"Creating green field opportunities for customer analytics through on-line experience" - a presentation by The crazy Colombian at IAPA's "Customer Analytics 2009" conference
In this talk, I present some new ideas on how the analytical field is being transformed by the development and maturity of social media and the Internet.
This document discusses the SMAC stack, which refers to an ecosystem of four converging technologies - Social, Mobile, Analytics, and Cloud - that can enhance IT architecture and enable digital transformation.
It provides details on each component: social for networking and marketing, mobile for accessibility, analytics for analyzing large datasets, and cloud for data storage and sharing resources. When implemented together, the SMAC stack provides a holistic solution.
Trends show customers now rely on online information, reviews, and easy mobile access. Companies use social media, mobile apps, big data analysis, and cloud services. The author's main interest is analytics due to growing data volumes and the potential to help companies manage this data.
A statistical approach to big data, Gustav Haraldsen and Arild Langseth, Stat...Tilastokeskus
This document outlines Statistics Norway's statistical approach to utilizing Big Data sources. It discusses using stable data sources that have representative and valid coverage of main social and economic processes. It also addresses challenges in interpreting new data sources and fitting them into traditional data matrices. The document suggests establishing a Center of Data Expertise to combine new data sources with traditional ones and produce innovative statistics through statistical projects and activities.
This document discusses the use of data science in modern banking. It provides an overview of Raiffeisen Bank, which uses data science for applications like customer profiling, churn prediction, and fraud prevention. It then describes a datathon use case to build predictive models for new customers using external open data to supplement limited internal customer information. Finally, it outlines the daily work and benefits of being a data scientist at Raiffeisen Bank.
Victoria L. Lemieux presented on using mixed-initiative social media analytics and visualization to innovate regulatory practices. The methodology uses sentiment analysis and visual analytics tools to explore historical Twitter data and measure public trust in regulatory processes. It aims to provide regulatory agencies support in notice-and-comment rulemaking by analyzing social media sentiment and discussion. The presentation highlighted the mixed-initiative social media analytics methodology and visualization tool developed, potential applications in regulatory impact assessment and rulemaking, and lessons learned from the project.
This document discusses trends in IT spending and the future of work. It notes that IT spending is expected to reach $5 trillion by 2020, driven by social, mobile, analytics and cloud technologies. This "SMAC" model allows functions to maximize their impact through integration. Mobility in particular will be transformative for banks as smartphone penetration increases access to financial services and education. The author is interested in mobility services and addressing multiple device formats to accommodate increasing diversity.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
In this presentation, Vandana introduces the topic of SMAC and associated trends. She says that she wants to build marketing solutions on social media to improve word of mouth marketing for brands.
This document discusses the SMAC stack, which refers to an ecosystem of four converging technologies - Social, Mobile, Analytics, and Cloud - that can enhance IT architecture and enable digital transformation.
It provides details on each component: social for networking and marketing, mobile for accessibility, analytics for analyzing large datasets, and cloud for data storage and sharing resources. When implemented together, the SMAC stack provides a holistic solution.
Trends show customers now rely on online information, reviews, and easy mobile access. Companies use social media, mobile apps, big data analysis, and cloud services. The author's main interest is analytics due to growing data volumes and the potential to help companies manage this data.
A statistical approach to big data, Gustav Haraldsen and Arild Langseth, Stat...Tilastokeskus
This document outlines Statistics Norway's statistical approach to utilizing Big Data sources. It discusses using stable data sources that have representative and valid coverage of main social and economic processes. It also addresses challenges in interpreting new data sources and fitting them into traditional data matrices. The document suggests establishing a Center of Data Expertise to combine new data sources with traditional ones and produce innovative statistics through statistical projects and activities.
This document discusses the use of data science in modern banking. It provides an overview of Raiffeisen Bank, which uses data science for applications like customer profiling, churn prediction, and fraud prevention. It then describes a datathon use case to build predictive models for new customers using external open data to supplement limited internal customer information. Finally, it outlines the daily work and benefits of being a data scientist at Raiffeisen Bank.
Victoria L. Lemieux presented on using mixed-initiative social media analytics and visualization to innovate regulatory practices. The methodology uses sentiment analysis and visual analytics tools to explore historical Twitter data and measure public trust in regulatory processes. It aims to provide regulatory agencies support in notice-and-comment rulemaking by analyzing social media sentiment and discussion. The presentation highlighted the mixed-initiative social media analytics methodology and visualization tool developed, potential applications in regulatory impact assessment and rulemaking, and lessons learned from the project.
