What is Predictive Analytics?
By: Johar M. Ashfaque
Tags: Artificial Intelligence, Machine Learning, Predictive Analytics, Analytics, Deep Learning

Source: Unsplash

Predictive analytics uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes. Predictive analytics has received a lot of attention in recent years due to advances in supporting technology, particularly in the areas of big data and machine learning.

Why It Matters?

Big Data

Predictive analytics is often discussed in the context of big data. Engineering data, for example, comes from sensors, instruments, and connected systems out in the world. Business system data at a company might include transaction data, sales results, customer complaints, and marketing information. Increasingly, businesses make data-driven decisions based on this valuable trove of information.

Increased Competition

With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways. Companies use predictive analytics to create more accurate forecasts, such as forecasting the demand for electricity on the electrical grid. These forecasts enable resource planning (for example, scheduling of various power plants), to be done more effectively.

Cutting-Edge Technologies

To extract value from big data, businesses apply algorithms to large data sets using tools such as Hadoop and Spark. The data sources might consist of transactional databases, equipment log files, images, video, audio, sensor, or other types of data. Innovation often comes from combining data from several sources.

With all this data, tools are necessary to extract insights and trends. Machine learning techniques are used to find patterns in data and to build models that predict future outcomes. A variety of machine learning algorithms are available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.

Predictive Analytics in Action

Predictive analytics helps teams in industries as diverse as finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing.

Automotive — Breaking new ground with autonomous vehicles: Companies developing driver assistance technology and new autonomous vehicles use predictive analytics to analyze sensor data from connected vehicles and to build driver assistance algorithms.

Aerospace — Monitoring aircraft engine health: To improve aircraft up-time and reduce maintenance costs, an engine manufacturer created a real-time analytics application to predict subsystem performance for oil, fuel, liftoff, mechanical health, and controls.

Energy Production — Forecasting electricity price and demand: Sophisticated forecasting apps use models that monitor plant availability, historical trends, seasonality, and weather.

Financial Services — Developing credit risk models: Financial institutions use machine learning techniques and quantitative tools to predict credit risk.

Industrial Automation and Machinery — Predicting machine failures:

A plastic and thin film producer saves 50,000 Euros monthly using a health monitoring and predictive maintenance application that reduces downtime and minimizes waste.

Medical Devices — Using pattern-detection algorithms to spot asthma and COPD: An asthma management device records and analyzes patients’ breathing sounds and provides instant feedback via a smart phone app to help patients manage asthma and COPD.

How It Works

Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events.

The term “predictive analytics’’ describes the application of a statistical or machine learning technique to create a quantitative prediction about the future. Frequently, supervised machine learning techniques are used to predict a future value (How long can this machine run before requiring maintenance?) or to estimate a probability (How likely is this customer to default on a loan?).

Predictive analytics starts with a business goal: to use data to reduce waste, save time, or cut costs. The process harnesses heterogeneous, often massive, data sets into models that can generate clear, actionable outcomes to support achieving that goal, such as less material waste, less stocked inventory, and manufactured product that meets specifications.

Predictive Analytics Workflow

We are all familiar with predictive models for weather forecasting. A vital industry application of predictive models relates to energy load forecasting to predict energy demand. In this case, energy producers, grid operators, and traders need accurate forecasts of energy load to make decisions for managing loads in the electric grid. Vast amounts of data are available, and using predictive analytics, grid operators can turn this information into actionable insights.

Step-By-Step

1. Import data from varied sources, such as web archives, databases, and spreadsheets. Data sources include energy load data in a CSV file and national weather data showing temperature and dew point.

2. Clean the data by removing outliers and combining data sources.

Identify data spikes, missing data, or anomalous points to remove from the data. Then aggregate different data sources together — in this case, creating a single table including energy load, temperature, and dew point.

3. Develop an accurate predictive model based on the aggregated data using statistics, curve fitting tools, or machine learning.

4. Energy forecasting is a complex process with many variables, so you might choose to use neural networks to build and train a predictive model. Iterate through your training data set to try different approaches. When the training is complete, you can try the model against new data to see how well it performs.

5. Integrate the model into a load forecasting system in a production environment. Once you find a model that accurately forecasts the load, you can move it into your production system, making the analytics available to software programs or devices, including web apps, servers, or mobile devices.