Understanding big data – and why it's vital for big business decisions

You may have plenty of questions about big data...
You may well have plenty of questions about big data...

Big data is such a complex and game-changing tool, it is not surprising that businesses are wary and sometimes confused by it. The benefits are significant, and with so many potential uses, it is important that organisations fully understand it before engaging with it.

While data does not always have to be "big", a good way of describing this recent trend is multiple sets of data which are too large and complex to be processed through traditional tools.

The key for organisations is combining the right data sources to answer business questions. Data can be any size, the critical point is relevance. It can be about almost anything in any format, from customer data, financial data, social media, manufacturing data to sports data, and when analysed, can provide insight and understanding of complex issues. In an increasingly IT-focused digital age, data is being collected from more sources and locations.

In the last few years we have seen an explosion in data. There are very few industries which are not using data and could not benefit from the insight it provides. Until recently, the focus for much of this insight has been for marketing, but it is increasingly being used for different applications. One of the most exciting use cases is in sport. The Bolton Wanderers Football Club is using data blending and visualisations to help them understand the movement of players and improve their game.

Before being able to analyse and learn from data, businesses need some key questions answered: where is data captured and stored, how is it processed, what is the right data to use to answer the most pressing questions, and what do businesses get from it?

Where is data stored and captured?

Data can be stored almost anywhere. When it comes to data, it is often so large and from multiple sources that it needs to be stored across multiple databases which are then clustered together. The benefit of a system like this is the scalability. To increase the size of this type of database, companies can simply install more storage and put in place enough hardware to manage it.

There are generally two main ways data is stored: SQL and NoSQL. SQL (Structured Query Language) is a type of programming language designed for data. From the 1970s until recently, SQL-based databases were the dominant force. However, SQL has begun to lose its appeal as the means to store data because the code is not fully portable. It can also be a bit restrictive as the standard is not always maintained leaving businesses unable to blend certain data sources together.

NoSQL (Not only SQL) was designed to solve these issues. NoSQL supports SQL along with multiple other languages, adapted to the demands of data. With NoSQL, speed comes first, and unlike SQL, there is no structure so the system is horizontally scalable. This makes growth very easy. If an organisation has enough space to store data then further databases can be added to grow the overall data cluster. For this reason, NoSQL is the system of choice for heavily data dependent organisations such as Google, Amazon and the CIA.


Hadoop is a software ecosystem which enables SQL and NoSQL databases. When introduced it dramatically speeds up processes by clustering databases in parallel. Because the data is stored in separate places, a data analysis or blending procedure which might take 20 hours can take just three minutes.

As data requirements have grown, Hadoop has enabled this growth, allowing for the management of structured (SQL) and unstructured (NoSQL) data.

Hadoop is one of the key factors for the current data revolution we are experiencing. When combined with data analysis and blending software, Hadoop can be used by largely anyone able to understand the software, often without the need for a data scientist.