This document discusses trends in IT spending and the future of work. It notes that IT spending is expected to reach $5 trillion by 2020, driven by social, mobile, analytics and cloud technologies. This "SMAC" model allows functions to maximize their impact through integration. Mobility in particular will be transformative for banks as smartphone penetration increases access to financial services and education. The author is interested in mobility services and addressing multiple device formats to accommodate increasing diversity.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
In this presentation, Vandana introduces the topic of SMAC and associated trends. She says that she wants to build marketing solutions on social media to improve word of mouth marketing for brands.
This document summarizes a project that used R and advanced analytics to predict when ATM machines would become critically low in performance, saving the company 20 million euros. It describes the end-to-end process used, including data architecture, engineering, analytics, predictive modeling, visualization, and storytelling. Key aspects involved cleaning and validating data from multiple sources, developing predictive logic from past projects, creating metrics and visuals, and negotiating solutions with stakeholders.
Change is the only constant, and who knows it better than us.
Since internet and mobile is changing every industry so I decided to have a look at the impact of these two game-changing factors on MR and summed up my thoughts in the attached presentation.
Here is a summary of what I think will happen in Marketing research:-
1. Traditional MR will be replaced by Social Media Listening and Integrated data solutions
2. Revenues of marketing research will mostly come from emerging economies and hence more understanding of local languages and cultures will be required to interpret data meaningfully
3. Integrated data mining tools that will integrate social media data, sales data/ retail data and with customer profile will be used in predictive models to forecast behaviors and trends.
4. Impact of each data based decision will be assessed through financial value i.e. cost of analysis and revenue generated
5. A Research manager will be required to understand the complete value chain of client and how the data is flowing in the value chain. Plus, he will be required to understand strategic planning, forecasting models, data integration methodologies
Abstract
Learn how artificial intelligence helps CI professionals to get unprecedented understanding of what’s going on in their industry : key trends, new technologies, competitor's moves and more…
This session is both theoretical and practical. This will be illustrated on how Machine Learning brings context when it comes to identifying information about companies.
Key topics
Provide an understanding of the current trends in the information industry, especially concerning MI/CI
Give an overview of the solutions offered by artificial intelligence technologies
Explain how disruptive providers can help, versus the challenge you are likely to face if you try alone
Getting Ready For 3rd Generation Platform
Data Science Thailand Meetup#4
Asst. Prof. Dr. Jirapun Daengdej
Vincent Mary School of Science and Technology
Assumption University
jirapun@scitech.au.edu
Data Product Discovery: The product perspective on digital transformationinovex GmbH
Event: Dataconomy Düsseldorf, 19.10.2017
Speaker: Dr. Christoph Tempich
Weitere Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Tech-Artikel im inovex-Blog: https://www.inovex.de/blog/
Michael Henderson shares stories around the campfire while enthralling an audience. Matt Church also captivates a crowd with his presentation. The event was sponsored by The Crazy Colombian, who aims to teach people how to achieve excellence through innovation.
Analytics for Customer Acquisition - Presentation at Nasscom Product Conclave...Arun Agrawal
Don't jump into Google Analytics without defining your KPIs first. Set your targets and analyse with this guide.
Includes strategies and tactics to solve the low traffic and low web site conversion problems. Apply these ideas to improve your sales and leads by a huge margin at a low cost.
How to use Customer Analytics to get the maximum out of Marketing Automation ...Universem
A detailed presentation on the importance of Customer Analytics to really understand your customers and take actions to improve retention and lifetime value. Presentation done at the Marketing Automation Summit on September 18th 2015 in Brussels by Hubert de Cartier, Partner at Universem.
This document discusses how Google Analytics can help businesses make effective decisions by providing powerful data. It outlines 5 common challenges with data, such as having too much or too little data, or not knowing what to do with it. The document then shows how Google Analytics can answer questions about online traffic sources, user demographics, top pages, engagement, and competition. It provides tips on using data to test ideas, make decisions, and create a data-driven company culture.
