Introduction to Analytics - Data
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This document provides an introduction to analytics. It discusses the objectives of the course which are to analyze contexts, qualify available data, apply appropriate methodologies, and integrate visual communications. It also provides an agenda that covers data types, structures, big data, and the Fourth Industrial Revolution. The document directs readers to other resources on data science, decision making, and using data in management.
Introduction to Analytics - Data
- 2. The Digital Photography School (2016) ©2016 L. SCHLENKER • A picture is worth a thousand words • Elucidate your customer’s challenge in six pictures • Take two pictures each of the context, the challenge, and the “happy end” • Tell your story in front of the class or with tools like Replay or Stellar Digital Photography School Introduction
- 3. 3 The objective of this course is to introduce the students to Analytics ©2016 LHST sarl • Analyze the context of each case to document the key processes of the organization or the market • Qualify the data at hand to understand the nature of the business challenges • Apply the appropriate methodologies in your predictive and prescriptive analyses, and • Integrate elements of visual communications in transforming the data into a call for collective action In this module , you will Introduction www.dsign4change.com
- 4. This a place where managers and students of management can discuss and debate best practises in the digital economy, new developments in data science and decision making. Ask questions and get practicable answers, and learn how to use data in decision making. Analytics for Management https://www.linkedin.com/ groups/13536539 Introduction
- 5. Introduction ©2016 L. SCHLENKER Agenda Introduction Data Types Data Structures Big Data The Fourth Industrial Revolution
- 6. • “The truth is, 9 out of 10 startups fail.” • Behind every statistic is an opinion: • What to measure and how to collect the data • How to interpret, visualize, and present the results • Where to distribute the results and amplify the reach • How to finance the analysis…. We are what we measure (2017) Data Types Carine Carmy
- 7. • More data has been created in the past two years than in the previous history of the human race • « Strategists still confuse technology with purpose … instead of garnering context and empathy to inform change…” - Brian Solis What is data? Data Types
- 8. Categorical (nominal) Data Data placed in categories according to a specified characteristic Categories bear no quantitative relationship to one another Examples: - customer’s location (America, Europe, Asia) - employee classification (manager, supervisor, associate) Ordinal Data Data that is ranked or ordered according to some relationship with one another No fixed units of measurement Examples: - football rankings - survey responses (poor, average, good, very good, excellent) Ratio Data Continuous values and have a natural zero point Ratios are meaningful Examples: - monthly sales - delivery times Interval Data Ordinal data but with constant differences between observations No true zero point Ratios are not meaningful Examples: - temperature readings - SAT scores Data Types
- 10. • From an objective point of view, information refers to date in context that conveys meaning to an individual. • From a subjective point of view, we could suggest that it’s the individual’s perspective of the data that implies meaning. • Given these definitions what meaning do Wikileaks, Facebook or Whatapp have? Assane, The Conversation Structures
- 11. • Structured data refers to data that can be easily represented in textual/numeric form and stored in a database. • Structured data is often logically organized around a data model or data object. • Such models permit companies to compare and aggregate data in databases, datamarts and data warehouses. Structures
- 12. • Data is considered « non-structured » if we can’t predefine its attributes and store it in a table or data base • Examples of this kind of data include press clippings, videoclips, and songs • In reality, this data isn’t « non-structured » - its just that its attributes involve « complex » relationships http://jean.marie.gouarne.online.fr/bi.html Structures
- 13. Big Data
- 14. Big Data
- 15. • How does the author define the “Fourth Industrial Revolution”? • The concept of looking “outside-in” suggests that we must understand the shifting business context affects our work, our careers and our business. Give at least one example. • What are digital natives and how do they look at business differently? • How are values changing in a digitally intermediated world?How Business Can Thrive in the Digital Economy (2016) Revolution?
Editor's Notes
- #10: Data Files Delimited Text Files XML Files Log Files Application-specific Files Databases Relational Databases Graph Databases Document Stores Columnar Databases Key-Value Stores