It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the 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 fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
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.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.
This session will cover:
Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:
Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Architecture, 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.
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.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.
This session will cover:
Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:
Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Architecture, 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.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDATAVERSITY
The increasing focus on data in today’s organization has increased demand for critical roles such as data architect, data engineer, and data modeler. But there is often confusion and ambiguity around what these roles entail, and what overlap exists between them. This webinar will discuss these data-centric roles and their place in the data-driven organization.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
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.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
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.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
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.
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDATAVERSITY
The increasing focus on data in today’s organization has increased demand for critical roles such as data architect, data engineer, and data modeler. But there is often confusion and ambiguity around what these roles entail, and what overlap exists between them. This webinar will discuss these data-centric roles and their place in the data-driven organization.
A Work of Zhamak Dehghani
Principal consultant
ThoughtWorks
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam
How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
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.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
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.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
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.
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
ADV Slides: Building and Growing Organizational Analytics with Data LakesDATAVERSITY
Data lakes are providing immense value to organizations embracing data science.
In this webinar, William will discuss the value of having broad, detailed, and seemingly obscure data available in cloud storage for purposes of expanding Data Science in the organization.
[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
We will dive into modern data management approaches that have become prevalent and popular across many industries, built on top of good old data lakes: Lakehouse. Here are some of the most common problems that are being solved with this novel approach: Data Silos Demolished: Discover how organizations are breaking down data silos that have plagued them for decades, unifying structured and unstructured data from diverse sources. Inefficient Data Processing: We'll unveil real-world examples of how inefficient data processing can grind productivity to a halt and explore how Data Lakehouses provide a powerful solution while improving governance and security. Real-time Analytics: Learn how modern businesses are striving to achieve real-time analytics and the role Data Lakehouses play in achieving this. Have one data copy that will serve BI, Reporting, and ML workloads
ADV Slides: 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.
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.
Modern apps and services are leveraging data to change the way we engage with users in a more personalized way. Skyla Loomis talks big data, analytics, NoSQL, SQL and how IBM Cloud is open for data.
Learn more by visiting our Bluemix Hybrid page: http://ibm.co/1PKN23h
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Precisely
The advanced analytics and AI that run today’s businesses rely on a larger volume, and greater variety, of data. This data needs to be of the highest quality to ensure the best possible outcomes, but traditional data quality tools weren’t designed for today’s modern data environments.
That’s why we’ve developed Trillium DQ for Big Data -- an integrated product that delivers industry-leading data profiling and data quality at scale, in the cloud or on premises.
In this on-demand webcast, you will learn how Trillium DQ:
• Empowers data analysts to easily profile large, diverse data sources to discover new insights, uncover issues, and report on their findings – all without involving IT.
• Delivers best-in-class entity resolution to support mission-critical applications such as Customer 360, fraud detection, AML, and predictive analytics.
• Supports Cloud and hybrid architectures by providing consistent high-performance processing within critical time windows on all platforms.
• Keeps enterprise data lakes validated, clean, and trusted with the highest quality data – without technical expertise in big data or distributed architectures.
• Enables data quality monitoring based on targeted business rules for data governance and business insight
The Shifting Landscape of Data IntegrationDATAVERSITY
Enterprises and organizations from every industry and scale are working to leverage data to achieve their strategic objectives — whether they are to be more profitable, effective, risk-tolerant, prepared, sustainable, and/or adaptable in an ever-changing world. Data has exploded in volume during the last decade as humans and machines alike produce data at an exponential pace. Also, exciting technologies have emerged around that data to improve our abilities and capabilities around what we can do with data.
Behind this data revolution, there are forces at work, causing enterprises to shift the way they leverage data and accelerate the demand for leverageable data. Organizations (and the climates in which they operate) are becoming more and more complex. They are also becoming increasingly digital and, thus, dependent on how data informs, transforms, and automates their operations and decisions. With increased digitization comes an increased need for both scale and agility at scale.
In this session, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which platforms have made, or are in the process of making, the leap to this new generation of Data Management and integration capabilities.
DataLakes kan skalere i takt med skyen, nedbryde integrationsbarrierer og data gemt i siloer og bane vejen for nye forretningsmuligheder. Det er alt sammen med til at give et bedre beslutningsgrundlag for ledelse og medarbejdere. Kom og hør hvordan.
David Bojsen, Arkitekt, Microsoft
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
Managing Large Amounts of Data with SalesforceSense Corp
Critical "design skew" problems and solutions - Engaging Big Objects, MuleSoft, Snowflake and Tableau at the right time
Salesforce’s ability to handle large workloads and participate in high-consumption, mobile-application-powering technologies continues to evolve. Pub/sub-models and the investment in adjacent properties like Snowflake, Kafka, and MuleSoft, has broadened the development scope of Salesforce. Solutions now range from internal and in-platform applications to fueling world-scale mobile applications and integrations. Unfortunately, guidance on the extended capabilities is not well understood or documented. Knowing when to move your solution to a higher-order is an important Architect skill.
In this webinar, Paul McCollum, UXMC and Technical Architect at Sense Corp, will present an overview of data and architecture considerations. You’ll learn to identify reasons and guidelines for updating your solutions to larger-scale, modern reference infrastructures, and when to introduce products like Big Objects, Kafka, MuleSoft, and Snowflake.
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
Weet u nog waar uw bedrijfsdata zich bevindt? Uw data bevindt zich (straks) overal. In samenwerking met Commvault laten we zien, hoe uw organisatie ‘in control’ kan blijven over én meerwaarde kan geven aan uw data ongeacht of deze zich on-premise, in de cloud of op een end-user device bevindt.
