Predictive hiring analytics provide high-value insights for creating workforce hiring models. Executing that innovative HR strategy will become the new norm, especially in the demanding industrial Internet of Things ecosystem.
Why? Because the globally competitive IIoT ecosystem is demanding. Workforce hiring strategy becomes a competitive advantage.
The new norm for IIoT workforce hiring strategy uses data-driven insights for predictive purposes. Executing strategy involves hiring HR professionals who are data-savvy and analytics conversant: a shift from the current role of HR in most corporations.
Where does your organization fall within this continuum?
Creating a data-driven, IIoT enabled organization starts by asking relevant and practical questions.
In the IIoT, predictive and prescriptive analytics are the analytics methods of choice.
- The first question, then, to answer is whether your organization predicts or reacts?
- The second question to answer is whether the current workforce understands how to ask the right questions of the data?
- The third question is whether the workforce comprehends the difference between an individual prediction and forecasting trends?
Predictive analytics is a form of advanced analytics that gives the probability of an outcome for a specific individual – a job candidate, an employee, a customer, etc. The focus: What has a higher probability of happening for this particular individual?
On the other hand, traditional business forecasting estimates or predicts what trend is likely to happen in the future, based on what happened in the past.
My advice: To move yourself, your team, and your organization into predictive data-driven mindset, start by asking the right questions of the data.
How can predictive hiring models become the new norm for IIoT workforce hiring strategy?
How can you transition your current HR model to become more data-driven and predictive? How will that shift make your organization more competitive?
I posed these questions to Greta Roberts, CEO, and Carla Gentry, Chief Data Scientist, at Talent Analytics, Corp. The company has developed a predictive hiring model platform, Advisor®. The platform predicts workforce performance and turnover for a specific individual, in a specific role, before you even hire them.
Roberts recommends: “Make the first question you try to answer with analytics focused, relevant… [and] useful to the company.” (SHRM Foundation, Using Workforce Analytics for Competitive Advantage), 2016)
The predictive, data-driven HR professional translates the impact of strategic hiring on Line of Business (LOB) value creation.
A useful HR question to answer focuses on flight risk.
Determine the value of predicting, pre-hire, which top performing sales and service reps, engineers or DevOps coders are most likely to defect, post-hire.
- First, determine the practical and competitive advantage of decreasing poor hiring decisions and reducing wasted onboarding investment.
- Next, make the business case for a predictive hiring strategy.
Roberts and Gentry promote using a predicting analytics hiring model, like Advisor®, to correlate the impact of that predictive analytics model on sales rep performance, or amount of errors, or customer satisfaction scores, or injuries, within a respective Line of Business Unit.
These are real line of business metrics.
To achieve a predictive analytics-based hiring model, an HR professional is assigned to each line of business. They understand the structure, function, context and, most of all, strategic goals of that unit.
For example, a VP of Sales uncovers a challenge of low sales performance or too much turnover. They could work with internal data scientists or external consultants to co-develop a model to predict sales rep quota performance or turnover of new sales candidates.
HR’s Talent Acquisition Team implements the sales predictive models. Sales and Turnover outcomes are measured, post-implementation. Machine learning tools recalibrate the predictive models to increase accuracy over time.
This approach promotes collaboration. The outcome is no longer ‘fulfilling a forecasted body count.’ Rather, the goal is increasing sales and business profitability: again, real line of business metrics.
What is the translational value of predictive hiring models in achieving data-driven decision making for digital transformation?
Open-source, big data platforms like TAP, can be used to test hypotheses and create proprietary manufacturing models for an organization. According to Talent Analytics Corp.’s Carla Gentry, open-source platforms like TAP create a sandbox or testbed for cross-functional teams to look at cause-and-effect scenarios.
Now let’s take things a step further. In my Playbook, what happens when HR becomes part of plant operations? What impact might HR have on hiring the right type of data-savvy workforce for the increasingly interoperable smart plant ecosystem?
In this predictive and prescriptive business model, data-savvy operations professionals also work with HR. Their focus? Sharing outcomes data with HR to correlate the impact of various workforce shifts on overall plant productivity. That integrated, interoperable approach involves big data from machines correlated with those small, practical workforce questions which HR now asks of the data very well.
HR can now correlate the impact of various workforce combinations on manufacturing line productivity per shift. As a result, the business case for value of predictive hiring models is championed across LOBs. This outcome only happens when HR and the line of business combine their data resources.
Similar use cases in healthcare use TAP to analyze at risk patients in hospital admissions environments. In addition, manufacturing sites have used the platform to explore retrofitting or bridging legacy software settings.
Predictive hiring models put HR professionals at the intersection of What’s Going to Happen? and What Should Happen?
Does the future of HR involve data-savvy HR professionals hired as part of a predictive hiring model for digital transformation?
Not only will IIoT-focused HR professionals become part of a company’s competitive strategy. So will the data scientists assigned to LOB teams, along with those specialized HR professionals.
Chew on this: The impact of the industrial Internet of Things on business models is a given. Companies can no longer hire yesterday’s professionals according to yesterday’s hiring practices. Interoperability is key for machines, software and, most of all, people.
What does your organization’s IIoT hiring model look like? Is yours predictive or reactive?
Babette Ten Haken writes, speaks, consults and coaches about collaborative value creation for customer success and customer retention. She connects the dots between strategy and execution. She works across leadership, human capital / HR and technical/IT/engineering teams within the industrial Internet of Things ecosystem.
Her focus? Creating enduring business outcomes. Babette’s playbook of technical / non-technical collaboration hacks, Do YOU Mean Business? is available on Amazon. Visit the Free Resources section of her website for more tools.
Image source: Fotolia