The Advantages of Predictive Modeling

by | Jun 15, 2020 | analytics, artificial intelligence, Data Science, new data

The Advantages of Predictive Modeling

In a rapidly morphing technological landscape, artificial intelligence is becoming more common in business. Predictive modeling, a long-standing method with thousands of applications, uses statistics or trial and error methods to predict or describe an unknown outcome. Machine learning refers to a system’s ability to learn and improve through experience without explicit programming.  As a subset of Ai, it can help businesses improve their performance by overcoming challenges and seizing opportunities. Machine learning platforms use multiple algorithms to determine which statistical or non-statistical model businesses should use when performing a particular action.  The choice of which model to use depends on several success criteria, including on precision and accuracy of predictions, consistency, adaptability and practicality. 

As stated above, predictive modeling has many applications and use cases. It can aid with email engagement, insurance claims, medical scheduling, pricing optimization, customer satisfaction, manufacturing failures, and much more. Because of its applicability in nearly every facet of a business’s operation, predictive modeling’s utility spans across industries and business units. In every case, the goal remains to measurably improve performance.

Machine learning platforms seek to solve problems efficiently, a qualifier that has multiple features. One such feature is low error, a key aspect of quality scientific observations. Error is comprised of two components: accuracy and precision. To illustrate these concepts, picture a dart board. Accuracy refers to how close a measurement is to a true or accepted value. On a dart board, this would equate to the bullseye. Precision, however, refers to how close measurements of the same item are to one another regardless of that true or accepted value. This would be represented by a player’s ability to throw multiple darts and hit the same location. In this example, if a player threw five darts and hit the same general area of the outermost ring of the board, they would be considered a precise, but not accurate, player. Low error requires both accuracy and precision, which machine learning platforms provide. To continue with the metaphor, this would look like a player hitting the bullseye with multiple darts.

In addition to low error, our machine learning platforms are fast, low cost, and scalable. Robust predictive modeling frameworks allow for automation, which results in speedy operations that can be managed at relatively low costs. Moreover, our machine learning platforms are run on massive servers, which means that our platforms are scalable to your needs and no data is too large.

At newData, we approach challenges using an artificial intelligence integration process during which we Discovery, Realization, Education, Action, and Measurement, or, simply, DREAM.

Discovery

In the discovery phase, we start by defining the business problem with key stakeholders. We identify the target (the problem being solved), the levers (anything actionable which can impact the target), and the users (people or machines which will pull the levers after the model is implemented). Without clear definitions, the plan cannot be properly developed.

Realization

Next, we run and validate machine learning models. The predictive model is a mathematical equation that works through data (the target and levers mathematically quantified) and shows which levers are important and how they should be pulled. The model quantifies the relationship between the target and levers, which allows for the development of the plan (the instructions or rules for how a person or machine will pull levers).  During the realization phase, the most accurate and reliable models are deployed onsite or on our remote servers.

Education

The educate phase consists of instructing users on how to pull the appropriate levers as prescribed by the model. This involves going over the plan and ensuring that every participant knows how to perform the appropriate action. In some cases, it is a system or application that will be pulling the lever automatically.  Ensuring the proper deployment for either humans or machines happens during this phase. 

Action

As the name implies, the action phase is when the models actually get used by a human or a machine (applications that utilize the modeling output to affect a decision).  This phase varies in duration.  In some cases, the model deploys continually.  In other cases, the model may be deployed once a month or whenever an input (for example, when a customer applies for a loan at a bank) is received.

Measurement

After the plan has been implemented, we measure its success by comparing performance levels before and after implementation or using a live control group.  We often use lift (the incremental improvement that is attributed to the predictive modeling solution) and return of investment (the dollars a business saves or gains by implementing the plan) as metrical units to deliver results to our partners.

By incorporating artificial intelligence integration processes such as ours described above into decision-making, businesses can benefit greatly from the wonders of artificial intelligence. Having a proven and comprehensive process like DREAM is a way newData differentiates itself among other providers of machine learning.  Predictive modeling using our machine learning framework contributes to the creation of more efficient business practices, a relationship between technology and business that will only continue to strengthen as the AI field continues to advance.

 

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