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In the last few years, we have seen the rapid surge of Analytics instruments, adopted by companies worldwide for extracting value from data to boost sales and net profits.

In this context, a particular kind of analytics, Predictive Analytics, emerged with the purpose of giving a precise estimate of the future by employing different mathematical and statistical techniques.

By the mean of Predictive Analytics, companies can reduce risks, predicting adverse events and mitigating their triggering factors, and to improve their decision-making process by leveraging information and insights.

Let’s take a look at the major steps to introduce Predictive Analytics in your company.

6 Steps to effectively introduce Predictive Analytics

Problem Definition

It may seem obvious, but the very first step to introduce Predictive Analytics is to precisely define its scope. There could be various applications that may change accordingly to their purpose and to the company’s industry. Some well-known examples come from forecasting models, anomaly detection algorithms or Churn Analysis tools.

During this phase is also important to understand which data are necessary and where do they exist.

Data Collection

In this step we take the necessary data (both structured and unstructured) from different sources. In the ideal scenario there is a Data Lake, designed and maintained for this purpose, or at least a Data Warehouse with its staging area from which we can retrieve the data.

Data Manipulation and Descriptive Analysis

During this phase data are organized for their final scope: being used by Predictive Analytics’ models to solve problems defined in Step 1. In this very phase, it is vital to pay attention to Data Quality and business rules to be embedded to provide a satisfactory prediction. At the same time, it is necessary to check that data aren’t biased, which would impair the quality of chosen models.

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Statistical Analysis

Once the final forma of data is obtained, it is possible to go on with a Statistical Analysis of parameters, so that previous hypotheses are directly tested, or insights are extracted thanks to metrics visualization. During this phase it is possible to adjust the statistical sample, with which models are fed, with operations of rescaling (values normalization), discretization, encoding and other feature engineering techniques. The aim of the latter operations is to adapt the model to its usage in later employed models


Once thoroughly setting up data, predictive models can be tested, and necessary experiments can be carried out to obtain a model with a satisfactory predictiveness.

In this precise stage, for example, file-tuning operations are performed to optimize models and achieve the best predictions. The evaluation of model performance stage is particularly important and depends on specific features of the variable to be predicted.


It is the stage of the actual deploy. After performing all the required tests, evaluating the quality of models, and validating output data, it is possible to implement the Predictive Analytics tool in production, so that it provides predictions able to solve the problem stated in the first point.

Mind the pitfalls – Strategic obstacles in Predictive Analytics

Even following all the steps mentioned above, implementing a Predictive Analysis tool, which can provide accurate predictions, can be anything but simple and it can easily lead to various mistakes.

A study carried out by Ventana Research for IBM[1] identified main causes for strategic pitfalls faced by companies during the implementation of Predictive Analytics projects.

These pitfalls can be summarized in the following list and can be grouped in two macro-categories: technical issues and organizational issues.
Technical Issues:

  1. Difficulties in integration between Predictive Analytics and the Company’s IT Architecture
  2. Troubles with accessing data;
  3. Hardships in practical use of results;
  4. Excessive requested data volume compared to what corporate resources can handle.

Organizational Issues:

  1. Lack of budget and technical skills, needed to handle such projects;
  2. Lack of awareness: lack of vision about using Predictive Analytics for fixing business issues;
  3. Lack of internal or external experts, which could develop Predictive Analytics projects and/or assess their results;
  4. Excessive focus on past pattern and schemes, which don’t always predict the future;
  5. Unreasonable costs for obtaining required data.



The total amount of available company data offers the chance to exploit such information for building Predictive Analytics solutions, which may address many business problems that companies face.

The adoption of these means may turn out to be a particularly effective way to gain competitive advantage by offering better products and services and cutting costs in different business areas.

Likewise, an effective implementation of Predictive Analytics  requires a careful planning as well as high expertise. If you want further information about this topic, please contact us.

[1] Ventana Research. (2015). “Ventana Research Benchmark Research: Next-Generation Predictive Analytics.”

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