Sunday, October 1, 2023

Modernizing Predictive Analytics with AI

By Anushree Bhattacharya - August 08, 2023 7 Mins Read

Modernizing Predictive Analytics with AI

Modernizing the analytics landscape with Artificial Intelligence (AI) allow data analysts to improve business operations. Businesses can take smarter approaches to integrate and align AI to get details about functionalities.

Businesses today use more and more predictive analytics to view operations and forecast trends. They can improve the delivery of solutions and products in time to the right audience, increase sales and reduce operational costs.

According to Markets and Markets report Predictive Analytics Market-Global Forecast to 2026, the global Predictive Analytics Market stood at USD 10.5 billion in 2021.

Growing at 27.7%, it is projected to reach USD 28.1 billion by 2026.

The considerable rise in the market valuation is due to one of the factors—the high use of AI and ML. These technologies are driving the adoption of predictive analytics by businesses worldwide.

The modern versions of analytics have artificial intelligence. Platforms paired with AI allow businesses to get deeper data insights.

Due to the technology, businesses are going way forward in forecasting, data insights, and making informed data-driven decisions accurately.

Why Need AI-Powered Predictive Analytics

Modernized analytics provide businesses to make decisions based on operations’ actionable insights.

The technology learns to fix or eliminate what it learns as an issue. Apart from this, it provides better options and predicts a better path to make better decisions.

For instance, in supply chain management, analytics systems interpret data to predict demand. As companies today require real-time updates about supply, AI tools provide improved data and deep insights into the chain.

Companies can gain operations and logistics data using predictive analytics. They can also predict costs, and based on this, they can decrease the pricing.

Predictive analytics applications are useful in managing risks, quality control, marketing, risk management, fraud detection, and cybersecurity. There is high demand for analytics to improve these areas of business operations.

However, the important thing is AI needs to fit with predictive analytics. It is possible with data. Companies can extract more value from data by integrating AI into predictive analytics. Valuable data gives power to business leaders for fast decision-making.

In the current business landscape, historical data is not only the option to understand or improve operations. They can help AI analytics to gather information. But to predict historical data are less invaluable.

Enterprise Use Cases of AI-Powered Predictive Analytics

  • Quality Control

Businesses can boost quality control activities through AI-based predictive analytics to get real-time data on tools and devices.

The latest supply chain management, cloud, and marketing platforms have IoT-based tracking systems, automatic data capabilities, and risk detection. These combine to control all quality options throughout the processes.

AI analyzes data collected from the operations. The result helps to plan, execute and improve operations by maintaining quality parameters.

  • Supply Chain Management

According to a new survey by Deloitte and MHI in The 2023 MHI Annual Industry Report, AI-powered predictive analytics will expand in the supply chain management process.

It showed that 31% of companies already use AI predictive analytics for supply chain management compared to 28% in 2022. Whereas 48% plan to integrate the technology in the next five years.

The report “The Responsible Supply Chain: Transparency, Sustainability, and the Case for Business” by Deloitte, says that AI can transform supply chains into more sustainable, transparent, and responsible operations.

Furthermore, 74% of supply chain leaders are increasing supply chain technology investments. Almost 90% of them, plan to spend over $1 million on the technology.

AI investment in predictive analytics includes improvising supply chain resiliency, transparency, and sustainability and boosting the workforce.

Companies today need to know when and how much to buy by understanding the market position.

These aspects may open ways regarding customers and revenue-generating opportunities. Statistical models that accurately forecast future events are necessary.

Predictive analytics with artificial intelligence gives that opportunity to companies. The technology provides data insights in advance and current trends. Meanwhile, historical data is vanishing.

This has forced companies to adopt or update their predictive analytics technologies focused on future trends.

AI looks into real-time data and alerts any discrepancies in the overall management lifecycle.

  • Marketing

Predictive analytics with artificial intelligence benefits marketing for customer-driven decisions, products, and services.

Marketers use analytics platforms—online and offline to understand customers’ buying patterns. They also learn buying behavior and search activities, and provide relevant information.

These AI analytics platforms also help marketers provide personalized product and solution services based on their online and offline activities.

Businesses can also use AI in predictive analytics for various reasons. It could help optimize pricing, sales opportunities, market research, backend sales operations, testing, and determining customer needs.

In addition, analytics has become even more important in helping enterprises navigate fast-changing customer behaviors and their rising expectations.

  • Management of Risks

AI-powered predictive analytics also helps manage risks as enterprises deal with massive and large volumes of data.

However, they are at risk all the time. Predictive analytics with artificial intelligence detect glitches and controls threat in advance.

  • Cybersecurity

Predictive analytics can help predict the safety limitations of a new application or website. It scans if they are likely to be malicious or whether a particular user’s behavior is suspicious in cyber data.

IBM’s report Cost of a data breach 2022 finds that data breaches will happen for 83% of companies. The costs of a data breach have already hit a 17-year high. The average data breach now costs a company worth $4.24 million.

45% of breaches occurred in the cloud. Organizations having a hybrid cloud model had lower average data breach costs of about USD 3.80 million.

That calls AI-based predictive analytics. It has a crucial role in protecting the enterprise. In the same report, enterprises using AI and automation had a 74-day shorter breach lifecycle. They saved an average of USD 3 million in 2022.

  • Put Predictive Data to Work

Enterprise applications of predictive analytics with artificial intelligence today cover a broad spectrum of use cases.

Using AI in analytics helps accelerate product and business operation life cycles, improves functionalities, and decreases operational error challenges.

Businesses can make assumptions with data, test it, and learn results to make decisions in the future. This also grows future opportunities for leaders to invest more.

Introducing AI systems into existing analytics platforms and functionalities requires a strong data foundation. Enterprises can put data to work in real-time for the benefit of businesses by decreasing complexities and legacy infrastructure.

Also Read: Five Industries That Benefit the Most from Predictive Analytics

Shaping the Future of Businesses with AI

Enterprises acknowledge that fact of advancing their analytics capabilities is the first step. The real matter is to know what tools they require to do the tasks right.

Here’s what business leaders must know to conduct tools appropriately.

  • Get AI Ready

To begin with advanced analytics, the leader needs data in place. Building adequate data infrastructure is a strong foundation for AI-based predictive analytics capabilities. This needs to be the next big business priority.

  • Turn Data into Action-based Intelligence

Data visualization is a key part when adopting AI-based analytics. Organizations can make data-driven decisions by allowing smart applications for effective data visualization and intelligent dashboards.

  • Get Ready with 5Vs

Business leaders must know how to manage data velocity, volume, veracity, variety, and value to turn data into a strategic asset. These attributes will help them gain maximum value and meet business goals.

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Anushree Bhattacharya

Anushree Bhattacharya is a Senior Editor with Ondot Media, where she covers stories on B2B business strategy, thought leadership, and corporate technology culture. She is a quality-oriented professional writer with eight years of experience. She has been curating content for the B2B industry, and her writing style is inclined toward how businesses want to perceive information about emerging digital transformations and technology developments. Anushree blends the best information on trending digital transformations, technology-driven stories, and SEO-optimized content. Anushree is proficient in technology journalism and curates information-driven stories about enterprise tech for EnterpriseTalk publication.

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