The document discusses trends in enterprise advanced analytics for 2021 and beyond. Some key trends include remote work continuing, strong tech spending rebound led by cloud capabilities, leading organizations increasing focus on AI/ML with model deployment taking center stage, more edge AI, rise of data lakes, new technology stacks focusing on data fabrics and AI pipelines, increased automation, open source becoming more prevalent, Kubernetes becoming the standard analytics stack, and general AI beginning to emerge. Winning approaches for 2021 include cloud, AI, data lakes, data warehousing, MDM, agile development, Kubernetes, automation, data quality, and DevOps/MLOps.
Measuring Data Quality Return on InvestmentDATAVERSITY
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.
ADV Slides: Organizational Change Management in Becoming an Analytic Organiza...DATAVERSITY
The disparity between expecting change and managing it – the “change gap” – is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril, because the “soft stuff” is truly hard. In this webinar, William McKnight will outline:
• The change readiness activities that focus on identifying and addressing people risks
• The tasks that will mobilize and align leaders to create outstanding business value
• The strategies to manage stakeholders, ensure change readiness, and address the organizational implications
• The methodologies to train the workforce as required to fully embrace and utilize the system
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...DATAVERSITY
There’s a lot of confusion out there about the differences between a data catalog, a data dictionary and a business glossary, and it's not always easy to understand who needs which and why. Join Malcolm Chisholm, Ph.D., President of Data Millennium, and Amichai Fenner, Product Lead at Octopai, as they help decode the mystery. Spoiler alert: one of these enables collaboration across BI and IT, which is it?
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This 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.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
ADV Slides: Comparing the Enterprise Analytic SolutionsDATAVERSITY
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Slides: Beyond Metadata — Enrich Your Metadata Management with Deep-Level Dat...DATAVERSITY
Today’s growing complexity to the data ecosystem requires organizations to understand data at the data element level. Challenges in data collection such as open text boxes/free form text fields added to the velocity of incoming data increases risk for organizations. This risk is amplified when those organizations rely exclusively on metadata scanning when it comes to discovering and actioning their data. The need to look deeper than basic metadata becomes even more pronounced when dealing with semi-structured or unstructured data commonly found in file shares and email systems. Maintaining compliance and driving business value often requires scanning actual files, interpreting data, flagging risks, and integrating that risk into a data catalog. Going beyond metadata to the actual data element level ensures that your data catalog is a source of truth, which ultimately allows organizations to create agile Data Governance programs.
We’ll walk you through key considerations for going beyond knowing what metadata you have by:
• Underlining the importance of an enhanced, AI-driven data discovery tool to better understand your data and how it is being used
• Discussing components of an effective Metadata Management strategy including data inventories, data dictionaries, and usage requests
• Highlighting how the OneTrust platform embedded with regulatory intelligence helps you to go beyond metadata and address key use cases around unexpected or at-risk unstructured data
Measuring Data Quality Return on InvestmentDATAVERSITY
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.
ADV Slides: Organizational Change Management in Becoming an Analytic Organiza...DATAVERSITY
The disparity between expecting change and managing it – the “change gap” – is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril, because the “soft stuff” is truly hard. In this webinar, William McKnight will outline:
• The change readiness activities that focus on identifying and addressing people risks
• The tasks that will mobilize and align leaders to create outstanding business value
• The strategies to manage stakeholders, ensure change readiness, and address the organizational implications
• The methodologies to train the workforce as required to fully embrace and utilize the system
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...DATAVERSITY
There’s a lot of confusion out there about the differences between a data catalog, a data dictionary and a business glossary, and it's not always easy to understand who needs which and why. Join Malcolm Chisholm, Ph.D., President of Data Millennium, and Amichai Fenner, Product Lead at Octopai, as they help decode the mystery. Spoiler alert: one of these enables collaboration across BI and IT, which is it?
