Big Data provides a huge amount of structured and unstructured data that requires critical thinking to interpret and ensure the right insights are drawn. A manager should focus on using all available data science tools and technologies like IBM Watson to interlink problems, save time, and harness big data to predict outcomes and resolve past issues. However, a manager must consider multiple perspectives and possibilities to avoid drawing precisely wrong conclusions from an overreliance on any single metric.
How To Make The Most Out of Enterprise DataSnapShot
CHAT 2016 - China Hotel And Tourism Conference Presentation by Stefan Tweraser. SnapShot Hotel Analytics CEO Stefan Tweraser explains how business leaders can make the most out of the data available to them.
How To Make The Most Out of Enterprise DataSnapShot
CHAT 2016 - China Hotel And Tourism Conference Presentation by Stefan Tweraser. SnapShot Hotel Analytics CEO Stefan Tweraser explains how business leaders can make the most out of the data available to them.
Presentation from PechaKucha part of IT-Jam conference (Odessa, August 2011) about building of small hyper-productive teams and managing them to drive projects to success.
4 Steps to Successful Big Data Product ManagementTravis Turney
This deck was the basis for a talk about big data product management I gave at Big Data Mornings (@BigDataAM) in Atlanta at @Hypepotamus on Wed August 28, 2013.
The Australian economy is going great but for how long?
There's new principles to consider in digital strategy, if you don't think about them now it may be too late.
Grow Your Own - How to Create a Data Culture at Your OrganizationLuciano Pesci, PhD
80% of data projects fail. How can something so promising be failing so badly? While organizations are scrambling to stay competitive by hiring data-talent, they don't fully understand the types available, how to integrate them into existing workflows, what to expect from their efforts, and how to gauge success.
You can watch the accompanying Webinar here: https://youtu.be/MUv-tqMHbvs
DataArt gathered statistics and insights on how retailers are dealing with warehousing, delivery, returns, and many more.
Read full version here 👉 https://bit.ly/3cYHiCh
Big Data for small business transform your business into more efficient and smarter operation. Stay ahead of the game and take advantage of Big data analytics for small business to now how can big data help small businesses.
Die fortgeschrittene Digitalisierung – insbesondere der zunehmende Einsatz von Künstlicher Intelligenz (KI) - wird das Arbeiten in Organisationen tiefgreifend verändern. In diesem Kontext sind auch neuartige Kompetenzen für Führungskräfte gefordert. Artistic Intelligence etwa spielt dabei eine besondere Rolle. In diesem Workshop wird die fortgeschrittene Digitalisierung kurz illustriert. Im Zentrum stehen die Diskussion der Kernkompetenzen von Führungskräften im Zeitalter der KI und die Frage, wie diese entwickelt werden können.
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
Big data is a term that describes the huge amount of data (structured and unstructured) that floods the enterprise every day.
Big Data includes the quantity of data , the speed or speed at which it's created and picked up , and therefore the variety or scope of the info points being covered. It very often comes from several sources and arrives in multiple formats.
From the perspective of a project manager or project manager, big data does not necessarily revolve around the amount of data that individuals and companies deal with. Data can be obtained from any source and analyzed to find the answer for the following purposes:
Reduce the time cut costs
Wise decision
Optimized product
New products development
Your present project management and soft skills are likely ultimate for establishing the framework for a replacement or existing Big Data project team and their projects. you only got to enhance the talents and knowledge you have already got .
This is where Tonex training can help.
Tonex Offers Big Data for Project and Program Managers Training
participants will find out how to profit from big data in their projects and programs
Why does one Need This Training?
Need project managers with big data expertise and business awareness
Must have expert judgment ability to use technology
The plan manager should assist in expanding and coordinating tasks throughout the project
Audience
Project managers
Program managers
Big data analytics
Decision makers of organizations
Strategic leaders
Executives
Training Objectives
Describe the big data analytics
Explain the business values of massive data
Talk about the opportunities and challenges of using big data
Choose if big data analytics serve their client’s interest, situation and knowledge
Manage data analytic projects
Assess risks related to the large data
Distinguish between a knowledge analytic project and a fishing expedition
Decide the best approach
Conclude the time to stop the analysis
Talk about how project management can be used to sustain your data analytics capability
Elaborate how big data can be used to secure the progress of the project
Identify what analytics should be implemented
Course Outline:
Overview to Big Data and Project/Program Management
Project Management Process
Where Does Big Data Analytics expertise is Required?
Introduction to Big Data Management
Big Data Challenges
The Status of Big Data Management
Data Science Methods
Technical Practices for Big Data Management
Analytic Exercises and Big Data Management
Applicable Programming Languages
Corporation Practices for Big Data Management
Top Priorities of Big Data Management
Choosing the Best Strategy
Organizational Leadership
Tonex Hands-On Sample Workshop
Learn More:
https://www.tonex.com/training-courses/big-data-project-program-managers-training/
Presentation from PechaKucha part of IT-Jam conference (Odessa, August 2011) about building of small hyper-productive teams and managing them to drive projects to success.
4 Steps to Successful Big Data Product ManagementTravis Turney
This deck was the basis for a talk about big data product management I gave at Big Data Mornings (@BigDataAM) in Atlanta at @Hypepotamus on Wed August 28, 2013.
The Australian economy is going great but for how long?
There's new principles to consider in digital strategy, if you don't think about them now it may be too late.
Grow Your Own - How to Create a Data Culture at Your OrganizationLuciano Pesci, PhD
80% of data projects fail. How can something so promising be failing so badly? While organizations are scrambling to stay competitive by hiring data-talent, they don't fully understand the types available, how to integrate them into existing workflows, what to expect from their efforts, and how to gauge success.
