Why Data Veracity must be a #1 priority for public services
Data derives its value not out of its quantity, but out of its trustworthiness. In our increasingly data-driven economy, the consequences of bad data being used by a company or public service cannot be underestimated. How to regulate, legislate and protect our data is occupying the minds of decision-makers at national and European level. Data Veracity is also identified as one of the five trends in Accenture’s 2018 Technology Vision.
Determining the accuracy and trustworthiness of data is one of the toughest challenges private and public organizations are confronted with. Scrutinizing and verifying raw unfiltered data takes time, money and an understanding of the different types of biases, noise and abnormalities that may occur. The reality of utilizing massive datasets makes it highly likely that they will bear at least some degree of addition (the corruption of data by the introduction of false data into an existing data set) or falsification (the corruption of existing data).
Data generated by public organizations comprises information on the population, city infrastructure, national and regional government operations, and much more. Yet despite having copious amounts of data within its grasp, public institutions often lack the proper know-how to guarantee the veracity of their data, and thereby come up short in tapping into its full potential.
There are many instances where data-driven governments or public institutions have gotten it wrong. A case in point would be the US Federal Reserve, which in the years leading up to the global financial recession of 2008 could not foresee the collapse of the housing market, despite basing their monetary policy decisions on data-driven predictive models. The same could be said for the credit rating agencies at the time (in particular Moody’s Investors Service, Standard & Poor’s, and Fitch Ratings), which gave AAA ratings to mortgage-backed securities that eventually turned out to be junk-bonds. These ratings were nevertheless determined by means of mathematical models based on large amounts of financial data.
Closer to home, the EU and European Central Bank’s policies during the early years of the European Sovereign debt crisis left much to be desired, as its contractionary measures seemed theoretically sound, but exacerbated the indebtedness of many of its Member States. The EU’s rescue program for Greece is a case in point: financial data pointed in favor of austerity measures. Fiscal discipline was namely thought to be is a prerequisite for growth.
