Which one of the following is a task within the process of converting data to information?

The 6 Stages of Data Processing Cycle by PeerXP

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• Stages of the Data Processing Cycle:
• The data transformation process
• Step 1: Data interpretation
• Step 2: Pre-translation data quality check
• Step 3: Data translation
• Step 4: Post-translation data quality check

“Data is like garbage. You’d better know what you are going to do with it before you collect it.” — Mark Twain

Much of data management is essentially about extracting useful information from data. To do this, data must go through a data mining process to be able to get meaning out of it. There are a wide range of approaches and techniques to do this, and it is important to start with the most basic understanding of processing data.

Data processing is simply the conversion of raw data to meaningful information through a process. Data is technically manipulated to produce results that lead to a resolution of a problem or improvement of an existing situation. Similar to a production process, it follows a cycle where inputs (raw data) are fed to a process (computer systems, software, etc.) to produce output (information and insights).

Generally, organizations employ computer systems to carry out a series of operations on the data in order to present, interpret, or obtain information. The process includes activities like data entry, summary, calculation, storage, etc. Useful and informative output is presented in various appropriate forms such as diagrams, reports, graphics, doc viewers etc.

Stages of the Data Processing Cycle:

1) Collection is the first stage of the cycle, and is very crucial, since the quality of data collected will impact heavily on the output. The collection process needs to ensure that the data gathered are both defined and accurate, so that subsequent decisions based on the findings are valid. This stage provides both the baseline from which to measure, and a target on what to improve.

2) Preparation is the manipulation of data into a form suitable for further analysis and processing. Raw data cannot be processed and must be checked for accuracy. Preparation is about constructing a data set from one or more data sources to be used for further exploration and processing. Analyzing data that has not been carefully screened for problems can produce highly misleading results that are heavily dependent on the quality of data prepared.

3) Input is the task where verified data is coded or converted into machine readable form so that it can be processed through an application. Data entry is done through the use of a keyboard, scanner, or data entry from an existing source. This time-consuming process requires speed and accuracy. Most data need to follow a formal and strict syntax since a great deal of processing power is required to breakdown the complex data at this stage. Due to the costs, many businesses are resorting to outsource this stage.

4) Processing is when the data is subjected to various means and methods of powerful technical manipulations using Machine Learning and Artificial Intelligence algorithms to generate an output or interpretation about the data. The process may be made up of multiple threads of execution that simultaneously execute instructions, depending on the type of data. There are applications like Anvesh available for processing large volumes of heterogeneous data within very short periods.

5) Output and interpretation is the stage where processed information is now transmitted and displayed to the user. Output is presented to users in various report formats like graphical reports, audio, video, or document viewers. Output need to be interpreted so that it can provide meaningful information that will guide future decisions of the company.

6) Storage is the last stage in the data processing cycle, where data, and metadata (information about data) are held for future use. The importance of this cycle is that it allows quick access and retrieval of the processed information, allowing it to be passed on to the next stage directly, when needed. Anvesh use special security and safety standards to store data for future use.

The Data Processing Cycle is a series of steps carried out to extract useful information from raw data. Although each step must be taken in order, the order is cyclic. The output and storage stage can lead to the repeat of the data collection stage, resulting in another cycle of data processing. The cycle
provides a view on how the data travels and transforms from collection to interpretation, and ultimately, used in effective business decisions.

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These days, understanding the steps involved in the data transformation process is important, even if data transformation is not a primary part of your job.

Because we live in a world where data is collected, stored, and analyzed in so many different formats, being able to perform the basic steps required to transform data from one form to another is a common requirement for many of us.

This article explains what those steps are by outlining a typical data transformation process.

The data transformation process

While the exact nature of data transformation will vary from situation to situation, the steps below are the most common parts of the data transformation process.

Step 1: Data interpretation

The first step in data transformation is interpreting your data to determine which type of data you currently have, and what you need to transform it into.

Data interpretation can be harder than it looks. As a simple example, consider the fact that many operating systems and applications make assumptions about how data is formatted based on the extension that is appended to a file name. Thus, your computer is likely to assume that a file name video.avi is a video file, or that text.doc is a Microsoft Word file.

The problem with these labels is that the actual data inside a given file (or a directory or database) could be very different from what the file name suggests. Users can add whichever extensions they want to a file name; changing the extension doesn’t actually transform the data.

For this reason, interpreting data accurately requires tools that can peer deeper inside the structure of a file or database to see what is really inside, instead of what a file name or database table name suggests is inside. Tools like the Linux command-line utility file are useful for this purpose.

Of course, you also need to determine the target format – in other words, the format that your data should have after transformation is complete. If you do not already know that format, you’ll want to read the documentation for the tool or system that will receive your transformed data to determine which formats it supports or expects.

Step 2: Pre-translation data quality check

Once you (or your data transformation tool) have figured out which kind of data formats you are working with and which forms you will transform data into, you should run a data quality check. A data quality check allows you to identify problems, such as missing or corrupt values within a database, in the source data that could lead to problems during later steps of the data transformation process.

Step 3: Data translation

After the data quality of your source data has been maximized, you can begin the process of actually translating data. Data translation means taking each part of your source data and replacing it with data that fits within the formatting requirements or your target data format.

For example, you may be transforming an old HTML file that was written using an outdated HTML standard into HTML5, the latest standard, and the one that most modern Web browsers expect. Part of the data translation process, in this case, would involve replacing deprecated HTML tags, such as <dir> (a tag that was used in old versions of HTML to help create lists), with <ul> (the list tag supported by modern HTML).

Data translation often entails not just replacing individual pieces of data with another piece, but also restructuring the overall file in a significant way.

For example, a CSV file that is formatted as a series of comma-separated words would require considerable restructuring to convert into an XML file, which organizes information using cascading hierarchies of tags.

Step 4: Post-translation data quality check

In order to ensure that your translated data will be maximally useful, you will also want to perform a data quality check. In this step of the process, you look for inconsistencies, missing information or other errors that may have been introduced during the data translation process.

Even if your data was error-free before translation, there is a decent chance that problems will have been introduced during translation.

Conclusion

In most real-world scenarios, the data transformation steps described above would be performed automatically by software tools. So, if these steps sound like work that you are not prepared to perform, then worry not.

Still, it’s valuable for human operators to understand what their data transformation tools are doing at each step of the data transformation process, and how each action adds up to make data transformation possible.

For more information about the importance of data quality, read this Forbes Insights report: The Data Differentiator – How Improving Data Quality Improves Business

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