Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
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. 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.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
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. 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.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
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.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Data Analyst vs Data Engineer vs Data Scientist | Data Analytics Masters Prog...Edureka!
** Data Analytics Masters' Program: https://www.edureka.co/masters-program/data-analyst-certification **
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Analyst vs Data Engineer vs Data Scientist" will help you understand the various similarities and differences between them. Also, you will get a complete roadmap along with the skills required to get into a data-related career. Below topics are covered in this tutorial:
Who is data analyst, data engineer and data scientist?
Roadmap
Required skill-sets
Roles and Responsibilities
Salary Perspective
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
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.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Data Analyst vs Data Engineer vs Data Scientist | Data Analytics Masters Prog...Edureka!
** Data Analytics Masters' Program: https://www.edureka.co/masters-program/data-analyst-certification **
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Analyst vs Data Engineer vs Data Scientist" will help you understand the various similarities and differences between them. Also, you will get a complete roadmap along with the skills required to get into a data-related career. Below topics are covered in this tutorial:
Who is data analyst, data engineer and data scientist?
Roadmap
Required skill-sets
Roles and Responsibilities
Salary Perspective
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
Quels bénéfices d'une intégration du big data dans les CRMSparklane
Retour sur le Forum Big Data organisé par Zebaz à Paris le 4 juin dernier.
Dans un contexte marqué par l’émergence du Big Data et des technologies associées pour le marketing B2B, ce forum ZEBAZ a fait le point sur les nouvelles tendances, les nouvelles méthodes et les nouveaux outils à disposition des professionnels.
Au cours de cette matinée de conférences, cinq experts se sont succédés à la tribune. Compte rendu de l’intervention de Didier Gaultier (Business & Décision), consacrée aux bénéfices de l’intégration du Big Data dans les CRM des entreprises.
1. Quelques notions fondamentales liées à la connaissance client
2. Le positionnement de la Data Science au sein du Big Data
3. Deux cas d’illustration du Big Data en B to B
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
It’s Not About Big Data – It’s About Big Insights - SAP Webinar - 20 Aug 201...Edgar Alejandro Villegas
Presentation slides of:
It’s Not About Big Data – It’s About Big Insights - SAP Webinar - 20 Aug 2013 - PDF
Scott Mackenzie - Sr. Director, Platform & Analytics CoE
Michael Golzc - CIO for SAP Americas
Ken Demma - VP, Insight Driven Marketing
20 Aug 2013 - Webcast - http://goo.gl/T74WAL
How Analytics Has Changed in the Last 10 Years (and How It’s Staye.docxpooleavelina
How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same)
· Thomas H. Davenport
June 22, 2017
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Deutsche Allgemeinversicherung
Photo by Ferdinand Stöhr
Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics.
Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade.
The last decade, of course, was the era of big data. New data sources such as online clickstreams required a variety of new hardware offerings on premise and in the cloud, primarily involving distributed computing — spreading analytical calculations across multiple commodity servers — or specialized data appliances. Such machines often analyze data “in memory,” which can dramatically accelerate times-to-answer. Cloud-based analytics made it possible for organizations to acquire massive amounts of computing power for short periods at low cost. Even small businesses could get in on the act, and big companies began using these tools not just for big data but also for traditional small, structured data.
Insight Center
· Putting Data to Work
Analytics are critical to companies’ performance.
Along with the hardware advances, the need to store and process big data in new ways led to a whole constellation of open source software, such as Hadoop and scripting languages. Hadoop is used to store and do basic processing on big data, and it’s typically more than an order of magnitude cheaper than a data warehouse for similar volumes of data. Today many organizations are employing Hadoop-based data lakes to store different types of data in their original formats until they need to be structured and analyzed.
Since much of big data is relatively unstructured, data scientists created ways to make it structured and ready for statistical analysis, with new (and old) scripting languages like Pig, Hive, and Python. More-specialized open source tools, such as Spark for streaming data and R for statistics, have also gained substantial popularity. The process of acquiring and using open source software is a major change in itself for established busines ...
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Communicating your BRAND today is best done by stories. An effective way for people to understand your uniqueness thru VISUAL Stories. From why stories to using "PAR" as a way to communicate.
In todays workforce their are 5 active generations. How has our past shaped us and how do we interact?. What values do we all share? What is best way to communicate and learn. This presentation tries to address the basics.
Order to Cash. Cash is King. Prime elements, points that block successful ETE flow. KPI's/metics and how to guage where your company really ranks: a Business leader, Average, or Laggard.