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This 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.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
ADV Slides: Comparing the Enterprise Analytic SolutionsDATAVERSITY
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Slides: Beyond Metadata — Enrich Your Metadata Management with Deep-Level Dat...DATAVERSITY
Today’s growing complexity to the data ecosystem requires organizations to understand data at the data element level. Challenges in data collection such as open text boxes/free form text fields added to the velocity of incoming data increases risk for organizations. This risk is amplified when those organizations rely exclusively on metadata scanning when it comes to discovering and actioning their data. The need to look deeper than basic metadata becomes even more pronounced when dealing with semi-structured or unstructured data commonly found in file shares and email systems. Maintaining compliance and driving business value often requires scanning actual files, interpreting data, flagging risks, and integrating that risk into a data catalog. Going beyond metadata to the actual data element level ensures that your data catalog is a source of truth, which ultimately allows organizations to create agile Data Governance programs.
We’ll walk you through key considerations for going beyond knowing what metadata you have by:
• Underlining the importance of an enhanced, AI-driven data discovery tool to better understand your data and how it is being used
• Discussing components of an effective Metadata Management strategy including data inventories, data dictionaries, and usage requests
• Highlighting how the OneTrust platform embedded with regulatory intelligence helps you to go beyond metadata and address key use cases around unexpected or at-risk unstructured data
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
DataEd Slides: Data Management + Data Strategy = InteroperabilityDATAVERSITY
Few organizations operate without having to exchange data. (Many do it professionally and well!) The larger the data exchange burden (DEB), the greater the organizational overhead incurred. This death by 1,000 cuts must be factored into each organization’s calculations. Unfortunately, most organizations do not know if their organization’s DEB is great or small. A somewhat greater number of organizations have organized Data Management practices. Focusing Data Management efforts on increasing interoperability by decreasing the DEB friction is a good area to “practice.”
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
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Analytic Platforms Should Be Columnar OrientationDATAVERSITY
A columnar database is an implementation of the relational theory, but with a twist. The data storage layer does not contain records. It contains a grouping of columns.
Due to the variable column lengths within a row, a small column with low cardinality, or variability of values, may reside completely within one block while another column with high cardinality and longer length may take a thousand blocks. In columnar, all the same data — your data — is there. It’s just organized differently (automatically, by the DBMS).
The main reason why you would want to utilize a columnar approach is simply to speed up the native performance of analytic queries.
Learn about the columnar orientation and how it can be effective for your needs. This is the native orientation of many databases and several others that have optional column-oriented storage layers.
There is also the equivalent in the cloud storage world, which is open format Parquet.
Slides: Migrate BI Dashboards to Run Directly on a Cloud Data Lake in Five Ea...DATAVERSITY
While BI dashboards are great at democratizing analytics in organizations, the architecture that traditionally powers them has hidden consequences that have serious impacts on the business.
This architecture is based on a 30-year-old paradigm that requires many different systems, ETL jobs, and copies of data in data marts, data warehouses, and BI extracts. One downside of many is that it takes many days if not weeks to answer a different business question with this architecture. The negative consequences are further multiplied by the tens, hundreds, or even thousands of dashboards needed to run a data-driven organization.
Now, there’s a straightforward way to overcome these challenges that many organizations are already taking advantage of, an open cloud data lake architecture and Dremio
Join Jason Hughes, Technical Director at Dremio, for this webinar to learn how you can migrate BI dashboards to Dremio to quickly provide interactive dashboards to data consumers without the issues of the traditional architecture — and finally deliver the benefits always promised by BI.
What you’ll learn:
• Why BI dashboards’ traditional architecture implemented at scale causes many issues, which hinder the very insights it promises.
• How a Dremio-powered cloud data lake architecture eliminates or mitigates the negative consequences of the traditional approach.
• Step-by-step instructions for migrating a BI dashboard to run directly on a cloud data lake, both a self-contained example and your own dashboards.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
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
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
Advanced Analytics: Analytic Platforms Should Be Columnar OrientationDATAVERSITY
A columnar database is an implementation of the relational theory, but with a twist. The data storage layer does not contain records. It contains a grouping of columns.
Due to the variable column lengths within a row, a small column with low cardinality, or variability of values, may reside completely within one block while another column with high cardinality and longer length may take a thousand blocks. In columnar, all the same data — your data — is there. It’s just organized differently (automatically, by the DBMS).