Google Analytics is a free web analytics service that provides statistics and analytical tools. It tracks data on user behavior, including sessions, users, pageviews, bounce rate, goals, and conversions. The document defines key Google Analytics terms and provides an overview of the Google Analytics interface and reporting features. It explains how to set up custom reports, dashboards, and segments. It also describes how to set up goals to track user conversions and customize campaigns to attribute traffic sources.
Case Studies - Customer & Marketing Analytics for Retail Gurmit Combo
The document discusses three case studies involving customer intelligence and marketing effectiveness services:
1. A luxury retailer case study where customer segmentation and profiling identified their most valuable customers to focus relationship management efforts.
2. A technology company case study where product association analysis and scoring identified accounts likely to purchase docking stations for targeted cross-selling.
3. A CPG company case study where regression modeling decomposed the impact of price, promotion, competition and cross-category effects on sales volumes, revealing promotion strategy optimizations.
Role of Analytics in Customer Relationship ManagementDexlab Analytics
Major telecommunication companies are now using data analytics to derive strategies to manage and analyze customer interactions. This presentation explains how these companies utilize big data for customer retention. It is high time you join the league of data scientists. Enroll in our data science course and make a successful career in the field of data science.
Borys Pratsiuk is the Head of R&D at an unnamed company. He has over 15 years of experience in engineering roles related to Android development, embedded systems, and solid state electronics. He holds a PhD in Solid State Electronics from Kiev Polytechnic Institute and has worked in both academic and industry roles in South Korea and Ukraine. The presentation discusses big data, analytics, artificial intelligence and machine learning applications across various industries. It provides examples of deep learning solutions developed for clients in areas like computer vision, natural language processing, predictive analytics and process automation. The presentation emphasizes Ciklum's full-service approach to developing and deploying deep learning solutions from data collection and modeling to deployment and ongoing support.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
Womenswear retailer Monsoon Accessorize IT & Ecommerce Director John Bovill explains his hopes for the Project Customer big data project at multichannel consultancy Practicology's 2016 client conference.
This document summarizes a project that used R and advanced analytics to predict when ATM machines would become critically low in performance, saving the company 20 million euros. It describes the end-to-end process used, including data architecture, engineering, analytics, predictive modeling, visualization, and storytelling. Key aspects involved cleaning and validating data from multiple sources, developing predictive logic from past projects, creating metrics and visuals, and negotiating solutions with stakeholders.
Change is the only constant, and who knows it better than us.
Since internet and mobile is changing every industry so I decided to have a look at the impact of these two game-changing factors on MR and summed up my thoughts in the attached presentation.
Here is a summary of what I think will happen in Marketing research:-
1. Traditional MR will be replaced by Social Media Listening and Integrated data solutions
2. Revenues of marketing research will mostly come from emerging economies and hence more understanding of local languages and cultures will be required to interpret data meaningfully
3. Integrated data mining tools that will integrate social media data, sales data/ retail data and with customer profile will be used in predictive models to forecast behaviors and trends.
4. Impact of each data based decision will be assessed through financial value i.e. cost of analysis and revenue generated
5. A Research manager will be required to understand the complete value chain of client and how the data is flowing in the value chain. Plus, he will be required to understand strategic planning, forecasting models, data integration methodologies
Abstract
Learn how artificial intelligence helps CI professionals to get unprecedented understanding of what’s going on in their industry : key trends, new technologies, competitor's moves and more…
This session is both theoretical and practical. This will be illustrated on how Machine Learning brings context when it comes to identifying information about companies.
Key topics
Provide an understanding of the current trends in the information industry, especially concerning MI/CI
Give an overview of the solutions offered by artificial intelligence technologies
Explain how disruptive providers can help, versus the challenge you are likely to face if you try alone
Getting Ready For 3rd Generation Platform
Data Science Thailand Meetup#4
Asst. Prof. Dr. Jirapun Daengdej
Vincent Mary School of Science and Technology
Assumption University
jirapun@scitech.au.edu
Data Product Discovery: The product perspective on digital transformationinovex GmbH
Event: Dataconomy Düsseldorf, 19.10.2017
Speaker: Dr. Christoph Tempich
Weitere Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Tech-Artikel im inovex-Blog: https://www.inovex.de/blog/
Michael Henderson shares stories around the campfire while enthralling an audience. Matt Church also captivates a crowd with his presentation. The event was sponsored by The Crazy Colombian, who aims to teach people how to achieve excellence through innovation.
