E strategies process
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This document provides an overview of an e-strategies course. It discusses the following key points: - The objective of the course is to explore the link between business strategy and information technology. - Students will analyze the foundations of business information systems and apply these analyses to explore the strategic impact of IT today. - The course will cover topics like enterprise resource planning, supply chain management, customer relationship management, and social media from different strategic perspectives.
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Role of the Content Strategist
A content strategist brings an understanding of targeted audiences and relevant information. They identify gaps between a website's current and ideal future states based on structure, access, and relevancy. Using a repeatable methodology of discovery, analysis, design, development, testing and maintenance, a content strategist works with others to create strategic, audience-focused digital content.
Training in Analytics and Data Science
This document provides an introduction and overview of a summer school course on business analytics and data science. It begins by introducing the instructor and their qualifications. It then outlines the course schedule and topics to be covered, including introductions to data science, analytics, modeling, Google Analytics, and more. Expectations and support resources are also mentioned. Key concepts from various topics are then defined at a high level, such as the data-information-knowledge hierarchy, data mining, CRISP-DM, machine learning techniques like decision trees and association analysis, and types of models like regression and clustering.
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This document discusses how The Data People uses data analytics to help businesses better understand their customers. They identify a company's most valuable customers through analyzing data on profitability, attitudes, demographics and more. The Data People then builds customer profiles and segments to tailor marketing strategies. Case studies show how they helped clients like Alliance & Leicester and Holmes Place increase response rates, reduce costs and lower customer churn through data-driven insights.
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This document discusses how The Data People uses data analytics to help businesses better understand their customers. They identify a company's most valuable customers through analyzing data on profitability, demographics, lifestyle, and usage. The Data People then builds customer profiles and segments customers to tailor marketing strategies. Case studies show how they helped Alliance & Leicester increase website visits by over 100% and Holmes Place reduce customer acquisition costs and increase retention rates through predictive modeling.
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Trust by Design
Support for the keynote “Trust by Design, Keynote, the Futura Group, Nov 8-9,2018 Post GDPR/FODP summit, Berlin
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Support for the keynote “Augmenter l’humain, » CDEFI Directors’ Conference, Keynote, June 6-7, 2019, Toulouse
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Support for the keynote "Data, Ethics and Health Care,”, Keynote, Creating Value in Health Care through Innovation Management, May 16,2019, Deusto, San Sebastien
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Technology and Innovation - Introduction
This document provides an introduction to a training module on digital technologies and innovation. It discusses artificial intelligence (AI) and its role in innovation. The training will cover topics like the building blocks of AI, digital economics, the internet of value, decision making, data ethics, and how AI can be used as a lever for innovation. It outlines the course administration, including grading based on a final exam and an innovation project. The introduction discusses using data to help management make better decisions and defines the key elements involved in applying analytics, including defining the problem, making predictions, evaluating outcomes, and taking action. It also outlines the machine intelligence continuum from rule-based automation to developing human-like intelligence.
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This document discusses ethics in digital technologies and business analytics. It addresses issues such as personal privacy, biases in automated decision-making, transparency in data collection and use, and the relationship between truth, trust and new technologies like blockchain. Key topics examined include personal data rights, accountability for data loss or misuse, limiting data extrapolation and manipulation, and understanding the context and limits of data science methods.
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This document discusses digital technologies and innovation in decision-making. It covers several topics:
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Technologies and Innovation – The Internet of Value
The document discusses digital technologies and innovation, including the building blocks of innovation, digital economics, and the internet of value. It covers topics like the data revolution, time and space organization, analytical methods, and decision making with data ethics. The agenda includes an introduction and sessions on these various topics related to digital innovation.
Technologies and Innovation – Innovation
This document provides an agenda and overview for a session on digital technologies and innovation. The agenda includes introductions on the building blocks, sources, and practice of innovation. It discusses Peter Drucker's views on where innovative ideas come from by analyzing opportunities within companies, industries, and the social environment. It also summarizes theories on innovation from Joseph Schumpeter, who distinguished invention, innovation and imitation, and from Birkinshaw, who categorized types of innovation. The document outlines frameworks for innovation from Bessant and Tidd, Tidd et al., and Rothwell's five models of the innovation process. It discusses open innovation models like InnoCentive and examples from Spotify and Giffgaff. It concludes with perspectives
Technologies and Innovation - Introduction
This document provides an introduction and agenda for a course on digital technologies and innovation. The 3-sentence summary is:
The course aims to help students understand innovation in various industry settings through 6 sessions covering topics like artificial intelligence, digital economics, decision making, and data ethics. The objective is for students to analyze case studies, qualify available data, apply appropriate analytical methodologies, and integrate visual communications to transform data into calls for collective action. Students' grades will be based 50% on participation in an innovation project presentation and 50% on a final multiple choice exam.