The main reason why you would want to utilize a columnar approach is simply to speed up the native performance of analytic queries.
Learn about the columnar orientation and how it can be effective for your needs. This is the native orientation of many databases and several others that have optional column-oriented storage layers.
There is also the equivalent in the cloud storage world, which is open format Parquet.
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Then Data Scientist Gaby Lio will engage with the audience about project dos and don’ts and leave you with a checklist to ensure your projects success.
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
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
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.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house the metadata that builds organizational confidence in your data. First and foremost, the people in your organization need to be engaged in leveraging the tools, understanding the data that is available and who is responsible for the data, and knowing how to get their hands on the data they need to perform their job function. This metadata will not govern itself.
Join Bob Seiner for the April RWDG webinar, where he will discuss how to govern the metadata in a data catalog, business glossary, and data dictionary. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must be governed. Learn how to govern that metadata in this webinar.
Bob will discuss the following subjects in this webinar:
• Successful Data Governance relies on value from very important tools
• What it means to govern your data catalog, business glossary, and data dictionary
• Why governing the metadata in these tools is so important
• The roles necessary to govern these tools
• Value expected from governing the catalog, glossary, and dictionary
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
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.
Data Centric Development: Supercharge your web & mobile application developmentBright North
Many businesses are finding that their web and mobile applications aren’t providing the long-term solution they were hoping for. As consumers provide more and more useful data, these digital platforms don’t allow businesses to take advantage of the huge opportunities that data presents.
Our new whitepaper details the practical steps you can take to supercharge your web and mobile application development and stay ahead of the data revolution.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/3fpitC3
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Architecting a Modern Data Warehouse: Enterprise Must-HavesYellowbrick Data
The goal of modern data warehousing is to not only deliver insights faster to more users, but provide a richer picture of your operations afforded by a greater volume and variety of data for analysis.
This presentation from a Database Trends and Applications webcast will educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them.
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
DataEd Slides: Data Management + Data Strategy = InteroperabilityDATAVERSITY
Few organizations operate without having to exchange data. (Many do it professionally and well!) The larger the data exchange burden (DEB), the greater the organizational overhead incurred. This death by 1,000 cuts must be factored into each organization’s calculations. Unfortunately, most organizations do not know if their organization’s DEB is great or small. A somewhat greater number of organizations have organized Data Management practices. Focusing Data Management efforts on increasing interoperability by decreasing the DEB friction is a good area to “practice.”
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
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Analytic Platforms Should Be Columnar OrientationDATAVERSITY
A columnar database is an implementation of the relational theory, but with a twist. The data storage layer does not contain records. It contains a grouping of columns.
Due to the variable column lengths within a row, a small column with low cardinality, or variability of values, may reside completely within one block while another column with high cardinality and longer length may take a thousand blocks. In columnar, all the same data — your data — is there. It’s just organized differently (automatically, by the DBMS).
The main reason why you would want to utilize a columnar approach is simply to speed up the native performance of analytic queries.
Learn about the columnar orientation and how it can be effective for your needs. This is the native orientation of many databases and several others that have optional column-oriented storage layers.
There is also the equivalent in the cloud storage world, which is open format Parquet.
Slides: Migrate BI Dashboards to Run Directly on a Cloud Data Lake in Five Ea...DATAVERSITY
While BI dashboards are great at democratizing analytics in organizations, the architecture that traditionally powers them has hidden consequences that have serious impacts on the business.
This architecture is based on a 30-year-old paradigm that requires many different systems, ETL jobs, and copies of data in data marts, data warehouses, and BI extracts. One downside of many is that it takes many days if not weeks to answer a different business question with this architecture. The negative consequences are further multiplied by the tens, hundreds, or even thousands of dashboards needed to run a data-driven organization.
Now, there’s a straightforward way to overcome these challenges that many organizations are already taking advantage of, an open cloud data lake architecture and Dremio
Join Jason Hughes, Technical Director at Dremio, for this webinar to learn how you can migrate BI dashboards to Dremio to quickly provide interactive dashboards to data consumers without the issues of the traditional architecture — and finally deliver the benefits always promised by BI.
