The Next Step in Predictive Analytics

Data collection has been a vital part of business process for decades. For the past 50 years, the process of collecting data has evolved to where it is today. With the use of early computers, companies were finally able to collect large amount of data in one location efficiently. In the 1980’s, data collection moved to data access. It was no longer good enough to just have the data; businesses needed the ability to access the data and report on it thus emerged the relational databases, SQL, and ODBC, and etc. In the 1990’s, data access took the form of Data Warehouses and Decision Support systems. Today, the question is “now what?” Companies have massive quantities of data which can be accessed easily, but what good is the data? What insight is gained from the company’s data? At Armada Consulting, we have a saying that “information is only as good as the action it initiates.”

Using the Data to Make Forward Decisions

Companies are learning to develop insight and create value by using analytics on the massive amount of data they possess. Through the uses of simple and complex statistical research methods, companies are able to use their data to provide proactive information delivery. Predictive analytics is one of the most successful and powerful analytical uses of data. Predictive analytics’ definition can be as narrow as “understanding why a variance occurs in real time and what to do about it moving forward” or as broad as “delivering the right insight to the right people in real time for making decisions.” Regardless of how narrow or broad the definition, two common aspects exist:

  1. It explains the impact of changes in interacting variables and
  2. It provides for real time actionable information.

The early adapters of predictive analytics were insurance, telecommunications, retail, travel, healthcare and pharmaceuticals business sectors because of ease to understand the effect of interacting variables. For example, in the travel industry it is easy to relate seasonality to the amount of visitor at a beach town. Thus, creating seasonal models to predict the need for staff at hotel in the beach town during different times of the year makes perfect sense. However, the uses of predictive analytics are not limited to easily definable relationships and can have lasting impacts across the organization.

Newton’s third law of motions states, “For every action, there is an equal and opposite reaction.” All activities of an organization are reactions to an event. Without customer demand, an organization would not exist. The key step to creating an effective and accurate predictive model is the identification of actions that drive business activities. IT departments are one of the large consumers of capital within organizations because creating and maintaining data collection systems and warehouses take a lot of time, effort, and money. A common folly of a corporation is viewing this department, and others, as the cost of doing business and as an expense to the company. By identifying the driving activity and forces behind the IT department, and combining it with predictive analytics, corporations can begin to understand IT cost and use it for a more effective business decision making process.

For many years, corporations have had excess data they have collected. It is now time for them to be put it to use. Predictive analytics is more than predicting future events, if done thoroughly it can help explain the causational relationship between activities in all areas of the organization. The next phase in the evolution of data uses is here. It is time to start using predictive analytics to deliver insight in real time to the right people for making a positive financial impact in all areas of the organization.