We live in a hyper-connected, globalised world in which we are constantly sharing information, even if we don’t realise it. This constant flow of information comes not only from what we choose to share, directly or indirectly, on the internet, but also from all the little things we do.
Companies of all sizes are becoming increasingly aware of the importance of data, whether it be to make far-reaching business decisions, devise winning marketing strategies or shed light where there was once darkness.
That is why, in this article, we are going to explain in detail what Big Data is and what Big Data analysis involves, as well as why it is so valuable for a company to have experts in Big Data Analytics as part of its team. Are you interested in making the leap into the world of data? This post from EDUCA Open is sure to hook you!
What is Big Data and its analysis?
To begin with, when we talk about Big Data, we are referring to a large volume of structured data (well-organised, catalogued and stored), semi-structured data and unstructured data (uncategorised, without a standard format, and distributed irregularly).
By extension, Big Data analysis, or Big Data Analytics, is a complex process in which These vast amounts of data are analysed in order to draw some valuable conclusions for the company, whether regarding correlations, market trends or the behaviour of the target audience.
How does big data analysis work?
Big Data analysis is a complex process involving the collection, processing and analysis of large volumes of data to obtain valuable information and useful insights. Below, we outline the basic steps involved in this process:
Setting objectives
We cannot plunge into a sea of data without a clear direction, a clear goal of what we want to achieve. That is why the first step will always be to clearly identify the objectives and the questions we wish to answer through the analysis. With a clear approach in mind, we will be able to select the data that is most relevant to us and know how to analyse it.
Data collection
At this stage, the data required for the analysis is collected. This data may come from various sources, such as internal databases, application logs, social media, sensors, amongst others. It is important to ensure that the data is reliable and well-structured.
To this end, it is useful to understand the “inner workings” of all the data measurement tools we use, as each one measures data in a different way. This is essential for being aware of the margins of error in our data and knowing how to identify more realistic data.
Data storage
The data collected is stored in appropriate storage systems, such as NoSQL databases or distributed file systems. It is also common to use big data platforms such as Hadoop or Spark to manage and process large volumes of data.
Particular emphasis must be placed on ensuring that data storage is secure; in other words, it must not be exposed to IT vulnerabilities or potential cyber-attacks, particularly if the databases contain commercial and/or confidential customer information.
Data preparation
At this stage, a the process of cleaning and transforming the data. This involves removing duplicate data, correcting errors, filling in missing values and converting the data into a consistent format for analysis.
Although it may sound paradoxical, a large part of the time spent on Big Data analysis is devoted to data cleaning. In other words, preparing the data using programming languages such as R or Python to bring its format into line with the standards of data visualisation tools such as Tableau or Power BI.
Data analysis
At this point, the following apply statistical techniques and machine learning algorithms to extract information and patterns significant aspects of the data. The type of analysis may vary depending on the objectives set, and may include descriptive, predictive or prescriptive analysis.
Descriptive analysis involves describing and analysing historical data to interpret the current situation and understand the behaviour and characteristics of our users. Predictive analysis, on the other hand, goes beyond space and time to provide forecasts and estimates based on past data and multiple variables.
Data visualisation
Data visualisation tools are used to gain a better understanding of the results of the analysis. Charts, tables and other visual elements make it possible to present the information in a clearer and more comprehensible way.
These charts, which can take a wide variety of forms depending on the type of data in question, not only help big data analysts to interpret the information, but also to to present it to other senior staff within the company. That is why we aim to simplify the complexity of the information and present only the most relevant charts.
Interpreting results and decision-making
At this stage, the findings of the analysis are interpreted in order to answer the questions initially posed. This may involve analysing trends, identifying relationships between variables or formulating recommendations based on the results obtained.
With the information and e insights obtained from the analysis, informed decisions are made which can improve processes, optimise strategies or solve specific problems.
Monitoring and updating
Big Data analysis is a cyclical process that requires you to be constantly assessing the rigour of analytical procedures that are observed, and ensures that the data being analysed is up to date.
This is because data changes over time (sometimes on a daily basis), and it is necessary to monitor and update the analyses regularly to ensure the results remain relevant and accurate.
Why is Big Data transforming businesses?
Big Data is revolutionising many fields, but it is particularly useful in helping businesses to gain a better understanding of their audience, create more tailored products, adapt to public preferences and identify problems that may not be immediately obvious.
Here’s a brief overview of how Big Data can boost a company’s brand and revenue:
It offers customisation options
Have you ever wondered how video platforms such as Netflix or YouTube provide you with recommendations? Well, big data analysis is the answer. These platforms use algorithms to identify trends and patterns of preference amongst thousands of users, with the aim of offering you content that is sure to interest you.
More effective products are being developed
Our audience may exhibit behaviours that suggest a need for a new product or feature. With Big Data, we could draw conclusions about what users are looking for most and what is missing from our range or features in order to fully meet their needs.
They improve the customer experience
The field of UX, or user experience, can benefit greatly from big data analysis, as it provides insightful information about the problems and confusion users may be experiencing on the website and within the conversion funnel. Big data therefore makes it possible to quickly identify whether something is going wrong from a technical or visual perspective, so that it can be resolved.
Optimisation of production processes
Big Data can help us identify areas for improvement in work processes, eliminate bottlenecks, streamline the most resource-intensive tasks and, ultimately, create new, more efficient workflows.
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