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Summary Data Analytics
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Summary of 40 pages for the course Data Analytics & Professional Skills at UvA (Summary by week)
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Session 1 Introduction to Data AnalyticsDevelopments in the Profession
• Business Intelligence (BI), Big Data analytics, artificial intelligence are used extensively by firms,
especially for internal reporting and decision making.
• BI and data warehouses form the foundation of nowadays corporate reporting.
• Audit firms have started using data analytics to support and automate auditing (e.g. PwC Halo, E&Y
Helix, KPMG Clara).
The Impact of Technological Innovations
• Cloud-based services provide access to digital capabilities to all kinds of firms that have previously only
been accessible to large companies.
• Virtually all data is digital and accessible.
• The software takes over the task of processing and recording business transactions as well as traditional
bookkeeping activities.
Changing Role of the accountant
Information for Managerial Decision Making
• Management is a process by which organizational goals are achieved by using resources.
• Decision making: selecting the best solution from two or more alternatives
o To select the best solution management requires sufficient information
o Managers usually make decisions by following a four-step process
§ Intelligence: Define the problem (or opportunity).
§ Design: Construct a model that describes the real-world problem, defines evaluation
criteria, and searches for alternative solutions.
§ Choice: Compare, choose, and recommend a potential solution to the problem.
§ Implementation: Implement the chosen solution.
o Simon 1977:
,Models
• Decision-making processes involve the inclusion of at least one model.
• A model is a simplified representation or abstraction of reality.
• Modeling is a combination of art and science.
• The Benefits of Models
o Manipulating a model is much easier than manipulating a real system.
o Simulation is easier and does not interfere with the organization’s daily operations.
o Compression of time, years of operations can be simulated in minutes or seconds.
o The cost is much lower than experiments conducted on a real system.
o The consequences of making mistakes are less severe.
o Mathematical models enable the analysis of a very large number of possible solutions.
o Models enhance and reinforce learning and training.
o Models and solution methods are readily available.
= MIS Domain (lower and middle
management responsibility
= Both DSS Domain (top
management responsibility
An Early Decision Support
Framework
• Degree of Structuredness (Simon, 1977)
o Highly structured (programmable)
o Semi-structured
o Highly unstructured (nonprogrammable)
• Types of Control (Anthony, 1965)
o Strategic planning (top-level, long-range)
o Management control (tactical planning)
o Operational control
System: a set of two or more interrelated components interaction the active a goal
• Has a boundary
• Has inputs and outputs
• Interacts with its environment
• Is governed by processes, rules, and procedures
Data vs. Information
• Data are facts that are collected, recorded, stored, and processed. à
Insufficient for decision making
, • Information is processed data used in decision-making. à Too much information, however, will make
its data more, not less, difficult to make decisions. This is known as ‘data overload’ or ‘information
overload’.
The Concept of Decision Support Systems (DSS)
• Interactive computer-based systems, which help decision-makers utilize data and models to solve
unstructured problems (Gorry and Scott-Morton, 1971)
• Couple the intellectual resources of individuals with the computational capabilities of the computer to
improve the quality of decisions.
• Primarily emerged from science.
Evolution of Computerized Decision Support to Business Intelligence and Data Science
Business Intelligence (BI)
• BI is an evolution of decision support concepts over time
o Before: Executive Information System (EIS/DSS)
o Now: Everybody’s Information System (BI)
• BI systems are enhanced with additional visualizations, alerts, and performance measurement
capabilities.
• Primarily emerged from the industry.
• Combines architectures, tools, databases, analytical tools, applications, and methodologies.
• Is a content-free expression, so it means different things to different people.
• The major objective is to enable easy access to data (and models) and business managers to analyze it.
• Helps transform data into information, to improve decisions, and finally to implement action.
A BI system has four major components
• a data warehouse with its source data
• business analytics (a collection of tools for manipulating, mining, and analyzing the data)
• business performance management (BPM) capabilities for monitoring and analyzing performance
• a user interface (e.g. a dashboard)
Typical BI functionality: statistics and data mining, ad hoc queries, cube analysis, enterprise reporting, and report
delivery and altering
Differences between DSS and BI
, Business Analytics
• Combination of; computer technology, management science techniques, statistics à to solve problems
• They usually categorized as Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics
Alternative Classification
Big Data
• The growing availability of information -> Big Data (e.g. personal devices connected to the Internet and
equipped with digital sensors)
• The term has been used with several and inconsistent meanings (Lacks a formal definition)
How to define Big Data?
• “data is the new oil, the source for corporate energy and differentiation in the 21st century” (ECM,
2011)
• “seriously massive and often highly complex sets of information” (Microsoft Research, 2013).
• “when the processing capacity of conventional database systems is exceeded” (Dumbill 2013)
• “a cultural, technological, and scholarly phenomenon” Boyd and Crawford (2012, p. 663)
• “Big Data is the Information asset characterized by such a High Volume, Velocity, and Variety to
require specific Technology and Analytical Methods for its transformation into Value.” (De Mauro et
al. 2016)
What is a Data Scientist?
• a high-ranking professional with the training and curiosity to make discoveries in the world of big data.
• forming theories, testing hunches, and finding patterns to predict.
• understand how to fish out answers to important business questions from today’s tsunami of
unstructured information.
• can bring structure to large quantities of formless data and make analysis possible.
• identify rich data sources, join them with other, potentially incomplete data sources.
• display information visually and communicate patterns they find clear and compelling.
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