability to deliver on business demands and readiness of existing technology to support those needs. We aggregated the results into following major themes: Strategy vs Reality, Agility & Technology Readiness, and Culture, Structure & People.
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.
DataEd Slides: Expressing Data Improvements as Business OutcomesDATAVERSITY
Join us and learn how you can better align your Data Management projects with business objectives to justify funding and gain management approval. Failure to successfully monetize Data Management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand that data practices are the root causes of many business problems, they become more willing to make the required investments. However, we need to also approach them. The No. 1 reason that data programs fail to deliver is that they do not set or measure specific objectives that are meaningful to management. While there are opportunities to assist at the project level, data improvements are better able to be leveraged at the organization level. An improvable, dedicated data program can only be achieved by repeated application of data practices in service of specific business objectives. Data improvements typically do not maintain an ROI calculation. ROIs expressed in terms that board/executive management cares about deeply ensure data program viability. Improving organizational execution of specific data practice improvements must lead directly to specific improvements in organizational KPIs. While organizations may not be currently practiced in this ability, it is quite easy to learn. This presentation uses a number of specific examples calculating the business impact of data improvements. Program learning objectives include:
• Coming to grips with the state of practice
• Understanding the need for a comparable baseline measure
• Seeing application in a number of contexts
Data Prep - A Key Ingredient for Cloud-based AnalyticsDATAVERSITY
Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.
DataEd Slides: Leveraging Data Management TechnologiesDATAVERSITY
Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of technologies that can be used to increase the productivity of Data Management efforts. The goal is to invest in as little infrastructure as possible while still achieving business/program objectives. This program’s learning objectives include:
• Understanding technology considerations
• Appreciating the overview of data technologies and then specifically
• CASE technologies
• Repositories
• Profiling/discovery tools
• Data Quality engineering tools
• Appreciating the complete Data Quality life cycle
Data-Ed Online: Emerging Trends in Data JobsDATAVERSITY
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
Slides: How Automating Data Lineage Improves BI PerformanceDATAVERSITY
BI landscapes are becoming increasingly complex with the surge in adoption of cloud technologies. Your BI group may have one foot in legacy systems and the other in more modern cloud-based systems, and this alone makes managing and understanding your data virtually impossible.
From needing to understand the impact of a change in a source system from the ETL through to reporting, to finding the source of a reporting error that an end-user questioned you on, to quickly responding to auditors’ demands – these recurring daily BI tasks and more turn into weeks-long projects.
Join us for our upcoming webinar where you’ll learn:
• How to enable your BI group to fix problems sooner for quicker access to accurate data
• The advantages of moving from manual to automated data lineage
• Use cases for BI and analytics groups in a variety of industries, including finance and insurance
DataEd Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing Data Strategy concepts often goes underappreciated, especially the multifaceted nature of the challenges that need to be met. 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.
In this webinar, we will discuss these barriers—the titular “Seven Deadly Data Sins”—and in the process will also:
- Elaborate on 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 and alternative approaches
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
Learn how to launch your data governance program, by answering three questions:
- What does my data mean: collect and manage business definitions and relations, taxonomies and classifications, business rules and ontologies;
- How can I involve all stakeholders: engage them across business units and geographies, with stewards, data owners, … in a guiding workflow;
- How do I operationalize data governance: link MDM, DQ and BI to the business, use business-driven semantic modelling, achieve end-to end traceabilitiy. During this session we will use examples from different verticals: Finance, Government, Utilities,… .
We discuss their main drivers for starting a Data Governance initiative, as well as their pragmatic approach in moving from gradual roll out to support and sustain their Data Governance program.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
DataEd Slides: Data Management vs. Data StrategyDATAVERSITY
Organizations across most industries make some attempt to utilize Data Management and data strategies. While most organizations have both concepts implemented, they must understand their required interoperability to fully achieve their goals.
