“Big Data” is a term which really sprang to the fore in late 2015 and seems to have almost pervaded every aspect of business ever since. And rightly so. Even now, almost two years on, it’s still one of the hottest topics and trends across global business with its implications and application becoming wider and wider in scope.
The term “big data” essentially denotes two things. Firstly, the data sets themselves that are so complex and so large that traditional data processing methods and software are frankly insufficient/inadequate to deal with them. And secondly, how that data is gathered, digested and applied.
The challenges of big data include: initially capturing the data; storing the vast amounts of data; analysing the data; as well as other analysis-associated activities such as searching, transferring, sharing, presenting etc.Obviously, big data has implications across every field, but arguably none more so than in recruitment. Especially when one considers the myriad of variables involved and the genuinely important -and sometimes- truly difficult process of placing the ideal person in their ideal role. Getting to grips with data, every single relevant granule of detail, and digesting it and then reacting accordingly is therefore a potential game changer.
And it’s here where the use of some of big data’s most important aspects like user behaviour analytics, predictive analytics, and certain other advanced data analytics methods, can really have a crucial impact and allow us, as recruiters, to extract the key data and its value in the ever-increasing war for talent.The internet has given recruiters an almost incomprehensible wealth of information on candidates, both professional and personal. Whilst this is undoubtedly a blessing, it does mean that, due to the vastness and massiveness of the information available, ploughing and labouring through all of that data is a substantial and sometimes extensive drain on time and hard cash.
The real challenge here is aggregating the data, organising it, analysing it, parcelling it up and then interpreting it. Furthermore, it is essential, that with the ever-growing speed with which customers and candidates expect results, all of the above can be achieved as quickly as possible.Now we all know that the recruitment industry loves its technology and there are a number of technologies already in existence and in use (VMS, CRM, various HR and payroll systems); and some even have applications as far as big data is concerned already – workforce analytics tools and applicant tracking systems (ATS) – at least applications and capabilities (of sorts) when it comes to making the most of big data.
ATS’s and workforce analytics tools, to their credit, are excellent when it comes to pulling together and assembling the massive amount of transactional statistics and data. However, where they really lack, and where the real sweet-spot of big data is not in the aggregation of the information at hand, but rather the digestion, analysis and conversion of the raw data into tangible knowledge that allows us, as recruiters, to predict and forecast future trends and be able to, at worst, react quicker and at best, pre-empt candidate market trends.
This is where the big data analysis really comes to the fore. Enter the Data Scientist...
This is a relatively new role that combines the disciplines of Statistics, Database Development, Data Mining and Data Analysis. Data Scientists bring these skills to bear upon so called "Big Data" to produce and provide structure and insight from very large data sets. As mentioned previously these data sets may come from disparate sources or departments such as Finance, Advertising, Human Resources or Marketing.
These data superheroes use tools such as Hadoop (a database) and NoSQL (another database) as storage components, Python (a programming language) and R (another programming language) to standardize, manipulate and cleanse the data. They then apply analytical techniques such as ‘Machine Learning’ and ‘Deep Learning’, as well as statistical methods such as ‘Decision Trees’ and ‘Regression Analysis’, to extract insight (predictive or historical) from these large data sets.
Finally, they use data visualization tools such as Tableau to present that insight to the decision makers.
These insights, and the level of detail that can be extracted and leveraged, are ultimately used to help businesses make better decisions. At Yocto, we use the information that we’re able to gather via application of big data and the associated analytics to help our clients find the ideal person for their requirements, and ensure that people find their ideal jobs.
We call this the ‘Information-Inspiration.’ There aren’t any easy shortcuts. In order to master the complexity of recruitment, we master data. Our decisions are based on rigorous insight and data analysis using tangible information that actually mean something. This attention to detail at every stage of the candidate journey and the associated forensic data analysis enables our recruiters to focus on laser-like sourcing, ensuring that we make the perfect match every time.
Written by Lazar Tomović, Bid Manager.