Data as a Business Resource

Key Performance Indicators are metrics that companies use often to measure, manage, and communicate results. There are two types of KPIs: Leading Indicators and Lagging Indicators. Many organizations will focus on revenue, profit, and growth as key measures of success; all lagging indicators that tell a story about what has happened. While these are important metrics, they do not provide foresight to where the business or industry is headed. According to Gartner, companies that focus on more leading indicators earn five percent more than businesses that do not. Why is that? Leading indicators represent metrics that forecast a high probability of future success, and they tend to communicate change in the environment. In 2010, we saw a shift in the entertainment industry when Blockbuster, the video rental giant, filed for bankruptcy. The image of the big blue and white sign that was seen on main street corners was quickly replaced with a white and red jpeg. Netflix disrupted the market. They measured viewer reach, the number of video rentals, and not the number of brick-and-mortar stores. The key is to identify a winning combination of leading and lagging indicators for your business. The next crucial step is to evaluate the data and mitigate risks associated with bad data–either data that is out-of-date, incorrect, or irrelevant.

Bad data can happen to good companies: Trouble with maintaining ‘data quality’ is an ongoing problem that plagues numerous businesses, and if IT leaders don’t take steps to improve the accuracy of their information, there could be serious consequences.As CIOs, it is our responsibility to mitigate the risks associated with bad data. Many organizations fall into the trap of collecting data and immediately trying to make decisions from the data, without first evaluating the sample size and distribution. We often assume that the data is normally distributed and the sample size is sufficient. For example, poor data quality at one real estate company resulted in huge opportunity costs. The company used historical data to predict the optimal location for sales events. Come to find out they were sending sales representatives to Philadelphia when they should have been sending them to Chicago, all due to one outlier that skewed the results. There are many sources of bad data and this is just one example. According to the Forbes Insights and KPMG “2016 Global CEO Outlook”, 84 percent of CEOs are concerned about the quality of the data they’re basing their decisions on. Gartner measures the average financial impact of poor data on businesses at $9.7 million per year.

I encourage CIOs to champion data governance. Despite the bureaucratic-sounding name, data governance has become an essential requirement for any organization that aspires to derive insight and business value from their data assets.

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