Predictive Analytics 101: Where We’ve Been, Why it’s Growing, and Where it’s Going.

Jeff Bezos wrote, “As a senior executive, what do you really get paid to do? You get paid to make a small number of high-quality decisions. Your job is not to make thousands of decisions every day.” 

The same could be said of most all enterprise decision making. If the decision hasn’t already been automated, typically, there’s judgment, nuance, and a human element involved in the decision. Which means decision quality counts. 

The question, then, is how do you make the highest quality decisions? Without having a crystal ball to know exactly what’s going to happen in the future, what’s the next best thing?

Predictive analytics.

What is Predictive Analytics?

Predictive analytics uses statistical models and data mining techniques to analyze historical data, and make predictions about future events or behaviors. 

It’s an extremely powerful tool that can help you better understand the complex relationships between variables in your world. Using past data to predict future outcomes can generates insights into how different factors interact and influence each other, rather than relying solely on intuition or guesswork. 

Which also means we can reduce bias and improve our prediction accuracy. Plus, our decision quality. However, it’s important to remember that predictive models aren’t perfect. They can never fully capture the complexity of real-world systems. So, we need to constantly evaluate and refine our models, while maintaining an awareness of their limitations and the uncertainties associated with them.

How has Predictive Analytics changed?

Predictive analytics has grown significantly over the past five years as more businesses have adopted data-driven strategies for increasing their decision quality. 

As the amount of data continues to grow, and technology continues to improve to keep up, the predictive analytics space is likely to continue to evolve in new and exciting ways.

Here are the key changes that have unfolded in the past five years;

• Increased adoption.
  Five years ago, predictive analytics was still a relatively new concept for many businesses, and adoption rates were lower. Today, as more organizations have seen the benefits of using predictive analytics, adoption rates have increased significantly.
• Improved technology.
  In the past five years, there have been significant advances in technology related to predictive analytics, including the development of more powerful algorithms, better data visualization tools, and more accessible cloud-based platforms.
• Greater emphasis on machine learning.
  Machine learning has become a more prominent part of the predictive analytics space over the past five years. Many businesses are using machine learning algorithms to train models that can predict outcomes more accurately.
• Increased use of big data.
  With the explosion of big data over the past several years, many businesses are using predictive analytics to make sense of vast amounts of data from a variety of sources.
• Expansion into new industries.
  In the past five years, predictive analytics has expanded into new industries, including healthcare, sports, and manufacturing, where it is being used to improve decision-making and optimize operations.

Is Predictive Analytics Still Growing?

As more organizations recognize the benefits of using predictive analytics to gain insights into their data and make better decisions, the field continues to grow into new use cases.

Now, predictive analytics is widely deployed to gain insights into customer behavior, market trends, and many other use cases. 

On the flipside, businesses who aren’t using predictive analytics continue to fall behind their competition. Here are a few examples of how it is applied:

1. Customer behavior analysis:
   Analyzing customer data such as purchase history, browsing patterns, and demographic information to predict future buying behavior. This allows businesses to create targeted marketing campaigns, personalize offers, and improve customer retention rates.
2. Risk management:
   Predictive analytics is used to assess and manage risk in various industries such as insurance, finance, and healthcare. For example, insurance companies use predictive models to estimate the likelihood of claims and set appropriate premiums.
3. Sales forecasting:
   Predictive analytics is used to forecast sales and revenue based on historical data and market trends. This helps businesses make informed decisions about product development, pricing, and marketing strategies.
4. Finance:
   Predictive analytics is being used in finance to analyze financial data and make predictions about stock prices, market trends, and consumer behavior. This can help banks and other financial institutions make better investment decisions and reduce risk.
5. Marketing:
   Predictive analytics is being used in marketing to identify patterns in customer behavior and predict which products or services they are most likely to purchase. This can help businesses tailor their marketing strategies and increase sales.
6. Manufacturing:
   Predictive analytics is being used in manufacturing to predict equipment failure and optimize production processes. For example, manufacturers can use predictive analytics to identify equipment that is likely to fail and schedule maintenance before a breakdown occurs, reducing downtime and improving efficiency.

Predictive analytics is helping organizations that impact your personal life, too. 

Your doctor, favorite sports channel, bank, and even many government agencies use predictive modeling and machine learning to improve your experiences and day to day life:

• Personalized medicine:
  Predictive models are being used to analyze patient data and predict how individuals will respond to different treatments. This can help healthcare providers develop personalized treatment plans that are tailored to the specific needs and characteristics of each patient.
• Climate change modeling:
  Predictive models are being used to simulate future scenarios of climate change and assess the potential impacts on different regions and ecosystems. This can help policymakers and stakeholders make informed decisions on climate adaptation and mitigation measures.
• Fraud detection:
  Predictive models are being used to analyze financial data and detect fraudulent transactions in real-time. Machine learning algorithms can identify patterns and anomalies in the data, and alert authorities to potential fraud before it causes significant harm.
• Sports:
  Predictive analytics is being used in sports to analyze player performance and make predictions about game outcomes. For example, sports teams can use predictive analytics to determine which players are most likely to perform well in certain game situations or predict which team will win a game based on historical data.

Today’s Predictive Analytic technology stack is powered by the common brands supporting your everyday connectivity; but tend to be highly expensive and tailored to data scientists, or strong mathematical expertise roles; making it hard for some businesses to leverage.

1. SAS: SAS is a leader in predictive analytics software and services, providing solutions for a wide range of industries, including healthcare, financial services, and manufacturing.
2. IBM: IBM offers a range of predictive analytics tools, including its Watson Studio platform, which uses machine learning and artificial intelligence to help businesses analyze their data.
3. Microsoft: Microsoft offers predictive analytics tools through its Azure Machine Learning service, which allows businesses to build, train, and deploy machine learning models on a cloud-based platform.
4. Oracle: Oracle offers a range of predictive analytics tools and services, including its Oracle Analytics Cloud platform, which provides a range of analytics capabilities, including predictive modeling, data visualization, and data discovery.
5. Amazon: Amazon offers a range of predictive analytics tools through its AWS platform, including Amazon Machine Learning, which allows businesses to build and deploy machine learning models in the cloud.
6. Google: Google offers a range of predictive analytics tools, including its Cloud AI Platform, which provides a range of machine learning and data analytics capabilities for businesses.
7. Alteryx: Alteryx is a provider of self-service data analytics software, which includes predictive analytics capabilities, allowing users to build predictive models without the need for extensive coding or data science skills.

Where is Predictive Analytics Going?

Despite all this growth, business units report struggling to unlock the full value of predictive analytics and support from their data science teams. According to a recent study surveying marketing teams who have integrated predictive analytics into their program:

• “38% of respondents say data isn’t updated quickly enough to be valuable.
• 35% say it takes too long to build the models.
• 42% say data scientists are overwhelmed and don’t have the time to meet requests.
• 40% say those building the models don’t understand marketing goals.
• 37% of respondents indicate that wrong or partial data is used to build models.”

Which is why a new generation of machine learning platforms have been developing—including Analyzr—to make predictive analytics more accessible.

The future of predictive analytics is “low to no code” with simple and fast analytics, available anywhere at an affordable cost.

This is exactly what Analyzr has been designed to do for both:

1. Technical users interested in outsourcing the routine data work like pre-processing, without having to manage data infrastructure or write another line of code in Python.
2. Everyday business users interested in generating deep insights using their data—and no code—delivered inside their native systems or PowerPoint graphics.

If you’d like to dive deeper into either the predictive analytics field or the Analyzr platform, contact us.