The ability to continuously innovate is crucial for business growth – and often necessary for survival. Leaders in an uncertain and fast-paced global business regularly seek innovation to revitalise rigid business models and processes. However, they are aware that ‘innovation is hard’ and fraught with uncertainty. I contend that Big Data Analytics – in addition to its many other business benefits – can guide the innovation process to make it more efficient, effective and predictable.
Big Data Analytics promotes the application of a data-driven mindset that ‘listens to the data’ for new insights and disrupts entrenched thinking that hinders innovation. It applies what-if analysis to assess impact of new ideas on key business metrics and uses evidence-based business performance analysis to track the impact of innovation. Integrating Big Data Analytics into the business planning and operational processes provides valuable feedback loops and enables an adaptive innovation process.
In short, Big Data Analytics can spark innovation, guide its refinement and adoption processes and sustain its ongoing implementation.
Our report will provide a look into the technology landscape of the future, including:
- Importance of AI in enabling innovation
- Catalysts of future innovations
- Top technology trends in 2023-2024
- Main benefits of AI adoption
- Steps to prepare for future disruptions.
Download your free copy now and implement the key findings to improve your business.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
Data Driven Advanced Analytics using Denodo Platform on AWSDenodo
Watch full webinar here: https://buff.ly/3JC8gCS
Accelerating cloud adoption and modernizing analytics in the cloud has become a necessity to facilitate timely, insightful, and impactful decision making. However, with the widespread data in an organization across disparate hybrid cloud data sources poses a challenge with real time and well governed analytics. Data Virtualization is a modern data integration technique in which a single semantic layer can be built to help drive data democratization and speed up the analytics in an efficient and cost-effective manner.
Watch this session to learn:
- How various AWS services (Redshift, S3, RDS) can be quickly integrated using Denodo Platform’s logical data management by implementing a logical data fabric (LDF)
- How LDF helps you manage and deliver your data for data science and analytics programs, supporting your business users.
- How governed Data Services layer enables self-service analytics in your complex AWS data landscape
Our report will provide a look into the technology landscape of the future, including:
- Importance of AI in enabling innovation
- Catalysts of future innovations
- Top technology trends in 2023-2024
- Main benefits of AI adoption
- Steps to prepare for future disruptions.
Download your free copy now and implement the key findings to improve your business.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Data Science Training | Data Science For Beginners | Data Science With Python...Simplilearn
This Data Science presentation will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
Learn more at: https://www.simplilearn.com
Data Driven Advanced Analytics using Denodo Platform on AWSDenodo
Watch full webinar here: https://buff.ly/3JC8gCS
Accelerating cloud adoption and modernizing analytics in the cloud has become a necessity to facilitate timely, insightful, and impactful decision making. However, with the widespread data in an organization across disparate hybrid cloud data sources poses a challenge with real time and well governed analytics. Data Virtualization is a modern data integration technique in which a single semantic layer can be built to help drive data democratization and speed up the analytics in an efficient and cost-effective manner.
Watch this session to learn:
- How various AWS services (Redshift, S3, RDS) can be quickly integrated using Denodo Platform’s logical data management by implementing a logical data fabric (LDF)
- How LDF helps you manage and deliver your data for data science and analytics programs, supporting your business users.
- How governed Data Services layer enables self-service analytics in your complex AWS data landscape
COMEX2017 Smart Talks by Amjid Ali , Muscat, Oman. Covering Introduction to big data, Big Data Definitions, Big Data Revolution, Big Data Timeline, Hadoop and Map Reduce covers importance of storage and DNA, Oceanstore 9000, Microsoft R, Spark,
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
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.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Building an Effective & Extensible Data & Analytics Operating ModelCognizant
Building an effective and scalable operating model requires a strong basis in data and analytics management. Creating such an operating model is a step-by-step process, as outlined here.
Some Preliminary Thoughts on Artificial Intelligence - April 20, 2023.pdfKent Bye
Bye, K. (2023, April 20). Some Preliminary Thoughts on Artificial Intelligence. [Presentation] The King Library Experiential Virtual Reality Lab (KLEVR) Tech Talks: AI Tools, Tips, & Traps; San Jose State University, San Jose, California via Zoom.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
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.
Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for ...Edureka!
( ** Hadoop Training: https://www.edureka.co/hadoop ** )
This Edureka tutorial on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this tutorial:
1. Why do we need Big Data Analytics?
2. Big Data Applications in Health Care.
3. Big Data in Real World Clinical Analytics.
4. Big Data Analytics in Education Sector.
5. IBM Case Study in Education Section.
6. Big data applications and use cases in E-Commerce.
7. How Government uses Big Data analytics?
8. How Big data is helpful in E-Government Portal?
9. Big Data in IOT.
10. Smart city concept.
11. Big Data analytics in Media and Entertainment
12. Netflix example in Big data
13. Future Scope of Big data.
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships 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.
