principles and methods of data cleaning pdf

Principles and methods of data cleaning pdf

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Data cleansing

What is data cleaning?

Engineering Asset Lifecycle Management pp Cite as.

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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, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information.

EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.

All of the above are varieties of data analysis. Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. Analysis , refers to dividing a whole into its separate components for individual examination. Data analysis , is a process for obtaining raw data , and subsequently converting it into information useful for decision-making by users.

Data , is collected and analyzed to answer questions, test hypotheses, or disprove theories. Statistician John Tukey , defined data analysis in , as:. There are several phases that can be distinguished, described below.

The phases are iterative , in that feedback from later phases may result in additional work in earlier phases. The data are necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analysis or customers who will use the finished product of the analysis.

The general type of entity upon which the data will be collected is referred to as an experimental unit e. Specific variables regarding a population e. Data may be numerical or categorical i. Data are collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data; such as, Information Technology personnel within an organization.

The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation. Data, when initially obtained, must be processed or organized for analysis.

For instance, these may involve placing data into rows and columns in a table format known as structured data for further analysis, often through the use of spreadsheet or statistical software. Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning , will arise from problems in the way that the datum are entered and stored. Data cleaning is the process of preventing and correcting these errors.

Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. For example, with financial information, the totals for particular variables may be compared against separately published numbers, that are believed to be reliable. There are several types of data cleaning, that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values.

Quantitative data methods for outlier detection, can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Textual data spell checkers, can be used to lessen the amount of mis-typed words, however, it is harder to tell if the words themselves are correct.

Once the datasets are cleaned, it can then be analyzed. Analysts may apply a variety of techniques, referred to as exploratory data analysis , to begin understanding the messages contained within the obtained data.

The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned in the lead paragraph of this section.

Descriptive statistics , such as, the average or median, can be generated to aid in understanding the data. Data visualization is also a technique used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights, regarding the messages within the data. Mathematical formulas or models known as algorithms , may be applied to the data in order to identify relationships among the variables; for example, using correlation or causation.

In general terms, models may be developed to evaluate a specific variable based on other variable s contained within the dataset, with some residual error depending on the implemented model's accuracy e.

Inferential statistics , includes utilizing techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising independent variable X , provides an explanation for the variation in sales dependent variable Y.

In mathematical terms, Y sales is a function of X advertising. Analysts may also attempt to build models that are descriptive of the data, in an aim to simplify analysis and communicate results.

A data product , is a computer application that takes data inputs and generates outputs , feeding them back into the environment.

It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. Once the data are analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative. When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques, to help clearly and efficiently communicate the message to the audience.

Data visualization uses information displays graphics such as, tables and charts to help communicate key messages contained in the data. Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts e.

Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the process. Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include:.

For the variables under examination, analysts typically obtain descriptive statistics for them, such as the mean average , median , and standard deviation. They may also analyze the distribution of the key variables to see how the individual values cluster around the mean.

The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the MECE principle. Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them.

For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as the revenue of divisions A, B, and C which are mutually exclusive of each other and should add to the total revenue collectively exhaustive. Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false.

For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type I and type II errors , which relate to whether the data supports accepting or rejecting the hypothesis.

Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y e. This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X. Necessary condition analysis NCA may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y e.

Whereas multiple regression analysis uses additive logic where each X-variable can produce the outcome and the X's can compensate for each other they are sufficient but not necessary , necessary condition analysis NCA uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it they are necessary but not sufficient.

Each single necessary condition must be present and compensation is not possible. Users may have particular data points of interest within a data set, as opposed to the general messaging outlined above.

Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points. 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.

Daniel Patrick Moynihan. Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion , or 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. There are a variety of cognitive biases that can adversely affect analysis.

For example, confirmation bias is the tendency to search for or interpret information in a way that confirms one's preconceptions. 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. Effective analysts are generally adept with a variety of numerical techniques. 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. For example, whether a number is rising or falling may not be the key factor. 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 [8] 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 analysis , they 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.

Data cleansing

Data cleansing or data cleaning is the process of detecting and correcting or removing corrupt or inaccurate records from a record set, table , or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict such as rejecting any address that does not have a valid postal code , or with fuzzy or approximate string matching such as correcting records that partially match existing, known records.

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, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.

When using data, most people agree that your insights and analysis are only as good as the data you are using. Essentially, garbage data in is garbage analysis out. Data cleaning, also referred to as data cleansing and data scrubbing, is one of the most important steps for your organization if you want to create a culture around quality data decision-making. Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled. If data is incorrect, outcomes and algorithms are unreliable, even though they may look correct. There is no one absolute way to prescribe the exact steps in the data cleaning process because the processes will vary from dataset to dataset.

What is data cleaning?

This chapter is about processing completed questionnaires: analysing them, and reporting on the results. Even in developing countries, most surveys are analysed by computer these days, so this chapter focuses mainly on computer analysis. Note, this content is now a bit dated, with the advent of online survey processing one example, and we'll try to update key areas so it remains useful and relevant.

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6 steps for data cleaning and why it matters

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