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Predict the Future of Your Company with Predictive Analytics

What if someone told you there was a way to predict what could happen to your company in the future? Would you want to know? With the help of predictive analytics, now you are able to do so!



What is Predictive Analytics?

Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future. We can see it being used in many different fields. For example, retailers often use predictive models to forecast inventory requirements, manage shipping schedules and configure store layouts to maximize sales. Hotels, restaurants and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.

Models of Predictive Analytics

Models are the foundation of predictive analytics. They are the templates that allow users to turn past and current data into actionable insights. Some typical types of predictive models include:


  1. Classification model: Usually considered the simplest model which categorizes data for simple and direct query response.

  2. Clustering model: This model clusters data together which common attributes such as shared characteristics, behaviors, or plan strategies.

  3. Forecast model: This model works with numerical value-based things and usually works based on historical data. For example, companies can look at historical data when figuring how many calls customer service can handle in a week.

  4. Outliers model: This model utilizes analyzing abnormal or outlying data points.

  5. Time series model: This model looks over a sequence of data points based on time.

Model users have access to an almost endless range of predictive modeling techniques. Many methods are unique to specific products and services, but a core of generic techniques are now widely supported across predictive analytics platforms. Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that's used to determine a course of action or to show a statistical probability. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next. Regression techniques are often used in banking, investing and other finance-oriented models to forecast asset values and help users understand the relationships between variables, such as commodities and stock prices. On the cutting edge of predictive analytics techniques are neural networks. Neural networks are algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind works. While getting started in predictive analytics isn't exactly something easy, it's a task that virtually any business can handle if it is committed to the approach and is willing to invest the necessary time and funds.

What Steps Need to be Taken?

Step 1: Pinpoint the problem that needs to be solved. Identify the different aspects of this problem; what needs to be solved, what do you want to change about the past, what do you want to happen in the future.

Step 2: Data is needed to continue. This data can be from any storage or system and it should be prepared for analysis. Keep in mind that data preparation can be one of the most time consuming part of this whole process.

Step 3: Thirdly, the predictive model building will take place. You can do this with the help of easy to use software or even work with your own professionals to help come up with the one that fits you best. Here, you will also put the models to work with the data you analyzed in the previous step.

Step 4: Sometimes, predictive analytics can be a long project so you need a team approach. Different people should work on different parts. Someone who understands the problem and how to solve it should be there to guide you along.

If you have a business, take the opportunity to understand your company even better! Don’t wait around and have more control over what your company will be like in the future by finding professionals!

Kitameraki has expertise on these areas. Feel free to contact us.

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