What you’ll learn:
• Why BI dashboards’ traditional architecture implemented at scale causes many issues, which hinder the very insights it promises.
• How a Dremio-powered cloud data lake architecture eliminates or mitigates the negative consequences of the traditional approach.
• Step-by-step instructions for migrating a BI dashboard to run directly on a cloud data lake, both a self-contained example and your own dashboards.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
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
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
Advanced Analytics: Analytic Platforms Should Be Columnar OrientationDATAVERSITY
A columnar database is an implementation of the relational theory, but with a twist. The data storage layer does not contain records. It contains a grouping of columns.
Due to the variable column lengths within a row, a small column with low cardinality, or variability of values, may reside completely within one block while another column with high cardinality and longer length may take a thousand blocks. In columnar, all the same data — your data — is there. It’s just organized differently (automatically, by the DBMS).
The main reason why you would want to utilize a columnar approach is simply to speed up the native performance of analytic queries.
Learn about the columnar orientation and how it can be effective for your needs. This is the native orientation of many databases and several others that have optional column-oriented storage layers.
There is also the equivalent in the cloud storage world, which is open format Parquet.
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Then Data Scientist Gaby Lio will engage with the audience about project dos and don’ts and leave you with a checklist to ensure your projects success.
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
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
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.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house the metadata that builds organizational confidence in your data. First and foremost, the people in your organization need to be engaged in leveraging the tools, understanding the data that is available and who is responsible for the data, and knowing how to get their hands on the data they need to perform their job function. This metadata will not govern itself.
Join Bob Seiner for the April RWDG webinar, where he will discuss how to govern the metadata in a data catalog, business glossary, and data dictionary. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must be governed. Learn how to govern that metadata in this webinar.
Bob will discuss the following subjects in this webinar:
• Successful Data Governance relies on value from very important tools
• What it means to govern your data catalog, business glossary, and data dictionary
• Why governing the metadata in these tools is so important
• The roles necessary to govern these tools
• Value expected from governing the catalog, glossary, and dictionary
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
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.
Data Centric Development: Supercharge your web & mobile application developmentBright North
Many businesses are finding that their web and mobile applications aren’t providing the long-term solution they were hoping for. As consumers provide more and more useful data, these digital platforms don’t allow businesses to take advantage of the huge opportunities that data presents.
Our new whitepaper details the practical steps you can take to supercharge your web and mobile application development and stay ahead of the data revolution.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/3fpitC3
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Architecting a Modern Data Warehouse: Enterprise Must-HavesYellowbrick Data
The goal of modern data warehousing is to not only deliver insights faster to more users, but provide a richer picture of your operations afforded by a greater volume and variety of data for analysis.
This presentation from a Database Trends and Applications webcast will educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them.
Watch here: https://bit.ly/3i2iJbu
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
Join us for an exciting session that will cover:
- The most interesting trends in data management.
- Our predictions on how those trends will change the data management world.
- How these trends are shaping the future of data virtualization and our own software.
Enable Better Decision Making with Power BI Visualizations & Modern Data EstateCCG
Self-service BI empowers users to reach analytic outputs through data visualizations and reporting tools. Solution Architect and Cloud Solution Specialist, James McAuliffe, will be taking you through a journey of Azure's Modern Data Estate.
Power to the People: A Stack to Empower Every User to Make Data-Driven DecisionsLooker
Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds.
Infectious Media shares:
- Why they chose their current stack
- Why faster data means happier customers
- Advantages and practical implications of storing and processing that much data
Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
Become More Data-driven by Leveraging Your SAP DataDenodo
Watch full webinar here: https://bit.ly/3K2SaCQ
In today’s world, management of data can be a major challenge. For many systems, including SAP, data in real-time and integrating it with other disparate sources has historically been difficult to accomplish. The traditional Data Warehouse approach can also be quite expensive to keep data fresh and control access to meet new and future data protection requirements. Denodo and Gateway Architect’s Meister Core™ offers a high-performance data virtualization solution, designed to fulfill those needs.
