Today, we are more connected as a society than ever before. Data is continuously being mined and stored from various sources by a plethora of companies and individuals. Data may be, among others, data from any type of sensor, data tracked by companies or data relevant to the public at large. Examples of data affecting the public at large may be traffic data, weather data, stock price data, etc.
Companies often use sensors to track the condition of their equipment and inventory. For example, sensors are used in oil depots (“tank farms”) to monitor various statistics of the oil stored at the tank farms as well as conditions of the equipment storing the oil. Sensors may be used to track the amount of oil stored in each storage tank, the water density in each tank, the inflow into each storage tank, the outflow of oil from each tank, etc. In addition, sensors may be used to track the temperature of the interior of the tank, the exterior or the humidity surrounding the tank.
In addition, companies track their inventory and sales at their distribution centers. For example, an oil distribution company will track the amount of oil it sells to each gas station, airport, shipping yard, etc. The company likely tracks the price at which each barrel of oil was sold, the date of the sale, etc. The company may also track its supply chain and distribution processes such that the time and steps taken to refine the oil as well as the location of each transport vessel (e.g., ship or truck).
Furthermore, some forms of gathered types have been used to predict future events. For example, weather data, e.g., data relevant to the public at large, is routinely collected and used to predict future weather systems in a given geographic area. For example, data may be collected from thermometers, barometers, windsocks, humidity sensors, air pressure sensors, etc.
However, even though all of this data may be collected and stored by various sources, the use of such data in predictive analytics has thus far been limited. For example, the data used to predict the weather forecast (e.g., data relevant to the public at large) has not been combined with data collected by companies regarding their inventory and shipments (e.g., business applications data such as the enterprise resource planning (ERP) of the company).