The importance of big data for businesses

Today, one of the most popular concepts in the business world is big data, which has become an essential practice for all professionals. This methodology is present in much of the technology we use every day, such as GPS apps, the offers we see in digital media and research involving large amounts of analytical data.

As this practice has become more visible, more and more companies are looking for professionals who understand how it works and what it is for, and who can develop business solutions or strategies based on this new methodology.

What do we mean by big data?

When we talk about this concept we mean large volumes of varied and complex data that are very hard to capture using traditional methods or processing techniques such as databases.

We can consider data to be big data if it meets the following requirements, better known as the 5 Vs:

Volume

Data is collected on a massive scale through websites, social media, studies or research published on search engines, electronic devices, and so on.

Velocity

The data collected is generated in real time thanks to the collection sources.

Variety

The data, whether structured or not, can come from tables, videos, databases, texts, and so on.

Veracity

The sources from which we collect the data must be of good quality and trustworthy.

Value

The data must be able to provide value or a benefit to the companies that will use it.

Why is big data important for businesses?

The main reason companies want to use it is that it provides answers to questions they had not asked themselves. This is a very strong point, since it helps anticipate problems they may face in the future.

In addition, because we can gather a large amount of data in a much shorter period of time, companies’ analyses become faster and we can use them to identify new opportunities, strengths, threats and weaknesses.

Therefore, big data brings the following benefits to companies:

Cost reduction

Data technology tools let us store large amounts of data in less time, and they are far more efficient and cheaper than traditional methods.

Faster and better decision-making

By combining the information from our analyses with new data sources, we can make faster, more confident decisions based on the knowledge we gain from the data we have already collected.

Knowing everything about our products and services

We can measure the satisfaction and needs of our target audience more easily, which gives us the opportunity to offer them the product or service that best suits their needs.

Applying big data step by step

Companies are increasingly treating data as a strategic resource. But what steps do we need to follow to implement big data successfully?

1. Define why we will use big data

Many companies have spent time and resources implementing big data in their processes only to find they did not know what to do with the information, which taught them how important it is to be clear about how to use and implement this methodology.

Therefore, the first step to implementing big data successfully is to define which objectives we will pursue with the new technology and why. This first step is essential, since reinventing yourself is a complex task that requires a great deal of creativity and business vision.

2. Identify the data and analytical algorithms

We need to identify the types of data we want to collect, bearing in mind the objectives we are pursuing. In this way we will be able to identify other data that may also be useful for achieving our objectives.

Once we have identified the data we want to collect, we will have to select the different algorithms we will use, which will allow us to analyse large volumes of information in real time.

3. Choosing the technology

To make use of big data we need technological tools, so we will have to define the exact requirements we will need to cope with the complexity of the unstructured data we will collect.

4. Managing big data

This is one of the most important phases, especially the first few times we implement this methodology. In it we will decide who will be on the team that starts the process and analyses the data we collect. For this reason, we must limit the team to the strictly necessary profiles in order to save costs.

In addition, each team member will need to be clear about their role, so that the whole process is well organised and productivity increases considerably.

5. Data collection

We will start the data collection process using the technological tools we have previously selected.

6. Data extraction

The main aim of this phase is to transform the data we have collected. Thanks to this transformation we will be able to present the data in formats that are much easier to understand for subsequent analysis.

7. Data validation

Some of the data we collect will be wrong or invalid, so it will be essential to validate everything we have obtained in order to arrive at the most relevant data, which meets the objectives we have set ourselves.

8. Data analysis

Finally, once the data has been verified, we will analyse all of it with the aim of turning data into information and information into knowledge.

Big data and machine learning

As with most technologies and methodologies, combining them with machine learning makes tasks considerably more efficient. And big data and machine learning work perfectly well hand in hand.

But what is machine learning? It is the discipline that teaches machines how to learn and/or act using previously unknown data that helps these machines act on their own and so automate tasks or processes.

If we combine the two disciplines we will be able to carry out complete data extraction analyses, which big data will turn into useful information, and with this information machine learning can predict outcomes, business situations, and so on.

As a result of this combination, we will get autonomous machines that learn and improve automatically on an ongoing basis.