Summary of three National webinars. Three V's, market, Functional areas showing most traction, Hot Revenue/ROI areas, Architecture options and using Use cases to overcome objections.,
Shows how RDS supports HANA, new Assemble to order Strategy utilizing RDS, Business Case studies tied to Technology and an evolution path for CRM utilizing RDS, HANA and the Cloud.
Understanding new Rapid Deployment Solutions. 150+ applications taht help solve business problems in weeks not years. Written from a basic user viewpoint.what
So what is SAP HANA? How can it help my area (Line of Business) and our business overall!. Presentation lays out BASICS and how can help users enable their area/business "Real time".
A beginners guide to scrum. Not only software. Defines roles, key meetings and artifacts. 7 certifications available thru Scrum alliance. Make the journey.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
How world-class product teams are winning in the AI era by CEO and Founder, P...
Big Data Analytics
1. JOHN CHOATE – PMMS SIG CHAIR
JAMES HAIGHT - BLUE HILL RESEARCH
RAGHU BANDA - SAP
BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S)
SESSION #1
2. The MARKET ( 2011 – 2017 )
Forecast – Components – 2013 Actual
Why Big Data? (Big 3: B – T – F)
Big Data Sponsorship – “C” Level Action
Big Data Focus Areas
Priority of Need
Infrastructure Priorities
The 4 V’s - Revisited
Top 10 Trends for 2014
PRESENTATION CONTENT - BIG DATA 2014 UPDATE
3. What is Analytics / Business Analytics
Market Projection
The 4 Key Types
Domains of Analytics
Capability Needs
Making Analytics Work – 10 Steps!
Building an Approach
Key Take Away’s
PRESENTATION CONTENT - ANALYTICS
5. • Hadoop software and related hardware and services;
• No SQL database software and related hardware and services;
• Next-generation data warehouses/analytic database software and related hardware and services;
• Non-Hadoop Big Data platforms, software, and related hardware and services;
• In-memory – both DRAM and flash – databases as applied to Big Data workloads;
• Data integration and data quality platforms, tools and services as applied to Big Data deployments;
• Advanced analytics and data science platforms, tools and services;
• Application development platforms, tools and services as applied to Big Data use cases;
• Business intelligence and data visualization platforms, tools and services as applied to Big Data use cases;
• Analytic and transactional applications and services as applied to Big Data use cases;
• Cloud-based Big Data services including infrastructure, platform and software delivers as a service.
• Other Big Data support, training, and professional services.
BIG DATA PRODUCTS & SERVICES
7. BIG DATA 2013 MARKET - ACTUAL
Big Data Adoption Barriers
A lack of best practices for integrating
Big Data analytics into existing business
processes and workflows.
Concerns over security and data privacy
in the wake of numerous high-profile
data breaches and the ongoing NSA
scandal.
Continued “Big Data Washing” by
legacy IT vendors leading to confusion
among enterprise buyers and
practitioners, as well as “political” factors
that make it difficult for enterprise
buyers to engage new vendors.
A still volatile and fast developing
market of competing Big Data vendors
and, though to a lesser degree in 2013,
competing technologies and
frameworks.
A lack of polished Big Data applications
designed to solve specific business
problems.
Big Data Growth Drivers
Both mega-IT-vendors and pure-play Big
Data vendors took steps to better articulate
their product & services roadmaps and
larger visions for Big Data in the enterprise,
creating greater confidence from enterprise
buyers.
The products and services related to Big
Data continued to mature from a features
perspective in 2013, further spurring
adoption. Big Data technologies also took
important steps towards greater enterprise-
grade capabilities in 2013, critical for mass
enterprise adoption. These steps included
better privacy, security and governance
capabilities, as well as improved backup &
recovery and high-availability for Hadoop
specifically.
Partnerships also played an important role
in maturing the Big Data landscape in 2013.
Of particular importance are a number of
reseller agreements and technical
partnerships between Big Data vendors and
non-Big Data vendors, the results of which
that make it easier for practitioners to adopt
and integrate Big Data technologies.
