I n 2011, venture capitalist and co-developer of the Mosaic web browser Marc Andreessen proclaimed, “Software is eating the world.” He further explained, “We are in the middle of a dramatic and broad technological and economic shift in which software companies are poised to take over large swathes of the economy.”

Andreessen offered multiple examples showing that in the twenty-first century every company must be a great software company, whether its industry is bookselling or oil and gas or financial services or automobiles.

No matter what industry or business function you’re in you need to use software effectively. But, in 2014, it’s not enough to just be great at software. Now data is eating the world, and you need to be great at making use of it. I’m not only talking about reporting and charting and dashboards – though those are all important and form a critical basis for more impactful uses of data. You should be using exploratory visualizations, sophisticated research designs, advanced statistical methods, and clever machine learning to really move the needle on outcomes you care about.

I’ll illustrate data’s importance by extending an example taken from Andreessen’s 2011 article: Amazon beat Borders with software. Since then, Amazon has expanded its sales to include almost anything you could think of buying, including shoes. But how can Amazon beat software-based competitors like Zappos when both are equally good at enabling shoe sales with web software? The win will go to the company that best collects and exploits data about shoe-buying to make good purchase recommendations, price footwear for optimal profits, and select the right mix of goods to sell.

Data science for contingent workforce management

Most successful companies have implemented both talent and vendor management systems for hiring and managing the best labor available. Now it’s time to use the data gathered in such systems to get even better at finding and hiring the best talent at the right time for the right price. The challenge we face in the VMS space is that advanced data analytics for talent management are in their infancy. We don’t know what will work.

Some of the challenges we face include:

  • What sort of measures can we use to meaningfully reflect how a contingent workforce management program is doing?
  • How do we figure out what works from observational data? The best way to determine whether something achieves what we want often involves controlled experimentation, but sometimes the best we can do is analyze correlational data instead.
  • How do we predict the future from historic data, and, more important, once we’ve figured out how to do that, how can such predictions be used to change the future for the better?
  • How do we make sense of the vast amounts of text data we collect in managing our workforce in the form of job descriptions and resumes?
  • How do we make the data available in a meaningful way? It can’t just be in reports and graphs, it needs to be incorporated into the app.

Data science will help us address these challenges. We will need to marshal techniques from psychometrics, research methodology, statistical methods, and machine learning as well as borrow from evaluation research, business intelligence, and decision automation to make progress.

Introducing IQN Labs: Collaborative data-driven innovation

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At IQNavigator, we see a need for innovative, data-based approaches to optimizing contingent labor and statement-of-work management. Furthermore, we see a need for a collaborative approach that brings business experts, data scientists, application developers, and big data technologists together to figure out how we can exploit the vast data reserves we’ve collected to help companies find and manage the best talent available in a cost-effective manner.

Because of the need for creative and innovative yet rigorous scientific approaches, we’re launching an innovation lab, IQN Labs, which provides a forum for collaborative data-driven innovation. We’ll work with the smartest people at the smartest businesses to design meaningful measurement approaches, test best practices with real evidence, surface important insights, and build computer support for optimized decision-making in contingent workforce management.

Next steps

In the coming weeks as we prepare for the official IQN Labs launch, I’ll be sharing more ideas about how data science and big data technologies can promote a new era of effective contingent workforce management based on the creative and innovative use of data resources and I’ll tell you about some of the early results from our experiments at IQN Labs.

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