The document discusses the role of data scientists and trends in data science. It describes how data scientists identify business needs, prepare and analyze data, interpret results, and communicate findings. However, emerging tools are automating some of these tasks using techniques like machine learning and natural language processing. This could change the role of data scientists and enable more self-service data analysis. The document also lists some vendors developing tools to support self-service data science through augmented intelligence.
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
Smarter businesses apply AI to learn and continuously evolve the way they work. To extract full value from AI, companies need data strategy that gives them access to all their data – no matter where it lives – in an environment that easily scales and applies the latest discovery technology including advanced analytics, visualization and AI. Learn how IBM Watson and Data provides all the tools companies need to embed AI, machine learning and deep learning in their business, while enabling professionals to gain the most from their data to drive smarter business and lead industry-changing transformations.
Smart Data Webinar: Knowledge as a ServiceDATAVERSITY
Building a successful ModernAI application often requires large volumes of data for training ML models or data that has been organized into knowledge using taxonomies or ontologies to support specific vertical markets (healthcare, insurance, pharma, etc.) or horizontal functions (HR, legal, supply chain, etc). While tools do exist to help developers ingest and organize the required data into meaningful knowledge stores, using pre-built data or knowledge packages can make application development faster, more reliable, and less expensive than starting from scratch.
In this webinar we will look at trends and examples of specific proprietary and open source data sets that offer prebuilt knowledge, representations, or models to serve these markets.
Noise to Signal - The Biggest Problem in DataDATAVERSITY
Our ability to produce, ingest and store data has grown exponentially, but our ability to parse out insights from data has not. In the 90s, an organization’s data would live in a data warehouse with an ETL pipeline and one reporting layer on top. Information was well controlled if not somewhat limited in breadth and slow to trickle down. Now with the onset of self-service analytics, anyone can create a report and an insight and there are many different sources of “truth.” For example, a seemingly straightforward question like "how many customers do we have?" will likely return difference answers from sales, finance and customer success, depending on their definitions and the data at hand. There is simply too much data (and duplicate data), too many tools, and too many systems storing data -- leading to time consuming searches, confusion and a lack of trust. Hear Stephanie discuss how a data catalog can help solve the noise to signal problem - making information easier to find, easier to understand and more trustworthy. She will describe how organizations like Safeway, Albertsons, Munich Re and Pfizer leverage a data catalog to find data and collaborate on data, gain a fuller understanding of its meaning and ultimately, solve important problems.
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
To gain insights from Business Intelligence, you need to easily see and understand what the data tells you by using data visualizations. While these charts and graphs can be eye candy, without proper context they are nothing more than pretty pictures. Data analysts and business analysts may use a variety of techniques to create the reports that they must generate for the business, and can benefit from a database tool that helps to simplify the task and accelerate the process. Join IDERA's Stan Geiger as he explains how to convey the meaning of data effectively and quickly create useful data visualizations for various audiences within your organization.
Many federal funding agencies, including NIH and most recently NSF, are requiring that grant applications contain data management plans for projects involving data collection. To support researchers in meeting this requirement, ICPSR is providing a set of tools and resources for creating data management plans. This presentation will covers:
• ICPSR’s Data Management Plan Website
• Suggested Elements of a Data Management Plan
• Example Data Management Plan Language
• Designating ICPSR as an Archive in a Data Management Plan
• Additional Resources for a Preparing Your Data Management Plan
Presented by Amy Pienta, Research Scientist, University of Michigan
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
Smarter businesses apply AI to learn and continuously evolve the way they work. To extract full value from AI, companies need data strategy that gives them access to all their data – no matter where it lives – in an environment that easily scales and applies the latest discovery technology including advanced analytics, visualization and AI. Learn how IBM Watson and Data provides all the tools companies need to embed AI, machine learning and deep learning in their business, while enabling professionals to gain the most from their data to drive smarter business and lead industry-changing transformations.
Smart Data Webinar: Knowledge as a ServiceDATAVERSITY
Building a successful ModernAI application often requires large volumes of data for training ML models or data that has been organized into knowledge using taxonomies or ontologies to support specific vertical markets (healthcare, insurance, pharma, etc.) or horizontal functions (HR, legal, supply chain, etc). While tools do exist to help developers ingest and organize the required data into meaningful knowledge stores, using pre-built data or knowledge packages can make application development faster, more reliable, and less expensive than starting from scratch.
In this webinar we will look at trends and examples of specific proprietary and open source data sets that offer prebuilt knowledge, representations, or models to serve these markets.
Noise to Signal - The Biggest Problem in DataDATAVERSITY
Our ability to produce, ingest and store data has grown exponentially, but our ability to parse out insights from data has not. In the 90s, an organization’s data would live in a data warehouse with an ETL pipeline and one reporting layer on top. Information was well controlled if not somewhat limited in breadth and slow to trickle down. Now with the onset of self-service analytics, anyone can create a report and an insight and there are many different sources of “truth.” For example, a seemingly straightforward question like "how many customers do we have?" will likely return difference answers from sales, finance and customer success, depending on their definitions and the data at hand. There is simply too much data (and duplicate data), too many tools, and too many systems storing data -- leading to time consuming searches, confusion and a lack of trust. Hear Stephanie discuss how a data catalog can help solve the noise to signal problem - making information easier to find, easier to understand and more trustworthy. She will describe how organizations like Safeway, Albertsons, Munich Re and Pfizer leverage a data catalog to find data and collaborate on data, gain a fuller understanding of its meaning and ultimately, solve important problems.
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
To gain insights from Business Intelligence, you need to easily see and understand what the data tells you by using data visualizations. While these charts and graphs can be eye candy, without proper context they are nothing more than pretty pictures. Data analysts and business analysts may use a variety of techniques to create the reports that they must generate for the business, and can benefit from a database tool that helps to simplify the task and accelerate the process. Join IDERA's Stan Geiger as he explains how to convey the meaning of data effectively and quickly create useful data visualizations for various audiences within your organization.
Many federal funding agencies, including NIH and most recently NSF, are requiring that grant applications contain data management plans for projects involving data collection. To support researchers in meeting this requirement, ICPSR is providing a set of tools and resources for creating data management plans. This presentation will covers:
• ICPSR’s Data Management Plan Website
• Suggested Elements of a Data Management Plan
• Example Data Management Plan Language
• Designating ICPSR as an Archive in a Data Management Plan
• Additional Resources for a Preparing Your Data Management Plan
Presented by Amy Pienta, Research Scientist, University of Michigan
Slides: The Automated Business GlossaryDATAVERSITY
You can’t do business without being able to successfully extract insights from your organization’s data supply chain. You need a strong foundation for visibility and control of data. Flying by the seat of your pants, when it comes to analyzing your market, your performance, and your competitors’ performances, just doesn’t work.
In this webinar, we’ll examine the real-life daily struggles and frustrations plaguing the data supply chain and discuss how these struggles can be eliminated by automation of metadata operations such as data lineage, data discovery and business glossary.
When you attend this webinar, you will learn about:
• What data consumers are really spending their time on and why they are so frustrated
• The challenges of building a business glossary
• How to get started with an automated business glossary and why it’s critical for BI intelligence
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
The Digital Economy is changing the way organizations do business across the globe, and is set to transform the economy on an unprecedented scale. Business optimization, and entirely new business models are emerging as data-driven technology provides unprecedented opportunity for innovation and change. In many organizations, data not only supports business profitability, but data itself has become the critical business asset.
What does it mean to leverage data as a business asset? And how can today’s data-centric technologies support the data-driven revolution? Join our expert panelists as they discuss the latest innovations in the data landscape.
Slides: Metadata Management for the Governance MindedDATAVERSITY
Do you have data governance on your mind? Do you envision an environment where people are held formally accountable for the data they define, produce and use? Does metadata play a big role in that governed environment? Of course, it does. To manage any “thing” requires that you have quality information about that “thing".
Join Bob Seiner, of KIK Consulting and TDAN.com, with Gal Alon, Senior Director of Business Development for an industry leading metadata management automation software provider Octopai, as they discuss the who’s, what’s, why’s and how’s of automating and leveraging your metadata environment to successfully govern your organization’s data. Bob and Gal will spend the hour chatting about the role metadata management plays in data governance as well as discuss specific use cases to improve probability of Data Governance success.
In this webinar, Bob and Gal will demonstrate:
- Data Governance’s dependency on quality metadata
- How a great tool will lead to increased use of your metadata assets
- What to look for in a metadata management tool and how it will help
- Automation of metadata collection and management processes
- People that will benefit from improved metadata automation and delivery
Data Insights and Analytics Webinar: CDO vs. CAO - What’s the Difference?DATAVERSITY
At one time, there were well-stated distinctions between the Chief Data Officer and Chief Analytics Officer roles. But not today. In some organizations, this role confusion actually causes serious concerns.
John and Kelle will revisit the definitions, suggest where lack of clarity first began, and discuss how best to manage the role distinctions going forward.
This webinar will address:
Differences in the CAO and CDO roles
CDOs who aren’t responsible for all organizational data
Why role clarity matters
Organizational success without one or both roles
Improving Data Analytics with Data GovernanceDATAVERSITY
Organizations are dedicating tremendous resources to improve their analytical capabilities. The focus for many is to improve the quality, understanding, availability and thus the value of the data for data scientists and analysts. These people are focused on providing descriptive, predictive and prescriptive analytics for the betterment of their organization. It all starts with governed data.
Join Bob Seiner and a special guest for this month’s installment of the Real-World Data Governance webinar series where they will discuss the importance of using Data Governance to improve Data Analytics. Bob will challenge the guest with questions about why and how data governance has a positive impact on getting the most out of your data.
In this webinar, Bob and his guest will discuss:
The relationship between Data Governance and Data Analytics
Getting management to understand why Data Governance is necessary
How to focus your Data Governance program on analytics
Using the focus on analytics to bolster your Data Governance program
Final words on the symbiotic relationship between Data Governance and Data Analytics
Data-Centric Analytics and Understanding the Full Data Supply ChainDATAVERSITY
While model development is an important part of analytics, this activity can be compromised by a lack of understanding of the data used in these models and poor Data Quality. For insights to be relied upon and truly actionable, data-related issues must be addressed.
The data supply chain (the set of architectural components that moves data around the enterprise from points where it is created or acquired to points where it is used) must be managed to supply the needs of analytics and other constituencies.
This webinar describes how the data supply chain should be designed and operated to provide analytics with the data it needs, and how Data Scientists should interact with the data supply chain to obtain the data they need. It also covers:
Data-centric considerations that must be taken into account in the development of analytic models
Features of a modern data supply chain
Major components in the data supply chain, with a focus on Data Lakes
Major roles and responsibilities in the data supply chain
How analytics must interact with the data supply chain
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
It’s been almost two years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. This complex but critical practice still has most enterprises grappling to master it for a myriad of reasons.
In this webinar, we’ll examine how Data Governance attitudes and practices continue to evolve and discuss what new research reveals as the most predominant challenges. We’ll delve into technology trends, including how adding certain capabilities will benefit your organization in terms of data asset availability, quality, and usability, including data consumer literacy and confidence.
When you attend this webinar, you will learn about:
• The requirements for a successful and sustainable Data Governance program
• Increasing confidence in data analytics for faster speed to insights
• How to automate data preparation and intelligence and where to start
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Advanced Analytics Governance - Effective Model Management and StewardshipDATAVERSITY
There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics.
Join this webinar to learn best practices and vetted approaches for how to:
Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship
Adapt governance best practices for different analytics use cases
Confirm alignment of your analytics and BI strategy with critical business objectives
Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
Data monetization is a cross-functional discipline that draws from best practices in Enterprise Data Management (EDM), technology, legal engineering, and finance to leverage data to increase revenues, reduce costs, and manage risk. EDM programs have generally found it extremely difficult to get senior management buy-in the absence of regulatory pressures or the fear of a data breach. Data monetization is an approach to drive quantifiable business benefits from data and information. This bottom-line driven approach is key to generating business adoption with stakeholders.
This session will review the key aspects of data monetization:
• Introduction to Data Monetization
• Identify Stakeholders
• Build Inventory of Use Cases
• Develop Business Cases
• Execute Initiatives
• Realize Business Benefits
• Legal Engineering and Regulatory Compliance
• Data Marketplace
Slides: Case Study — How J.B. Hunt is Driving Efficiency with AI and Real-Tim...DATAVERSITY
J.B. Hunt, one of the leading providers of transportation and logistics services in North America, recognizes the criticality of customer responsiveness, service quality, and operational efficiency for its success. However, with its data spread across multiple sources, including legacy mainframe systems, the organization was struggling to meet data requirements from multiple departments. They struggled to troubleshoot operational issues and respond to customers quickly.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems. Discover how J.B. Hunt now leverages a modernized data environment to accelerate data delivery and drive various AI and analytics initiatives such as real-time service-pricing, competitive counterbidding, and improving their customer experience.
Learn how you can:
• Ingest data in real-time from legacy mainframe systems, enterprise applications, and more
• Create a reliable cloud data lake to accelerate AI and Analytic Initiatives
• Catalog, prepare, and provision data to empower data consumers
• Drive operational efficiency and customer experience with AI-augmented insights
Business Value Metrics for Data GovernanceDATAVERSITY
As data professionals, we recognize and understand the need for data governance, focusing on data quality in particular. We have made progress in this area, as illustrated by the emergence of the Chief Data Officer role in recent years. However, in many organizations, the need for governance is still largely unrecognized, and remains very tough to sell internally. You may need some detailed information and metrics to demonstrate the business value. This session will focus on business justification for establishing a data governance framework, including:
Data classification
Data quality
Business value metrics (KPIs)
Slides: Applying Artificial Intelligence (AI) in All the Right Places in the ...DATAVERSITY
Data and Analytics are fundamental to digital transformation, yet many companies are still under-utilizing them. To go full throttle, AI and automation technologies can be added across the full spectrum of your data journey to truly re-imagine processes and business models.
Join Information Builders for this webinar on how AI:
• Augments your traditional business intelligence and analytics systems
• Minimizes manual inefficiencies with the way data is generated, collected, cleansed, and organized
• Helps you realize substantial performance gains with use cases such as churn forecasting, predictive maintenance, supply chain planning, risk mitigation, and more
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
Better Business From Exploring Ideas - AWS Summit Sydney 2018Amazon Web Services
Better Business from Exploring Ideas - Modern Data Architectures on AWS
In this session you will learn how organisations are able to drive better business outcomes from products and deliver more personalised and real-time experiences to customers. We will walk through a common customer journey that shows how to quickly test ideas and get insights, built on top of data lakes, data pipelines, and sandboxes using the same platform advantages of Modern Data Architectures on AWS as some of our most prominent customers. See how to serve the needs of your business users, business analysts, and data scientists using AWS services for analytics, Big Data, and Machine Learning.
Craig Stires, APAC Head of Analytics, Big Data, and AI, Amazon Web Services
Slides: The Automated Business GlossaryDATAVERSITY
You can’t do business without being able to successfully extract insights from your organization’s data supply chain. You need a strong foundation for visibility and control of data. Flying by the seat of your pants, when it comes to analyzing your market, your performance, and your competitors’ performances, just doesn’t work.
In this webinar, we’ll examine the real-life daily struggles and frustrations plaguing the data supply chain and discuss how these struggles can be eliminated by automation of metadata operations such as data lineage, data discovery and business glossary.
When you attend this webinar, you will learn about:
• What data consumers are really spending their time on and why they are so frustrated
• The challenges of building a business glossary
• How to get started with an automated business glossary and why it’s critical for BI intelligence
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
The Digital Economy is changing the way organizations do business across the globe, and is set to transform the economy on an unprecedented scale. Business optimization, and entirely new business models are emerging as data-driven technology provides unprecedented opportunity for innovation and change. In many organizations, data not only supports business profitability, but data itself has become the critical business asset.
What does it mean to leverage data as a business asset? And how can today’s data-centric technologies support the data-driven revolution? Join our expert panelists as they discuss the latest innovations in the data landscape.
Slides: Metadata Management for the Governance MindedDATAVERSITY
Do you have data governance on your mind? Do you envision an environment where people are held formally accountable for the data they define, produce and use? Does metadata play a big role in that governed environment? Of course, it does. To manage any “thing” requires that you have quality information about that “thing".
Join Bob Seiner, of KIK Consulting and TDAN.com, with Gal Alon, Senior Director of Business Development for an industry leading metadata management automation software provider Octopai, as they discuss the who’s, what’s, why’s and how’s of automating and leveraging your metadata environment to successfully govern your organization’s data. Bob and Gal will spend the hour chatting about the role metadata management plays in data governance as well as discuss specific use cases to improve probability of Data Governance success.
In this webinar, Bob and Gal will demonstrate:
- Data Governance’s dependency on quality metadata
- How a great tool will lead to increased use of your metadata assets
- What to look for in a metadata management tool and how it will help
- Automation of metadata collection and management processes
- People that will benefit from improved metadata automation and delivery
Data Insights and Analytics Webinar: CDO vs. CAO - What’s the Difference?DATAVERSITY
At one time, there were well-stated distinctions between the Chief Data Officer and Chief Analytics Officer roles. But not today. In some organizations, this role confusion actually causes serious concerns.
John and Kelle will revisit the definitions, suggest where lack of clarity first began, and discuss how best to manage the role distinctions going forward.
This webinar will address:
Differences in the CAO and CDO roles
CDOs who aren’t responsible for all organizational data
Why role clarity matters
Organizational success without one or both roles
Improving Data Analytics with Data GovernanceDATAVERSITY
Organizations are dedicating tremendous resources to improve their analytical capabilities. The focus for many is to improve the quality, understanding, availability and thus the value of the data for data scientists and analysts. These people are focused on providing descriptive, predictive and prescriptive analytics for the betterment of their organization. It all starts with governed data.
Join Bob Seiner and a special guest for this month’s installment of the Real-World Data Governance webinar series where they will discuss the importance of using Data Governance to improve Data Analytics. Bob will challenge the guest with questions about why and how data governance has a positive impact on getting the most out of your data.
In this webinar, Bob and his guest will discuss:
The relationship between Data Governance and Data Analytics
Getting management to understand why Data Governance is necessary
How to focus your Data Governance program on analytics
Using the focus on analytics to bolster your Data Governance program
Final words on the symbiotic relationship between Data Governance and Data Analytics
Data-Centric Analytics and Understanding the Full Data Supply ChainDATAVERSITY
While model development is an important part of analytics, this activity can be compromised by a lack of understanding of the data used in these models and poor Data Quality. For insights to be relied upon and truly actionable, data-related issues must be addressed.
The data supply chain (the set of architectural components that moves data around the enterprise from points where it is created or acquired to points where it is used) must be managed to supply the needs of analytics and other constituencies.
This webinar describes how the data supply chain should be designed and operated to provide analytics with the data it needs, and how Data Scientists should interact with the data supply chain to obtain the data they need. It also covers:
Data-centric considerations that must be taken into account in the development of analytic models
Features of a modern data supply chain
Major components in the data supply chain, with a focus on Data Lakes
Major roles and responsibilities in the data supply chain
How analytics must interact with the data supply chain
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
It’s been almost two years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. This complex but critical practice still has most enterprises grappling to master it for a myriad of reasons.
In this webinar, we’ll examine how Data Governance attitudes and practices continue to evolve and discuss what new research reveals as the most predominant challenges. We’ll delve into technology trends, including how adding certain capabilities will benefit your organization in terms of data asset availability, quality, and usability, including data consumer literacy and confidence.
When you attend this webinar, you will learn about:
• The requirements for a successful and sustainable Data Governance program
• Increasing confidence in data analytics for faster speed to insights
• How to automate data preparation and intelligence and where to start
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Advanced Analytics Governance - Effective Model Management and StewardshipDATAVERSITY
There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics.
Join this webinar to learn best practices and vetted approaches for how to:
Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship
Adapt governance best practices for different analytics use cases
Confirm alignment of your analytics and BI strategy with critical business objectives
Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations
Slides: Data Monetization — Demonstrating Quantifiable Financial Benefits fro...DATAVERSITY
Data monetization is a cross-functional discipline that draws from best practices in Enterprise Data Management (EDM), technology, legal engineering, and finance to leverage data to increase revenues, reduce costs, and manage risk. EDM programs have generally found it extremely difficult to get senior management buy-in the absence of regulatory pressures or the fear of a data breach. Data monetization is an approach to drive quantifiable business benefits from data and information. This bottom-line driven approach is key to generating business adoption with stakeholders.
This session will review the key aspects of data monetization:
• Introduction to Data Monetization
• Identify Stakeholders
• Build Inventory of Use Cases
• Develop Business Cases
• Execute Initiatives
• Realize Business Benefits
• Legal Engineering and Regulatory Compliance
• Data Marketplace
Slides: Case Study — How J.B. Hunt is Driving Efficiency with AI and Real-Tim...DATAVERSITY
J.B. Hunt, one of the leading providers of transportation and logistics services in North America, recognizes the criticality of customer responsiveness, service quality, and operational efficiency for its success. However, with its data spread across multiple sources, including legacy mainframe systems, the organization was struggling to meet data requirements from multiple departments. They struggled to troubleshoot operational issues and respond to customers quickly.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems. Discover how J.B. Hunt now leverages a modernized data environment to accelerate data delivery and drive various AI and analytics initiatives such as real-time service-pricing, competitive counterbidding, and improving their customer experience.
Learn how you can:
• Ingest data in real-time from legacy mainframe systems, enterprise applications, and more
• Create a reliable cloud data lake to accelerate AI and Analytic Initiatives
• Catalog, prepare, and provision data to empower data consumers
• Drive operational efficiency and customer experience with AI-augmented insights
Business Value Metrics for Data GovernanceDATAVERSITY
As data professionals, we recognize and understand the need for data governance, focusing on data quality in particular. We have made progress in this area, as illustrated by the emergence of the Chief Data Officer role in recent years. However, in many organizations, the need for governance is still largely unrecognized, and remains very tough to sell internally. You may need some detailed information and metrics to demonstrate the business value. This session will focus on business justification for establishing a data governance framework, including:
Data classification
Data quality
Business value metrics (KPIs)
Slides: Applying Artificial Intelligence (AI) in All the Right Places in the ...DATAVERSITY
Data and Analytics are fundamental to digital transformation, yet many companies are still under-utilizing them. To go full throttle, AI and automation technologies can be added across the full spectrum of your data journey to truly re-imagine processes and business models.
Join Information Builders for this webinar on how AI:
• Augments your traditional business intelligence and analytics systems
• Minimizes manual inefficiencies with the way data is generated, collected, cleansed, and organized
• Helps you realize substantial performance gains with use cases such as churn forecasting, predictive maintenance, supply chain planning, risk mitigation, and more
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
Better Business From Exploring Ideas - AWS Summit Sydney 2018Amazon Web Services
Better Business from Exploring Ideas - Modern Data Architectures on AWS
In this session you will learn how organisations are able to drive better business outcomes from products and deliver more personalised and real-time experiences to customers. We will walk through a common customer journey that shows how to quickly test ideas and get insights, built on top of data lakes, data pipelines, and sandboxes using the same platform advantages of Modern Data Architectures on AWS as some of our most prominent customers. See how to serve the needs of your business users, business analysts, and data scientists using AWS services for analytics, Big Data, and Machine Learning.
Craig Stires, APAC Head of Analytics, Big Data, and AI, Amazon Web Services
Smart Data - The Foundation for Better Business OutcomesDATAVERSITY
This webinar will explore emerging technologies that enable a new generation of intelligent applications and enterprise systems. It will also act as a roadmap for evaluating and integrating these technologies and practices, and set the stage for our 2016 series of Smart Data webinars.
In the last few years, we have witnessed an AI renaissance with significant advances in areas such as machine-learning/deep learning, natural language processing, and biologically-inspired processor architectures. Simultaneously, the rise of the Industrial Internet of Things - which together with the “traditional” Internet form the Internet of Everything – foreshadows a connected world of smarter homes, cities, and even business relationships.
These “cognitive connections” are supported by advanced analytics and smart data. Join the discussion to see how you and your organization can benefit from getting started now.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
Who is a Data Scientist? | How to become a Data Scientist? | Data Science Cou...Edureka!
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Who is a Data Scientist" will help you understand what a data scientist does, their roles and responsibilities, and what the data science profile is all about. You will also get a glimpse of what kind of salary packages and career opportunities the data science domain offers.
Below topics are covered in this PPT:
Who is a Data Scientist?
What is Data Science?
Who can take up Data Science?
How to become a Data Scientist?
Data Scientist Skills
Data Scientist Roles & Responsibilities
Data Scientist Salary
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DataRobot 머신러닝 자동화 플랫폼은 전 세계 Top Data Scientist 들의 지식, 경험 및 모범 사례를 바탕으로 최고 수준의 자동화와 사용 편리성을 확보한 가장 진보된 머신러닝 자동화 솔루션 입니다. DataRobot을 통해 비즈니스 관계자, 분석가 및 데이터 과학자 등 기술 수준과 관계 없이 모든 사용자가 기존 모델링 기법에 비해 아주 빠르게, 매우 정확한 예측 모델을 수립하고 구축, 관리할 수 있습니다.
The intersection of AI and IoT presents new opportunities to create value for your business, capturing new insights from the vast amounts of IoT data available, which results in stronger customer relationships and new efficiencies. In this session, we discuss the future of operations and product development when AI and IoT meet to make autonomous decisions faster and better.
Graph technology has truly burst onto the scene with diverse new products and services, proving that graph is relevant and that not all graph use cases are equal. Previously relegated to niche implementations and science projects, graph now finds itself deployed as the foundational technology for enterprise analytics solutions and enterprise Data Fabric strategies. It is no surprise that many are calling 2018 “The Year of the Graph”.
Streaming Analytics for IoT-Oriented ApplicationsDATAVERSITY
The growth of connected devices on the Internet of Things (IOT) is already creating huge volumes of sensor- and system-oriented data. The pace will no doubt continue to increase for years to come. Finding and leveraging opportunities from this data faster and more effectively than your competition requires the effective use of streaming analytics. We simply don’t have time to apply legacy techniques and can’t interrupt the flow to analyze it. The good news is that tools and techniques enable us to get usable analytics in near-real time today, and to integrate current streams of data with historical archives to create prescriptive systems that direct next best action scenarios.
This webinar will present an overview of streaming analytics technologies and tools, and help participants identify opportunities for streaming analytics-enabled IOT applications.
Designing a Successful Governed Citizen Data Science StrategyDATAVERSITY
To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents.
In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
AI now ron tolido, capgemini cwin18_toulouseCapgemini
Now that corporate enthusiasm for Artificial Intelligence is skyrocketing, the challenge for the industry is to deliver on the incredible promise. Although lots still needs to be discovered around ethics, culture and effects on society, there is no reason to procrastinate: AI can deliver benefits and impact already today. Here’s 3 ways of how to do it.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
Natural Language Understanding at AI and Machine Learning in Clinical Trials ...Saama
Karim Damji, SVP of Products and Marketing, and Malaikannan Sankarasubbu, VP of AI Research at Saama Technologies spoke at the AI and Machine Learning in Clinical Trials Summit 2018 on Accelerating Clinical Trials using Natural Language Understanding.
Pharma has a big text problem. Lots of useful information buried in unstructured data formats that is difficult to use. Natural Language Understanding will help to turn what was once unusable data into meaningful insights that can be applied to the clinical trial development continuum. NLU engines also open up the possibility for users to have a more interactive relationship with their vast data stores using speech or chat messaging in a conversational experience.
Big Data analytics have increasingly gained prominence in business because it has provided beneficial insights regarding emerging trends, behaviors and preferences. Relying exclusively on analytics to address the vast majority of business uncertainties, however, is detrimental to our ability to solve problems. Madsbjerg and Rasmussen, in a WSJ article, insightfully captures the essence: “By outsourcing our thinking to Big Data, our ability to make sense of the world by careful observation begins to wither, just as you miss the feel and texture of a new city by navigating it only with the help of a GPS.”
If we are to gain a better understanding of our customers and the business itself, we must not miss “the feel and texture.” We need to see problems and opportunities in terms of human experience and capture and interpret data with a human context. We need to examine and understand how people live their lives from their own perspective, rather than from traditional business’ perspective. This applies to markets and products, as much as it applies to corporate culture because humans are complex and difficult to qualify and quantify. By using ethnographic research methods, we can uncover and understand the needs and desires – the whys and the feel and texture - that drive the emotional lives of customers.
This paper argues that business needs to combine analytics with ethnography for richer and even more valuable insights to move ahead in a global market. It also provides some suggestions on how to combine analytics with ethnography.
Lead Author: Matt Artz, Azimuth Labs
Co-author: Dr. Uldarico Rex Dumdum, Marywood University
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Securing your Kubernetes cluster_ a step-by-step guide to success !
The Disappearing Data Scientist
1. JULY 12, 2018
The Disappearing Data Scientist
Adrian J Bowles, PhD
Founder, STORM Insights, Inc.