A Workflow is an automated series of actions that produces a specified outcome. At certain points, it requires actions from users in the form of tasks.
Workflows help people collaborate on assets, automate processes, ...
A workflow is useful in case of:
- Asset approval
- Asset intake
- Issue management
- Escalation by default
- User on-boarding
The design of a workflow starts with defining a process definition. Collibra Data Governance Center uses the Activiti Workflow engine to manage its process definitions.
In this first lesson, I’ll show you how you have to set up the Activiti Workbench.
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
BIG Data & Hadoop Applications in HealthcareSkillspeed
Explore the applications of BIG Data & Hadoop in Healthcare via Skillspeed.
BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of providing superior patient care. They are used for optimizing clinical trials, disease detection & boosting healthcare profitability.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
COMEX2017 Smart Talks by Amjid Ali , Muscat, Oman. Covering Introduction to big data, Big Data Definitions, Big Data Revolution, Big Data Timeline, Hadoop and Map Reduce covers importance of storage and DNA, Oceanstore 9000, Microsoft R, Spark,
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
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.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Building an Effective & Extensible Data & Analytics Operating ModelCognizant
Building an effective and scalable operating model requires a strong basis in data and analytics management. Creating such an operating model is a step-by-step process, as outlined here.
Some Preliminary Thoughts on Artificial Intelligence - April 20, 2023.pdfKent Bye
Bye, K. (2023, April 20). Some Preliminary Thoughts on Artificial Intelligence. [Presentation] The King Library Experiential Virtual Reality Lab (KLEVR) Tech Talks: AI Tools, Tips, & Traps; San Jose State University, San Jose, California via Zoom.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
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.
Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for ...Edureka!
( ** Hadoop Training: https://www.edureka.co/hadoop ** )
This Edureka tutorial on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this tutorial:
1. Why do we need Big Data Analytics?
2. Big Data Applications in Health Care.
3. Big Data in Real World Clinical Analytics.
4. Big Data Analytics in Education Sector.
5. IBM Case Study in Education Section.
6. Big data applications and use cases in E-Commerce.
7. How Government uses Big Data analytics?
8. How Big data is helpful in E-Government Portal?
9. Big Data in IOT.
10. Smart city concept.
11. Big Data analytics in Media and Entertainment
12. Netflix example in Big data
13. Future Scope of Big data.
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships 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.
A Workflow is an automated series of actions that produces a specified outcome. At certain points, it requires actions from users in the form of tasks.
Workflows help people collaborate on assets, automate processes, ...
A workflow is useful in case of:
- Asset approval
- Asset intake
- Issue management
- Escalation by default
- User on-boarding
The design of a workflow starts with defining a process definition. Collibra Data Governance Center uses the Activiti Workflow engine to manage its process definitions.
In this first lesson, I’ll show you how you have to set up the Activiti Workbench.
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
BIG Data & Hadoop Applications in HealthcareSkillspeed
Explore the applications of BIG Data & Hadoop in Healthcare via Skillspeed.
BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of providing superior patient care. They are used for optimizing clinical trials, disease detection & boosting healthcare profitability.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The traditional data warehouse has served us well for many years, but new trends are causing it to break in four different ways: data growth, fast query expectations from users, non-relational/unstructured data, and cloud-born data. How can you prevent this from happening? Enter the modern data warehouse, which is able to handle and excel with these new trends. It handles all types of data (Hadoop), provides a way to easily interface with all these types of data (PolyBase), and can handle “big data” and provide fast queries. Is there one appliance that can support this modern data warehouse? Yes! It is the Analytics Platform System (APS) from Microsoft (formally called Parallel Data Warehouse or PDW) , which is a Massively Parallel Processing (MPP) appliance that has been recently updated (v2 AU1). In this session I will dig into the details of the modern data warehouse and APS. I will give an overview of the APS hardware and software architecture, identify what makes APS different, and demonstrate the increased performance. In addition I will discuss how Hadoop, HDInsight, and PolyBase fit into this new modern data warehouse.
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Geoffrey Fox
Keynote at Sixth International Workshop on Cloud Data Management CloudDB 2014 Chicago March 31 2014.
Abstract: We introduce the NIST collection of 51 use cases and describe their scope over industry, government and research areas. We look at their structure from several points of view or facets covering problem architecture, analytics kernels, micro-system usage such as flops/bytes, application class (GIS, expectation maximization) and very importantly data source.
We then propose that in many cases it is wise to combine the well known commodity best practice (often Apache) Big Data Stack (with ~120 software subsystems) with high performance computing technologies.
