Bourgeois Learning Objectives Upon successful completion of this chapter, you will be able to: Introduction You have already been introduced to the first two components of information systems: However, those two components by themselves do not make a computer useful.
Retrieve Value Given a set of specific cases, find attributes of those cases. What is the value of aggregation function F over a given set S of data cases? What is the sorted order of a set S of data cases according to their value of attribute A? What is the range of values of attribute A in a set S of data cases?
What is the distribution of values of attribute A in a set S of data cases? What is the correlation between attributes X and Y over a given set S of data cases?
Barriers to effective analysis[ edit ] Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.
Confusing fact and opinion[ edit ] You are entitled to your own opinion, but you are not entitled to your own facts. Daniel Patrick Moynihan Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinionor test hypotheses.
Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. This makes it a fact.
Whether persons agree or disagree with the CBO is their own opinion. As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects.
When making the leap from facts to opinions, there is always the possibility that the opinion is erroneous. Cognitive biases[ edit ] There are a variety of cognitive biases that can adversely affect analysis.
In addition, individuals may discredit information that does not support their views. Analysts may be trained specifically to be aware of these biases and how to overcome them.
In his book Psychology of Intelligence Analysis, retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.
However, audiences may not have such literacy with numbers or numeracy ; they are said to be innumerate. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques.
More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy GDP or the amount of cost relative to revenue in corporate financial statements.
This numerical technique is referred to as normalization  or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation i. Analysts apply a variety of techniques to address the various quantitative messages described in the section above.
Analysts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysisthey will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock.
Smart buildings[ edit ] A data analytics approach can be used in order to predict energy consumption in buildings. Analytics and business intelligence[ edit ] Main article: Analytics Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.
Initial data analysis[ edit ] The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question.
The initial data analysis phase is guided by the following four questions: Data quality can be assessed in several ways, using different types of analysis: Test for common-method variance. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.
One should check whether structure of measurement instruments corresponds to structure reported in the literature. There are two ways to assess measurement: If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in sample.
Other possible data distortions that should be checked are: It is especially important to exactly determine the structure of the sample and specifically the size of the subgroups when subgroup analyses will be performed during the main analysis phase.
The characteristics of the data sample can be assessed by looking at: Basic statistics of important variables Scatter plots Cross-tabulations  Final stage of the initial data analysis[ edit ] During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.
Also, the original plan for the main data analyses can and should be specified in more detail or rewritten. In order to do this, several decisions about the main data analyses can and should be made:In the world of libraries, academia, and research there is an important distinction between data and statistics.
Data is the raw information from which statistics are created. Put in the reverse, statistics provide an interpretation and summary of data. Often, the words information and data are used interchangeably, yet they are not the same thing. Data is, or are (depending on your knowledge of Latin), fundamental to business intelligence.
First Things First: Data vs Information. There’s a really simple way to understand the difference between data and information. When we understand the primary function of the item we are looking at, we quickly see the distinction between the two.
The origin of the DIKW (Data, Information, Knowledge, Wisdom) hierarchy is ably presented in Sharma () highlighting the first appearances of the hierarchy in both the Metaphorical analysis is a great tool to do this. However, whilst the concepts and transitions to structure or necessary relationship between them.
In addition, data. between having a security policy and realizing a reduction in data security breaches. This dissertation was a case study to explore the relationship between information technology.
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, .