Updated: 5 days ago
Technology is collecting a deluge of information that has the potential to make or break any organization. In a 2019 Gartner survey of HR leaders, only 21% believe their organizations effectively use talent analytics to make better decisions. To extract value from the vast amount of available information and thrive in the reality of everyday chaos, organizations must face two significant challenges. The first challenge involves gathering, processing, and warehousing high-volume, high-speed, and highly diverse data sets. The second challenge is managing the sheer volume of information mined from the data. If not done well, organizations can get buried in the data missing the opportunity presented.
"Analytics-based decision making is essential for making big decisions and thousands of little ones." Bartlet, 2013
Talent Data Analytics Strategy
If you have a talent strategy, the data analytics journey begins for many organizations with moving from bad data to high-quality data. This involves working to clean existing data and restructure the data into a format aligned with the talent strategy. Based on my personal experience, this step can be costly and sometimes involve moving two steps back for every single step upward. It is easy to feel like the journey is not worth the challenge, but you will love the view when you complete this first step. The next part of the journey then involves figuring out how to get clean data into the hands of decision-makers to be analyzed and used in a timely manner. The journey's final leg typically involves developing or hiring data science expertise for advanced analysis and tools such as R, Python, and SPSS. Taking these steps can unlock a competitive talent advantage and lead to transforming the work, the worker, and the workplace.
Unlocking Competitive Advantage
There are many creative examples of how descriptive, predictive, and prescriptive data analytics create a competitive advantage through performance improvements, efficiency, better management decisions, and increased revenue. One company used the competing values framework and operational performance descriptive analytics from 250 firms to identify which operational practices best benefit specific corporate cultures. In another example, a company used insights to ensure the right talent is placed in the right jobs and ensure optimal workforce planning levels to provide outstanding customer experience. Another company used health and wellness data to deliver preventative care avoiding spending millions of dollars on urgent care costs. The trick to creating a competitive advantage through organizational talent data analytics is to discern what is needed from what is wanted.
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Bartlett, R. (2013). A practitioners guide to data analytics: Using data analysis to improve your organizations decision making and strategy. McGraw-Hill. New York.
El-Khalil, R. (2015). Simulation analysis for managing and improving productivity: A case study of an automotive company. Journal of Manufacturing Technology Management, 26(1), 36-56. doi:10.1108/JMTM-03-2013-0024
Gambi, L. D. N., Boer, H., Gerolamo, M. C., Jørgensen, F., & Carpinetti, L. C. R. (2015). The relationship between organizational culture and quality techniques, and its impact on operational performance. International Journal of Operations & Production Management, 35(10), 1460-1484. doi:10.1108/IJOPM-12-2013-0563
Ledet, E., McNulty, K., Morales, D., & Shandell, M. (2020). How to be great at people analytics. McKinsey & Company. https://www.mckinsey.com/business-functions/organization/our-insights/how-to-be-great-at-people-analytics
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About the Author:
Jeff's knowledge and expertise include leadership development, coaching, and workforce strategies to achieve influence and grow organizations. Jeff Doolittle is the founder of Organizational Talent Consulting in Grand Rapids, MI. He can be reached at firstname.lastname@example.org or by calling (616) 803-9020. Visit https://www.organizationaltalent.com/executive-coaching to learn more about executive coaching services provided.