Big Data Analytics: find out all you need about it

Big data became a significant part of many development and marketing processes. With its help, thousands of companies create better products. At the Global Cloud Team, we use big data analytics as well.

Learn all about the ways data analytics is used by companies, why it is so important, and how to implement it in your business in the article below.

What is Big Data Analytics?

When we speak of Big Data, we mean a huge amount of information that is usually impossible to process manually. Special software and a variety of methodologies are applied to uncover hidden patterns along with different correlations. As an example of application, this helps developers find new ideas to simplify the usage of their software.

Another example of the usage of big data analytics is enhancing security features and searching for new opportunities for development. It provides a complete insight into what’s going on with a certain product. User behavior can be analyzed as well, bringing even more understanding of what should be improved.

We are confident that we have what it takes to help you get your platform from the idea throughout design and development phases, all the way to successful deployment in a production environment!

4 steps of big data analytics

You might have already guessed that processing such a huge amount of information requires a structured approach. In most cases, all the work is divided into four stages.

1. Collecting the data

While this sounds like a simple job, data collection varies depending on the organization and specifications of the gathered information. Modern technologies allow specialists to gain both structured and unstructured data from different sources. The most frequent ones are cloud storage and software.

Then, the information is gathered in a data center or data pool, depending on its form. The former is required for processed data, while the latter is more suitable for raw data.

2. Processing the data

When you get the information, it will first be available in a raw form. It’s hard to make any sense of it, so some processing is required. This will help the specialist get proper results and certain answers regarding analytics and research.

There are two options to transform the data into an easy-to-read form:

• Batch processing;
• Stream processing.

The first option covers huge blocks of information over time. It is a reasonable approach when there is a big gap between gathering and processing the data.

The second option covers small blocks of information, transferring everything significantly faster. Although it is usually very complex and too expensive, stream processing helps companies save a massive amount of time.

3. Cleaning the data

If you do not want to have false information, you’ll have to swipe it like a dirty window. Specialists must ensure proper formatting of the gathered data and the absence of duplicates and irrelevant info. That’s the only way big data analytics can bring strong and clear results. Misleading content could lead to huge losses in millions of dollars.

4. Final analysis

It will take some time before the gathered data get a readable form. Once this happens, the company’s analytics team can start getting new ideas regarding the improvement of the product’s performance. Some analysis methods could be:

• Mining data – involves checking large blocks of info for patterns and similarities. This is done by identifying unusual data and creating clusters.
• Predictions – involves the usage of historical data of an organization to predict what could happen in the nearest future. This helps companies see risks and new chances for profits.
• Deep learning – involves the application of AI technologies to imitate human activity. This simplifies the analysis of algorithms, patterns and work with complex data.

Each of these methods is good in its own way. Usually, Big Data scientists combine different approaches for the best results. It might take a lot of time, although the results are almost always worth the effort.

What are the benefits of big data analytics?

Generally speaking, your company would get the same advantages as in any other type of analytics. It is more about being informed about important events on time to prevent huge financial losses.

In most cases, businesses benefit from big data analytics in the following ways:

1. Reduced expenses due to an improved process.
2. Enhanced product development due to better understanding.
3. Increased market awareness due to new knowledge regarding trends and user behavior.

While these are only the general advantages you would get from using big data analytics, there are multiple other pros you’d acquire depending on the area you work in.

What challenges do big data specialists face?

Big data is a relatively new area, meaning that there are still multiple challenges for all scientists and specialists who work within it. Some of the main big data challenges involve:

• Creating clear and readable data charts;
• Keeping high-quality data without errors, duplicates, etc.;
• Maintaining data security;
• Applying the proper tools, software, and platforms.

The last option can be easily changed by developing a new big data solution specifically for your business. The Global Cloud Team specializes in this area, so we know how to help!

How to get started with big data analytics?

Now that you know how it works and which methodologies are applied, it is time to implement big data and analytics solutions in your company. This will help you reach the top of the competition and bring down any competitors on your way. Try it out now with the Global Cloud Team!

Author

Ivan Kolesnikov

Total Articles: 211