Everything You Need to Know about Data Processing

The use case of data has increased exponentially as users today employ it in each aspect of their lives, from social media or researching information to streaming. So, for business entities functioning in the hyper-competitive, digitally-driven world, each second, a massive amount of data is being generated, some structured while a major part of it remains unstructured.

But what exactly happens to this enormous amount of data generated and stored every other minute? How does one take the raw data and decipher it into some actionable item that holds valuable information? What role does this data play in honing an organization's competitive edge? Is it relevant to operate on raw, unstructured data to acquire crucial insights?

A simple answer to these questions is DATA PROCESSING.

Data processing, in the simplest form, is the manipulation of raw data, which includes transforming data to analyze and interpret its meaning and significance.

Before diving deeper into the nitty-gritty of data processing, it is important first to understand what it means and how automated data processing can revolutionize businesses.

What is Data Processing?

Any company cannot use data in its raw form. As a result, data processing is frequently done in stages by a group of data professionals. Therefore, in essence, collecting and transforming raw data into relevant information is data processing. Anybody, from data scientists to business analysts and decision-makers to managers, can use this information once it has been processed for various objectives. However, regardless of the end-user or activity, data processing's ultimate purpose is always the same - converting data into knowledge.

Having understood what data processing is, the next big question is - Is there a one-size-fits-all approach to data processing? Read on to find out the answer.

Types of Data Processing

Based on the process followed to analyze the data, the source from which it is acquired, and the processing function's use case, different methodologies are employed to transform raw data into a usable form.

  • Batch Processing - Processing data in multiple small batches, ideally for processing data in memory efficient and cost-effective manner.
  • Real-time Processing - Processing data as soon as it is acquired and stored in the system, ideally for processing data where the latency period is short.
  • Online Processing - Processing online data as soon as it is made available to the system, another type of real-time processing.
  • Distributed Processing - Processing of data distributed over a shared network of different computers, ideally performed for memory-intensive jobs.
  • Multiprocessing - Data processing with two or more central processors within a system to run multiple processing concurrently.

Benefits of Data Processing

Data processing is of utmost significance for businesses to create effective strategies and ensure a competitive edge. In addition, employees across the organization can understand and use the data by translating it into a usable format such as graphs, charts, and texts. In addition, much of the processing in modern data analytics employs sophisticated technology and algorithms, resulting in the automated data processing.

The various benefits of data processing include:

  • Convenient Data Storing

When processed data is stored in relational databases rather than unstructured, text-heavy documents, it be comes easier to store, manipulate, and examine the data.

  • Easier Reporting

Once efficiently processed, you may easily construct reports, dashboards, and other summaries of a dataset's features.

  • Enhanced Productivity

Processed data saves users from reprocessing a dataset every time they wish to utilize it since it is easier to explore.

  • Logical Housekeeping

Automated data processing is a continuous process, not a one-time event. Reprocessing helps keep things in order and reduces the number of errors or faults in your data.

  • Improved Accuracy in Analysis

The accuracy of your insights is improved by deleting outliers, errors, and unneeded data points regularly (and by using properly defined data models).

Stages of Data Processing

The next big question that comes to your mind is - What goes into conducting data processing. Here's how data is processed and results are generated. The process includes a series of steps:

1. Collection

The type of data used for processing considerably impacts the system, making this first step of critical significance. Therefore, raw data is generally gathered from defined, reliable and accurate sources to ensure that the output is valid.  

2. Preparation

Also known as data cleaning, this step involves filtering and sorting data to remove inaccurate and irrelevant data. During this step, miscalculations, errors, duplications, and missing datasets are removed.

3. Input

At this stage, the raw data is converted into a machine-readable format and entered into the system for processing.

4. Data Processing

The raw data is subjected to different processing methods to generate results using machine learning and artificial intelligence algorithms. This step may vary based on the purpose of processing and the type of data used.

5. Output

The data is finally transformed into user readable formats like charts, graphs, etc., which can be used to derive information or further processed depending on the need.

6. Storage

The last step of the processing cycle includes storing data for further use to ensure quick and convenient data retrieval.

Automated Data Processing

As technological advancement tops the list of trending technovations, Artificial Intelligence, Machine Learning, Automation, and Cloud Computing continue to explode, becoming the future of modern data analytics.

With almost all the business data shifting to the cloud, cloud computing takes center stage, improving the speed and efficacy of data processing. The increasing importance of real-time processing brings automated data processing into business operations. Automated data processing does all the heavy lifting, reducing human interference by relying on tools and software to manage the structure and movement of data across the organization. From simplifying omnichannel processing to reducing the high cost of errors, artificially intelligent automated data processing is one innovation that is here to stay.

Best Data Processing Tool

The data analytics market is brimming with various tools and software that can help users conduct data analysis and processing effortlessly. Among the most sought-after solutions is Needl.Ai, a holistic solution for an organization's data management, processing and collaboration needs. Needl.Ai is a cloud-based AI platform that unifies all unstructured data from emails, conversations, notes, files, websites, blogs, podcasts, RSS feeds, discrete SaaS platforms and applications and offers seamless data processing and collaboration services.

With big data entering the picture, data workers are expected to function on larger volumes of fragmented datasets, revolutionizing how businesses function. Needl.Ai assists in ensuring a transparent, productive data processing system, helping organizations remain agile and stay ahead of the competition.

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