Learning Objectives
• Gaining a good understanding of both important topics
• Understanding that data only operates at a very intricate, specifically dependent, intent and what this means
• Understand state-of-the-practice
• Coordination is key, requiring necessary but insufficient interdependencies and sequencing
• Practice makes perfect
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
2016 Building Bridges - Need for a Data Management StrategyBrad Bronsch
An institution’s success is either hampered or realized by the the lack of or availability of accurate, timely information. Without a comprehensive, enterprise approach to data management, it’s difficult to meet this need.
Social media and relationship development for salesEconsultancy
Econsultancy Director Peter Abraham's presentation on the topic of social media and relationship development for sales. (originally presented at Chicago Booth University School of Business)
The Chief Data Officer's Agenda: What a CDO Needs to Know about Data QualityDATAVERSITY
Data is the ultimate cross-enterprise asset. Data and information flow throughout an organization and require both business and IT expertise to manage them effectively. The same data is available for multiple users, unlike physical products that once sold to a buyer cannot be resold to the next customer who walks through the door or places an online order. These properties create unique challenges for the CDO chartered to oversee the data management organization. The purpose of any data management work is to ensure high quality, trusted data and information, yet data quality is a profession within itself. Join us to learn what a CDO needs to know about data quality:
The relationship between data quality, governance, and other data management functions
Options for structuring within your organization
The difference between data quality programs and projects
What a CDO can do to help both data quality programs and projects succeed
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Data-Ed: Unlocking business value through data modeling and data architecture...Data Blueprint
When asked why they are architecting data, many in the practice answer: "Because that is what must be done." However, a better approach to this question is to speak in terms that are understood in the executive suite – business results! All of our organizations are faced with various organizational challenges that require analysis. Building new systems is just one example. This webinar describes the use of data architecting as a basic analysis method (one of many that good analysts should keep in their “toolbox"). I will demonstrate various uses of data architecting to inform, clarify, understand, and resolve aspects of a variety of business problems. As opposed to showing how to architect data, I 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.
Learning Objectives:
Understanding how to contribute to organizational challenges beyond traditional data architecting
Realizing the fundamental difference between "definition" and "purpose"
Guiding analyses through data analysis
Using data modeling in conjunction with architecture/engineering techniques
Understanding foundational data architecture concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data architecting in support of business strategy
Data-Ed: Get the Most Out of Your Tools: Data Management TechnologiesData Blueprint
This webinar originally aired on Tuesday, November 13th, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
This presentation provides an overview of the many types and classes of useful technology available to data managers. These include: computer aided software/systems engineering (CASE) tools, repositories, profiling/discovery tools, data quality engineering technologies, and data integration servers.
Data-Ed: Unlocking Business Value through Data Modeling and Data Architecture...Data Blueprint
This webinar originally aired on Tuesday, January 8, 2013. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
When asked why they are modeling data, many in the practice answer: “Because that is what must be done.” However, a better approach to this question is to speak in terms that are understood in the executive suite – money! All of our organizations are faced with various organizational challenges that require analysis. Building new systems is just one example. This webinar describes the use of data modeling as a basic analysis method (one of many that good analysts should keep in their “toolbox.”) I will demonstrate various uses of data modeling to inform, clarify, understand, and resolve aspects of a variety of business problems. As opposed to showing how to data model, I will show you how to use data modeling to solve business problems. The goal is for you to be able to envision a number of uses for data modeling that will raise the perceived utility of this analysis method in the eyes of the business.
Learning Objectives:
Understanding how to contribute to organizational challenges beyond traditional data modeling
Really understanding the fundamental difference between “definition” and “purpose”
Guiding analyses through data analysis (Hubbard chapter 7)
Using data modeling in conjunction with architecture/engineering techniques
Data-Ed: Show Me the Money: The Business Value of Data and ROIData Blueprint
This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management. Join us and learn how you can apply similar tactics at your organization to justify funding and gain management approval.
