What is Talent Intelligence

When I started my career as a consultant, I found it very helpful to have templates for building analyses.  They helped me factor the thought process of an older, smarter and more experienced consultant into my thinking about a problem.  As I grew in the world of HR Consulting, I found that as I learned, I created templates to help my peers and those I was mentoring, to understand how my thinking led me to analyze a business problem.  Detailing what we are thinking helps others understand our thought processes and is important as we set the course for the use of analytics in the world of Human Resources and business.

We Need to Show our Work

When I took the Johns Hopkins course in Data Science, they taught us to fully document our steps in analyzing a problem… “show your work.” This enables those after us to buy into how more fully we achieved our goal and developed our conclusions.  As we use evidence from analytics and the data around us to solve business problems, we are going to need to “show our work” to get leaders and others to buy into our conclusions.  We have built HR Analytics on top of HR Reporting which is based on the underlying transactional data.  But we never switched the thinking of the analyses being deeper business questions.

Building the Next Generation of HR Analytics

The evolution of HR analytics has been slow.  In fact, many of us are still leveraging single data entities: reports, spreadsheets, access databases, best of breed systems.  These entities are mostly disconnected from the rest of an organization’s datasets.  They provide a level of insight into the process being managed. Although they may lack context, these insights typically provide just enough background to satisfy current needs.

What we need or are looking for are new strategies that will help us build the next generation of analytics which provides answers to the questions we know how to ask and more importantly, ones we should be asking.

Maybe it's because the templates haven't been created or we haven't thought to ask these questions. Or maybe it's because we're being too easy on the technologies and the data we rely on for answers.

This evolution of analytics needs to take a leap, in some ways it needs to be transformative of HR processes.  Whether it’s called people analytics or talent analytics or talent insights, we need to break free and start asking technologists and data scientists to connect datasets, go deeper and uncover powerful insights that are buried in the data. This lack of powerful insight might not necessarily be the fault of technologists nor the data scientists.  HR has been very accustomed to using reporting as the way we extract insight from our processes.  However, it looks, feels and acts like disconnected insights to our clients.
For example, let’s examine how people analytics look at turnover. We struggle to find the reasons why people leave our organizations. There are processes that collect exit information not from the manager but directly from the employees themselves. Unfortunately, this information doesn't go back into the core HR management system. Instead, it is held separately in an exit interview report or a separate database and due to privacy reasons, we can't access it, so we lose the context for why people leave.

Another example is from a candidate experience perspective where we take a one-sided approach to collecting information about a candidate requisition and the process needed to get to a successful hire. We lose the context of all the other candidates and the reasons why they weren't chosen for the position. There could be very telling information about the process itself and how to fix it if we looked at the application and candidate process as well as the hiring and onboarding processes.

Talent Intelligence Tells a Deeper Story

A new, interconnected thought process around how we leverage data and analytics to not only tell a story but to tell that story in context is the next generation of People Analytics.  Talent Intelligence borrows from analogs in other organizational analytical systems. It is the coming together of disparate data sources across HRIS and beyond to tell a deeper, richer story about business decisions and the way in which managers, leaders and employees can be provided with insights that enabled them to make better decisions.

Talent Intelligence is at the crossroads of all types of data and processes and borrows advanced analytical and computing innovations to provide appropriate context for those stakeholders to make better business decisions. When you bring together large, disparate sets of our data it enables you to provide that better context, but Talent Intelligence is beyond just the intersection of large datasets. Talent Intelligence utilizes next-generation thinking about how we ask questions and how we get insights and evidence and context to be able to see the bigger picture and make better business decisions.

Instead of asking a question like “what's my headcount?” Talent Intelligence would train us to go further and ask about our cost structure and how people fit into the cost structure. It would enable us to ask if we are optimized in the way in which we spend money on people based on the markets that we compete in for talent as well as the markets we serve from a customer perspective. We would ask: “Is our employee base as diverse as the markets in which we compete for talent?” That seems like a question that people analytics should be able to answer today. But as we know many times when we ask questions like that the answers, we get back are challenged by how we measure those things and where do we find those kinds of datasets to be able to provide accurate estimations.  In the world of Talent Intelligence there's clear documentation and assumptions are laid out completely for us to understand where the data came from and how it all works together.

Workforce planning may prove valuable in the context of Talent Intelligence. Utilizing HR data in isolation when following a workforce planning process disambiguates the reason or reasons for doing workforce planning in the first place. It would be much better to have the superset of organizational data available to draw what might be a more complex picture but certainly a more complete picture of how the organization operates. So, the question can then be put to the data “how do we grow our business in order to meet potential demand?” The complete data set has no problem answering that question while the HR data set requires a ton of assumptions all of which would need to be tweaked and dialed to make a more accurate plan.

Data Accuracy is Critical to Talent Intelligence

Talent Intelligence provides the opportunity to ask questions about our organizational capabilities, strengths and weaknesses with evidence context and history. Of course, one of the keys to success lies in the underlying data model and the completeness and accuracy of the data components that complete that model. So, the challenges to Talent Intelligence become the richness of history, the accuracy of data, and the willingness of the organization to deal with the inadequacies or potential inadequacies of the data. As good scientists we know that no matter what we do we are going to face error. As we try and measure a population, we know that those measurements will come with some errors and therefore that error will be built into our assumptions as a part of the process.

Think about the sets of data necessary to provide this context. The core includes data from:

• Internal - Organization, Hierarchy, Location, Industry, Jobs/Skills, Payroll, Time/WFM, Recruiting, Candidates/Applicants/Employees/Alumni, Compensation, Benefits, Engagement, Performance, Relationships/ Teams (ONA), Products, SOPs, Customers/Clients/Accounts, Finance – budget/goal/GL, Supply Chain, Inventory, Banking, Taxation, Compliance/Regulatory
• External - Government – Local/State/Federal, Markets/Exchanges, Academia, Healthcare – Exchanges/Brokers, Real Estate, Weather, Entertainment, Vendors, Prospects, Schedules

The goal of the nexus of the insights is to create an environment for various stakeholders not just to access data, but to get access to a plethora of data and insight that explains the situation around the data. Why can I do, or not do something is a question that a stakeholders should be able to ask the data. Why can’t I ask a worker in my shop to work an extra hour for tomorrow’s shift? “In this jurisdiction, once a part-time employee works 30+ hours in a week, the organization is required to …. Do you want to do that? It will cost the organization $xx,xxx. Instead, there are 4 other workers not bumping up against this limitation that are showing free scheduling for this time. Other managers in this situation have used other resources instead of adding hours to the part-time associate. You may expect increased engagement of the other team members by 4% and the affected employee may have 5% less probability of turnover.  How should you proceed? Choices are outlined below with the table of costs for your comparison aiding your business decision.”

Talent Intelligence Drives Business Success

Talent Intelligence creates the set of intellectual process maps and linkages stakeholders need to run their business. There are myriad questions that can be asked of the data with the key being that AI (Artificial Intelligence) can learn and create new linkages, new questions that can be built enabling the creation of further synapses or linkages. Welcome to the real HAL (HR Automation Language), but 20+ years after originally predicted.

The beautiful part of our journey into building a talent intelligence framework is that as we do so we will be building templates that our successors and future generations will utilize to improve on these models. Just look those past generations that have created templates for me to use in describing a market analysis to a client, future generations will look back at talent intelligence 1.0 and laugh but hopefully at the same time they will also see how big of a leap it was and ask, “how do we make decisions before without all the facts?”

For more insights into Talent Intelligence and People Analytics, check out the HR Data Labs podcast.