Join Denodo, Gateway Architects and W5 Consulting to learn about the value of a logical Data Fabric and delivery platform and its role in this new solution. The webinar will overview the solution including how it provides support for SAP Migrations and sharing of SAP data across geographic boundaries. In addition, you will see how this solution provides the added value of improved agility for supply chain management, and much more. We will also share a demonstration to showcase the benefits of this solution.
Do not miss this opportunity to learn all this as well as how the Joint Denodo/Meister Core solution can:
- Create an agile, real-time, robust data virtualization solution.
- Work with combinations of SAP and Non-SAP data in “Actual” real time scenarios.
- And deliver a true 360 degree view of analytics from multiple systems and seemingly tie that to all your SAP FICO documents 10X faster then previously possible.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
On May 19, 2020, analyst firm IDC; vendors Intel, MemVerge, Net
App and Penguin Computing; and end-user Credit Suisse, introduced a new category called Big Memory. Big Memory hardware and software together transform scarcity and volatility into abundance, persistence, and high-availability.
Building Resiliency and Agility with Data Virtualization for the New NormalDenodo
Watch: https://bit.ly/327z8UM
While the impact of COVID-19 is uniform across organisations in the region, a lot of how the organisation can recover from the impact and strive in the market would depend on their resiliency and business agility. An organisation’s data management strategy holds the key, as they tackle the challenges of siloed data sources, optimising for operational stability, and ensuring real time delivery of consistent and reliable information, irrespective of the data source or format.
Join this session to hear why large organisations are implementing Data Virtualization, a modern data integration approach in their data architecture to build resiliency, enhance business agility, and save costs.
In this session, you will learn:
- How to deliver clear strategy for agile data delivery across the enterprise without pains of traditional data integration
- How to provide a robust yet simple architecture for data governance, master data, data trust, data privacy and data access security implementation - all from single unified framework
- How to deploy digital transformation initiatives for Agile BI, Big Data, Enterprise Data Services & Data Governance
Take Action: The New Reality of Data-Driven BusinessInside Analysis
The Briefing Room with Dr. Robin Bloor and WebAction
Live Webcast on July 23, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=360d371d3a49ad256942f55350aa0a8b
The waiting used to be the hardest part, but not anymore. Today’s cutting-edge enterprises can seize opportunities faster than ever, thanks to an array of technologies that enable real-time responsiveness across the spectrum of business processes. Early adopters are solving critical business challenges by enabling the rapid-fire design, development and production of very specific applications. Functionality can range from improved customer engagement to dynamic machine-to-machine interactions.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, who will tout a new era in data-driven organizations, and why a data flow architecture will soon be critical for industry leaders. He’ll be briefed by Sami Akbay of WebAction, who will showcase his company’s real-time data management platform, which combines all the component parts needed to access, process and leverage data big and small. He’ll explain how this new approach can provide game-changing power to organizations of all types and sizes.
Visit InsideAnlaysis.com for more information.
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
Hadoop and the Relational Database: The Best of Both WorldsInside Analysis
The Briefing Room with Dr. Robin Bloor and Splice Machine
Live Webcast on August 5, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=71551d669454741c8bd56f2349bdf140
As the pressure of Big Data collides with the reality of daily operations, many organizations are trying to solve the challenge of meeting new requirements without disrupting the flow of business. One solution focuses on the data layer itself, by combining the well known functionality of relational database technology with the scale-out capabilities of Hadoop.
Register for this episode of The Briefing Room to hear from veteran Analyst Dr. Robin Bloor as he outlines the critical components of a business-ready data layer. He’ll be briefed by John Leach and Rich Reimer of Splice Machine who will explain how their solution delivers the best of both data worlds: the trusted capabilities of relational with the infinite scalability of Hadoop. They will also discuss how Hadoop has transformed from a batch-oriented workhorse into a scale-out layer capable of supporting real-time applications and operational analytics using traditional SQL.
Visit InsideAnlaysis.com for more information.
