Some people have compared this big data phenomenon with the California Gold Rush of the 1800s. It seems reasonable to compare the two in that today’s big data can translate to money. The Gold Rush happened in Northern California; the big data rush is being propelled largely by many tech companies in the Silicon Valley, in Northern California. However, there’s a big difference between the Big Data Rush of today and the California Gold Rush of the 1800s. The California Gold Rush only lasted for 7 years and that’s mainly because gold eventually got mined out and retrieving gold became extremely difficult. The big data rush of today, however, is here to stay and that’s because data continues to grow exponentially. Big data’s volume, velocity, and variety are all expected to increase. Running out of data to mine is simply unimaginable.
In the last few years, big data, analytics have experienced a tremendous increase in buzz and popularity simply because data is everywhere. It is as if floodgates have opened up and huge waves of data came gushing out. It really has been only in the last decade and a half that digitization and automation really bloomed into maturity. And now there is widespread realization that these data exist and are accessible and that they can be used to analyze virtually all aspects of existing operations. Big data, predictive analytics, and business intelligence are not just buzzwords for many companies because they provide tremendous strategic importance. There has always been an interest in gathering data and making data-driven decisions but in the last several years, the term and concept of data analytics have seemed to evolve and have thus gained tremendous popularity in the business world. With automated technology, the internet, and now mobile technology, the collection of data does not have to be proactively pursued anymore. Data collection just happens and happens virtually everywhere from a customer purchasing a book on Amazon, to a baseball hitter striking out, to an employee leaving a position. We now have long historical data to make fairly accurate predictions on the weather, stock market fluctuations, and almost every aspect of everyday life.
With modern technology such as the internet, social media, mobile technology, and other electronic gadgets, data collection just happens inevitably and happens virtually all the time. It is up to the analysts and the decision-makers to determine what queries to make, what data to focus on, how to extract and transform the data, and how to tell a meaningful story.
For HR analysts, just like for everybody else, there’s plenty of data out there but where does one begin and how does one begin to analyze any of it? HR transactions are now automated and so if you’re interested in measuring the time it takes to fill a position, you may perhaps look at the number of days it takes from when you posted the vacancy to when you are able to fill that position. You may even want to further break down the process and look at the different touchpoints so you can see where the hang-ups in this HR process. Most, if not all, of these steps are automated and digitized already from posting the job vacancy online to when the applicants send in their applications to when interviews get scheduled.
However, with so much data available nowadays, not all data are meant to be mined and analyzed. Analysts and decision-makers need to be judicious in profiling the data available because it can be overwhelming and it can even cause analysis paralysis. The challenge now is not so much what data to collect but what data to select that would make for meaningful analyses.
In a later blog, I will discuss the research methodologies that can be used in this age of big data.
In : Data
Tags: big data data collection
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