Predictive Analytics

What is predictive analytics?

By better understanding the past, businesses can gain insights into the future. 

Predictive analytics software is all about making business outcomes predictable. Data scientists and data analysts can do this by using data mining and predictive modeling to analyze historical data. 

Predictive analytics goes a step further than general business intelligence, which businesses use to pull actionable insights from their data sets. Instead, users can develop machine learning algorithms and predictive models to help forecast and achieve business-critical numbers.

Types of predictive analytics

Depending on what predictive analytics is being used for or the industry a company is in, one of the two different types will be utilized.

• Regression analysis: This type of analysis looks at the relationship between a dependent variable and correlates it to one or more independent variables. It can be useful for forecasting and predicting outcomes. For example, a logistics company can analyze their deliveries and predict the impact of rain and other weather conditions on their speed of delivery.
• Time series analysis: This type of analysis analyzes variables over time and can help predict how a variable might change over time. For example, a retail company might forecast sales over a given period and attempt to ascertain how they will do financially across different quarters.

Benefits of using predictive analytics

Predictive analytics can be beneficial to companies across industries. Although no company has a crystal ball, they can use past data to help them predict future outcomes, allowing them to be prepared for what may come.

• Improve planning: Data can allow business users to foresee what is coming. With predictive analytics, they can use historical data and trends to plan for everything from sales to product performance and more.
• Identify risks: Things can go wrong in any business. Systems can fail, customers can churn, and supply chains can be disrupted. With predictive analytics, companies can identify these risks before they occur.
• Increase efficiency: Data-driven decisions allow businesses to make more thoughtful choices. For example, they can optimize processes using predictive maintenance or use demand forecasting to make efficient staffing decisions.

Impacts of using predictive analytics

Supply chain management, for example, can be positively impacted by predictive analytics. 

• Demand forecasting: Businesses can have a better understanding of their supply chain, knowing where their items are, how fast they are moving, and more.
• Transport optimization: Predictive analytics can help supply chain experts better understand their transportation needs and improve their shipping operations. For example, they can use predictive models to optimize routes, reduce fuel costs, and improve delivery times.
• Dynamic pricing: Businesses looking to make the prices for their products dynamic can use predictive analytics to choose the best price for a given time, location, or person. This type of prediction-powered pricing can help with a company’s bottom line.

Basic elements of predictive analytics software

The format for a predictive analytics solution can vary, but a comprehensive  solution will include the following elements:

• Data preparation: Robust predictive analytics tools support data blending and data modeling, giving the end user the ability to combine data across different databases and other data sources and allowing them to develop robust data models of this data. This is a critical step in making meaning out of the chaos by combining data from various sources.
• Data management: Once the data is properly integrated, it must be managed. This includes the ability for data access to be restricted to certain users, for example. 
  Although some companies opt for a standalone data management product, such as a data warehouse, predictive analytics solutions must provide some level of data management by definition.
• Reports and dashboards: Multilayered, real-time dashboards are a core feature of predictive analytics. Users can program their analytics software to display metrics of their choice and create multiple dashboards that show analytics related to specific teams or initiatives. 
  From predictive analytics of website traffic to customer conversion rates over a specified period of time, users can pick and choose their preferred metrics to feature in dashboards and create as many dashboards as necessary. 

Predictive analytics best practices

To make predictive analytics work, follow these best practices:

• Ensure data quality is high: As the saying goes: “garbage in, garbage out.” If a business wants to get good results with its predictive models, they must prepare and clean their data.
• Focus on security: Companies must consider security options to ensure the right users see the correct data to guarantee strict data security. Effective analytics solutions should offer security options that enable administrators to assign verified users different levels of access to the platform based on their security clearance or level of seniority.
• Ensure seamless integration: Without integration, it becomes challenging to get a complete view of a business’s operational performance. If an integration experiences a communication error or another issue during a data query, it causes an incorrect or incomplete reading. 
  Users should monitor these connections and any potential performance issues throughout their software stack to ensure that correct, complete, and up-to-date information is being processed and displayed on dashboards.

Predictive analytics vs. prescriptive analytics

Predictive analytics tells a user what could happen in the future based on previous patterns. 

Prescriptive analytics, however, goes a step further and gives recommendations to the user, telling them what to do next.

Take a look at these 8 examples of industries using predictive analytics today.