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.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. 2021 Trends in Enterprise
Advanced Analytics
Presented by: William McKnight
“#1 Global Influencer in Data Warehousing” Onalytica
President, McKnight Consulting Group
A 2 time Inc. 5000 Company
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET
#AdvAnalytics
2. Dataversity Webcast
Vertica and Pure Storage address your variable workloads
on-premises with a cloud-optimized architecture
Jeff Healey
Sr. Director of Vertica Marketing
E: jeff.a.healey@vertica.com
Miroslav Klivansky
Field Solution Evangelist
mklivansky@purestorage.com
3. What is Vertica?
SQL Database
Load and store data in a data
warehouse designed for
blazingly fast analytics
Query Engine
Ask complex analytical
questions and get fast
answers regardless of
where the data resides
Vertica is the leading unified analytics warehouse built for the scale and complexity of today’s data-
driven world. It combines the power of a high-performance, MPP query engine with advanced
analytics and Machine Learning.
Analytics & ML
Create, train, and deploy advanced
analytics and machine learning
models at massive scale
4. Remove scale, performance, and capacity constraints
3
Get data quickly enough to act upon it, explore your data interactively,
and enable everyone to make their own data-driven decisions
Fear of more users or growing data volumes is a thing of the past
Scale Data Volumes Scale Users
SQL Database
+
Vertica Analytics Platform
+
Get data quickly enough to act upon it, explore your data interactively,
and enable everyone to make their own data-driven decisions
Analytics & ML Query Engine
5. Benefits of Vertica in Eon Mode
Deliver Vertica with the
Cloud Economics Promise
Consuming only what you
need when you need it
Through separation of
compute from storage.
Scale Infrastructure Linearly. Elastically
scale your analytics for workload changes,
seasonality, or peak load times.
Improved Database Operations. Faster node
recovery, superior workload balancing, and
more rapid compute provisioning.
Isolate Analytic Workloads. Designate
specific nodes as a subcluster to isolate
workloads and support multi-tenancy.
Hibernate. Stop and start analytics more
efficiently by hibernating compute nodes
when they’re not needed..
6. Vertica powers the applications and services that enable our data-driven world.
A Day in the Life with Vertica
7. Learn More - Vertica in Eon Mode for Pure Storage
Visit www.vertica.com/pure today
9. Vertica Eon Mode Requirements
Data Safety
Performance at Scale +
Capacity for Data Growth +
Linear Scalability +
Tuned for Everything +
Easy to Manage +
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Separation of Compute from Storage =
10. FLASHBLADEPURPOSE-BUILT FOR MODERN ANALYTICS
BLADE PURITY SCALE-OUT FABRIC
Powerful, Elastic Data
Processing & Storage Unit
Massively Distributed Software
for Limitless Scale
Software-defined fabric that scales
linearly with more data & clients
3
11. BORN FOR UNSTRUCTURED DATA
FLASHBLADE WAS BUILT TO ADDRESS MODERN DATA CHALLENGES
1980 20202005 20152000 20101990
GPS/GIS
1983
NFS
1985
WWW
1989
LDAP, Wikis,
Java and IPv6
1995
MP3
1996
Machine Learning recognizes
cats
2012
iPhone
2007
Edge computing widely
adopted
2018
1st SSD
ships
1991 Era of Analytics
2005
Hadoop
2005
S3
2006
bitcoin
2009
Nest
Thermostat
2011
Dropbox
2007
AWS
2002
Self Driving
Cars
2018
Amazon Echo
2015
Kubernetes
2015
IoT
1999
LinkedIn
2003
First human
genome sequence
completed
2003
14. INTEGRATED NETWORKING
SOFTWARE-DEFINED NETWORKING
2x BROADCOM TRIDENT-II
ETHERNET SWITCH ASICS
Collapses three networks – frontend, backend,
and control – into one high-performance fabric
8x 40Gb/s QSFP
Connections into customer
top-of-rack switches
13
FlashBlade Chassis
Up to 15 Blades
4RU Height
N+2 Redundant, Heals in Place
Blades
Capacity & Performance
DirectFlash NAND
Embedded NVRAM
15. FLASHBLADE
BLADE
INTEL XEON
SYSTEM-ON-A-CHIP
Compute + Networking + Chipset
Low-Power, Low-Cost Design