Group 5 - Narayana Health
Narayana Health is an Indian healthcare organization that operates hospitals and cardiac care centers. It uses a low-cost business model focusing on economies of scale. Narayana Health leverages data and analytics to reduce costs, improve processes, and enhance patient care. It utilizes tools like PowerBI and Azure to conduct real-time analysis, predictive modeling, and AI-driven initiatives to optimize resource usage, surgery costs, and length of hospital stays. The organization's strategic use of data science supports its mission to deliver affordable, high-quality healthcare.
Group 4 - DHL
DHL uses data science and AI throughout its operations to improve efficiency, customer experience, and decision making. It analyzes data such as origin/destination, size/weight of shipments, vehicle locations, and customer interactions to predict delays, demand, and risks. Machine learning helps optimize routing, staffing, and warehouse management. AI tools identify damaged packages and monitor suppliers for risks. This informs decisions that lead to reliable delivery, increased profits and customer satisfaction.
Group 3 - BBVA
BBVA underwent a transformation process focused on digitalization and improving the customer experience. It developed new data analytics capabilities including algorithms and machine learning models to gain insights from customer data and optimize processes like trading. This allows BBVA to better predict trends, evaluate outcomes, and take actionable steps to create opportunities for customers.
Group 2 - Byju's
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Group 1 LinkedIn
LinkedIn uses Hadoop and other big data tools to analyze data from its over 400 million user profiles. It focuses on data science techniques like recommendation engines to build products that suggest connections, jobs, and skills comparisons to users. LinkedIn embeds data scientists with product teams and uses an AI Academy to equip employees to optimize AI experiences for members. It analyzes user data like profiles, activities, likes and searches to build a comprehensive understanding of professionals and their networks.
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This document provides an overview of a business analytics course. It includes the following key points:
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Analytics in Action - Storytelling
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Smart cities use data and technology to improve operations and services. Data is collected from sensors in infrastructure, assets and citizens. This data is analyzed to manage resources efficiently and provide information to citizens. Some benefits of smart cities include better maintenance of infrastructure, improved traffic management, and increased safety. However, smart cities also face risks like privacy and security issues. Data scientists play a key role in smart cities by ensuring open access to data and using it to support decision making. Future trends may include different cities focusing on customized data and solutions based on their unique needs.
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E strategies process
- 2. ©2016 L. SCHLENKER Agenda Introduction Administrative Details Business Basics Storytelling Mind Mapping Introduction
- 3. 3 The objective of this course explore the link between strategy and information technology ©2016 LHST sarl Administration Analyze the foundations of business information systems Apply these analyiss to explore the strategic impact of information technology today Develop the associated concepts and applications in the context of each student’s work Transpose these concepts and applications in a small group project In this module , you will
- 4. To help us understand the motivations, experience and objectives of the internal and external clients of the organization ROI Real time data ... Stockholders Competition “made in” “made by” ... The State Lower entry barriers Acquisitions, OPA... Partners Loyalty Real costs ... Clients The Enterprise Mobility Empowerment ... Employees Introduction
- 5. Focus Improve Knowledge Leverage Mesure Organization Processes Explicit Transactions Efficiency
- 6. Introduction
- 8. The financial perspective (enterprise resource planning) The logistics perspective (supply chain management) The client perspective(client relationship management) The community perspective(social media) ERP SCM CRM History MRP Total Quality Management Client contact systems Measure Quantitati ve Static Quantitative Dynamic Qualitative Perimeter s Inside a firm Between firms Outside-in ©2016 L. SCHLENKER
- 10. Gartner, 2010
- 11. AppsRunTheWorld ©2016 L. SCHLENKER Customer Relationship Management
- 14. • A unique data model • Data dictionnary • Single data base
- 15. ©2016 L. SCHLENKER Three-tier architectures
- 17. Focus Improve Knowledge Leverage Mesure Organization Processes Explicit Transactions Efficiency Services Delivery Implicit Interactions Effectiveness
Editor's Notes
- Is David taking decisions? How does the oven know what do to? How does data change David’s world? How we use Data to make better decisions?