In the digital era, companies collect and process huge amounts of data. We will help you maximize the power of your data.
What is Data Processing?
Data processing is the process of collecting raw data and transforming it into a usable form. This is done through a sequence of operations. To achieve this manually, data scientists or data engineers are typically required.
However, automation software has made it easy to carry out these steps with little to no human intervention needed.
The entire process involves the collection, cleansing (filtering and sorting), processing, analysis, and storage of data so that internal and external parties can utilise the valuable information.
Data processing helps organisations achieve smoother operations, increased productivity, enhanced security, and, ultimately, can positively impact the bottom line.
What Types of Data Need Processing?
Mostly every type of data needs some sort of processing. When you think of data, it’s likely for numbers to come to mind. But, data can be in the form of images, graphs, survey answers, transactions, and more.
Data can be categorised into things like: personal information, financial transactions, banking details, etc.
Depending on what type of information you want to glean and the type of data available, the processing time or steps can vary.
What are the Applications of Data Processing?
Data processing is used across industries and businesses of all sizes. Here’s a look at some of the most common applications of data processing:
Commercial Data Processing:
Commercial data processing is when a large volume of input data is used to produce a large volume of output. For enterprises and big businesses, you can quickly understand the value of being able to process massive amounts of data.
For example, banks and insurance companies withhold countless records that are both sensitive and necessary for doing business.
Real-World Applications:
Outside of business, data processing is needed in realms where the information can literally change lives. To exemplify, the healthcare industry may be able to process massive amounts of data to better understand public health crises so that solutions can be found and executed.
Additionally, data is processed in academic settings so researchers can use scholarly material that is accurate.
Data Analysis:
Data analysis is a main function that involves data processing where algorithms and forecasts can be used to make important choices today that will affect what’s yet to happen.
Data Collection
The first step of data processing is, of course, data collection. At this step, data is pulled from all available sources, be it a data lake, data warehouse, or disparate systems. Since this is the raw information that will be translated into insights, it’s important that the data is of the highest possible quality.
Some examples of raw data may include: user behavior, monetary figures, and website cookies.
Data Preparation
Once the data is collected and ready to be transformed, the data preparation stage is initiated. This is where data is organised and cleaned before it moves into the next step. At this stage, data is checked to remove redundancies, incorrect data, or incomplete data (records with gaps).
While this could be an immense time suck when performed manually, Yeti Tech can do this work for you in seconds.
What are the Stages of Data Processing?
There are six main stages of data processing, namely:
Data Input
Now, the pre-processed data gets entered into the system in which it will be utilised. This could be a CRM or data warehouse, for example. The data gets translated into a language that the machine can understand and starts to take its shape of usable information.
Data Processing
With the same name as the overall procedure, the data processing step is the core component of these steps. Here, the computer system in which the data was input begins to process the data by way of algorithms and machine learning.
The way in which this happens will depend on the data source and intended output (or use case).
Data Output
After being processed, the data output stage equates to the data interpretation stage. It’s at this point that individuals (who are not data scientists) can understand the data.
The information takes the format of graphs, plain text, images, videos, etc. which makes it easy for anyone to be able to utilise. SolveXia allows users to create customisable dashboards for this purpose.
Data Storage
Even though the main goal of data processing is to reap the benefits of the insights, data storage is a ever important piece of the process. After data has been used, it should be stored for future reference.
Not only is it valuable to have it accessible to review should the need arise, but there is also compliance involved when collecting and storing information.
To ensure that your business is covering all its bases and adhering to regulations, the bank-grade security inherent in SolveXia’s product can protect your data as an overall component of your data management.
Future of Technology and Data Processing
Given the fact that businesses collect data every second from different sources, the overall value appears when data is centralised so that all data records are processed for use. The future of data processing exists in cloud technology.
Cloud technology makes it possible to access data from various systems without timing delays to maximise effectiveness. When software updates (which is inevitable), the cloud technology used to process data is unaffected.
Additionally, cloud platforms are a more cost effective option than having to store data on servers on-premise. Since most work is being done remotely, cloud software also ensures that users can access data securely, wherever they may be.
What are the Types of Data Processing?
As we’ve briefly mentioned, data processing methods vary based on the source of the data and the intended output.
Here’s a summary of the different types of data processing and use cases for each:
Batch Processing: For large amounts of data, batch processing makes it easy to process data in bulk (aka batches). This is often used in payroll systems, for example.
Online Processing: For continuous processing of data, online processing makes it possible for data to enter the CPU as soon as it’s available. Think of using this for something like barcode scanning as a retailer.
Real-time Processing: For smaller sets of data, real-time processing can be used to process data seconds after the system receives the data inputs. A real world example of real-time processing happens any time you take money out of an ATM.
Time-sharing: To accommodate several users using the same computer resources, data can be process in time slots.
Multiprocessing: Also known as parallel processing, multiprocessing breaks data down into frames and processes it using two or more CPUs in the same computer system. Weather forecasts use this type of data processing.
What are the Methods of Data Processing?
The most efficient way to perform data processing is with automated processing. However, you also have the option to use manual data processing or mechanical data processing. Here’s an overview of what each method of data processing involves.
Manual Data Processing
As the name implies, manual data processing is performed by hand. Without the aid of any machine or software, an individual human being or team is responsible for data collection, filing, cleansing, calculating, and processing the data.
Although this method is one of low cost, it may ultimately cause more costs in the long run due to the high rate of errors and the cost of human resources/time.
Mechanical Data Processing
Mechanical data processing involves the usage of tools and devices, such as: calculators, printers, etc. The increased data involved in this method may lead to extra complexity; however, your rate of errors will be lessened since you lower the amount of human intervention.
Electronic Automated Data Processing
If you’re trying to process data as efficiently and cost effectively while minimising errors, then automated data processing is the only way to go. Programs and automation software solutions follow directions step-by-step to transform data inputs into data outputs.
Although it may be an upfront investment that makes this method seem more expensive, it can save a lot in reduced errors and opportunity cost. The accuracy of the output is maximised and made easy to share and access at any given point in time.
SolveXia is an example of electronic automated data processing. The software can perform data aggregation as it connects all your sources of data into a centralised repository for use.
Even if your business has an existing toolstack, you can integrate the software so you won’t lose any of your important data. Instead, this data can be automatically pulled, organised, and processed into visual representations and reports.
The advent of automation technology has reshaped how businesses operate. Businesses that adopt this technology can remain agile, proactive, and ensure that all their data is an accurate reflection of reality in real-time.
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Our consultation is free and doesn’t require any commitment from your side. We can explore whether this solution will be the right choice for your company. Contact us today and learn how we can help you maximize the value of your business.