A crucial element in modern business workflows, data processing empowers organizations to make thoughtful decisions based on true and powerful insights. By collecting, parsing, and interpreting data, organizations are armed with all they need to make key business decisions with a greater chance of success.

What is Data Processing?

In the digital-first era, data is king. Chances are, your business is more accessible than ever, which means you can collect data from virtually any interaction with prospective and established clients.

Between phones, tablets, laptops, and wearable devices, data is being transmitted constantly. As your business collects data from website visitors, contact forms, payments, fulfillment, email subscribers, and more, not putting it to use means leaving money on the table. Data processing and analysis help to make sense of the data you have, converting raw data into meaningful insights.

Stages of Data Processing

To make the most of the information you have, consider the following data processing steps:

Collection

This self-explanatory stage requires the collection of raw data. Pull this data from accurate, reliable, and trustworthy sources to ensure quality.

Preparation

Before analyzing the data, it must first be prepared in a pre-processing stage. Clean raw data by scrubbing for errors and eliminating any redundant or incomplete data, leaving only the highest-quality points to carry into the next phase.

Input

At this stage, convert your raw data into a digestible, readable format. This crucial step prepares the data for analysis and can be done manually or with automated data processing tools.

Take quality control into account in this stage (garbage in, garbage out) and ensure the accuracy of your data, and that it can be trusted to generate insights.

Processing

Now that you have a data pool, this is the stage where it will be processed and interpreted using machine learning and AI algorithms.

Output

Once the data is parsed and analyzed, the resulting information will be transferred to a user-readable format. Depending on the use case and data set, this may include files, graphs, documents, or other formats.

Storage

Once the data has been generated, transmitted, and displayed, this is the stage where it will be stored. Data insights may be useful in future requirements and can be included in additional processing.

Types of Data Processing

There are a few key types of data processing to be aware of:

Online

Don’t confuse online with real-time data processing, though they have similarities. Online data processing allows users to extract and parse data at any time, and from any location. 

Ex: A purchase transaction starts with scanning a UPC or entering a product code, collecting payment, and then marking the product sold in the store’s inventory.

Real-Time

Similar to online, real-time data processing collects data from anywhere at any time and generates results.

Ex: An application for maps and directions can interpret traffic data and generate recommendations as a result of current conditions.

Batch

Batch data processing refers to assigning actions across multiple data sets from a single point or command.

Ex: Within a spreadsheet workbook, a macro or formula can analyze or sort data with ease, and update with changes made to cells or functions.

Automatic

With automatic data processing, artificial intelligence takes the legwork of data entry and processing in real time without human intervention.

Ex: Invoices are automatically generated and sent as part of supply chain functions, without the need for manual entry and tracking.

The Future of Data Processing

In line with the transformation of modern business, the future of data processing moves from server rooms to the cloud. The fundamental steps remain as outlined above, relying on organizations to collect clean and accurate data in order to generate valuable insights. But cloud computing will offer faster, more advanced, more efficient, and more cost-effective results.

The cloud also offers the accessibility and agility that modern businesses require. Those who work with or require data insights will be able to access what they need, when they need it, from wherever they are. Integration with existing cloud applications and databases means data processing in the cloud will eliminate silos to support insight-driven workflows with scalability through congruence across the departments and systems.