The massive amount of complex data transactions circulating around in today’s transactional systems has spurred an ever-growing trend of predictive modeling and analytics. Tracking all elements of this extremely difficult equation involve juggling a great number of variables. How many of these variables bring valuable information in terms of future forecasting is the scope of the work of predictive models.
If you are new to the concept, the easiest way to understand what these models are like is to imagine them as a collection of predictive elements in the form of variables, which estimate the likelihood of a future outcome. As it accumulates enough data, this results in the formulation of a statistical model. It could be anything from a simple sequence of steps to a complex software algorithm with multiple dependencies. As more and more data get accumulated, the existing model is either validated or modified accordingly.
Applications of Predictive Modeling
While it might be considered most fitting in weather forecasting, predictive modeling actually has widespread application in a vast majority of businesses. Online marketing and advertising rely heavily on web user browsing history to determine and predict what products are most likely to interest the online audience.
It can also detect spam and fraudulent activity with the help of a predictive algorithm in the identification of unwanted communication and data breach. When it comes to customer relationships, such models can identify potential customers and yield much better turnout than random messaging. Engineering, disaster recovery and all forms of security management also rely a lot on predictive modeling techniques application for successful operation.
Methods for Modeling
Even though it is easy to assume that the more data we feed into a predictive analytics model, the more accurate it is going to be, things do not necessarily happen that way. Piling up information doesn’t always lead to greater precision of the predicted outcomes. What yields actual results is analyzing separate segments of the information at hand, thus speeding up model development times and enabling a faster deployment.
The sampling of data that is chosen for analysis is followed by a careful selection of the right model to process it against. The usual modeling method is linear and involves picking a dependent and an independent variable that is placed along the x-y axis, resulting in a string of data points that form a predictive line that marks the likelihood of future occurrences involving the dependent variable.
Undoubtedly, the greatest difficulty before data scientists when it comes to predictive modeling is obtaining the right data to feed their algorithms when developing them. They have estimated it that no less than 80 percent of the time spent creating the models is occupied by proper data selection.
Despite the fact that such algorithms are considered a part of the realm of Mathematics, the creation of predictive models goes above and beyond that. It includes a technical aspect, overcoming organizational challenges, connecting to a centralized data warehouse that is to provide the input information, as well as a lot of the latter considered confidential by some businesses, which immediately makes access to it quite difficult in the first place. If you already feel overwhelmed by everything that needs to be taken into account, here is a great article that will give you a more gentle introduction to the topic. In short, dependencies and correlations that appear interesting might be pursued by scientists at a given time only to show up as irrelevant later. This makes the process of data collection even longer and more complex. Business relevance is different from statistical relevance, and some valid interdependence between socio-economic phenomena might be useless to the entrepreneur.
What is a Useful Model?
Predictive models of the recent past comprised of packages of bundled statistical data that was decipherable only by a chosen few and meant nothing to the user who would take the real advantage of feeding this information to their own application and database data in order to take executive operational decisions on the basis of immediate outcomes. This excluded the application of any BI resources or capabilities that every modern model would have.
In a few words, it will incorporate a usable and efficient predictive analysis tool into an intuitive system that supports data integration, reporting and a user-friendly application interface that will enable operators to read, understand and manipulate the results delivered.
Introducing predictive modeling in business enterprises is usually ill-received by management. The reason for this is expectations that the complexity of resulting data will render it unusable and impossible to understand. It places the effort that goes into model development and creation in the proper context and form that will ensure their business relevance and intuitive character.
Operations teams, on the other hand, are often slow to adopt the approach due to fixation on past mistakes and shortcomings that are no longer valid or true. It also takes time to understand that the model will be a guiding tool to help them take the most appropriate decisions and not an extra method of checking their performance and putting past decisions to the test.
Staying Up to Date
Last but not least, it is important to remember that predictive tools are not universal. They represent a snapshot of trends and correlations at a given moment and get stale as time passes by. In order for a model to remain valid, it needs to be revised and refreshed periodically. How often that should take place is a question which the valid answer does not always fit all businesses.
There are, however, a few criteria that can help decide whether a revision is necessary.
The most important elements to consider are how often your data changes, whether the business entity it pertains to has had a major change in direction or operation since the time the model was created, as well as whether there are any external social or economic factors that might have strong implications. The various nuances of model upgrade can range from slight revisions to the need to rebuild it from scratch.