The Harvard Business Review (“HBR”) made Big Data the subject of its “Spotlight” feature in the December 2013 issue. There are lots of definitions for this term on the web, but I like the one from Wikipedia as it’s written for the layman: “Big data is a blanket term for any collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.”
In the popular press, Big Data is primarily discussed in the context of collecting massive amounts of data about customers – both current and potential — so that organizations can tailor and market their products effectively. Perhaps you recall the story in 2012 about how the retailer Target found that women who purchased certain vitamins and lotions together were likely pregnant. This led to the home delivery (no pun intended) of coupons and mailers aimed at expectant mothers. This strategy makes use of what I will term, “External Big Data” because the derived information describes the demographics, behaviors, and preferences of people outside of the organization.
Companies spend billions investing in big data and analytics. A 2010 story in The Economist reports, “In recent years Oracle, IBM, Microsoft and SAP between them have spent more than $15 billion on buying software firms specialising in data management and analytics. This industry is estimated to be worth more than $100 billion and growing at almost 10% a year, roughly twice as fast as the software business as a whole.“
But what about the data companies already have?
Let’s call it “Internal Big Data.” When I was a Project Manager, my employer mandated the regular submittal and analysis of performance metrics, risk registers, quality metrics, and forecast information, to name just a few. But this project management data was not aggregated nor analyzed at the product line level or the organizational level. In the same HBR issue referenced above, Jeanne W. Ross, Cynthia M. Beath, and Anne Quaadgras write, “the biggest reason that investments in big data fail to pay off, though, is that most companies don’t do a good job with the information they already have” (p. 90). Good point.
Are there other uses of Big Data that organizations should explore: chiefly, how to improve the successful completion of their projects? (“Successful completion” here means on time, on budget, and all deliverables meet requirement and quality specifications.) Does your supplier management system show all the organization’s projects that utilize a specific part? Do Project Managers have insight into that information so they can create volume discount opportunities or share information? Does your risk management system provide an overall assessment at the portfolio level? Are there two (or more projects) with similar risks? Can the firm leverage their combined resources to mitigate?
If the company already collects the data, what would it take to aggregate, publish, analyze, and turn it into actionable information?
Every aspect of project management could benefit. Perhaps an even more mature data collection model could expand the standard Human Resource Information System to include how frequently employees have worked together. The same HBR issue referenced above reports in “Idea Watch” that “when [team] familiarity increased by 50%, defects decreased by 19%, and deviations from budget decreased by 30%” (p. 28). Sounds like a powerful (and profitable) use of Internal Big Data.
How does your organization use Internal Big Data to help achieve project success?