Analytics for Customer Acquisition - Presentation at Nasscom Product Conclave...Arun Agrawal
Don't jump into Google Analytics without defining your KPIs first. Set your targets and analyse with this guide.
Includes strategies and tactics to solve the low traffic and low web site conversion problems. Apply these ideas to improve your sales and leads by a huge margin at a low cost.
How to use Customer Analytics to get the maximum out of Marketing Automation ...Universem
A detailed presentation on the importance of Customer Analytics to really understand your customers and take actions to improve retention and lifetime value. Presentation done at the Marketing Automation Summit on September 18th 2015 in Brussels by Hubert de Cartier, Partner at Universem.
This document discusses how Google Analytics can help businesses make effective decisions by providing powerful data. It outlines 5 common challenges with data, such as having too much or too little data, or not knowing what to do with it. The document then shows how Google Analytics can answer questions about online traffic sources, user demographics, top pages, engagement, and competition. It provides tips on using data to test ideas, make decisions, and create a data-driven company culture.
Google Analytics is a free web analytics service that provides statistics and analytical tools. It tracks data on user behavior, including sessions, users, pageviews, bounce rate, goals, and conversions. The document defines key Google Analytics terms and provides an overview of the Google Analytics interface and reporting features. It explains how to set up custom reports, dashboards, and segments. It also describes how to set up goals to track user conversions and customize campaigns to attribute traffic sources.
Case Studies - Customer & Marketing Analytics for Retail Gurmit Combo
The document discusses three case studies involving customer intelligence and marketing effectiveness services:
1. A luxury retailer case study where customer segmentation and profiling identified their most valuable customers to focus relationship management efforts.
2. A technology company case study where product association analysis and scoring identified accounts likely to purchase docking stations for targeted cross-selling.
3. A CPG company case study where regression modeling decomposed the impact of price, promotion, competition and cross-category effects on sales volumes, revealing promotion strategy optimizations.
Role of Analytics in Customer Relationship ManagementDexlab Analytics
Major telecommunication companies are now using data analytics to derive strategies to manage and analyze customer interactions. This presentation explains how these companies utilize big data for customer retention. It is high time you join the league of data scientists. Enroll in our data science course and make a successful career in the field of data science.
Role of Analytics in Customer Relationship Management
Similar to "Creating green field opportunities for customer analytics through on-line experience" - a presentation by The crazy Colombian at IAPA's "Customer Analytics 2009" conference
Borys Pratsiuk is the Head of R&D at an unnamed company. He has over 15 years of experience in engineering roles related to Android development, embedded systems, and solid state electronics. He holds a PhD in Solid State Electronics from Kiev Polytechnic Institute and has worked in both academic and industry roles in South Korea and Ukraine. The presentation discusses big data, analytics, artificial intelligence and machine learning applications across various industries. It provides examples of deep learning solutions developed for clients in areas like computer vision, natural language processing, predictive analytics and process automation. The presentation emphasizes Ciklum's full-service approach to developing and deploying deep learning solutions from data collection and modeling to deployment and ongoing support.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
Womenswear retailer Monsoon Accessorize IT & Ecommerce Director John Bovill explains his hopes for the Project Customer big data project at multichannel consultancy Practicology's 2016 client conference.
KEY CHALLENGES FOR MONETIZING BIG DATA POWERED AI: AN OVERVIEWTyrone Systems
YOU’RE NOT THE ONLY ONE FACING THIS PROBLEM
according to recent articles in the Harvard Business Review and McKinsey. But don’t blame big data for that. It’s all your fault
Neo4j GraphTour New York_ State of the State_Amit Chaudhry Neo4jNeo4j
The document outlines an agenda for the Neo4j Graph Tour in New York that included discussions on graph databases, data management trends, case studies, and the future of graphs. It also provided examples of how various organizations like Caterpillar, Comcast, and the German Center for Diabetes Research are using Neo4j graph databases for applications like equipment maintenance, smart home services, and medical genomic research.
The document proposes two ideas to improve search engines: 1) Allow webmasters to block specific websites or keywords from being crawled or indexed on their pages to provide more relevant search results. It also suggests building a search engine that provides results for related links in addition to keywords by analyzing outgoing links and traffic to find similar websites. The document uses an example of a search on Google India for "career objectives" returning an irrelevant US site to illustrate issues with current search engine indexes.
