With an increasing number of businesses undergoing the process of digitization with the assistance of document scanning services, data preparation is considered as an important step to improve the ability to use data in a distributed manner for data discovery, data mining, and advanced analytics. Data preparation refers to the process of collecting, combining, and organizing data. This organized data is used in business intelligence, analytics and data visualization to extract valuable insights and make informed decisions. Good data preparation helps in efficient analysis, limits errors and inaccuracies that may happen during processing, and makes all processed data more accessible to users.

Purpose of Data Preparation

One of the main purposes of data preparation is to ensure that the raw data is kept ready for processing, and the analysis is accurate and consistent so that the results of BI and analytics applications will be valid and authentic. Often, data is found to be missing and inaccurate or separate data sets often have different formats that need to be reconciled. Correcting these data, checking its quality and joining data sets are the main activities in the data preparation process.

Best Practices in Data Preparation

  • Verify Data Formats: The process of data preparation begins with raw data files that come in various formats and sizes. The data that comes from PCs will be different from Mainframe data, similarly spreadsheet data is differently formatted and this goes on. So, the first step is to ensure that you can read the files that you are given and take time to look at each field’s contents.
  • Check Data Types: The entire data falls into any of the four categories that affect what sort of statistics you can appropriately apply to it:
    • Nominal data is important just as a name or an identifier.
    • Ordinal data puts records into order from lowest to highest.
    • Ratio data is similar to interval data except that it also allows for a value of 0.
    • Interval data represents values where the differences between them are comparable.

     

    Understanding which data falls into which category before feeding into the statistical software is important.

  • Graph your Data: Knowing how your data is distributed is important. You can run statistical procedure unit you are exhausted but none of them will give you as much insight into what your data looks like as a simple graph.
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  • Check the Accuracy of the Data: Once you become comfortable with the way the data is formatted, the next step is to ensure that it is accurate, which requires some knowledge of the subject area that you are working in. There is no definite approach to verify accuracy of the data. The main idea is to formulate some properties that you think the data should exhibit and test the data to see if those properties hold. This is mainly to ensure that the data that you have is correct.
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  • Identify the Outliers: Outliers are data points that do not work with other data. These types of data are either very large or very small when compared with the rest of the dataset. Outliers are problematic because they can seriously compromise statistics and statistical procedures. A minor outlier can make a significant impact on the value of the mean. This is because mean is supposed to represent the center of the data, in a sense, this one outlier renders the mean useless. If you come across any outlier, the best practice is to delete them. Sometimes you may want to take them into account. The best method is to do your analysis twice, once with outliers included and once with the outliers excluded so that you can evaluate which method gives better results.
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  • Find the Missing Values: Missing values are very common and the most annoying data related issue that you may encounter. Your first impulse might be to drop records with missing values from your analysis. However, this is not advisable because missing values may not be just small data glitches.
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  • Look Into your Assumption about How the Data is Distributed: Almost all statistical procedures are based on the assumption that the data is distributed in a certain way and if the assumptions fail, the accuracy of the prediction will be affected. In cases where the data isn’t distributed as you need it to be, all is not necessarily lost. There are several ways of transforming data to get the distribution into the shape that you need it. One of the best ways to verify the accuracy of a statistical model is to actually test it against the data once it’s built. The best way to do that is to randomly split your dataset into two files and you might call these files Analysis and Test, respectively.
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  • Make a Backup of Everything that You Do: It has become very easy to use statistical software and you can run procedures just with the touch of a button. This helps to create several dozen graphs based on different data transformations in a matter of a few minutes. But with so many activities, it is very easy to lose track of what you have done. Make sure to keep a record of what you are doing, label all graphs with the names of the data that was used to create them. It’s also important to back up your data files. In the course of your analysis, you are likely to create many different versions of your data that reflect various corrections and transformation of variables. Save the procedures that created these versions and they should also be documented in such a way that it describes what changes you have made and why.

Data preparation makes data accessible to your customers, partners, suppliers etc. It allows business users to prepare their own data for analysis, organizations can bypass the IT bottleneck and accelerate time-to-insight, and thus make better business decisions. A document scanning service helps to improve the accuracy of the data and with accurate and quality data, organizations can perform better, faster and seamlessly meet business objectives.