Predictive Analytics, AI and ML


By Gonzalo Usandizaga

It is no surprise that experts predicted 2018 to be the year of best-in-class Business Intelligence (BI) tools, along with hybrid cloud strategies, machine learning (ML) and artificial intelligence (AI). Our data-driven world is highly dependent on, and highly served by, the evolved Big Data practices.

According to a Bains study I recently read, companies that use advanced analytics and machine learning are twice as likely to be top quartile financial performers, and three times more likely to execute effective decisions. That is a powerful outcome of predictive analytics.

So, while everyone has access to data and analytics – it’s imperative that BI tools become more predictive and proactive to not only deliver insights, but also drive greater intelligence and productivity across the enterprise. To stay relevant and stay ahead, businesses have to necessarily look into AI and ML for continuous and deep organizational and operational insights. Machine learning and deep learning, a subset of AI and advanced data analytics are all part of the Narrow AI that is transforming the way businesses operate and innovate.

ML & AI will power predictive analytics for innovation

As per Gartner’s 2018 Strategic Trends report, AI tops the list of BI trends. Artificial intelligence, immersive experiences, digital twins, event-thinking and continuous adaptive security will create a foundation for the next generation of digital business models and ecosystems.

AI and machine learning are revolutionizing the way we interact with our analytics and data management. The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience is set to drive the payoff for digital initiatives through 2025.

Machine Learning, AI combined with advanced analytics can eliminate business risks altogether

From Big Data to fast data analytics, the application of big data analytics in real-time helps solve business challenges before they occurred. Now, with machine learning, I see the concepts of big data and fast data analytics being used in combination with AI to eliminate these business challenges and risks altogether and build more and more improved business models. 

ML allows computers to develop the ability to learn through experience and search through data sets to detect patterns and trends. So instead of extracting intelligence for risk prevention, ML allows computers to adjust program actions predictively and continuously. 

With the advent of ML across industries, we’re seeing disruptive models in the form of image classification and detection, facial detection, proximity marketing, gaming and so much more. With the growing demand though there is also an evolving ecosystem of software specific to ML machine that creates disruptive business models through instant and predictive analytics. 

Industry applications of predictive analytics

Predictive analytics is changing the way companies across every industry operate, grow and stay competitive. Some examples of practical applications that come to mind include: 

  • Financial Services where predictive analytics allows fraud discovery, investment opportunities detection, identifying clients with high-risk profiles and the ability to determine the probability of an applicant defaulting on a loan. 
  • Telecommunications sector uses predictive analytics to analyse network performance, predict capacity constraints and ensure quality of service delivery to end customers. 
  • AdTech is using predictive analytics to optimize audience targeting, analyse visitor behaviour through A/B and multivariate testing, and predict user engagement patterns. 
  • In the manufacturing industry, predictive analytics is helping identify product defects, predict equipment maintenance needs, optimize supply chain planning and forecast demand. 

But what’s available today that can mine the combined power of ML and AI to create more and more disruptive business models through deep insights? While traditional tools try to suit up for the growing volume and velocity of data, the complexity of creating and deploying machine learning models becomes more complex, requiring more time and resources to bring predictive analytics projects to fruition. 

To know more about solutions that address common barriers preventing predictive analytics projects from getting off the ground, talk to us at Micro Focus. We can help you take your journey to the next level.

About the author: Gonzalo Usandizaga is the VP & GM, Emerging Markets at Micro Focus. Micro Focus is one of the largest pure-play software companies in the world and is uniquely positioned to help customers maximize existing software investments and embrace innovation in a world of Hybrid IT – from mainframe to mobile to cloud.