There is an unbelievable amount of data in the world. Moreover, it just keeps growing and growing: according to Domo, 2.5 quintillion bytes of data are created every single day. That’s 25, followed by 17 zeros. Alternatively, 2.5 billion billion bytes. It’s a lot!
It’s hard to articulate just how much data there is in the world. That’s where the term Big Data comes from - the exponential growth and availability of large data sets. Even more broadly, the term Big Data also encompasses the speed with which the data is created, and it’s inherent complexity due to its sheer volume. Photos, internet searches, credit card transactions, emails. Data comes at us from all angles, all the time.
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Big Data in Finance drives important business decisions
Data is so essential to motivating business decisions, what to invest in, where to expand, how to boost profits, these are all questions Big Data can help us solve. It helps with understanding customer needs, market trends, business patterns and revenue results. It is indispensable for keeping business margins positive and making critical decisions.
The words ‘Big Data’ get thrown around a lot, so what types of data are actually included? The term is used quite loosely and really refers to any set of data that encompasses the three Vs: Volume, Velocity and Variety. In other words, any data that comes in very fast, from several different sources, in a variety of formats, can be classified as Big.
In the financial world, these different sources of Big Data can refer to a variety of things, such as transactional banking data, personal information such as date of birth, or details on any outstanding loans or debt.
Before wide-spread smartphone use, it was hard to access data to this level that is common in many businesses today. Now, most companies have access to data from multiple sources thanks to better tracking methods and technology, such as APIs and other integrations.
With billions of financial transactions happening between millions of consumers every day, the trail in consumers transactional data can be revealing. The question of how we can access the data is not as relevant anymore. Instead, the question becomes, how do we interpret it?
Big Data leads to big knowledge
Historically, taking advantage of Big Data has been crucial for companies to stay ahead of the competition. In the past decade, those with a data-driven mindset have been quick to beat the curve. As Sir Francis Bacon once said, “knowledge is power” and in today’s world, knowledge comes from numbers.
Data-driven strategies are behind the success of many of the world’s biggest companies, who continue to use Big Data to innovate, optimise and ultimately win business.
Big Data has even given rise to new sectors - alternative credit scoring is only possible thanks to the regulated access to detailed financial transaction data. This is only a relatively recent development in the industry, but already we have gained new insights into spending behaviours and created better products from analysing data that wasn’t there before.
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How can my organisation harness my Big Data?
Before you go pouring thousands on hiring a new data scientist, data engineer, programmer or analyst, the good news is that you don’t need to break the bank to utilise Big Data. Data is lying all around us, and the solution to analysing this data can often be quite straightforward. By leaning on tools already in the marketplace, we can start to make sense of data.
Tools, such as machine learning (ML), take our information from a seemingly useless string of digits and facts to an easily digestible array of insights on customer demographics, buying patterns and behaviours, just to name a few. The trick is to know what data is vital for your business and then finding the right tool for the job.
Once the technology is matched correctly, the insights can be transformational for businesses. The right tools will allow your business to discover new opportunities, enabling you to identify and fix what is not working. In fact, this is part of the mission statement for SAS, one of the pioneers in predictive modelling and Big Data analytics: "Identify what's working. Fix what isn't. Moreover, discover new opportunities.
Connecting the right tools will take your business to the next level
One of the most exciting areas in Big Data analytics is the use of ML. ML models are built on Big Data. Businesses smartly implementing ML are harnessing the competitive edge ML can bring. There is no indication that this trend is slowing down, either.
So, what are the practical implication of using Big Data for ML? Well, Big Data can be used to figure out spending habits to continue to improve the accuracy of spending models or, automating credit risk scoring.
If Big Data can give us the much-needed information to make better decisions in our businesses, then we must learn how to harness its powers. Data volumes are only going to increase, so it makes sense for any organisation to take advantage of the insights it offers, particularly when the methods of understanding it are becoming cheaper and more readily available.
We know the secrets are there. The next step is to unlock them.