For the uninitiated, the term ‘Data Science’ can conjure up images of the famous scene from The Matrix – the one with the unending scroll of green characters on a black screen. It means it is indecipherable to all but a select few. As with many Hollywood inventions though, this just isn’t true.
There is some basis in fact, however. At its very simplest, data science is the extraction of useful information from structured and unstructured data. If it helps, think of this as the characters from the film extracting information from that scrolling green screen. The premise is the same – the details vastly different.
In the understanding that data science is extracting information from data then you may be questioning why we need it at all. The answer is simply that the amount, and complexity of data being created and analysed in our current world requires new approaches. Data science is more focussed on spotting future trends and predictive analytics than traditional, simple analysis.
Everyone has heard a different statistic for how much data is created every minute. The numbers are staggering. More importantly for this topic though is the prediction that by 2020, over 80% of data created will be unstructured. Unstructured data essentially means that simple Business Intelligence systems would not be able to process it. To be able to organise, analyse and extract useful information from this we need the power of data science.
Data science is a large field and encompasses many different specialisations. From start to finish it can include data collection, data storage, data preparation, visualisation, statistical analysis, machine and deep learning, predictive analysis and more.
It is important to note here that whilst data science often uses technologies such as Artificial Intelligence (AI) and Machine Learning, it should not be used as an interchangeable term. Data science uses the power of AI to interrogate and give meaning to large bodies of data. Where AI and Machine Learning can really help is with predictive analytics.
This is a tough question to answer. Depending on your organisation data science could help in a huge variety of different ways. Any task that could be improved, or even simply done, by processing and analysing large amounts of data could, in theory, be helped by data science.
For your organisation that could look like spotting abnormalities or emerging trends. Whether that is potential fraud or abnormal behaviour from a client or identifying ahead of time where bottlenecks in your operations could appear. It could also help you to craft future marketing campaigns for your customer base – but based on robust data analysis rather than hunches.
At this point, we hope data science sounds useful and worthwhile. But it isn’t for everyone, the outlays are simply too expensive for many organisations and for others it just isn’t quite the right fit. Businesses or organisations that can really take advantage of data science usually manage incredible amounts of data and make business-critical decisions on a daily basis. It’s more commonly used in finance, pharmaceutical and government industries currently, but this isn’t exclusive.
Primarily though, organisations looking to take advantage of data science need to really value data. Their decision-making process, and often their entire culture, should be built around it.