Why HR needs Contextual Analytics

Sales analysis, financials, customer orders, salary data – all of these can be easily computed by enterprise software which are used by organizations.

Feed some narrative text or paragraphs from a textbook, and these systems cannot interpret such type of data intelligently.  How could they comprehend the meaning of words and store them in the form of numbers in relational database systems or excel sheets? Let alone, figuring out the ‘context’ of words which is even more important when dealing with data of this kind. 

Look at this - Photoshop, Mary, Flash, 7.0 years in 3D Max, 5.3 years in Illustrator; superior UI design skills, Fremont College.

A machine will not be able to break down and process say such 100 pieces of data and give intelligence for decision making.  You, a human who is reading this can. But you will have to read each such piecemanually to make the judgement. The way recruiters do.

Human Resources (HR) is fraught with Unstructured Data. Think of resumes, job descriptions, appraisal notes, candidate footprints in social media, feedbacks and more. Existing HR automation systems have strong limitations when fed with such Unstructured Data. Causally, this major shortcoming percolates down to every level of HR operations. It limits the ways in which HR professionals understand data that concerns them and affects business as a whole. 

While there are numerous Business Intelligence enterprise solutions available world over, can you think of solutions which give Talent Intelligence and showorganizations how to leverage the same for overall benefit? 

You see, there are grave gaps here. First of all, it is that of the tricky ‘context’.

E.g.: HR deals with talent data.  A significant part of this concerns the skills and work experience of employees which organizations utilize and is extremely critical in determining business performance. But for a second, stop and think! Skills are usually interrelated with each other on a resume. Work experience is contextual to every individual. Such details are relative to organizations. Company A does not perceive Java skills in a manner as company B does because their businesses and applications are different. 

Can enterprise HR systems discern talent skills and capabilities in the context of businesses in a detailed manner? For otherwise, what is the point in having them? 

Here is a revelation! It is obvious that with such diversity of unstructured details, HR needs a great many level of comprehensive data analytics which could be used for making informed decisions. And what wonder, if all such intelligence could serve as evidence for crafting business strategies and increasing revenue per employee? 

Here is the simplest of an example. Joe’s Java skills cannot be compared to that of Stella’s. Stella’s Java skills relate to the development of websites, while Joe’s concerns enterprise software. Your HR automation system must inherently understand this contextual difference before suggesting either of the one as a suitable candidate for a job opening. 

Organizations don’t just need talent analytics. They need contextual talent analytics.  HR is the first place in a business plan whose elements need to be understood contextually.

HR needs to Relook at Data with Contextual Perspective and Realize Value from Data-Driven Analytics

The first step is to begin using a contextualautomation system and set the stage for intelligent interpretation of HR data. Contextual technology can be applied to the entire cycle of Human Capital Management (HCM). Below are a few examples of how HR professionals could leverage talent analytics with data-driven evidences. 

Data-driven contextual analytics serve as real evidences for CXOs to base their decisions on. 

To know more about the application of contextual analytics in your organization, follow #TalentScience, a content series.