BI Talent Maturity Model for Contingent Workforce Management

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BI Talent Maturity Model for Contingent Workforce Management

In a previous blog post, I discussed the challenges and opportunities surrounding talent data analytics. The opportunities are vast. A recent joint survey by MIT and IBM found organizations with advanced HR analytics, versus those without, see:

  • 8 percent higher sales growth
  • 24 percent higher net operating income
  • 58 percent higher sales per employee

But getting to the point where data analytics become actionable is a substantial challenge for many organizations. The problem is not at the executive layer. Seventy-five percent believe HR analytics are critical their businesses.[1] However, only 18 percent of HR organizations have the in-house skill sets needed to get actionable business intelligence (BI).[2] This explains why only 17 percent even use data analytics.[3]

Much work obviously remains to be done.

Components of a BI Talent Maturity Model

When it comes to talent analytics, decision-making is either strategic or tactical. Strategic decision-making examines issues across functions and departments, often at a macro-level. The analysis looks at emerging trends—both opportunities and threats—and helps organizations prioritize them in terms of importance. Tactical decision-making focuses on real-time issues, enabling organizations and individuals to make the best decisions based on available data.

Talent analytics also involve different business intelligence outcomes. The base level is descriptive. These analytics are retrospective in nature, using data to explain what happened. Predictive analytics are prospective in nature, using data to forecast future outcomes. Prescriptive analytics—the most advanced level—use artificial intelligence to show potential outcomes and prescribe optimal recommendations.

When these strategic and tactical business approaches and the three possible analytical outcomes are overlaid on top of each other, the result is a BI Talent Maturity Model. The six areas in the resulting grid define BI activities and outcomes as follows:

  • Tactical-Descriptive
  • Strategic-Descriptive
  • Tactical-Predictive
  • Strategic-Predictive
  • Tactical-Prescriptive
  • Strategic-Prescriptive

Requirements for Actionable BI

To executive on each of these different BI outcomes, organizations need to have four pillars in place. These include:

  • Internal and External Data
  • The Right Technology Platform
  • Visualizations
  • Humans (experience and expertise to interpret and apply data analytics)

When organizations have all four of these in place, they can compete better in the war for talent. The fourth one is something many often forget when using data analytics in general. The reality is that data analytics without an human layer fail to generate desired outcomes. Simply put, certain business insights are not possible without the involvement of humans—their business experience and expertise serve as an interpretive and navigational grid.

This is certainly something PRO Unlimited understands well. The data analytics generated from Wand, our vendor management system (VMS) solution, are supplemented with our Strategy, Analytics, and Metrics team to produce optimized business insights and guidance for our clients.

More Information

Organizations seeking more details on the BI Talent Maturity Model can download our eBook on “Actionable BI Analytics for Managing the Global Workforce.” You can also call us at 1-800-291-1099 or email us at

Disclaimer: The content in this blog post is for informational purposes only and cannot be construed as specific legal advice or as a substitute for legal advice. The blog post reflects the opinion of PRO Unlimited and is not to be construed as legal solutions and positions. Contact an attorney for specific advice and guidance for specific issues or questions.

[1] “Global Human Capital Trends 2015: Leading in the New World of Work,” Deloitte University Press, 2015.
[2] Matt Ariker, Peter Breuer, and Tim McGuire, “How to Get the Most from Big Data,” McKinsey, December 2014.
[3] “Advanced Analytics Report 2015,” Advanced Business Solutions, September 2015.

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