Did you know that nearly 80% of the data in the world is unstructured data? Unstructured data presents challenges for those who are in charge of data management and require enterprise systems that can control the data and access it as needed.
Simply put, unstructured data is data which cannot be quantified, it cannot be counted in numbers. For example: text files, powerpoints, word documents, instant messages (tweets, likes, posts), emails, web pages, jpeg images, audio files and videos.
It is hard to categorize and store unstructured data vis-à-vis structured data which can be easily quantified and stored in databases. Every day, the entire world creates unstructured data to the tune of 2.5 quintillion bytes, especially due to the proliferation of IoT and cloud computing. IDC estimates that 90% of Big Data is unstructured data.
It is obvious that the world is missing out on a lot of indispensable and valuable information since unstructured data remains undeciphered.
HR data is unstructured by its very nature. Resumes and Job Descriptions come in various formats. Sometimes important information present in the Cover Letters gets easily missed.
Typically, recruiters spend a lot of time and effort sifting through applications and lining up interviews. Nowadays, companies receive thousands of applications not just from local job seekers, but also from aspirants worldwide. The problem is simple: there is too much unstructured data to be handled, let alone make sense out of it.
In addition, most HR automation systems perform mere keyword searches. These systems cannot discern the quality of a resume the way human beings do. While there are a number of automation systems in use, getting hold of the right candidates is a perennial challenge. Ask any recruiter how difficult it is to hire the right fit candidates.
Legacy HR systems need to become intelligent to handle hordes of quadrupling HR data and make meaning out of it. Top honchos often base their decisions on data. How can then HR contribute significantly to strategies and business performance of companies with the help of all the available talent data without first understanding it; making it meaningful and reliable in real-time?
More and more CEOs are expecting CHROs to play important roles towards making impact on business outcomes and of course save money. It is imperative for companies to make their HR automation systems more intelligent and help them increase measurable factors such as revenue per employee, costs and time.
One way to make sense out of HR data is with predictive analytics the ability to see patterns within the data and then predict what may occur later. These analytics are often the type of rule-based analytics. By looking at the particular unstructured data, it is likely that when the computer sees something that triggers its pattern recognition, it can raise the flag to human operators that there is something that needs to be paid attention to.
It is tempting to think that with rule-based analytics, computers can interpret data more meaningfully. However, much of the rule-based analytics are programmed in by a person. The new trick is to have the computer develop its own form of analytics by learning from patterns within the data itself. This form of artificial intelligence, also called a machine-learning approach, gives the computer the power to analyze the data, recognize patterns and chart rules relative to the data.
However, it is contextual intelligence that makes all the difference by understanding data the way humans do. Contextual technology gives data (unstructured or structured) its unique ‘context.’ When combined with machine-learning, it gives advanced analytics which bring forth never-before-seen insights and metrics. For example: the entire bandwidth of skills that a candidate has and level of proficiency in each; the exact resume match for a particular job description suited to the specific needs of a company/brand/business rules; performance of employees vis-à-vis investments; identification of skill gaps and customization of L&D; and a host of other in-depth analytics.
Insightful data analytics can drive better people decisions. Contextual intelligence brings agility and speed enabling cost savings, time saving and reduction in manual effort. Contextual technology has the potential to set a benchmark in how otherwise ‘raw’ data is understood and applied in real life for real business outcomes.