8. Business
Opportunity to enable innovative new business models
Potential for new insights that drive competitive advantage
Technical
Data collected and stored continues to grow exponentially
Data is increasingly everywhere and in many formats
Traditional solutions are failing under new requirements
Financial
Cost of data systems, as a percentage of IT spend, continues to grow
Cost advantages of commodity hardware & open source software
KEY DRIVERS BIG DATA *
* http://hortonworks.com/blog/7-key-drivers-for-the-big-data-market/
10. Customer Centric Outcomes
Operational Optimization
Risk / Financial Management
New Business Models
Employee Collaboration
BIG DATA FOCUS AREAS
11. 1. A Greater Scope of Information
2. New Kinds of Data and Analysis
3. Real Time (HANA) Information
4. Data influx of New Technologies
5. Non-traditional forms of Media
6. Large Volumes of Data (Big Data!)
7. The Latest Buzz words
8. Social Media Data
PRIORITY OF NEED FOR BIG DATA
12. INFRASTRUCTURE PRIORITIES FOR BIG DATA
Information Integration
Scalable Infrastructure
Storage
High Capacity Warehouse
Security and Governance
Scripting and Development Tools
Columnar Databases
Complex Event Processing
Workload Optimization
Analytic Accelerators
Hadoop / Map Reduce
No SQL Engines
Stream Computing
13. THE 4 “V’s” (REVISITED)
VELOCITY
Data in Motion: Streaming data within fractions of a second to make “Real Time” (HANA) Decisions
VOLUME
Data at Scale: Terabytes to Zeta bytes (Big Data)
VARIETY
Data in Many Forms: Structured, Unstructured, Text & Multi Media
VERACITY
Data Uncertainty: Managing the reliability and predictability of imprecise data types.
Gartner Model
14. VOLUME
500+ Million records
Terabytes to Zetabytes
VELOCITY
Data in Motion
Streams
VARIETY
Structured, Semi – structured,
Unstructured
VALUE
Store everywhere
Billions of Records
10’s of TB’s of Data
“REAL TIME”
Text Processing & Search
Sentiment Analysis
High-Value
Low Volumes
of Low Value data
THE 4 “V’s” & In Memory (HANA)
15. Big Data and Analytic Top 10 Trends for 2014
Copyright Oracle - 2013
1. Business Users Get Hooked on Mobile Analytics
2. Analytics' Take to the Cloud
3. Hadoop-Based Data Reservoirs Unite with Data Warehouses
4. New Skills Bolster Big Data Investments
5. Big Data Discovery is the Secret to Workforce Success for HCM
6. Predictive Analytics Lend Fresh Insight into Big Data Strategies
7. Predictive Analytics Bring New Insight to Old Business Processes
8. Decision Optimization Technologies Enhance Human Intuition
9. Business Leaders Embrace Packaged Analytics
10. New Skills Launch New Horizons of Analysis
16. What is Analytics?
WHAT IS BUSINESS ANALYTICS?
Analytics is the discovery and communication of meaningful patterns in data.
Analytics uses data visualization to effectively communicate insight.
Business Analytics (BA) is comprised of solutions used to build analysis models and simulations to
create scenarios, understand realities and predict future states.
Business analytics includes;
Data Mining
Predictive Analytics
Applied Analytics
Statistics
According to market research firm IDC, the business analytics software market grew by 14.1 percent in 2011
and will continue to grow at a 9.8 percent annual rate, to reach
$50.7 billion in 2016, driven by the focus on Big Data.
17. TYPES OF ANALYTICS
“Business Intelligence”, or BI reporting
More the real time (HANA) the better!
Form of dashboard reporting or any other conventional reporting
Simply “analytics”
“Descriptive Analytics”
Gain insight from historical data with reporting, scorecards, clustering etc.
Terms such as profiling, segmentation, or clustering fall under descriptive analytics.
Example:
How many different segments of buyers are we dealing with? Where are they, and what
do they look like?
How do high value customers differ from our other Customers?
18. TYPES OF ANALYTICS
PREDICTIVE : Analyze current and historical facts to make predictions about future, or otherwise unknown,
events.
Need carefully structured statistical models, which will return “scores” that define likelihood of customers
behaving a certain way.
In terms of complexity, this is the most demanding type of analytics
EXAMPLES:
Predict market trends and customer needs (CRM)
Customized offers for each segment & channel (CRM)
Predict how market-volatility will impact business (CRM)
Foresee changes in demand and supply across entire supply chain (SCM)
Proactively manage workforce by attracting and retaining talent (HCM)
Optimization:
Requires a complex type of modeling, where “what if” type of questions are answered.
Type of analytics calls for different types of data in comparison to typical predictive modeling
19. BASIC DOMAINS WITHIN ANALYTICS
Behavioral Analytics
Cohort Analytics
Collections Analytics
Contextual Data modeling
Financial Services Analytics
Fraud Analytics
Marketing (Customer) Analytics
Pricing Analytics
Retail Sales Analytics
Risk and Credit Analytics
Supply Chain Analytics
Talent (Human Resources) Analytics
Telecommunications
Transportation Analytics
DOMAIN
(1) A group of computers and devices on a
network that are administered as a unit with
common rules and procedures. Within the
Internet, domains are defined by the IP address.
