Moving beyond the traditional: How data and analytics can predict your company’s future non-employee labor needs

July 25, 2018

New analytics platforms and tools allow companies to improve outcomes across the entire organization – now multiple departments can be reached through different components and aspects of analysis.  Consider what you could do if you had access to standardized benchmarks based on actual transactions worth more than $100 billion from more than 85 countries around categories such as quality, efficiency, cost, risk, job titles, and geographic region.
Think about human resources departments – their key focus is attracting top talent thorough a more holistic, predictive view based on looking at a company’s work needs in a more general sense.  Before the advent of such cutting-edge data and analytics, HR was only able to make a best guess based on limited data sets from their own VMS, without much knowledge of what was taking place across the industry.
Now, artificial intelligence linguistics models can break down job descriptions by individual components which can then be compared to global data.  Real-time analytics provide more strategic, actionable insights that allow companies to find suppliers that can provide the workers with the specific skills sets you need – no matter where they are around the globe.  For example, when looking at rate analysis, decisions can be made using larger, more global data sets.  This results in clear comparisons by either time-to-fill or quality metrics.  HR departments can now access prescriptive and predictive data, which can be used for strategic program planning.
Procurement departments tend to rely on internal financial data to make determinations about hiring and rates.  With the proper tools and expert partners, procurement can move beyond that, incorporating broader economic data to determine how changes in markets can influence factors such as bill rates and long-term impact of market changes.  SOW contracts can also be managed through automation, providing insight into total SOW spend and ending questions about where the company’s money is going.
To take advantage of these and other data analytics processes, deciding to fully integrate workforce decision analytics is the key.  Gaining access to wider, more detailed data sets changes how your company understands and acts regarding non-employee labor decisions.  That market-spanning data, compared to individual internal VMS data, generates answers to questions about every aspect of your company’s programs.  But how do you determine the best partner?
A powerful data analytics partner should be able to generate individualized models that best-serve clients, positioning them for program success.  Those strategic models should address:
  • What happened
  • Why it happened
  • What you should do next
That partner should also be able to provide data analytics processes and solutions that complement your existing VMS, interjecting more comprehensive market data and standardized benchmarking that answers some of your most nagging questions.  Specifically, those processes should be able to predict future job rates based on economic trends; show how a program performs against the market; determine the best market for a project; and deliver insights into aggregated contingent labor spend including SOW.
At this critical intersection of talent and technology, companies and workers are upping their games, seeking the best opportunities.  To ensure your company is at the pinnacle, your data analytics partner needs to have an unsurpassed level of sophistication, offering ways to make informed decisions based on facts.

Beeline welcomes this guest post from our partner, GRI. The post represents GRI's opinions and not necessarily those of Beeline.