You can watch the accompanying Webinar here: https://youtu.be/MUv-tqMHbvs
DataArt gathered statistics and insights on how retailers are dealing with warehousing, delivery, returns, and many more.
Read full version here 👉 https://bit.ly/3cYHiCh
Big Data for small business transform your business into more efficient and smarter operation. Stay ahead of the game and take advantage of Big data analytics for small business to now how can big data help small businesses.
Die fortgeschrittene Digitalisierung – insbesondere der zunehmende Einsatz von Künstlicher Intelligenz (KI) - wird das Arbeiten in Organisationen tiefgreifend verändern. In diesem Kontext sind auch neuartige Kompetenzen für Führungskräfte gefordert. Artistic Intelligence etwa spielt dabei eine besondere Rolle. In diesem Workshop wird die fortgeschrittene Digitalisierung kurz illustriert. Im Zentrum stehen die Diskussion der Kernkompetenzen von Führungskräften im Zeitalter der KI und die Frage, wie diese entwickelt werden können.
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
Big data is a term that describes the huge amount of data (structured and unstructured) that floods the enterprise every day.
Big Data includes the quantity of data , the speed or speed at which it's created and picked up , and therefore the variety or scope of the info points being covered. It very often comes from several sources and arrives in multiple formats.
From the perspective of a project manager or project manager, big data does not necessarily revolve around the amount of data that individuals and companies deal with. Data can be obtained from any source and analyzed to find the answer for the following purposes:
Reduce the time cut costs
Wise decision
Optimized product
New products development
Your present project management and soft skills are likely ultimate for establishing the framework for a replacement or existing Big Data project team and their projects. you only got to enhance the talents and knowledge you have already got .
This is where Tonex training can help.
Tonex Offers Big Data for Project and Program Managers Training
participants will find out how to profit from big data in their projects and programs
Why does one Need This Training?
Need project managers with big data expertise and business awareness
Must have expert judgment ability to use technology
The plan manager should assist in expanding and coordinating tasks throughout the project
Audience
Project managers
Program managers
Big data analytics
Decision makers of organizations
Strategic leaders
Executives
Training Objectives
Describe the big data analytics
Explain the business values of massive data
Talk about the opportunities and challenges of using big data
Choose if big data analytics serve their client’s interest, situation and knowledge
Manage data analytic projects
Assess risks related to the large data
Distinguish between a knowledge analytic project and a fishing expedition
Decide the best approach
Conclude the time to stop the analysis
Talk about how project management can be used to sustain your data analytics capability
Elaborate how big data can be used to secure the progress of the project
Identify what analytics should be implemented
Course Outline:
Overview to Big Data and Project/Program Management
Project Management Process
Where Does Big Data Analytics expertise is Required?
Introduction to Big Data Management
Big Data Challenges
The Status of Big Data Management
Data Science Methods
Technical Practices for Big Data Management
Analytic Exercises and Big Data Management
Applicable Programming Languages
Corporation Practices for Big Data Management
Top Priorities of Big Data Management
Choosing the Best Strategy
Organizational Leadership
Tonex Hands-On Sample Workshop
Learn More:
https://www.tonex.com/training-courses/big-data-project-program-managers-training/
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Big data is everywhere , although sometimes we may not immediately realize it . First thing to be believed is that most of us don't deal with large amount of data in our life except in unusual circumstance. Lacking this immediate experience, we often fail to understand both opportunities as well challenges presented by big data. There are currently a number of issues and challenges in addressing these characteristics going forward.
Presentation on using workflow to implement a highly used ECM system.
Provides a step-by-step outline how to understand user needs through marketing techniques such as user journeys and persona building.
Introduces the concept that ECM is an organically growing system rather than an architected software solution.
Challenges are consistent in Big Data environments; resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. Big Data has grown so large that traditional data analysis and management solutions are too slow, too small and too expensive to handle it. Many companies are in the discovery stage of evaluating the best means of extracting value from it. This Enterprise Tech Journal interview with Kevin Goulet, VP Product Management, CA Technologies, explores the challenges of Big Data, the approach to resolving them. With Big Data environments, the challenges are consistent – resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. For more information visit http://www.ca.com/us/products/detail/business-intelligence-and-big-data-management.aspx?mrm=425887
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Copy of presentation delivered at the CHASS 2015 National Forum in Melbourne (October 2015), The Council for Humanities, Arts and Social Sciences in Australia is the peak body supporting more than 75 member organisations in their relationships with Federal and State Government policy makers, Academia and the broader community within Australia.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
10. Where and how can a
manager use all these
information?
• For maintaining a very good culture
at office a manager should focus on
the bigger picture during a conflict
• He has to consider the possibility
that facts can mislead most of the
time.
• Hire people who are graduates of
highly computational studies that
involve mathematics in it.
• Saving time using Data Science.
• He should interlink most of the
problems
11. • Interlinking problems would save a lot
of time
• Big data can be harnessed with the
help of some technology
For instance:
• Technologies such as IBM Watson
(Artificial Intelligence) would save a lot
of time.
• Be it a doctor, engineer, marketer,
lawyer. AI can predict the possible
outcomes giving us the Archimedes
lever for making a decision.
12. • Structured big data can be used for
analysis that would help us to predict
the future or to resolve the past
constraints.
• A good manager should ignore the
occurrence of an issue in the first place.
• Sales forecasting, Productivity Metrics
Reviewing, Effort estimation, financial
Cost models constitute huge amount of
data.
• A manager should utilize all the
technologies available till date for
ensuring the quality of the deliverable.
13. • Organization Culture is
extremely important for
making it to thrive for a long
time.
• Professionals achieve their
target easily with some
assistance from a leader.
People don’t actually quit
companies. People quit their
managers.
14. • Manager has to consider the possibility
of chaos.
• Only then it can be avoided or mitigated.