Yellowfin BI: the business benefits of predictive analytics
Many businesses are turning to predictive analytic models to help them provide a better assessment of what will happen in the future. To do this, predictive analytics uses data statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
By using mathematical and statistical methods, predictive analytics can predict the value or status of something of interest.
With wide-ranging, and potentially game-changing, benefits, predictive analytics can drive significant improvements such as reduced costs, increased margins and profitability, better safety and reliability, and lower environmental impact.
Founded in 2003 by Glen Rabie and Justin Hewitt, Yellowfin BI create products that help businesses discover, understand, and act faster on the opportunities buried in their data.
Yellowfin solves data complexity with automation, data storytelling, collaboration and beautiful action-based dashboards that are simple to use allowing more people to see, understand and do more with their data.
CEO Glen Rabie spoke to AI Magazine to share his insights into the benefits of predictive analytics within a business.
How do predictive analytics services improve business operations?
Predictive analytics enables organisations to understand the patterns in past business activities and use that to forecast future performance. For example, it could be used to predict machine maintenance needed on a production line, or the likelihood of a banking customer requiring a new home loan. By having predictive analytics, businesses can use the insights provided to be proactive in their operations rather than reactive.
Are there ways that predictive analytics can support a business in times of unpredicted uncertainty, such as the Coronavirus pandemic?
External shocks, like a pandemic, are generally not anticipated when developing predictive models. So when they occur, models need to adapt quickly to the changed conditions. This is where AI-driven models come into play. AI models are self-learning as opposed to the traditional manual method of model creation. They can be updated and adapt far more quickly to changing conditions, therefore supporting the business far more rapidly than the manual approach.
How can it support customer relationships and interaction?
AI and predictive analytics is being used in a wide variety of customer relationship scenarios today. In the software industry, developers use it to track user behaviour and to use that data to optimise experiences and improve usability. It is used in loyalty programs to predict which offers are most likely to be taken up by certain customer segments. If you have ever received a suggestion from Amazon, Netflix or Spotify, that is an example of AI predicting what you like best, and trying to keep you more engaged with that platform or purchase more from them.
How can companies ensure they have built a successful predictive analytics pipeline?
Predictive analytics and AI is a program, not a project. Like all analytics, your business and the market conditions in which you operate change. In addition, predictive models are just that - predictive. Each model needs to be tested in the real world and a process of trial and error undertaken to ensure the best outcomes. As a result of these two factors, you need to keep updating your predictive models to ensure they are achieving the outcomes you seek from them.
How can companies know if they’re choosing the right one for their business?
Each model serves a specific purpose. Depending on the problem you are trying to solve, you will need one or more of a variety of models. This is why having expert advice is so important. Either in-house or externally sourced data science skills are critical to ensure you choose the right solution for your business.
