According to the latest report 'The Rise of Talent Intelligence' by Talent Tech Labs, the quantity of data about potential hires in the recruitment process is only going to continue to grow as companies expand. AI and data-driven recruitment is essential in processing the sheer quantity of data and information collected. Smart solutions such as the emergence of Talent Intelligence can help to solve this problem.

What Is Talent Intelligence?

Talent Intelligence is broadly used to describe the tools and technology platforms that apply AI to the vast quantity of data that lives in companies' hiring systems, as well as data that lives on the open web. It is used to provide a holistic view of candidates and help clients make better strategic decisions around talent. Some tools are optimised to provide big picture insights, for example which markets have more female software engineers, while others give a detailed view at the individual talent level, answering questions such as which candidates are most likely to succeed in a role.

Talent Intelligence can be divided into 3 areas:

  1. Talent Acquisition
  2. Talent Management
  3. Talent Fairness

Putting it all together, Ideal believes that Talent Intelligence means using a data-driven approach across the talent lifecycle, while ensuring that those decisions are always equitable and fair.

Main Uses of talent Intelligence:

While there are many use cases for Talent Intelligence, here are the three most commonly encountered:

  1. Screening and Shortlisting Candidates
  2. Diversity, Equity, and Inclusion Reporting
  3. Internal Mobility
The pressures recruiters face

Today's recruiter isn't struggling with too little information; their challenge is too much. Recruiters are expected to sift through thousands of applicants for a single position. They are required to collate resumes, application questions, chat bot conversations, complex psychological assessments and interview notes. Beyond the importance of candidate experience, recruiters are also responsible for driving and delivering on business goals, including cost, speed, quality, and diversity agendas.

Transforming Talent Data to Talent Intelligence:

Intel conducted an extensive search for Talent Intelligence solutions that could reduce two problems. Firstly, the burden on recruiters to identify the best applicants by providing explainable scoring; secondly, mitigating the strain on sourcers by automatically surfacing qualified candidates from both the Applicant Tracking System (ATS) and the Customer Relationship Management (CRM). After 3 years of de-biased Intel hiring data about what qualified talent looked like for every seniority level, location, and business unit, 'HiredScore's proprietary Brain' was built. The 'Fetch' feature within, gave the Intel team an opportunity to use automated Talent Intelligence to enrich the hiring managers' experience. With zero effort the recruiter could show up with candidates in hand that had already shown interest in Intel in the past and were known to be qualified.

Catenon decided to get ahead of the trend with their latest development called Talent Hackers. Talent Hackers is the first nodal distributed network platform for the search and recruitment of technology and digital professionals based on paid referrals. Through the dynamic distribution of offers, performed by their algorithm, these reach more profiles than any other platform, activating passive talent, that which is not actively looking for a job. It uses Data Intelligence to identify professionals in the IT Community, drawing conclusions about the ideal time, channel and message to impact potential candidates and their environment, and thus maximise the activation of that talent. Talent Hackers has been extremely successful and has also just won the 1st Edition of the INNOVARH Awards!

How an intelligent talent data system will innovate the future of recruitment

Talent acquisition today has become synonymous with a robust infrastructure pulling data from varied sources. A talent data system collates information from these different sources before manipulating the output into actionable insights for recruiters. As we prepare for arguably the most digitised workforce era in history, this system will become the ideal recruitment infrastructure to help hiring teams make a shift from data availability to data intelligence. Data intelligence doesn't dehumanise recruitmentand AI is not here to automate everything, in fact the opposite. Within this infrastructure, hiring teams can now let this talent data system run in the background and instead more time is created for these hiring teams to focus on personalisation; outreach becomes more intentional and a more diverse and inclusive scope of job seekers can be reached.

Get ahead of the trend today

The recruitment process is well positioned to benefit from the advancement of AI; large data sets consisting of multiple detailed sources can prove extremely time consuming to manually analyse, whilst AI systems can automatically extract relevant data on candidates and previous candidates, and simultaneously gives hiring teams more time to personalise their strategy. As Intel discovered, Talent Intelligence tools can make sense of the noise at scale and will be crucial in helping organisations increase their talent decision velocity. We have only scratched the surface with regard to Talent Intelligence; the 2020 Gartner Hype Cycle report calls it the 'innovation trigger.' Over the next 5 years, organisations who want to innovate, will continue to prioritise Talent Intelligence as part of their broader digital transformation strategies. The adoption of Talent Intelligence across the industry is therefore inevitable, so getting ahead of the trend will give you a competitive advantage and allow you to maximise the chances of finding your ideal candidate much quicker.

Post Views: 2

Attachments

  • Original document
  • Permalink

Disclaimer

Catenon SA published this content on 18 April 2021 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 19 April 2021 13:54:01 UTC.