8x Full XEON Cores
DRAM
MEMORY
PROGRAMMABLE
PROCESSORS
FPGA
NAND FLASH
17TB or 52TB
(per Blade)
INTEGRATED
NV-RAM
Supercapacitor-backed
write buffer
PCIE CONNECTIVITY
CPUs & Flash communicate via
custom protocol over PCIe
16. SOUL OF FLASHBLADE IS PARALLEL
POWERING 75 BLADE-SCALE IN SINGLE IP WITH PURITY FOR FLASHBLADE
KEY-VALUE DATABASE STORE FOR DISTRIBUTED PARTITIONS
KEY
VALUE
BILLIONS&
BILLIONS
OF OBJECTS
NATIVE OBJECT NATIVE NFS/SMB
17. MODERNIZE YOUR DATA EXPERIENCE
Expedite & automate
troubleshooting
VM Analytics
Plan with ease
AI driven workload planner
Take the guesswork out
of management
Cloud based management
Global information at
your fingertips
Pure1 mobile app
18. Learn More - Vertica in Eon Mode for Pure Storage
Visit www.vertica.com/pure today
19. 2021 Trends in Enterprise
Advanced Analytics
Presented by: William McKnight
“#1 Global Influencer in Data Warehousing” Onalytica
President, McKnight Consulting Group
A 2 time Inc. 5000 Company
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET
#AdvAnalytics
20. William McKnight
President, McKnight Consulting Group
• Frequent keynote speaker and trainer internationally
• Consulted to many Global 1000 companies
• Hundreds of articles, blogs and white papers in publication
• Focused on delivering business value and solving business
problems utilizing proven, streamlined approaches to
information management
• Former Database Engineer, Fortune 50 Information
Technology executive and Ernst&Young Entrepreneur of
Year Finalist
• Owner/consultant: 2018 and 2017 Inc. 5000 strategy &
implementation consulting firm
• 30 years of information management and DBMS experience
21. McKnight Consulting Group Offerings
Strategy
Training
Strategy
Trusted Advisor
Action Plans
Roadmaps
Tool Selections
Program Management
Training
Classes
Workshops
Implementation
Data/Data Warehousing/Business
Intelligence/Analytics
Master Data Management
Governance/Quality
Big Data
Implementation
3
22. Why Are Trends Important?
• It is imperative to see trends that affect your
business to know how to respond
• Plan for and deal with change
• Better to be at the beginning of the trend rather
than the end
• Wants, needs, and tastes of your customer changes
• Make you a leader, not a follower
• Grow your business ideas
• Give you ideas what to improve in your business
23. Information Management Leaders
• Information Management leaders of tomorrow
can advance maturity while also solving
business issues
– There’s no budget for “staying on trends”
• Information Management leaders must pick
their winning (i.e., multi-year sustainable)
approaches and get on board
24. The Money Tree Doesn’t Exist
Hitch your Trend Pursuit
Efforts to a Budget Delivering ROI
6
25. Those Who Were Less Impacted by 2020
• Cloud-First
• Microservices-Based
• Data is a separate function
• Agile Development
• Master Data
7
27. Last Year’s Trends
• Data Takes Steps to the Balance Sheet
• Explosion in Sensor-Based Time-Series Data
• Business Intelligence Interfaces Upheaval
• ETL will be Nearly Automated
• Cloud Object Storage
• More Edge AI
• Data’s New Highest Use Will Be Training AI Algorithms
• Explainable AI
• Kubernetes and Containers
• Hybrid Databases
9
28. Factors to Watch in 2021
• Pandemic Footprint
• Vaccine Rollout
• Resiliency of Corporations
• Prioritization of Forward Factors
• Continued Preparedness Awareness
10
29. Top Trends in Enterprise Analytics for
2021 and Beyond
30. Remote Work Continues
• Some projects done all remote
• Or multiple people to 1 seat arrangements
• Remote Conferences
• Some Offices Prepare for Return
31. Led by Cloud Capabilities, Strong Tech Spending
Rebound in 2021
• CXOs ready to release floodgates
• Storage strong growth
– AWS Storage Revenue Approaching $10B
• Artificial Intelligence, Kubernetes Approaches
and Automation are driving corporate tech
budgets
32. Leading Organizations are increasing a focus on
AI/ML
• Budgets for AI/ML increasing significantly
• Beyond Initial Use Cases
• Model Expansion in Production
14