All devices sharing a common part of the IP
address are said to be in the same domain.
(2) In database technology, domain refers to the
description of an attribute's allowed values. The
physical description is a set of values the attribute
can have, and the semantic, or logical, description
is the meaning of the attribute.
20. Query and Reporting
Data Mining
Data Visualization
Predictive Modeling
Optimization
Simulation
Natural Language Text
Geospatial Analytics
Streaming Analytics
Video Analytics
Voice Analytics
ANALYTICS CAPABILITY NEEDS
21. 1. Expand where feasible and effective!
2. Integrate across the organization
3. Bring to specific tasks: Strategy/Planning, Finance, Marketing, Sales, IT, Ops/SCM,
Product Development, Customer Service, & HR
4. Use the tools: Spreadsheets, KPI’s/Dash boards, Forecasting, Queries, General Stats,
data/Text Mining, Simulations, Models, Optimization, Web Analytics, & Data visualization
5. Create data strategy that includes “Real Time” access to data.
6. Deploy necessary Technology
7. Develop formal data-management processes
8. Secure Executive Buy In
9. Deliver and Communicate Value
10. Hire and train the right analytic talent
EFFECTIVE STEPS TO MAKE ANALYTICS WORK
22. BUILDING AN ANALYTIC APPROACH / ROADMAP / TEAM
Analytics Structure &
Change Management
Centralized Analytics Structure
Modern IT is a business enabler and
strategic partner
IT can take leadership to framework the
centralized analytics team, since data
and data management is essential to
analytics
Decentralized Analytics Structure
Data architects, analysts distribute cross
the business functions, the more
dynamic CoE (Center of Excellence) is
facilitated to share the progress and
best practices
Analytics Tips
Out-of-the-box analytics (RDS) with a
heavy focus on results
Increased demand by users and
continued data model development
analytics
Make it stick: Integrate the analytics
practitioners into everyday business
rhythms, also commit the measurement
Agile Analytics: A series of user-driven
deliverables, with frequent outputs and
check-in
Analytics KPIs & Maturity
The path to analytic maturity has three
key areas — leadership, breaking down
silos, and developing and keeping talent .
The maturity of the organization is based
on exploring the quality data, asking the
effective question, exploring the end-to-
end business process, building the
practical analytics model, measure the
KPIs.
Analytic Business Cases
Quick Win: Communicate and initiate
the business case base on business
priorities buy-in & support from
shareholders to deliver near-term
results
Strategic Project: Capture the hinder-
sight, insight and foresight, enable the
business to solve problems timely and
approach new market promptly.
Expansion: Cross-functional, multiple
analytic disciplines are required to solve
the wide variety of problems an
organization faces, while enabling the
greatest analytic bandwidth.
Transformation: Organizational change
and analytics capability expand effort
cross-functional track, evaluate and
measure the result, the analytics culture
has been nurtured, the key processes
have been optimized, the organization
has been transformed into agile, high-
performance business.
23. Analytics support business intuition with data decisions
Don’t expect an analytical model to give you “the answer”
Simpler is Better
The simplest approach that solves your problem is usually the best one
There is no correlation between analytic complexity and business value
Really understand the Customer’s Business Problem you’re trying to solve
Apply the 5 Why’s approach
Small steps lead to big wins!
POC as a 1st step!
TAKE AWAYS
24. #1 BIG DATA 2014 UPDATE & ANALTICS BASIC’S
#2 TYPES OF ANALYTICS – July 28
#3 INDUSTRIES / X INDUSTRIES
LINE OF BUSINESS (LOB) – Aug TBD
BUSINESS PERSPECTIVE
TECHNOLOGY PERSPECTIVE
UPCOMING SESSIONS IN ANALYTICS SERIES
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STAY IN TOUCH!
26. JOHN CHOATE – PMMS SIG CHAIR
JAMES HAIGHT - BLUE HILL RESEARCH
RAGHU BANDA - SAP
BIG DATA 2014 UPDATE & BUSINESS ANALYTICS (BASIC‘S)
SESSION #1
Editor's Notes
Better ways of managing your business is one of the key drivers behind the whole Big Data Movement although it’s not the only one.
For example, leveraging Big Data has enabled new innovative business models, for example analyzing social media feeds, or web log data and by analyzing Big Data it can give real competitive advantage.
But there are also technical reasons, for example, the amount of data that is being collected continues to grow exponentially and appearing many different formats and, frankly, conventional database solutions were finding it hard to cope. New technologies for handling the data needed to be found if it was going to be processed.
There are also financial reasons in that as data increased in volumes, lower cost methods of processing needed to be found.