Augmenting predictive analytics tools with AI

predictive analytics tools

Predictive analytics tools are one of the best investments a business can make. While the present situation with the pandemic indicates trouble in the economy. Having an advanced analytics tool that can predict company fortunes in the coming months or even years helps businesses lay out their future.

Forrester expects the market for predictive analytics to grow at a compound rate of 15% through 2021. This strong growth is a clear indicator of the value predictive analytics tools bring to different industries. Several big companies across retail, healthcare and ERP have benefitted from incorporating predictive analytics tools into operations.

However, predictive analytics can be improved with artificial intelligence. When coupled with AI, predictive analytics can be an even bigger asset for businesses. AI can augment the functionalities of organisations and eliminate the shortcomings. Such an asset would be very helpful to businesses during these troubling times.

How does AI strengthen predictive analytics tools

AI is the perfect complement to predictive analytics and studies show that predictive analytics has seen the demand for AI grow. Here are a few potential reasons why this is the case.

Incorporating the human element into decision-making

Predictive analytics tools analyse data on the past to understand developments in the future. The problem is that people are not always logical. When making decisions, people are swayed by several factors like intuition, culture and emotions. It can be very difficult to predict how people react emotionally using conventional tools.

However, we’ve been able to develop AI to the point where it is able to consider these factors into human decision-making. Businesses can use these tools to have a better understanding of their customer base and quantify factors that were once impossible to anticipate.

Anomaly detection

Anomaly detection is a system that analyses data to identify anything that is unusual about operations and expectations. It is often used in vital operations across different industries. For example, banks use predictive analytics tools with anomaly detection to anticipate and stop money laundering as it is happening.

So anomaly detection analysis is crucial to business operations and AI strengths anomaly detection capabilities of analytics tools.

This makes anomaly detection faster, more responsive and more effective. Given the current circumstances, it makes sense for businesses to augment their predictive analytics tools using AI.

Building on the advantages of anomaly detection is contribution analysis. In addition to detecting a threat, AI and predictive analysis tools provide you with the context in which an activity may have taken place. If there was an anomaly detected, contribution analysis provides context surrounding the actual anomaly itself.

Improved data governance

Data governance refers to the principles and processes that govern security, loss prevention, integrity, integration, lineage and completeness. As data grows in scope and size, managing it becomes more challenging. Having the tools to protect and manipulate data not only prevents data loss but also improves productivity because data scientists will have an easier time managing data.

High-quality data governance would reduce the time needed to clean and prep data for analysis, improve the speed of training for online models and develop a better understanding of an organisation’s capability through better profiling of the data asset.

A deeper layer of analysis

AI can analyse data with more depth than humans alone can manage. The reason(s) behind this is the neural networks that allow for multiple layers of analysis. The deeper layers of analysis have allowed businesses to leverage incredible computational power to analyse big data on multiple levels.

Furthermore, predictive analytics tools with AI benefit when there is a large volume of data to work with because AI models become more accurate with big data. This is because the learning models built from AI ‘learn’ from data, so the more data they are fed, the more accurate their findings will be.

Getting started with predictive analytics

Predictive analytics tools are going to be an invaluable asset for businesses. AI has the potential to augment these analytics tools, getting them to work more intelligently than before. Given the volatile business situation, business intelligence that can predict outcomes before they happen can be a vital investment.

To make use of predictive analytics tools, businesses need to work with a certified analytics reseller that can devise suitable solutions for installing, hosting and administering the analytics tools in a way that suits the businesses’ unique requirements.


Click Here to Leave a Comment Below

Leave a Comment: