Cisco established an Analytics Center of Excellence (CoE) to accelerate the company's competitive advantage through data-driven insights. The CoE aims to understand past performance, manage current operations, and influence future outcomes. It works with business functions and a governing body of senior leaders to prioritize initiatives, establish processes, and cultivate a culture where analytics drives decision-making. The long-term goal is to transform Cisco into a company where analytics provides a clear competitive differentiator.
The Analytics CoE: Positioning your Business Analytics Program for SuccessCartegraph
This Loras College Business Analytics Symposium breakout session presentation by Kiran Garimella, Ph.D., president and founder of XBITALIGN, explored the analytics center of excellence (CoE).
A business analytics program is more than the application of data science and Big Data technology to data. Success should be measured not only by the valuable insights the program delivers, but also by how well it is sustained and how much the ‘analytics mindset’ becomes part of the company’s DNA. The journey is not only from data to information, but also from information to knowledge, and from knowledge to intelligence. The foundation for making this happen is a well-structured Analytics Center of Excellence (CoE).
Building an Analytics CoE (Center of Excellence)Rahul Saxena
This deck is from a workshop I conducted at the Indian Institute of Management, Bangalore (IIMB) on 20th July, 2013.
Agenda:
* What does the organization want to do with analytics? What is the role of the CoE that they envision?
* What is the organizational context? Current providers of analytics? Leadership support?
* What will the Analytics CoE need to be like (now and in the future, up to the planning horizon)?
* Where do we stand with analytics capabilities now, compared to what we need?
* How will we evolve the CoE? Set expectations, drive the evolution, establish the value.
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.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
The Analytics COE positioning your business analytics program for successKiran Garimella
You should consider the following three aspects of your Business Analytics Program:
* The Business (not just data science, big data, and technology)
* Analytics as the DNA of the company (and not just a competency of an elite few)
* A Programmatic approach that sustainable for the life of the company (and not just a one-time project or initiative)
What role do classical statistics, Bayesian statistics, judgment under uncertainty, heuristics, biases, categorical data analysis, etc., play in such a program?
A COE (Center of Excellence) framework seeks to address these aspects and ensure the company can progress on all fronts.
The Analytics CoE: Positioning your Business Analytics Program for SuccessCartegraph
This Loras College Business Analytics Symposium breakout session presentation by Kiran Garimella, Ph.D., president and founder of XBITALIGN, explored the analytics center of excellence (CoE).
A business analytics program is more than the application of data science and Big Data technology to data. Success should be measured not only by the valuable insights the program delivers, but also by how well it is sustained and how much the ‘analytics mindset’ becomes part of the company’s DNA. The journey is not only from data to information, but also from information to knowledge, and from knowledge to intelligence. The foundation for making this happen is a well-structured Analytics Center of Excellence (CoE).
Building an Analytics CoE (Center of Excellence)Rahul Saxena
This deck is from a workshop I conducted at the Indian Institute of Management, Bangalore (IIMB) on 20th July, 2013.
Agenda:
* What does the organization want to do with analytics? What is the role of the CoE that they envision?
* What is the organizational context? Current providers of analytics? Leadership support?
* What will the Analytics CoE need to be like (now and in the future, up to the planning horizon)?
* Where do we stand with analytics capabilities now, compared to what we need?
* How will we evolve the CoE? Set expectations, drive the evolution, establish the value.
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.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
The Analytics COE positioning your business analytics program for successKiran Garimella
You should consider the following three aspects of your Business Analytics Program:
* The Business (not just data science, big data, and technology)
* Analytics as the DNA of the company (and not just a competency of an elite few)
* A Programmatic approach that sustainable for the life of the company (and not just a one-time project or initiative)
What role do classical statistics, Bayesian statistics, judgment under uncertainty, heuristics, biases, categorical data analysis, etc., play in such a program?
A COE (Center of Excellence) framework seeks to address these aspects and ensure the company can progress on all fronts.
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.
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.
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
NEED FOR CHANGE: Data is changing the world. We all know that. The real challenge will be to keep up with those changes by hiring the right team to help you take on the data that is already in your organization.
STAFF FOR SUCCES: Make sure you have an executive sponsor that has a vision for how the organization can become data-driven; hire experienced team members to lead the data engineering, and architecture teams; and adopt agile methodologies to allow for quick experimentation and quick failures.
SKILL UP: In a recent survey focused on Spark, over 60 percent indicated that the skills/training gap was their biggest organizational challenges with Spark, but 65% of respondents indicated that they either did not know or had no future plans for training. Cloudera University to get them ramped up quickly. Cloudera University helps organizations tackle the skill gaps issue they encounter when growing their teams and helps them stay up to date on the latest supported technologies.
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.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
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.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
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.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
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.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
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.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Big Data Maximizing Entrepreneurship Success - IE Alumni Weekend Miami - Juan...Juan Gorricho
My presentation at the IE Alumni Weekend in Miami (https://pickevent.com/e/iealumni-miami) on Big Data Maximizing Entrepreneurship Success. Entrepreneurs have plenty of opportunities to leverage data to create business value. Key is to have a vision and focus in the business value; resources required (people, technology) are commodities that are available.
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.
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.
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
NEED FOR CHANGE: Data is changing the world. We all know that. The real challenge will be to keep up with those changes by hiring the right team to help you take on the data that is already in your organization.
STAFF FOR SUCCES: Make sure you have an executive sponsor that has a vision for how the organization can become data-driven; hire experienced team members to lead the data engineering, and architecture teams; and adopt agile methodologies to allow for quick experimentation and quick failures.
SKILL UP: In a recent survey focused on Spark, over 60 percent indicated that the skills/training gap was their biggest organizational challenges with Spark, but 65% of respondents indicated that they either did not know or had no future plans for training. Cloudera University to get them ramped up quickly. Cloudera University helps organizations tackle the skill gaps issue they encounter when growing their teams and helps them stay up to date on the latest supported technologies.
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.
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help?
Given in Montreal on 14-Dec-2021
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
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.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
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.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
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.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
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.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Big Data Maximizing Entrepreneurship Success - IE Alumni Weekend Miami - Juan...Juan Gorricho
My presentation at the IE Alumni Weekend in Miami (https://pickevent.com/e/iealumni-miami) on Big Data Maximizing Entrepreneurship Success. Entrepreneurs have plenty of opportunities to leverage data to create business value. Key is to have a vision and focus in the business value; resources required (people, technology) are commodities that are available.
Loras College 2014 Business Analytics Symposium | Aaron Lanzen: Creating Busi...Cartegraph
Cisco Services is providing a behind-the-scenes perspective of its decision management and smart analytics programs. Success for Cisco is more than the technology or any one project. It's a mix of art, philosophy and technology that allows analytics to keep adding value to the business. You will hear how the program has evolved over the last 6 years and will explore different levels of smart analytics. Along the way, you will hear how the team grew a simple idea into a patent-pending resource allocation model.
For more information on the Loras College 2014 Business Analytics Symposium, the Loras College MBA in Business Analytics or the Loras College Business Analytics Certificate visit www.loras.edu/mba or www.loras.edu/bigdata.
we provide Visual BI insights and perspectives in the area of data governance and special considerations to take into account when approaching cloud-based solutions.
In this webinar recording, we will be introducing the core concepts of Data Science and the resources in Azure to deliver a complete Data Science solution. We will also walk through a demonstration on how best to use Azure Databricks as a data scientist to process enterprise data and build a machine learning model to deploy.
Digital Supply Chains & the Internet of ThingsKevin Ross
Kevin Ross, Senior Director of Supply Chain Transformation and New Business Model Enablement at Cisco will share their journey to a Digitized supply chain. For Cisco, this shift involves making big bets on five emerging technologies to digitize their supply chain: IoT or the Internet of Things, Big Data and Analytics, Cloud Based Applications, Mobility and Collaboration.
The Agile Analyst: Solving the Data Problem with VirtualizationInside Analysis
The Briefing Room with Radiant Advisors and Cisco
Live Webcast Jan. 21, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=05e9d4ccbd2505ce15bc8de699f9c961
Today’s business analyst needs data from all kinds of places: the data warehouse, data marts, web services as well as local and departmental files and spreadsheets. The fact is, even seasoned analysts typically spend more than half their time hunting and gathering data, which impedes analytical insights and limits time to value. Increasingly, innovative organizations are turning to data virtualization as a faster path to analytics, thus expediting business impact.
Register for this episode of The Briefing Room to hear Analysts Lindy Ryan and John O'Brien of Radiant Advisors explain how analytical sandboxes and data virtualization can enable true analytic agility. They will be briefed by Marc Breissinger of Cisco Data Virtualization Business Unit, who will tout his company’s upcoming analytic platform Data Collage, a desktop tool for designed for analysts who need agile access to enterprise data. He will discuss how Data Collage allows users to easily combine data and accelerate the development of new analytics.
Visit InsideAnalysis.com for more information.
Speed, agility and reduced time to market are becoming increasingly important for Technology organizations. As more business moves online, existing business models and industries are disrupted and new ones are enabled. Technology organizations are facing the challenge of how to transition to agile ways of working. Transform the existing team? Build a separate digital team? Or do both?
For more information, contact vicki.shillington@northhighland.com or kim.clarke@northhighland.com.
Building a 360 Degree View of Your Customers on BICSPerficient, Inc.
Why there is a need for Customer 360 and what the proposed cloud based solution is. We cover the stages of strategic marketing and how Oracle BI can help.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Anil Kaul, CEO and Co-Founder, AbsolutData delivered a session on institutionalizing Big Data analytics for organizations, at the Big Data Innovation Summit, London on 1st May, 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.
Visit us here : www.absolutdata.com
The Future of Digital Marketing and Advertising: 2023 PredictionsSG Analytics
The future of the digital marketing world is continuing to transition at a faster pace. The next phase of advertising will offer access to flexible testing.
Visit:
https://www.sganalytics.com/blog/the-future-of-marketing-and-advertising/
Similar to Forging an Analytics Center of Excellence (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”