The phrase “big data analytics” conjures images of voluminous server rooms, vast labyrinths of data-manipulating algorithms and a kind of Orwellian pervasiveness only a true technophile could embrace. The high-tech and high-spun image of big-time analytics, promoted both by its corporate advocates and true believers, promises ingenuity, efficiency, accuracy, prediction and a previously unheard-of frontier in quick data-driven decisions.
Sure, state and local governments have used analytics to relieve traffic congestion, monitor public utilities, evaluate and predict crime, follow education trends, and keep tabs on public resources. And the bigness of analytics seems to be getting bigger too, if private-sector Goliaths like Amazon are any indication. Amazon’s patented algorithms, for example, allege to predict shopping habits before orders are placed. Other private companies are also using analytics to create statistical treasure maps in market trends.
But for all of its potential, big data’s impact in government remains relatively small. Behind closed doors, government insiders are hopeful of possibilities but skeptical toward first steps. In a study of 150 federal IT professionals, the government IT networking group MeriTalk estimated that federal agencies could save 14 percent with analytics programs, or nearly $500 billion. However, the study also found that only 31 percent of those that had launched an analytics project believed their data strategies would deliver.