Statista projects global data creation to be more than 180 zettabytes by 2025. Besides, the total amount of data created, captured, copied, and consumed worldwide reached 64.2 zettabytes in 2020 and is forecasted to increase rapidly.
Whether you use the internet to order food, complete financial transactions online or learn about a certain topic, data is produced every single minute. The use of eCommerce sites, social media platforms, OTT apps, and other video streaming services, etc., contribute to such humongous volumes of data generated. And to gain useful insights from such huge amounts of data – businesses need an efficient data processing function.
Process at Glance
Organizations might be sitting on heaps of data, but it makes no sense until the data is processed properly. In other words, raw data is of no use for organizations.. So, data processing is the method of aggregating this raw data and transforming it into useful information.
This is a step-by-step process performed by a team of scientists in the company. The whole data processing cycle is explained here:
Data Collection
The first step of a data processing cycle includes the collection of raw data- it can be user behavior, monetary figures, profit/loss statements of a company, website cookies, etc. The type of raw data collected impacts the output produced. Therefore, it must be gathered from verified sources to ensure that the outcomes are valid and usable.
Data Cleaning/Preparation
This step includes sorting and filtering the raw data to eliminate inaccurate and irrelevant entries. Raw data is checked for duplication, errors, miscalculations, or missing data, and transformed into an appropriate form that facilitates data analysis and processing. Hence, only the highest quality data is fed into the processing unit.
The main purpose of this step is to filter out bad data (incomplete, redundant, or incorrect entries) to get only high-quality information and use it in the best possible way for business intelligence.
Input
The raw data is converted into a machine-readable format in this step. This machine-readable data can be in the form of data entry through a keyboard, QR code scanning through scanners, or any other input source and is then fed into the processing unit.
Data Processing
Next, the raw data is subjected to different data processing methods via AI/ML algorithms to get the desired output. Depending on the source of data being processed, which could be online databases, data lakes, connected devices, etc., this step may vary from process to process as well as the intended use of the output.
Output
After going through the above stages, the raw data finally becomes usable. It is converted to user-readable formats such as documents, audios, videos, graphs, tables, vector files, etc. This output can be stored and can be used in the next batch of the data processing cycle.
Storage
The final and last step of the data processing cycle is storage. In this, data and metadata are stored for future use. This enables seamless access and retrieval of information as and when needed. Also, this output can be directly used as input in the next data processing cycle.
Adding Value to the Business
Companies, whether big or small, old or new have piles of data in the store. This data must be properly sorted, filtered, and analyzed to harness its true potential. With effective data processing solutions in place, businesses can enjoy the benefits mentioned here:
Greater Productivity & Profits
Quick access to well-organized data facilitates seamless access/retrieval of meaningful information as well as offers a competitive advantage to organizations. Real-time data processing acts as the catalyst for organizations, regardless of the industries or verticals they deal in to drive better results and productivity.
Accelerated Decision Making
Most business processes, actions, and decisions are data-driven today. Data analysis and insight generation become easier with the data that is well-organized and stored. As a result, stakeholders can make faster and better company decisions.
Easy Data Storage
Storing loads of data is not an easy task. Besides, there are high chances of missing information and confusion. Instead, data gets stored in digital formats that can be easily accessed anytime when the data is processed via computers. Apart from this, data sharing and transferring becomes much easier with data processing.
Safe & Secure Data
If not stored and digitized properly, business-critical data is prone to theft. One might also end up losing important information. On the other hand, having organized, digitized, and properly stored, fulfills one of the very essential requirements of information being safe and secure.
Inhouse v/s Outsourcing
Though data processing is an important business function, it is equally time-consuming and tedious. So, a common dilemma for many companies is whether to have an in-house setup for such tasks or outsource them. Well, a better choice is engaging in professional data processing services.
As data processing requires the latest tools, enormous time, and resources, collaborating with experienced data processing service providers is the smart way to take this burden off and focus better on core operations. Here are the reasons why offshoring is relatively smarter option for companies:
Higher Expertise
Data processing and management tasks are better left to experts if they’re not your area of expertise. Hiring a data expert from an experienced outsourcing company offers you professional excellence and technological advantage. They have rich experience and can aptly handle data processing tasks, irrespective of their complexities; thus, you benefit from their high-quality offerings.
Faster Turnaround Time
Data processing is a time-taking process that includes different steps such as validation, sorting, summarizing data, aggregation, and analysis-this process when handled by experts helps you get desired results within the stipulated time. Therefore, offshoring data processing activities facilitate seamless access to properly organized data. And, this easy access to structure fosters quick actions & decisions based on data.
Seamless Data Management
Businesses can easily digitize data as well as achieve better storage and management. As a result, stakeholders already have the indexed data at their disposal that is necessary to make strategic business decisions.
Accurate Quality Data
Offloading data processing tasks to a company with rich experience helps you get data accuracy that is close to 100%. Leveraging the automated and manual procedures, the professionals ensure that the task is performed with utmost precision while ascertaining complete data security. They also monitor and double-check that the final output is error-free.
Conclusion
Data processing is vital for companies to create smart business strategies and gain a competitive edge in the industry. By transforming the data into a readable format such as charts, graphs, and documents, employees throughout the organization can perceive and use the data to make informed decisions, enhance efficiency, and drive success. All you need to do is begin with finding the right partner!
