What is big data?

What is big data?

With the constant development of digital tools on the market, we are generating increasing volumes of data. With the amount of data we used to generate, we could use standard tools to quantify, analyse and store it — and these processes were relatively simple. Today we generate much higher volumes of data. We need ways of storing it all, and processing it quickly — often in real time — to leverage its true value. Big data projects require architectures and infrastructures that are specially designed for this purpose, and cloud computing is here to meet these expectations.

big data

Big data definition

The term ‘big data’ is used to refer to very high volumes of data. In everyday life, a lot of our actions generate data. Similarly, when we use an application or website, we request high volumes of data. These volumes are simply impossible for a typical person or analysis tool to process. To automate the collection and processing of this data, many organisations are implementing big data projects. These include (but are not limited to) private companies, public administrations, social networks, mobile applications, and research institutes.

In order to meet this demand, new tools emerged for distributed data storage and processing —  like Hadoop, Apache Spark, Kafka, Flink, MongoDB, and more. The purpose of big data is to harness the value of data that would not be useful in small volumes. The emergence of these tools and practices has also led to the creation of new professions: for example, data analysts, data engineers, data scientists, specialist big data consultants, and much more. Their role is to support companies in their operations.

Big data — the 4 Vs

To get a better understanding of what big data is, we need to detail the three fundamental points: volume, velocity and variety. A fourth point is also important when we talk about processing a large volume of data: veracity.

  • Volume

Every company and organisation generates data. The high number of data sources — and the need to quantify and manage them — mean that companies must find ways of storing rapidly-growing volumes of data. While most of the data collected is of low quality, it becomes valuable data when it is structured and cross-referenced.

The infrastructure used as part of a big data project must have a very high volume of storage space to handle the influx of data, which can increase exponentially as the project evolves.

  • Velocity

Stored data can quickly become obsolete if it is not processed in time. The speed of data collection and processing is an important variable, so it requires real-time analysis tools. Standard tools are markedly slower in processing data streams, and there is little likelihood that they can cross-reference the data they process. This is why new tools designed for big data offer analysis and processing methods with increased performance, so that the data is processed before it becomes outdated.

  • Variety

The more varied the data sources are, the more qualitative the analysis will be. This variety is also influenced by the range of formats for the resources collected. This includes temporal, geographical and transactional data, as well as data extracted from its context (audio, video and text). The value of big data processing lies in the ability to cross-reference this data, then harness its value. It can be used for product improvement, service development, customer understanding, and even forecasting what actions to take in the future.

  • Veracity

Aside from considering how such high volumes of data will be stored, and how quickly it will be processed, another crucial factor to keep in mind is its accuracy.

Big data processing is an expensive operation that can present real challenges for a company’s future. If the data used is false or inaccurate, the data analysis results will also be incorrect. This can then lead to decisions based on false information.

Uses for big data

  • Developing products

Using predictive analytics and data visualisation, exploiting data on a product can help you better understand buyers’ needs, and how to meet them. This means that when you improve your current products and develop new ones, you can meet your customers’ requests as closely as possible.

  • Performing predictive maintenance

Anticipating hardware replacements and predicting mechanical failures are major industry challenges. By using predictive analytics, you can replace a machine at the end of its lifecycle, or near its point of failure — which can significantly control costs company-wide.

  • Predicting future needs

Predicting your long-term future requirements can be a very tricky task. To tackle this challenge, you can use big data to predict the strategies you need to adopt in the short, medium and long term — so it is a great tool for aiding decision-making processes.

  • Dealing with fraud

Due to their size, mid-to-large sized companies face increasingly sophisticated fraud attempts. Fraudulent behaviour can be difficult to perceive as it is hidden in vast data streams — but nevertheless, you can use big data to detect recurrent patterns and manipulations. By using big data to analyse suspicious behaviour, you can be more vigilant and responsive against attempted fraud.

  • Preparing data for machine learning

Machine learning for artificial intelligence requires data. In theory, the more data there is, the more accurate the learning outcome will be. Big data helps clean, qualify and structure the data that powers machine learning algorithms.

Big data OVHcloud

Big data technologies

  • Apache Hadoop

Apache Hadoop is an open-source framework that helps applications exploit huge volumes of data. Hadoop can store petabytes of data (very high volumes) by distributing it across the various nodes in a cluster. The data can then be queried efficiently using MapReduce architecture.

This software acts as a data warehouse, and can be used to store data. It also mitigates hardware failures that may occur on part of the infrastructure, so that they do not result in data loss or downtime.

  • Apache Spark

Apache Spark is another framework dedicated to big data, and is used for static or real-time data processing. Its data architecture makes it work quicker (with reduced processing time) than MapReduce, Hadoop’s processing system. Spark does not have a distributed data storage feature, so it can be used in conjunction with Hadoop for data optimisation, or with S3 object storage solutions.

  • MongoDB

Because big data comes in such high volumes, its users need to move away from the standard way of working with structured relational databases. This is why MongoDB, a NoSQL distributed database management system, was created. By completely redefining how to integrate and serve data, it is able to process information very quickly within a big data project.

  • Python

Python is considered the most widely-used language for machine learning, making it a perfect choice for your big data solution. It is very popular and compatible with most operating systems — so many developers and data scientists choose to use it for its ease of use, and the time it saves in creating algorithms. There are many libraries that make it easier for developers to work in IT domains such as data science, data analytics, data management, and much more.

Big data at the heart of digital transformation

There are many sources and types of non-structured data (web activity, connected devices, user habits, CRM, etc.). With a digital marketing strategy, companies can leverage data for analytics, and turn their raw data into an asset. A data analyst can interpret the available data, and participate in the decision-making process — with subjects such as customer relations or customer knowledge, for example. Part of the decision-making chain involves modelling your big data architecture, and integrating it into your digital transformation.

Artificial intelligence and big data

The same way that human beings need to access and process knowledge in order to learn, artificial intelligence relies on data. Theoretically, the more data it can access for its learning, the more efficient the AI will be. High volumes of data from various sources may be required to be exploited by the machine’s algorithm.