What exactly is data science?

23 November 2015 / Adrian Lingard

There is a lot of talk about the power of data science and how organisations today needs data scientists to extract insight from data to meet customer needs and achieve significant competitive advantage. But, many are left wondering what exactly data science is, what the components of it are and indeed, what/who is a data scientist.  

What do the academics say?

As with many wide-ranging industry phrases, who you ask partly defines the answer. In its new MSc course in Data Science, Sheffield University describe it as follows:
“Data science is an emerging field that seeks to discover and explore new ways of exploiting data to support decision making for a range of domains and problems”.

They go on to say:
“The UK requires a strong skills base, able to manage, analyse, interpret and communicate data, in order to extract insight and value” Seizing the Data Opportunity (HM Government, 2013). Data Science solutions involve knowledge and understanding of:

  • Technologies (e.g. data warehousing, Hadoop)
  • Data modelling (e.g., representing and aggregating multiple datasets)
  • Data standards (e.g., open data and Linked Data)
  • Data analysis (e.g., data mining)
  • Communication (e.g., data visualisation, generate data reports)
  • Wider context (e.g., business processes, governance and ethics)

It is a wide-ranging list, and one where I don’t particularly disagree with any of the points. The devil is in the detail as ever.

Unlocking the true value of data

I have worked with data analysts, modellers and technicians for over 20 years now – before most of you realised data is (are) cool! Over this time, the number of tools available to examine data has expanded hugely, as have the sources of data. Today, data is everywhere and it’s no longer just a table of customer and product records. Every time you open a browser you generate data, every time you click on a website or use an app on your phone you generate data. With the explosion of the digital era comes new challenges around getting data, understanding it, processing it, and just as importantly when to (and when not to) use it.

So, back to the original question, what is data science? My view is that it is a collection of disciplines used to organise, process, and get the best out of data – whether that’s to meet regulation, to gain business understanding, or to generate a better customer experience. Whether the action is technical, analytical, or to interpret an output, question it, and understand it. All these things are part of data science.  

What makes a Data Scientist?

The common question that then leads on from here is ‘what/who is a data scientist’? The simple answer, if you look at ALL the tools a data scientist could use and skills that a data scientist should have, is probably ‘no-one’! Clearly that isn’t a sensible answer but it highlights a point. Data science has a lot of disciplines and skills.

To call yourself a footballer you don’t have to be perfect in all disciplines. So to call yourself a data scientist you don’t have to be the fully rounded article. The ever-increasing number of technologies and opportunities (as well as responsibilities) around data all contribute to the number of increasing data scientists. The expertise of some goes very deep in a particular discipline, whereas others operate at a high level across a number of different disciplines. It is both the breadth and depth of skills that increases the quality of data science, and therefore the Data Scientist. Data scientists of all types have some very useful skills to offer organisations. The more complex the data environment, the greater the opportunity to explore the art of data science (yes, it’s an art!), both in terms of processing and interpreting data.

A highly-skilled team approach is key to success

At Jaywing, data science is a key specialism of ours so clearly we have to operate across the full breadth and depth of the discipline. We have around 60 data scientists, some are specialists and others operate across a broad range of skills. There is no such thing as ‘the perfect data scientist’ in the same way as there is no such thing as ‘the perfect footballer’. However, the question is often ‘how can you tackle the challenge in front of you?’ and/or ‘how can you maximise the opportunity?’ The answer is found by having a large number of people with various depths of skills across all areas and by having a large number of specialists who can complement those people.  I am fortunate in that my team at Jaywing bring together a complementary blend of backgrounds and experience to make a very unique data science team. By working with data science specialists with the right mix of skills and experience, you can gain valuable business insight from data to exceed customer expectations and gain significant competitive advantage.