Presentatie 9 juni 2016
Big Data Fabric: A Necessity For Any Successful Big Data InitiativeDenodo
Watch this webinar in full here: https://buff.ly/2IxM8Iy
Watch all webinars from the Denodo Packed Lunch webinar series here: https://buff.ly/2IR3q6w
While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users.
Attend this session to learn how big data fabric enabled by data virtualization:
• Provides lightning fast self-service data access to business users
• Centralizes data security, governance and data privacy
• Fulfills the promise of data lakes to provide actionable insights
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.
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.
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.
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.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Empowering the Data Driven Business with Modern Business IntelligenceDATAVERSITY
By consolidating data engineering, data warehouse, and data science capabilities under a single fully-managed platform, BigQuery can accelerate computation, reduce data analysis costs, and streamline data management.
Following in-depth interviews with a security services provider and a telecommunications company, Nucleus Research found that customers moving to Google Cloud BigQuery from on-premises data warehouse solutions accelerate data processing by over 75 percent while reducing data ongoing administrative expenses by over 25 percent.
As BigQuery continues to optimize its platform architecture for compute efficiency and multicloud support, Nucleus expects the vendor to see rapid adoption and further penetrate the data warehouse market.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
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).
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1. 2022 Trends in
Enterprise Advanced
Analytics
Presented by: William McKnight
“#1 Global Influencer in Big Data” Thinkers360
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
#AdvAnaly;cs
3. ChaosSearch helps modern organizations
Know Better™ by activating the data lake for
analytics.
The ChaosSearch Data Lake Platform indexes customers’ cloud
data, rendering it fully searchable and enabling analytics at scale
with massive reductions of time, cost and complexity.
9. Image Goes
Here
Our SRE teams used to struggle with managing
the vast amount of logs it takes to support
millions of users in real time in a consistent
manner across all our product lines. With
ChaosSearch, we are able to use a singular
solution for our various logs without the hassle
of managing the logging tools as well.”
Joel Snook, Director, DevOps Engineering
ChaosSearch Replaces Elasticsearch for Log Analytics
Activate your cloud object storage to become a hot, analytical data lake.
12. 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
William McKnight
The Savvy Manager’s Guide
The
Savvy
Manager’s
Guide
Information
Management
Information Management
Strategies for Gaining a
Competitive Advantage with Data
13. McKnight Consulting Group Offerings
Strategy
Training
Strategy
§ Trusted Advisor
§ Ac1on Plans
§ Roadmaps
§ Tool Selec1ons
§ Program Management
Training
§ Classes
§ Workshops
Implementa/on
§ Data/Data Warehousing/Business
Intelligence/Analy1cs
§ Master Data Management
§ Governance/Quality
§ Big Data
Implementa;on
3
14. 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
15. 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
16. Last Year’s Trends
• Remote Work Continues
• Led by Cloud Capabilities, Strong Tech Spending Rebound in 2021
• Leading Organizations are increasing a focus on AI/ML
• Model Deployment Takes Center Stage
• More Edge AI
• Explainable AI
• Strong Data Lake Adoption
• New Technology Stacks: Shift from only data warehouses, lakes, and
ETL to data fabrics, AI, and pipelines
• Strong DEVOps Adoption
• Strong MLOps Adoption
• Automation
• Open Source Adoption
• Kubernetes Adoption
• We are at the start of General AI
6
17. Top Trends in Enterprise Analytics
for 2022 and Beyond
18. • Embedded Databases at the edge
• AI baked into the chips
• Decision making at the edge
• High-Performance Edge AI
• Real-Time Data Wrangling
Edge AI and Edge Computing Dominate
Architectures
24. AI-Enabled Applications
• Venture Funds will shift from AI tools and
technologies to AI-enabled applications
• AI is coding- and trial-heavy; Customers will
begin demanding low- and no-code off-the-
shelf AI
• AI will focus on automation and deep,
complex analysis of big data for immediate
action
14
25. Data Catalogs Cross Chasm in Data Stack
• Data Catalogs serve as metadata store for
all services including data integration,
prep/transformation, data lake, DW, ML
• Identifies relationships
• Identifies data pipelines
• Serves preferences in data set selection
• Documents all data sets (including
connection info)
15
26. Data Quality Subsumed into Data
Observability (and Data Observability
Becomes Huge)
16
Predictive
data quality &
observability
Scale
detection
Leverage ML to generate
explainable and adaptive
DQ rules
Scale
architecture
Scan large and diverse
databases, files and
streaming data
Scale
adoption
Empower users with a
unified scoring system
and personal alerts
27. Streaming Analytics Grows with IoT
17
Data Prep /
Enrichment SQL on
Hadoop
Raw Data Topics
JSON, AVRO
Processed
Data Topics
and
/ or
Stream
Processing
or
Live device log data
32. AutoML Cements Itself as The Future of ML
22
AutoML features to look for:
• Algorithm availability
• Preprocessing capabili5es
• Search methods
• Ensembles
• Explainability
Automatically build in parallel
multiple models to select the best
Open-Source/Paid AutoML tools
• AutoWEKA
• Auto-sklearn
• TPOT
• Google Cloud AutoML
• H20 AutoML
Apply data
preprocessing
Research to pinpoint
the right ML
algorithm
Optimize
hyperparameters for
selected algorithms
Golden ML ensemble
Automate the design of
machine learning
models:
AutoML
34. § 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
35. Winning Approaches in 2022
• Edge AI
• Containerized Data with Kubernetes
• Synthetic Data for Training AI Models
• Avoid Miscommunication: i.e., Data Fabric
• AI-Enabled Applications
• Data Catalogs
• Data Observability
• Streaming Analytics
• AI Design
• AutoML
• GPT3
36. 2022 Trends in
Enterprise Advanced
Analytics
Presented by: William McKnight
“#1 Global Influencer in Big Data” Thinkers 360
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