Introduction

Whether the internet is used to research a topic, make transactions online, food ordering, data is continuously generating each second. The amount of data has increased due to the increased utilisation of online shopping, social media and streaming services. A study has estimated that 1.7MB of data is generated each second for every single human being on this earth in 2020. To avail and get intuitions from such huge amounts of data – data processing is useful.

So what is Data Processing? To be put in simple words is the collection, manipulation, and processing of collected data for the intended use. It translates huge amounts of collected data into a desirable form used by commoners to analyze and interpret the meaning of data processed. Data processing in computers refers to the manipulation of data by computers. This is inclusive of output formatting or transformation. Data flow through the memory and  CPU to the output device and of course, the reformation of raw data into machine language.

  1. What is Data Processing?
  2. How is data processed?
  3. Different Types Of Output
  4. Different Methods
  5. Types
  6. Why we should use Data Processing

1) What is Data Processing?

Concept of Data processing is collecting and manipulating data into a usable and appropriate form. The automatic processing of data in a predetermined sequence of operations is the manipulation of data. The processing nowadays is automatically done by using computers, which is faster and gives accurate results.

Thereafter, the data collected is processed and then translated into a desirable form as per requirements, useful for performing tasks. The data is acquired from various sources like excel file, database, text file data, and unorganised data such as audio clips, images, GPRS and video clips. The most commonly used tools for data processing are Storm, Hadoop, HPCC, Statwing, Qubole and CouchDB. The output is worthwhile information various file formats like a chart, audio, table, graph, image, vector file depending on software or application necessary.

Therefore the meaning of Data processing is a method of collecting raw data and converting it into useful information. Data Processing is performed in a predetermined procedure by a team of data scientists and data engineers in an organization.

2) How is data processed?

Data processing requires six steps, and those are:

  1. Data Collection: The primary stage of data processing is to collect data. Data is acquired from sources like data lakes and data warehouses. The collected data must be trustworthy and of high quality.
  2. Data Preparation: Also called “pre-processing”, this stage is where the collected data is cleansed by checking for errors and arranged for the following data processing stage. Elimination of useless data and to generate quality data for quality business intelligence is the motive of this stage.
  3. Data Input: The prepared data is translated into machine language by using a CRM such as Salesforce and Redshift, a data warehouse. 
  4. Processing: The processing of input data is done for interpretation. The processing is accomplished by machine learning algorithms. Their process is variable depending on the data which is processed (connected devices, social networks, data lakes, etc.) and the intended use (medical diagnosis, ascertain customer wants, examining advertising patterns, etc.).
  5. Data Interpretation: The non-data scientists find this data very helpful. The data is converted into videos, graphs, images and plain text. Members of a company can start analysing this data and applying it into their projects.
  6. Data Storage: Storage utilisation in future is the final step of processing. Effective Properly storage of data is necessary for compliance with GDPR (data protection legislation). Properly stored data to be accessed easily and quickly by employees of an institution as and when needed is of utmost importance.

3) Different Types Of Output

The different types of output files in data processing are –

  • Plain Text File – The text file is the simplest format of a data file will be exported as Notepad or WordPad files. 
  • Table/Spreadsheet – the data is represented in columns and rows, that helps in quick analysis and understanding of data. Tables/ Spreadsheet allows numerous operations like sorting & filtering in descending/ascending order and statistical operations.
  • Charts and graphs – The most common features in almost all software is the graphs and charts format. This format enables easy analysis of data by just a glance.
  • Maps/Vector or Image File – The requirement to store and analyse spatial data and export data can be fulfilled by this image and map formats.
  •  Specialised software can process software specific file formats.

4) Different Methods

The three prominent data processing methods are as follows:

  • Manual Data Processing: Data is processed manually in this data processing method. The entire procedure of data collecting, filtering, sorting, calculation and alternative logical operations is all carried out with human intervention without using any electronic device or automation software. It’s a low-priced methodology and needs very little to no tools; however, it produces high errors and requires high labour prices and much of your time.
  • Mechanical Data Processing: data is processed using machines and simple devices such as typewriters, calculators, printing press, etc. Simple data processing operations can be accomplished by this method. There are fewer errors compared to manual data processing, but the only drawback is that this method cannot be utilized with the increase of data. 
  • Electronic Data Processing:  Data processing softwares and programs are used to process data. A series of instructions are given to the software to process the data and produce the desired output. It is more expensive but provides faster processing with the highest reliability and accuracy.

5) Types

The types of data processing are as below:

  • Batch Processing: The collection and processing of data is done in batches where there is a huge quantity of data. E.g., the payroll system.
  • Real-time processing: For a small quantity of data, real-time processing is done where data can be processed within seconds of data input.

E.g., withdrawing money from ATM

  • Online Processing: As and when data is available, it is automatically entered in the CPU. This is useful for processing of data continuously.

E.g., barcode scanning

  • Multiprocessing: This also goes by the name parallel processing, where data is fragmented into small frames and processed in two CPUs within a single computer system.

E.g., weather forecasting

  • Time-sharing: Allocates computer resources and data in time slots to several users simultaneously.  

6) Why we should use Data Processing

In the modern era, most of the work relies on data, therefore collection of large amounts of data for different purposes like academic, scientific research, institutional use, personal and private use, for commercial purposes and lots more. The processing of this data collected is essential so that the data goes through all the above-stated steps and gets sorted, stored, filtered, presented in the required format and analyzed.

The amount of time consumed and the intricacy of processing will depend on the required results. In situations where large amounts of data are acquired, the necessity of processing to obtain authentic results with the help of data processing in data mining and data processing in data research gets inevitable.

Conclusion

Finally, to define data processing in simple terms, it is the procurement of worthwhile information by conversion of data. The processing of data is done in six stages which are data collection,  sorting of data, storage of data, processing of data,  data presentation and data analysis.

The three prominent methods of processing data are Mechanical, Electronic and Manual. Data processing is crucial for organizations to create better business strategies and increase their competitive edge. By changing the data into a legible format like graphs, charts and documents, workers throughout the organization will be able to perceive and use the data to analyse and interpret according to their requirements.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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