We describe this and give early results based on clustering running with different paradigms.
We identify key layers where HPC Apache integration is particularly important: File systems, Cluster resource management, File and object data management, Inter process and thread communication, Analytics libraries, Workflow and Monitoring.
See
[1] A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures, Shantenu Jha, Judy Qiu, Andre Luckow, Pradeep Mantha and Geoffrey Fox, accepted in IEEE BigData 2014, available at: http://arxiv.org/abs/1403.1528
[2] High Performance High Functionality Big Data Software Stack, G Fox, J Qiu and S Jha, in Big Data and Extreme-scale Computing (BDEC), 2014. Fukuoka, Japan. http://grids.ucs.indiana.edu/ptliupages/publications/HPCandApacheBigDataFinal.pdf
Cloud Solutions - what do we mean by Solution in the Cloud Era?Ahmed Fattah
The term solution in the context of Information Technology is frequently used ambiguously especially by vendors who apparently use the term to mean simply “what we sell” – sometimes referring to hardware, software or services. Business users on the other hand are, of course, clear on what they mean: it’s a readily useable application (or likely these days to be an app) that support business use case and achieves a desired business outcome. The fact that business users are seldom able to articulate precisely what these use cases or outcomes are may be beside the point. They are the customers after all and "the customer is always right". Vendors sometimes do a good job of understanding, interpreting or even shaping customers’ requirements (think of Steve Jobs who said “A lot of times, people don't know what they want until you show it to them”). In other situations, either out of incompetence, lack of imagination or expediency, some vendors sell the customers the promise of achieving their business outcomes without being able to deliver them. If we give these vendors the benefit of the doubt and assume that they are really trying hard, we can only assume that there is a semantic gap between what the customers want and what the vendors are supplying.
Analysing data analytics use cases to understand big data platformdataeaze systems
Get big picture of data platform architecture by knowing its purpose and problem it solves.
These slides take top down approach, starting with basic purpose of data platform ie. to serve analytics use cases. These slides categorise use cases and analyses their expectation from data platform.
Cognitive computing: Fad or Game Changer - The Skeptics GuideAhmed Fattah
This article tries to sidestep the hype and uncover what is Cognitive Computing from a practitioner’s point of view and how it differs from the previous generation of AI. The focus is not on the theoretical aspects of AI but on the practical perspective required to apply Cognitive Computing on real-life problems.
Analytics is a critical tool that allows business owners to make
fact-based decisions about taxonomies. Taxonomy management involves capturing terms and concepts, analyzing their usefulness, and managing the employment of the concepts and terms within different contexts.
This presentation offers best practices on design and maintenance of taxonomies, as well as discusses the role of the governance plan.
The presentation covers broad areas of design methodology, with sustainable methods for maintaining taxonomies and integrating changes into their systems design processes.
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Going Beyond ‘What Success Looks Like’ – Using Data to Achieve Successful Pro...Jamie Clouting (CSPO)
Delivering value is at the heart of the Business Analyst role, but how easy is it to identify tangible value and prove the success of a project or program?
In agile projects we’ll often define a “definition of done” or ask the question “what does success look like”. At LateRooms.com, we’ve developed a toolkit for our Business Analysts to support the business in using data to define what success looks like, and track it throughout the project lifecycle.
This presentation will look at the ways LateRooms.com collects, analyses and uses data to better define the problem space, setup up KPI driven Critical Success Factors and present Benefits Realisation.
A look at the evolution of analytics and its revolutionary potential to transform ordinary businesses, power new business models, enable innovation, and deliver greater value. http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends.html
Are you getting the most out of your data?SAS Canada
Data is an organizations most valuable asset, but raw data by itself has little value. To drive data’s worth, it must be managed and processed to extract value and information that decision makers can leverage and turn into actionable insights. It is the ways in which a company choses to put that information to use that will determine the true value of its data.
Through business intelligence and business analytic tools, businesses are enabling themselves to make more strategic, accurate decisions, while optimizing business processes. Hear from Info-Tech Research Group and learn what you need to consider when choosing an analytics solution provider. The webinar will highlight Info-Tech Research Group’s recently published vendor landscape for selecting and implementing Business Intelligence and Business Analytics solutions. The report positions SAS as the only leader across all four categories of Enterprise BI, Mid-Market BI, Enterprise BA and Mid-Market BA.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Big data analytics and innovation
1. Big Data Analytics and Innovation
How Big Data Analytics can spark, guide and sustain Innovation
Ahmed Fattah, October 2013
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2. Contents
§ Big Data Analytics: big talk or big promise?
§ What is Big Data Analytics?
