Organizations increasingly rely on analytics and advanced data visualization techniques to deliver incremental business value. However, when their efforts are hampered by data quality issues, the credibility of their entire analytics strategy comes into question.
Because analytics traditionally is seen as a presentation of a broad landscape of data points, it is often assumed that data quality issues can be ignored since they would not impact broader trends. But should bad data be ignored to allow analytics to proceed? Or should they stall to enable data quality issues to be addressed?
In this article, we use a shipping industry scenario to highlight the dependence on quality data and discuss how companies can address data quality in parallel with the deployment of their analytics platforms to deliver even greater business value.
Shipping companies are increasingly analyzing the financial and operational performance of their vessels against competitors, industry benchmarks and other vessels within their fleet. A three-month voyage, such as a round trip from the US West Coast to the Arabian Gulf, can generate a large volume of operational data, most of which is manually collected and reported by the onboard crew.
Fuel is one of the largest cost components for a shipping company. Optimum fuel consumption in relation to the speed of the vessel is a tough balancing act for most companies. The data collected daily by the fleet is essential to analyse the best-fit speed and consumption curve.
But consider an example of a speed versus fuel consumption exponential curve plotted to determine the optimum speed range at which the ships should operate. With only a few errors made by the crew in entering the data (such as an incorrect placement of a decimal point), the analysis presented is unusable for making decisions.