All you need to know about Data Science and Big Data (they're not the same)
Our Data Science and Big Data recruitment consultant brings his latest insight into his market. As many of you will know, he is certainly working a booming field, both for ourselves and the wider market. However, many people still feel the field is , and even a little confusing! Niall reveals a little more about that today.
So, give us an idea of how you see things with your experience as a niche recruiter in the area?
I’d like to start by saying this is all taking time to define because it is hugely important, and all things that are important, do. I read a cracking blog post by on the market that summed up our approach to Big Data as a recruitment agency recently, and I think it would be fair to say, the way lots of other people involved in the field see it too.
It is a pretty simple concept really, but something that certainly goes a long way to helping the situation. A shout out to @data_nerd on Twitter for posting it – Big Data needs Data Science, but Data Science doesn’t need Big Data.
Ok there’s a difference then? Tell us a little more about that.
We’ve seen lots of scepticism surrounding the field recently, after the buzzword “Big Data” rode in on a wave of adulation. It’s not without merit, either. You see, people have for a long time been calling Data Science, Big Data.
I suppose for myself and others, the Data Science element makes up one segment of the Big Data market. Data Science consists of the advanced analytics conducted by professionals capable of getting insight from really unusual or previously untapped sources of knowledge in data. Some of the success stories can be traced here to London, Hailo (the cab app) for example with their unusual location based sources, or analysis being conducted on social media – Twitter, etc. Real time analytics through updates for quicker insight are proving to be invaluable. I think that too is why we’re seeing people like Gil Press capable of writing a separate history timeline for each - Big Data/ Data Science (they’re not entirely new concepts!).
We’re seeing these new results which provide real insight for businesses, and this is where the real Data Scientists are coming into play (and proving themselves to be so valuable). These guys and girls can not only analyse these unusual sources of information, but also gain genuine business insight from them because they are so commercially minded. This naturally requires a large amount of technical skill, so new technologies and practices are emerging to deal with that. I talk a little more about that on my blog on the Xcede Recruitment Solutions website “What is a Data Scientist and how do I become one?”
If that is the case, what does Big Data include under its banner?
Again, a tricky one, and this opinion can vary from person to person. I believe the technical element, the engineers capable of using Hadoop, NoSQL databases (Mongo, Cassandra and the like) are a clear part of this.
Big Data was certainly given its name by a capability of dealing with Data en mass for analysis (hence the simplified Big) rather than previous analytical techniques. However, such techniques are still very much used within businesses; we see a huge number of clients looking for Data Scientists capable of modelling, clustering, regression. They’re still valuable skills.
This can often be the point of contention for many people; it marks a sea of change in the previous thinking of how to get the best insight from your Data. Not to spoil the show (you can still find it online), but I was recently at a talk given by Fiachra Woodman of Aimia at CWJObs’s Big Data Breakfast. It was interesting because he suggested that this was their major doubt about making the switch – a culture change as colossal as that is only magnified when it’s being considered at one of the largest insight companies in the world.
So is it only large companies that can take advantage of Big Data…and Data Science?
In my opinion, not at all. It all depends on the product that you have, and whether any results from analysis are genuinely going to get you some ROI. I think that has been one of the other major hang ups for business leaders – they’re scared that it won’t be worthwhile to them (especially at a smaller business level, or that at least is what I’ve been told by my contacts in the know in each sector).
I’ve worked with a hugely exciting start-up in London who recently (through me) recruited a Data Scientist and a Data Engineer. The engineer was very much capable of dealing with large data sets, and the scientist of analysing them and provided huge value to the business model (they are a market research company, so this is obviously hugely important to monetising their product).
It was particularly important that their Data Scientist was capable with something like Tableau or D3.js. Visualisation is obviously a huge aspect of communicating insight to those who need the information displayed in a way that makes sense to them (some can truly be beautiful as well as helpful), but maybe that talk is for another time.
The point is, they needed a two man team. The oracle himself, DJ Patil, wrote an article about a year back outlining how you could build a Data Scientist team. In it, he mentions that a number of skills are necessary (visualisation is a great example) and therefore people can scale their team accordingly.
Of course, smaller companies and start-ups will need people that can muck in with a little bit of everything (that’s why I encourage people to give that kind of environment a go at some point to really hone their skills), but at a larger company level you could split this up. If we’re talking about a multinational, you could imagine a Big Data/ Data Science unit being set up – a team of engineers being a part of that, Data Scientists also who are very good at their jobs, but in addition the ever increasingly popular job title of “Data Artisan”.
It sounds like there is a lot to consider when trying using it!
There is and there isn’t. Certainly it’s a large undertaking for anyone who wants to implement it properly; the possibilities are endless though. We’ve placed two Data Scientists within a large publisher a while back and their Head of Pricing and Analytics Eloy Sasot (a passionate advocate of Big Data in all of its forms) gave us a great interviews on his opinions – you can read his interview here.
I’m not as technical as Eloy, so it may be worth taking his word as well as my own, but both Data Science and Big Data are only going to grow in importance. As I mentioned at the beginning, the definitions of the field are still being set in stone and are going to continue to be a little fluid for a while yet. However, progress is being made – we’ve see “Big Data” entering into the dictionary in the past few months.
But hopefully we can help with that a bit!
Yeah absolutely, and as I say to all of my clients and candidates, anything that you are unsure about I’m always here to help. Big Data and Data Science as areas of expertise have grown organically, and will continue to do so if well all join in the discussion.
For more information on Data Science and Big Data contact Niall on 0203 301 9908 or email@example.com or check out all our current vacancies.