Lead Analyst, AI, Aragon Research
2. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FROM THE GUARDIAN CAREER CHOICES SECTION
“What's a data scientist and how do I become one?”
“There is currently a shortage of data scientists – with companies looking for
programmers and analytical thinkers to plug the gap”
“…the next three years offer a veritable goldmine for data scientists.”
June 30, 2015
3. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
What IS a Data Scientist Anyway?
The role, responsibilities, skills
How the role will change
Emerging tools to augment or automate data science
AGENDA
4. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
WHAT IS A DATA SCIENTIST ANYWAY?
Someone who can
Identify/Interpret Business Need for Insights
Identify and Prepare Data
Analyze - using tools and algorithms appropriate
for the problem at hand
Interpret the results
Tell the story
Wikipedia contributors. "Slide rule." Wikipedia, The Free Encyclopedia.
Wikipedia, The Free Encyclopedia, 11 Jul. 2018. Web. 11 Jul. 2018.
"Sextant (astronomical)." Wikipedia, The Free Encyclopedia.
Wikipedia, The Free Encyclopedia, 12 Oct. 2017. Web. 11 Jul. 2018
Probability and Statistics
Experimental Design
Communication Skills
5. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
TWO APPROACHES: REPORTING VS EXPLORING
Data
Discovery
Data
Preparation
Model
Analyze
Interpret
Problem
Definition
Data
Discovery
Data
Preparation
Model
Analyze
Interpret
Pattern
Detection
~Supervised ML ~unSupervised ML
7. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
TECHNOLOGY AUGMENTATION & AUTOMATION
PROGRAMMERS
SCOPE
TECHNOLOGY
IMPACT
8. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
DATA
SCIENTISTS
TECHNOLOGY AUGMENTATION & AUTOMATION
SCOPE
TECHNOLOGY
IMPACT
9. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
THE NATURAL PROGRESSION
Business
User
IT
Business
User
IT
Business
User
Data IT
Data
Scientist
Data
Data
12. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FORCES DRIVING SELF-SERVICE DATA SCIENCE
Data Growth Budgets for
Analysis Tools
Bypassing ITDearth of Skills
Issues Demand Supply
AI Technologies
Maturing to
Augment Business
Analysis Requirements
13. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
WHAT CAN BE AUTOMATED?
Identify/Interpret Business Need for Insights
Identify and Prepare Data
Analyze
Interpret
14. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
DATA SCIENCE TRENDS
Self-Service Data Science BI AI
15. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
AUTOMATION/AUGMENTATION: MODEL GENERATION
Properties of the Data
+
Comparative Analysis | User interrogation
+
Machine Learning
16. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
PROBLEM DEFINITION TREND: FROM SQL TO NLP
Structured Queries Visual Queries Natural Language
Data-Centric Business User-Centric
17. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
DATA EXPLORATION TREND
…Interactive to Conversational
Distributed Analysis - Put the Power Close to the Pain
18. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
VENDORS MEETING SELF-SERVICE DATA SCIENCE DEMAND (REPRESENTATIVE LIST)
IBM - Watson Explorer, Watson Analytics,
Microsoft PowerBI
MicroStrategy
Oracle Data Visualization
Qlik Sense
SAP Lumira, Analytics Cloud
SAS Visual Analytics
Sisense
Tableau
Tibco Spotfire
19. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
NATURAL LANGUAGE GENERATION FOR STORY TELLING
Narrative Science Automated Insights
SAP Lumira
Sisense
Microsoft PowerBI
MicroStrategy
Qlik Sense
Tableau
Tibco Spotfire
20. Copyright (c) 2018 by STORM Insights Inc. All Rights Reserved.
FINDINGS AND RECOMMENDATIONS
Findings
Increasing demand will continue to drive self service analytics
Quality of automation vs augmentation varies widely
Biggest benefits are coming from AI classification and NLP technologies
Recommendations
Don’t think in terms of “citizen data scientist” - think productivity
Evaluate your current BI vendor’s roadmap
Train user on analysis fundamentals and experimental design before
deploying the new tools - they may look easy, but they may also just help you
solve the wrong problem faster
Test the new interfaces in your environment before choosing a tool
If you’re a data scientist - relax