Time Magazine soon after the U.S. presidential election ran a fascinating piece on how the Obama team managed its data extremely well. From the piece:
For all the praise Obama’s team won in 2008 for its high-tech wizardry, its success masked a huge weakness: too many databases. Back then, volunteers making phone calls through the Obama website were working off lists that differed from the lists used by callers in the campaign office. Get-out-the-vote lists were never reconciled with fundraising lists. It was like the FBI and the CIA before 9/11: the two camps never shared data. “We analyzed very early that the problem in Democratic politics was you had databases all over the place,” said one of the officials. “None of them talked to each other.” So over the first 18 months, the campaign started over, creating a single massive system that could merge the information collected from pollsters, fundraisers, field workers and consumer databases as well as social-media and mobile contacts with the main Democratic voter files in the swing states.
The Democrats’ highly effective use of social media in 2008 has been well documented. Despite this, as the Time piece shows, its data management behind the scenes was clearly suboptimal. Multiple versions of the truth? Databases that didn’t talk to each other?
Sound familiar? You could pretty much say the same about thousands of large and mature organizations. Say what you will about the inner workings of the US government and political parties, but the example above proves that even the public sector can clean up its data. The technology to consolidate datasets and work from one single version of the truth is more accessible and less expensive than ever. Lacking in many organizations, however, is the desire and willingness to clean things up.
Organizations need to fix what’s broken.
Of course, when the stakes are so high (like they were in November), it’s not altogether surprising that the Obama team took data management so seriously. The role that data played in his victory will hopefully and finally change the way that many laggards treat data management.
Note that the Obama team’s use of Big Data complemented its consolidation of disparate data sources. One wouldn’t have been as effective sans the other. The lesson: Just because you use Big Data solutions to manage ungodly amounts of poly-structured data does not mean that you should ignore Small Data. All else being equal, fewer data sources are always better than more.
Simon Says: Normal Isn’t Good Enough
Far too many organizations and their employees tolerate data anarchy. Employees get used to taking four weeks to manually produce a report when six months of data cleansing will make that report run in four hours.
Even if you’re not an Obama fan – and, judging by the popular vote, many of you are not – learn from his campaign’s data management and use of Big Data. Adopting similar principles and practices might not get you elected president, but you’ll still emerge victorious.
What say you?