As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This presentation explores the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it outlines three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.
The document discusses big data analytics and related topics. It covers the evolution of technology, an overview of big data analytics including the 5 V's (volume, variety, velocity, value, and veracity). It also discusses research topics in big data, tools and software, literature surveys on various big data studies, identified research gaps, and a proposed activity chart and bibliography. The document provides a comprehensive overview of big data analytics, key concepts, potential research areas, and literature in the field.
This document provides a history of data collection and usage from ancient times to present day. It discusses early data storage methods like tally sticks and the abacus. Important developments highlighted include the Library of Alexandria, the Antikythera Mechanism, John Graunt's statistical analysis, and Herman Hollerith's tabulating machine. The document then covers the development of digital data storage and computing, the concept of business intelligence, and the rise of big data. It discusses challenges of managing marketing data across different systems and proposes using a data management platform to address these challenges.
Leveraging big data to drive marketing innovationAndrew Leone
Summary of the book: "The Big Data-Driven Company." Contains insights into leveraging data to drive marketing innovation. To buy this book: http://amzn.to/1YTdtqY
1. Web Mining – Web mining is an application of data mining for di.docxbraycarissa250
1. Web Mining – Web mining is an application of data mining for discovering data patterns from the web. Web mining is of three categories – content mining, structure mining and usage mining. Content mining detects patterns from data collected by the search engine. Structure mining examines the data which is related to the structure of the website while usage mining examines data from the user’s browser. The data collected through web mining is evaluated and analyzed using techniques like clustering, classification, and association. It is a very good topic for the thesis in data mining.
2. Predictive Analytics – Predictive Analytics is a set of statistical techniques to analyze the current and historical data to predict the future events. The techniques include predictive modeling, machine learning, and data mining. In large organizations, predictive analytics help businesses to identify risks and opportunities in their business. Both structured and unstructured data is analyzed to detect patterns. Predictive Analysis is a lengthy process and consist of seven stages which are project defining, data collection, data analysis, statistics, modeling, deployment, and monitoring. It is an excellent choice for research and thesis.
3. Oracle Data Mining – Oracle Data Mining, also referred as ODM, is a component of Oracle Advanced Analytics Database. It provides powerful data mining algorithms to assist the data analysts to get valuable insights from data to predict the future standards. It helps in predicting the customer behavior which will ultimately help in targeting the best customer and cross-selling. SQL functions are used in the algorithm to mine data tables and views. It is also a good choice for thesis and research in data mining and database.
4. Clustering – Clustering is a process in which data objects are divided into meaningful sub-classes known as clusters. Objects with similar characteristics are aggregated together in a cluster. There are distinct models of clustering such as centralized, distributed. In centroid-based clustering, a vector value is assigned to each cluster. There are various applications of clustering in data mining such as market research, image processing, and data analysis. It is also used in credit card fraud detection.
5. Text mining – Text mining or text data mining is a process to extract high-quality information from the text. It is done through patterns and trends devised using statistical pattern learning. Firstly, the input data is structured. After structuring, patterns are derived from this structured data and finally, the output is evaluated and interpreted. The main applications of text mining include competitive intelligence, E-Discovery, National Security, and social media monitoring. It is a trending topic for the thesis in data mining.
6. Fraud Detection – The number of frauds in daily life is increasing in sectors like banking, finance, and government. Accurate detection of fraud is a challenge. Da.
Chad Richeson gave a presentation on harnessing big data. He discussed how nearly every industry is trying to apply big data concepts to improve opportunities, efficiencies, and minimize risk. Examples of big data applications in different industries were provided. Richeson emphasized that successful big data projects require blending analytics, business, and technical skills. He outlined key steps for moving big data projects from development to implementation, including focusing on business goals and gaining user agreement.
Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.
Key note big data analytics ecosystem strategyIBM Sverige
This document discusses IBM's analytics portfolio and vision. It provides an overview of Big Data trends, how Watson has evolved to be faster and smaller, and the need for real-time analytics. It also discusses IBM's approach to Big Data challenges like volume, velocity, variety and veracity. The document outlines IBM's analytics platform capabilities including accelerators, information integration, governance, and Hadoop solutions. It highlights the evolution of IBM Netezza and DB2 in analytics and how IBM is committed to helping clients succeed with Big Data.
