steps through the process of transforming data into decision

In predictive analytics, the workflow follows different steps through the process of transforming data into decision.

1. Identify the business objective

Organizations should clearly establish the objectives before investing in predictive analytic solutions. When they define the outcomes, they can understand in a better way the path that will get them to that point. The first thing you need to get started using predictive analytics, is a problem to solve. The discovery process is driven by asking business questions that produce innovations. What do you want to know about the future based on the past? What do you want to understand and predict? So, the first step is defining what the business needs to know. And then, translating the business question into a mathematical representation of the problem, which can be solved with predictive analytics.

2. Obtain data from varied sources and understand the data model

Data mining for predictive analysis aims to collect information from different platforms for analysis. A data expert is needed as someone with data management experience who can help you cleaning and preparing the data for analysis. Firms should understand the variety of sources from which the data is generated. This activity helps the companies to evaluate their preparation for a predictive analytics solution. Data are the sources from which the variables are obtained, the relationships between them, the induced knowledge and the identified behavior patterns, becoming a vital element of all predictive analysis.

3. Prepare, clean and select the data

To prepare the data for a predictive modeling exercise also requires someone who understands both: data and the business problem. The way you define your target is essential to how you interpret the outcome. Data preparation is considered one of the most time-consuming aspects of the analysis process. It is necessary to look at the content of the data, which can be a challenge. Often, determining what is relevant and what is not can be complicated. When it comes to cleaning the data, it is necessary to use appropriate filtering and processing techniques. Organizations have to identify data spikes, missing data, or anomalous points to remove from the data. Then aggregate different data sources together and select the impotant data. All this is summarized in automatic processes of data classification, less expensive; greater capacity for information processing, maintaining the same personnel; high response speed, thus enabling real-time services that would be impossible if they required manual processing; and stronger business rules, based on the security of automatic classification.

4. Explore the data

Interactive, self-service visualization tools need to serve a wide range of users (from the business analyst with no statistical knowledge to the analytically savy data scientist) so they can easily search for relationships, trends and patterns to gain deeper understanding of the data. In this step, the question and the approach formed in the initial phase of the project where business questions was asked to produce innovations, will be refined. Ideas on how to address the business problem from an analytical perspective are developed and tested. While examining the data, you may find the need to add, delete or combine variables to create more precisely focused models. This step is fundamental, just because it allows to structure the data in a way it can recognize patterns which, potentially, helps data to extract future trends. The models will allow to describe and explain the data in a more formal way. Which, it is of great help to understand the results that will be obtained from the data analysis; but it is also a good starting point when it comes to visualizing the results. The statistical analysis allows you to validate your hypothesis and test them using standard statistical models.

5. Develop the Predictive model

After explore the data, the predictive model building begins. Increasingly easy-to-use software means more people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. As in the extraction of the data, the models must go through the same scrutiny. You should ensure that the models are a valid representation of the issue it is trying to predict.

 

Selecting the right algorithm, and knowing what the figures represent within is more important than anything else during this stage of the predictive analysis project. In this stage, numerous analytical and machine-learning modeling algorithms are applied to find the best relationships in the data that will help answer the business question. Once the correct assumptions between data have been defined, we must try to take advantage of them, which will be used to make predictions. After identifying the correlations between variables using computational learning techniques and establishing the correct assumptions, behavior patterns are identified that allow the creation of a predictive model.

6. Test the model

The second part of development is testing to ensure it works. Diversity and inclusion should be respected when choosing your analytical model. Once the model is identified, the data analyst should evaluate the model using a set of identified business metrics. This process has to be continued until the most effective model which gives the closest prediction is found. This is accomplished by repeatedly iterating the training data set to test various approaches. This is where it’s decided whether or not a different model is required or, for example, if significant time and attention are given to data collection and preparation. Once the training data have been entered and the predictive model is applied, a qualification will be obtained that will indicate the probability that the situation studied by the model will occur.

7. Deploy the model

Deployment of the predictive model should be a well-planned and a closely coordinated activity that involves all the key stakeholders within the organization. Identifying the best deployment approach helps eliminate post-deployment issues and enables organizations to quickly realize the business benefits. The implementation of predictive models allows to display the analytical results of the decisions of each day, building a process to obtain results and reports that allow to reach the automation of decisions. Successfully implementation of the predictive analysis from beginning to end requires governance of each stage of the process to keep the progress moving along the timelines and budget spending allocated to the project. Another significant function the project manager performs is either knowing when any component of the effort falls outside of company compliance or consulting the compliance department as the effort advances.

8. Act on new information

There are two types of decisions that can be made based on analytical results. First, humans who examine results and take action, usually looking to the future, make strategic decisions. Second, operational decisions are automated. They don’t require human intervention because the rules that humans would apply can be coded into the production systems. For strategic decisions, predictive analysis is used, since based on the results provided by said analysis, decisions can be made based on predictions of the future. At this point, prescriptive analysis comes into play. More and more organizations are looking to automate operational decisions and provide real-time results to reduce decision latencies. Basing operational decisions on answers from analytical models also makes these decisions objective, consistent, repeatable and measurable. Integration of models with enterprise decision management tools enables organizations to build comprehensive and complete operational decision flows. These combine analytical models with business-rule-based triggers to produce the best automated decisions.

9. Evaluate your results

This step and perhaps most important step is to evaluate the outcome of the actions produced by the analytical model. It is important to check the implemented solution. This helps resolve issues and find key enhancements that facilitate the improvement of the product. The questions we should ask ourselves are: Did your models produce the correct predictions? Were tangible results realized, such as increased revenue or decreased costs? With continuous monitoring and measurement of the models’ performance based on standardized metrics, you can evaluate the success of these assets for your business.

10. Ask again

Predictive models are not forever. The factors that drive the predictions in a model change over time, your customers change over time, competitors enter or leave the market, and new data becomes available. It is a constant and evolving process. If a model degrades, it is recalibrated by changing the model coefficients or rebuilt with existing and new characteristics. When the model no longer serves a business need, it is retired and a model is sought that does.

It is easy to imagine the many ways this process can go wrong. Organizations often take months, sometimes years, to move through this end-to-end process. There are many common complicating factors:

The needed data sources might be scattered across your organization.

Data may need to be integrated and cleansed multiple times to support a variety of analytical requirements.

It can take a long time for models to be manually translated to different programming languages for integration with critical operational systems, which can include both batch and real-time systems.

Organizations might be slow to recognize when a model needs to be changed, so they forge ahead with bad decisions based on outdated model results.