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

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

“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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noun • [day-tuh kon-ver-shun] • the process of converting data that is stored in one file format into another file format of a different structure

Converting data into formats that help you understand, analyze, and present information is required in all fields of work. Viewing data in a different format can help to unlock new insights that may otherwise go unnoticed. While data isn’t necessarily “hidden” in any format, by converting and restructuring data into something new, you often are exposed to the equivalent of a new perspective. It is this unique perspective that can give you a better and more well-rounded understanding of the data you have.

Data conversion is widely used for reasons related to accessibility. Often data is stored in a specific file format that only certain software can access and read. This is due to the innate structure of the file itself. While this was once a limitation, there are now many strategies for converting data which allows for greater flexibility and enables you to use the data in any way you’d like.

With more and more data being collected, it is inevitable that data conversion will be a task that you complete, no matter how simple or complex the conversion.

The structure of data describes not the data itself, but the file structure that the data is stored in. It is this structure that is programmatically built into software systems that allow them to read the information you have stored in a given file.

For data conversion to be effective, there needs to be an understanding of the structure of the file format that you have the data stored in and the format that you want to convert to. For most, this sounds like an incredibly daunting task. That’s why most people opt for using data conversion software rather than coding a data conversion process manually.

Example: Both a .txt and .doc file are similar formats in that they both are typically used for storing plain text. However, the .doc file structure, unlike the .txt file structure, accounts for formatting and other additional styling information.

File formats have typically been made to work with specific software programs. For example, .doc files were created for Microsoft Word, .dwg for AutoCAD, and .gdb for ArcGIS. While the popularity of some file formats has led to other software systems incorporating support for these files, this isn’t always the case.

This is one of the primary sources and causes of data silos. Without having a data conversion process put in place, data silos are somewhat unavoidable when you are using multiple applications that create, analyze, or store the data. This is another reason why most people opt for using data conversion software. These programs can help you overcome limitations with compatibility and allow data to flow freely.

Example: A .shp file (aka. shapefile) is used to display vector shapes (points, lines, and polygons). While shapefiles have tabular data for the associated vector shapes, this file is not compatible with other tabular software programs like Microsoft Excel.

It’s no surprise that with a process as complicated as data conversion there will be some hiccups along the way. One of the most common (and irritating) issues that often occur with data conversion is losing information during the conversion.

For example, if you convert a .doc to a .txt, you will inevitably lose styling and formatting information due to the structural differences of the files. If you convert a shapefile into an image, you will inevitably lose the tabular data because image files do not support that kind of additional information.

Data conversion programs can help you avoid this issue. In many cases, data conversion programs will offer extensive support for transforming data specifically to help you meet your unique needs. These capabilities make data conversion platforms optimal over the workaround of merely opening a file format in a program and then saving it as a different file format.

Data conversion software like FME can help you quickly and easily convert data from one format to another. FME supports over 450 formats and applications to help you connect data no matter the structure or level of compatibility the data has with other formats and applications. There is built-in support for the supported file structures so you can read and write data without needing to be trained on the design of any particular format formally.

While you could simply read and write data directly with FME, the main benefit of using FME comes from being able to build custom workflows. Building your own workflow means being able to control the exact way data is being converted.

For example, you may have an AutoCAD DWG file with coordinates that you want to convert into a shapefile. A DWG file is a drawing file used for storing 2D design data, while a shapefile is used to store spatial data. Using FME you can convert your DWG into a shapefile along with the metadata. By specifying that you want your DWG ID information to be stored as an attribute in your shapefile, you end up converting both the visual and descriptive components of your file.

To do this, add transformers to your workflow to transform data before conversion. This will help ensure that the exact data you need is being converted and that you are not losing the information you need. Additionally, you can use transformers for customizations like styling or formatting to help make your results that much better. Creating workflows in FME using transformers means being able to incorporate your specific rules and standards into the process as well.

With FME you aren’t limited to 1:1 data conversion workflows either. If you have multiple data sets that you’d like to combine into a single file, go for it! Alternatively, you may want to breakdown a file into separate, new formats. You can do that too.

FME is a data integration platform that was built with the intention of creating an optimal data conversion solution for data users in all industries. How did we know this was a tool that was needed? Because we too have gone through the trials and tribulations of trying to convert data and knew something had to be done.

FME is recognized as the data integration platform with the best support for spatial data worldwide. However, it can handle much more than just spatial data. FME can help you integrate business data, 3D data, and applications all within the same platform. FME has a range of supportive data transformation tools called transformers that make is easy to integrate over 450 formats and applications. With FME you have the flexibility to transform and integrate exactly the way you want to.

Safe Software, the makers of FME, are leaders in the technology world that strive to stay one step ahead of data integration trends. FME is continuously upgraded to ensure it has been adapted to support new data formats, updated versions of existing data formats, and large amounts of data. Gone is the idea that individual departments must work in their data silos, with IT structures limiting the company’s potential to truly work as one. Data should be able to flow freely no matter where, when, or how it’s needed.

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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.


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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