Keynote Address - Leaning Forward: Imagine Supply Chain 2030
Setting the stage for the conference, Lora Cecere will share recent research on the future of the supply chain. and insights on what drives value in supply chains.
Lora Cecere, Founder of Supply Chain Insights
From the 2017 Supply Chain Insights Global Summit
1. Major improvements in accuracy in speech recognition and image recognition opens up a new field in human computer interaction. With computers able to correctly interpret almost all interactions without direct contact with keyboard or mouse, a major data source has opened up for Data Scientists to explore.
2. A system which is 80% accurate may not usable, however, when accuracy crosses 95%, there is a major turnaround in large scale adoption.
3. Self driving cars will lead to major leaps in technologies for object recognition -> not just previously known objects, also to anticipate and correctly handle unexpected objects.
4. In my view, there are four key dimensions of Data science, these are Data, Domain Expertise, Machine learning algorithms and Technology of Deployment. Value creation is possible across all the dimensions of Data Science. Better quality data, higher volume of relevant and contextual data can create value, and domain expertise remains critical in making successful deployments of data science projects. Our focus on machine learning algorithms is important, however, value creation happens across all the four dimensions.
5. We have seen a 5X increase in jobs which require machine learning and neural networks expertise.
Data Science is now mainstream and it is important for every organization to invest in Data Science and benefit from it.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Similar to "Creating green field opportunities for customer analytics through on-line experience" - a presentation by The crazy Colombian at IAPA's "Customer Analytics 2009" conference (20)
Tired of chasing down expiring contracts and drowning in paperwork? Mastering contract management can significantly enhance your business efficiency and productivity. This guide unveils expert secrets to streamline your contract management process. Learn how to save time, minimize risk, and achieve effortless contract management.
Efficient PHP Development Solutions for Dynamic Web ApplicationsHarwinder Singh
Unlock the full potential of your web projects with our expert PHP development solutions. From robust backend systems to dynamic front-end interfaces, we deliver scalable, secure, and high-performance applications tailored to your needs. Trust our skilled team to transform your ideas into reality with custom PHP programming, ensuring seamless functionality and a superior user experience.
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Presentation by Herman Kienhuis (Curiosity VC) on Investing in AI for ABS Alu...Herman Kienhuis
Presentation by Herman Kienhuis (Curiosity VC) on developments in AI, the venture capital investment landscape and Curiosity VC's approach to investing, at the alumni event of Amsterdam Business School (University of Amsterdam) on June 13, 2024 in Amsterdam.
Discover the Beauty and Functionality of The Expert Remodeling Serviceobriengroupinc04
Unlock your kitchen's true potential with expert remodeling services from O'Brien Group Inc. Transform your space into a functional, modern, and luxurious haven with their experienced professionals. From layout reconfiguration to high-end upgrades, they deliver stunning results tailored to your style and needs. Visit obriengroupinc.com to elevate your kitchen's beauty and functionality today.
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Adani Group's Active Interest In Increasing Its Presence in the Cement Manufa...Adani case
Time and again, the business group has taken up new business ventures, each of which has allowed it to expand its horizons further and reach new heights. Even amidst the Adani CBI Investigation, the firm has always focused on improving its cement business.
AI Transformation Playbook: Thinking AI-First for Your BusinessArijit Dutta
I dive into how businesses can stay competitive by integrating AI into their core processes. From identifying the right approach to building collaborative teams and recognizing common pitfalls, this guide has got you covered. AI transformation is a journey, and this playbook is here to help you navigate it successfully.
Enhancing Adoption of AI in Agri-food: IntroductionCor Verdouw
Introduction to the Panel on: Pathways and Challenges: AI-Driven Technology in Agri-Food, AI4Food, University of Guelph
“Enhancing Adoption of AI in Agri-food: a Path Forward”, 18 June 2024
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"Creating green field opportunities for customer analytics through on-line experience" - a presentation by The crazy Colombian at IAPA's "Customer Analytics 2009" conference
9. The five stages of a customer analytics project 1. CONCEPTUAL 2. PROBLEM STRUCTURING 3. DATA PREPARATION & EXPLORATION 5. RECOMMENDATIONS & DEPLOYMENT 4. ANALYSIS & MODELING