33. Leading Organizations are increasing a focus on
AI/ML
• Collaborative AI
• Human/AI Hybrid Solutions
15
34. Model Deployment Takes Center Stage
• Model Deployment Will Rise to the Top Activity
of Data Professionals
• Data Scientists Will Continue to Wrangle Data
Since Most Data Environments are Mid/Low
Maturity
• Models Getting More Sophisticated
– Data Wrangling Increasing
– Continued Challenges to Data Maturity
• Organizations will struggle without MLOps
16
35. • Embedded Databases at the edge
• AI baked into the chips
• Decision making at the edge
More Edge AI
37. • Combatting Bias
• Responsible AI
• Regulations
• This trend will evaporate in time
Explainable AI
38. Data Lakes
• The Rise of the LakeHouse
• Explosion in Sensor-Based Time-Series Data and
Edge AI
• Leveraging Cloud Storage for Data Lakes
• Data Integration Automation
20
39. New Technology Stacks: Shift from only data warehouses, lakes,
and ETL to data fabrics, AI, and pipelines
21
41. MLOps
• MLOps applies DevOps principles to ML delivery
• The ML process primarily revolves around creating, training and deploying
models
• Once trained and validated, models are deployed into an architecture that
can deal with large quantities of (often streamed) data, to enable insights to
be derived
• Development of such models can benefit from an iterative approach, so the
domain can be better understood, and the models improved
• It also then needs a highly automated pipeline of tools, repositories to store
and keep track of models, code, data lineage and a target environment
which can be deployed into at speed
• The result is an ML-enabled application: MLOps requires data scientists to
work alongside developers, and can therefore be seen as an extension of
DevOps to encompass the data and models used for ML
23
42. • Automated Data Discovery
• Auto-generated pipelines based on global
experiences
• Joins by data
• Key variables updated with each new data point
• That, in turn, automatically execute the proper
next best action
• Next best action determined by AI
• Enterprises will automate data cataloguing and
profiling
Automation
43. • Lack of and expensive data engineers
• More vendors rearchitecting to open
source
• Vendors to compete on customer
satisfaction and execution
Open Source
44. • Data analytics stack goes Kubernetes for
both open source and commercial
• Winners go from thought to POC quickly
• Serverlessness
Kubernetes
45. We are at the start of General AI
• GPT-3 has opened a new chapter in machine learning.
– Its most striking feature is its generality.
– Only a few years ago, neural networks were built with functions
tuned to a specific task, such as translation or question answering.
Datasets were curated to reflect that task.
– GPT-3 has no task-specific functions, and it needs no special
dataset. It simply utilizes as much text as possible and plays
forward its output.
• Somehow, in the calculation of the conditional probability
distribution across all those gigabytes of text, a function
emerges that can produce answers that are competitive on
any number of tasks.
• It is a breathtaking triumph of simplicity that probably has
many years of achievement ahead of it.
27
46. There’s more maturity
in moving imperfectly
than in merely
perfectly defining the
shortcomings
Build credibility
Don’t be afraid to fail
Don’t talk yourself out
of having a new
beginning
Have an open mind
No plateaus are
comfortable for long
That resistance is not
about making
progress, it’s the
journey
47. Winning Approaches in 2021
• Cloud Computing
• Artificial Intelligence
• Data Lakes
• Data Warehousing
• Master Data Management
• Agile Development
• Kubernetes
• Automation
• Data Quality
• Graph Data
• Organizational Change Management
• DevOps and MLOps
• Data Catalogs
• Data Governance
48. 2021 Trends in Enterprise
Advanced Analytics
Presented by: William McKnight
“#1 Global Influencer in Data Warehousing” Onalytica
President, McKnight Consulting Group
A 2 Time Inc. 5000 Company
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET