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The talent landscape is changing rapidly, demanding more and more from Talent Professionals, particularly with respect to data and technology. As machine learning and artificial intelligence become more widespread and advanced, they are enhancing our capacity to both gather data and use it strategically.

Data analysis, interpretation, and evaluation have become essential skills in Human Resources and Talent Management in order to drive the function forward and demonstrate value to the business.

Now more than ever, Talent Professionals are required to demonstrate the ROI and the importance of talent initiatives in a language that speaks to how it can decrease expenses and increase the top line.

This article will help you become a strategic player as a Talent Professional.

Part 1: Data and Information – The Fundamentals

“You can have data without information, but you cannot have information without data.”

– Daniel Keys Moran

Data analysis moves along a continuum; different companies will be at different points in the continuum depending on their ability to parse and make sense of their organizational data.

The Data Continuum

  1. Data and Information:

Data is raw, unorganized facts; information is categorized, calculated, and condensed data. Both of these functions can now be completed in real time through machine learning technologies.

  1. Knowledge:

Knowledge is contextualizing information and understanding how it can be applied to a problem.

  1. Strategy:

Strategy arises when your data has become predictive, and you can therefore use it to inform future courses of action. Machine learning coupled with artificial intelligence can help develop robust predictive models with the large amount of data available to them.

As you can see, you must achieve these stages in sequence – it is difficult to have effective strategy without knowledge!

Gathering Data

To work effectively with data, it’s essential to understand how it is collected. Only then can you ensure the data accurately represents the group you’re trying to learn about.

There are many ways organizations can collect data to create samples of the population. Each approach comes with advantages and disadvantages. For example, a voluntary sample may be easier to obtain, but may skew the data toward specific results. If you receive data from a voluntary sample, it’s critical to know that it came from a voluntary sample, and not a statistical sampling approach that is more representative of the population.

Machine learning is a new approach to data collection that is becoming a huge source of information. This is shifting the paradigm for Talent Professionals;never before has it been so easy to gather so much data so quickly. Consider how your organization can take advantage of these new technologies.

Part 2: Knowledge – Interpreting and Evaluating Data

Validity and Reliability

The foundation of all statistical analyses: the measurement tool and the resulting data must be valid and reliable.

Data can be influenced by many factors, including how a question is phrased (whether in a survey or interview). Here are a few examples of question types to avoid in your own organization’s data collection tool(s):

  • Double-barreled: Groups together two different topics into one question. g. To what extent do you agree that the engagement survey process was fast and accurate?


  • Biased/Leading:Suggests the answer the question is looking for using non-neutral language. g. Do you approve of the CEOs innovative and ground-breaking new strategy?


  •  Ipsative: Forces a choice between two things that are not mutually exclusive. g. Would you say you are creative or analytical?

Part 3: Strategy – Data at the Predictive Level

Do you know how to use the data you have strategically?


  • How is this linked to business outcomes?
  • How will this generate a positive return on investment/increase bottom line?
  • How will this initiative increase the top line?
  • How will this reduce expenses and resource allocation?

Just as you would when setting a goal, you want to make sure that the problem statement is specific and measurable. This will ensure it can truly have a positive impact.

Strategic Talent Analytics Is Not a One-time Thing

Strategic analytics is not something for Talent Professionals to go through once, put on a shelf, and never think about again. It’s a constant evolution, requiring ongoing refinement. Organizations that continually improve – asking these questions repeatedly and getting better each time – are more profitable and competitive in the marketplace.

Artificial intelligence, relying on the data from machine learning, is becoming particularly good at updating predictive models. The technology can be refined to help predict and validate factors that impact the bottom and top line, including talent management, retention, and performance.

As you capture more data and your capability for talent analytics grows, your prediction model will only get better. When you are able to accurately predict outcomes based on data and make informed decisions about the future, you have truly moved into the realm of strategy.

At Self Management Group, we have been developing predictive models and using what is now called machine learning and artificial intelligence for over 35 years. To better leverage the power of data in your organization, contact us today!

Ask about our Talent Analytics Workshop to enhance your analytics expertise.

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John Marshall

About John Marshall

John is the President and Founder of The Self Management Group, and has a doctorate in psychology from York University where he also worked as a lecturer. For over three decades, John has helped hundreds of organizations develop into self-managed, high performance cultures. Using advanced statistical methods and principles, SMG has become a leader in applied research and using predictive analytics to assist organizations in attracting, selecting, and developing top performers.

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