§ Why is it hard to innovate?
§ Innovation and Big Data Analytics
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3. Big Data: big talk or big promise?
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4. What is Big Data Analytics?
The ability to capture, move and process enormous volumes of data combined with increased
sophistication and maturity of analytical capabilities enables significant economic and business
value.
Big Data
+
Analytics
Data generated
Growth in structured &
unstructured data
Ability to draw insights from data
Memory & storage cost
Network speeds
Moore’s Law
Data Mining, Machine Learning, Statistical Analysis, Operational Research, Content Analytics, Simulation, Stream Analytics, Map Reduce, …
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5. Characteristics of Big Data Analytics
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•
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Huge data: N è ALL
Correlation before causation
Messy: Errors, anomalies and outliers
New & unstructured data types (not
only transactions but interactions and
observations)
• Predictive -- facilitates decision making
• Near real time
• Built-in performance optimisation
capabilities
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Big Data is All Data in All Data Repositories
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6. Data-driven mindset
•
Data-driven mindset is a data-centric approach that “lets the data speak” which
starts by identifying and collecting data needed to understand a given business
area and ends with evidence-based confirmation of an improvement or a solution.
•
The data-mindset can be outlined in the following activities:
–
–
–
–
–
–
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Identify and collect data;
Diagnose the current situation;
Frame issues based on insights gleaned from the data;
Identify possible solutions based on relationships between data objects;
Forecast impact of candidate solutions on key business metrics; and
Track business performance and contribution of implemented solution.
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7. Correlation before causation
•
Data-driven mindset uses correlation because it is good enough for many practical purposes, for
example, in product recommendations.
•
Correlation fills a very important gap between implicit gut-feel models and elaborate causation
models that may take excessive time and effort to build.
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8. Why is it hard to innovate?
Barriers:
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Power of the established model
Following the experts
Inability to deal with incoherence
Uncertainty
No champions
Key questions:
– How can we come up with new novel ideas?
– How can we test new ideas for validity and impact and get them adopted?
– How can we track new ideas during and after implementation?
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9. Big Data Analytics and Innovation
BDA can spark, guide and sustain Innovation and thus improve its efficiency,
effectiveness and predictability.
• Spark
– Disrupt current models by ‘listening to the data’. In other words, it identify issues and triggers the
generation of new ideas
• Guide
– Allow modelling of what-if scenarios to understand the impact of new ideas thus allowing their
continuous evaluation and so reduce risk inherent in innovation and convince sceptics via irrefutable
evidence-based logic of the value of adopting innovative ideas
• Sustain
– Facilitate tracking KPIs verify impact of applying new ideas, hopefully encouraging more innovation
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10. Summary and call to action
•
The presentation argues that Big Data Analytics (BDA) can help overcome barriers
to innovation in three ways:
• Sparking innovation by promoting a data-driven mindset that listens to the data for new insights;
• Guiding innovation using data-driven hypothesis testing, what-if analysis and crowdsourcing; and
• Sustaining innovation by using ongoing evidence-based business performance management.
•
BDA’s contribution to innovation is not just a bonus but an integral part of the
essence of the new data-driven era.
•
Call to action
– Apply the BDA-inspired data-driven mindset to every problem at hand to see how data can shed new
light on the problem, verify the solution and track its implementation.
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11. Supporting Slides
For more information:
www.ibm.com/software/au/data/bigdata/
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13. IBM Big Data Platform
New analytic applications drive the
requirements for a big data platform.
§ Integrate and manage the full variety,
velocity and volume of data
§ Apply advanced analytics to
information in its native form
§ Visualise all available data for ad-hoc
analysis
§ Development environment for building
new analytic applications
§ Workload optimisation and scheduling
§ Security and Governance
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14. Abstract (and link to paper)
The ability to continuously innovate is crucial for business growth – and often necessary for
survival. Leaders in an uncertain and fast-paced global business regularly seek innovation to
revitalise rigid business models and processes. However, they are aware that ‘innovation is hard’
and fraught with uncertainty. I contend that Big Data Analytics – in addition to its many other
business benefits – can guide the innovation process to make it more efficient, effective and
predictable.
Big Data Analytics promotes the application of a data-driven mindset that ‘listens to the data’ for
new insights and disrupts entrenched thinking that hinders innovation. It applies what-if analysis
to assess impact of new ideas on key business metrics and uses evidence-based business
performance analysis to track the impact of innovation. Integrating Big Data Analytics into the
business planning and operational processes provides valuable feedback loops and enables an
adaptive innovation process.
In short, Big Data Analytics can spark innovation, guide its refinement and adoption processes
and sustain its ongoing implementation.
See full paper on: Big Data Analytics and Innovation paper
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