Having a lot of data is an advantage for companies, but it’s no use if they don’t know how to process them. What does “data processing” mean and why is it so important?

Data Processing: What Is It?

We’ve already seen what Big Data are and why they’re so important for businesses. Now let’s get into detail to understand exactly what data processing – entrusted to Data Scientists and Data Analysts, two of the most sought-after professions on today’s job market – really means.

Data processing starts with the collection of raw data and ends with their transformation into usable information. This operation must be carried out in great detail so as not to compromise the end results or the data output, as well as to enable companies to optimise their marketing strategies.

In order to become usable, all the raw data collected must be carefully converted into a readable format – like a chart – that may vary depending on form and specific context. This way, devices and AI can interpret the information correctly, allowing it to be used by professionals in different ways, all with the aim of optimising company processes.

The Stages of Data Processing

1. Collection

First of all, the data are pulled from available sources, which must be trustworthy so as to ensure the top quality of the information collected.

2. Preparation

Once the data are collected, they enter the preparation stage, also known as “pre-processing”. All the data are checked to avoid errors and selected based on quality as well as cleaned up and organised.

3. Input

At this point, the data are entered into their destination system, such as a CRM system or a data warehouse and then translated into a language that the chosen system can understand. For the first time, the raw data begins to take shape in a more usable way.

4. Processing

A crucial stage during which all data are processed for interpretation using machine-learning algorithms. Once again, the source of the processed data (social networks, data lakes, etc.) and their intended use (medical diagnoses, consumer needs, marketing, etc.) are extremely important.

5. Output

The data are transformed into information usable for the average use; indeed, the output stage is also called “interpretation”. The content finally takes shape as videos, images, plain text or charts, and company employees can start planning strategies and projects based on the data available.

6. Storing

Some data are used immediately, while the rest are stored for future use. This stage is extremely important because it is necessary for compliance with data protection regulations, like GDPR (General Data Protection Regulation).

From Processing to Analysis: the Future of Data Is in the Cloud

The cloud offers endless possibilities and, speaking of Big Data, it can speed data processing up and make it more effective. Speed is everything in business, and having access to such a large quantity of data in the shortest possible time is an incredible advantage for companies.

The cloud also enables companies – both large and small – to combine their platforms into a single system. Despite the many functions that can grow along with the company, the main cloud platforms are free. Implementing our own data processing strategy means offers an incredible advantage for competitiveness on the market.

Though the stages of Big Data processing might remain the same over time, the cloud is set to become the prevailing technology, with its offer of affordable, smart and sophisticated methods.

What Happens After the Data Have Been Processed?

At this point, it’s time to map out the ideal client, revisit company processes and decide on the best marketing strategy to grow our business. Data analysis allows us to make more conscious decisions in less time.

In such a hectic global economic context, competitiveness is key!