Data-Ed Online: Data Operations Management: Turning Your Challenges Into SuccessData Blueprint
This webinar aired originally on Tuesday, April 10, 2012. It is part of Data Blueprint’s ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
While database operations comprise the majority of the organizational data operations management focus, other data delivery options, e.g. portals and virtualization, are interacting with increasingly complex regulatory environments. This presents organizations with dense analysis challenges in order to understand reporting obligations. Using the Zachman Framework as a guide, you will learn how to understand and approach data operations challenges from tuning to real-time reconfiguration. This presentation provides you with an understanding of data operations management, including the initiation, operation, tuning, maintenance, backup/recovery, archiving and disposal of data assets in support of organizational strategies and other activities.
Data-Ed Online: Let's Talk Metadata: Strategies and Successes Data Blueprint
This webinar originally aired on Tuesday, September 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Commonly described as metadata management, properly implemented metadata practices incorporate data structures into more abstract processing. By using data about the data to enhance its value, its understandability, ease of use and many other options, organizations have developed sophisticated ways to enhance their data management and especially their data quality engineering efforts. Join us to learn more about specific metadata benefits and how to leverage it to achieve success within your organization.
This webinar aired originally on Tuesday, March 13, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
This presentation provides you with an understanding of the data modeling and data development components of data management. Participants will understand how the analysis, design, implementation, deployment, and maintenance of data solutions should be approached in order to maximize the full value of the enterprise data resources and activities. Architecting in quality is imperative at this level and complements a subset of project activities within the system development lifecycle (SDLC) focused on defining data requirements, designing data solution components, and implementing these components. Participants will understand the difficulties organizations experience when interacting with data development efforts and how to best incorporate these efforts into specific data projects.
View the video recording here: http://www.slideshare.net/aberkowitz/dataed-online-practical-data-modeling-12019990
Data-Ed Online: Building A Solid Foundation-Data/Information ArchitectureData Blueprint
This webinar aired originally on Tuesday, February 14, 2012.
It is part of Data Blueprint’s ongoing webinar series on data management. For more information and to sign up for future session, please visit www.datablueprint.com/webinar-schedule
Abstract:
All organizations have data architectures. The question is: How effectively do they use them? This presentation provides a clear and concise understanding of what is meant by the term data architecture and the requirement that data and information architectures must be simultaneously managed. More importantly, organizations must understand what it means to use data architecture to support the implementation of organizational strategy. Participants will understand the requirements for an iterative, incremental approach to data architecture reengineering, the complimentary role of the Zachman Framework, and the ability to articulate the business value of data architecture projects and components.
View the video recording here: http://www.slideshare.net/aberkowitz/dataed-online-building-a-solid-foundationdatainformation-architecture
Data-Ed Online: MDM: Quality is not an Option but a RequirementData Blueprint
This webinar aired originally on Tuesday, June 12, 2012. It is part of Data Blueprint’s ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
Our presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Data-Ed Online - Making the Case for Data GovernanceData Blueprint
This webinar aired orginally on Tuesday, January 24, 2012
It is part of Data Blueprint’s ongoing webinar series on data management. For more information and to sign up for future session, please visit www.datablueprint.com/webinar-schedule
Abstract:
When thinking about data management, data governance is not one of those topics that immediately come to mind. Although neglected and often poorly performed, it is a vital function of data management and it is absurd to even consider managing data without some form of formal guidance. Data governance is central to “defining, coordinating, resourcing, implementing, and monitoring organizational data program strategies, policies, and plans as a coherent set of activities.” This presentation provides you with a clear and concise understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity and confusion that often surround initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
View the video recording here: http://www.slideshare.net/aberkowitz/dataed-online-making-the-case-for-data-governance-11407047
Similar to DataEd Online: Building the Case for the Top Data Job (20)
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
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 comprehensive 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.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
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 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. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
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: “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.” This program refocuses efforts 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. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This 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
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
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 with 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
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.