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AI for Predictive Analytics: Everything You Need to Know

AI for Predictive Analytics: Everything You Need to Know

This blog, co-written with Thanneermalai Krishnappan (Technical Manager, Apexon), draws on their joint experience in the data and analytics environments. 

A constant fixture in the annual Gartner Hype Cycle for Emerging Technologies, predictive analytics has been part of the tech zeitgeist for what seems like a very long time. The concept of being proactive as opposed to reactive is nothing new, but the consensus is that companies have only recently realized just how beneficial this attitude can be in a business optimization strategy.  

When data scientists talk about predictive analytics, they are talking about the use of historical data to predict future events. By building complex models that incorporate significant trends and assumptions, predictive analytics provide organizations with vital insights into their customers, prospects, employees, competitors and supply chain.  

When you introduce Artificial Intelligence or, more specifically, Machine Learning into predictive analytics, the AI system can take analytics one step further. In other words, the technology can make assumptions, testing and learning autonomously.  

And as analytics systems become more advanced, they then move to a “prescriptive” model, helping businesses with decision-making based on the program’s insights.  

To the general public, AI is the scary element in the science fiction movies, the one that is going to take over the world. But from a business standpoint, integrating a system that not only learns but also predicts is a long way from a dystopian future. 

With that in mind, let’s take a deeper dive into what predictive analytics brings to the table and, importantly, how AI can deliver a competitive advantage. 

Why do we Need AI-powered Analytics? 

Let’s start with a simple fact; predictive analytics is already established in many business contexts. 

In manufacturing, for example, analytics systems interpret existing data to predict demand or when to schedule essential maintenance. In retail, predictive analytics provide insights into customer preferences, buying behaviors and anticipate spikes in demand or footfall. It’s why Netflix and Amazon Prime know what you (in theory) want to watch next.  

The applications for predictive analytics are already as wide-ranging as weather forecasting, detecting and diagnosing disease, enhancing sports performance or managing risk in the insurance and financial sectors. There is also high demand for analytics to improve specific areas within a business function such as HR or marketing.  

So, how does AI fit in with predictive analytics? The answer, unsurprisingly, is data. 

In a digital world, data is power, but only if you know how to take advantage of it. Integrating AI into predictive analytics enables organizations to extract a lot more value from data. You are most likely to see AI-powered predictive analytics applied in similar scenarios to stand-alone predictive analytics (without AI, basically), but the AI component is what will deliver more sophisticated, prescriptive results.  

AI-powered predictive analytics is increasingly favored where up-to-the-minute information from disparate data sources needs to be processed for fast decision-making. For example, a road-based pilot scheme in India which is helping to cut down on the number of car accidents by alerting drivers to potential dangers in time for them to prevent collisions relies on the AI element to be truly effective.  

Additionally, there are situations where historical data, used in traditional predictive systems, does not actually help in forecasting future behavior. For an illustration of this, think of how the COVID-19 pandemic affected demand for protective equipment. A predictive system alone would have no basis to anticipate the sharp increase in demand that occurred.  

Putting Data to Work 

Enterprise applications for AI in predictive analytics are wide-ranging. 

Leveraging AI alongside analytics accelerates product life cycles, improves resource allocation and increases operating efficiencies. By blending together a variety of external and internal data sources with an AI system that can make assumptions, test them and learn from the results, organizations don’t just improve their understanding of the business as it looks now, but its future growth opportunities too.  

There is, however, a caveat.  

“Analytics” is a broad term that utilizes different disciplines and solutions. And while it has become a significant part of the conversation, it isn’t a silver bullet.  

Introducing AI systems into your analytics capabilities requires a solid data foundation. Enterprises today have access to enormous amounts of data, but the ability to put it to work for the benefit of the business is constrained by increasing complexity, limited skill sets and ill-equipped infrastructure.  

This challenge is one of the reasons for Apexon’s recent acquisition of Gathi Analytics and addresses the acute need we see in enterprises to not only overhaul but also modernize data environments to deliver insights and business impact. Data is a valuable commodity, and the companies that use this resource in the right way are arguably demonstrating the level of digital maturity that the connected society requires.  

What do Business Leaders Need to Know?  

One question that needs to be considered is what businesses need to know, both in terms of how they can advance their analytics capabilities and the tools they will require. The latter is often dependent on which service providers can answer the question.  

In our experience, decision makers should be thinking about the following:  

The 5 Vs – Learn how to manage data volume, velocity, variety, veracity and value to turn data into a strategic asset. Data is being constantly generated, so enterprises need to understand how to manage the deluge of data in order to extract maximum value. Our expertise runs deep in covering the business, financial and technical aspects of your enterprise data to help you meet your business goals.  

Get AI-ready – If your end goal is advanced analytics, your starting point needs to be with your data. Getting data infrastructure and ecosystems in order first builds a strong foundation for future analytics capabilities. We call this process data engineering and the fact that digital engineering firms like ours are seeing such high demand for these data modernization services is a strong indicator that the next big business priority is AI.  

Turn data into actionable intelligence – Data visualization plays a key role in ensuring that insights turn into actions. Gone are the days when data could only be deciphered by the IT department. By enabling smart applications for data provisioning, effective visualization and an interactive, intelligent dashboard, organizations put the power of data-driven decision-making into the hands of their business and operations leaders.  

Anticipating and Addressing Analytics Challenges    

It’s no secret that data scientists and AI engineers are both skills that are in short supply in the current market. It’s easy to see why: AI-powered analytics turn your data into a strategic asset with the power to accelerate digital initiatives and increase ROI.  

However, a shortage of talent can curtail a project’s ambitions pretty quickly, so it’s important to be realistic about what is achievable and how much external help will be required. A data engineering partner, like Apexon, helps to keep initiatives on track by tapping into their deep technical and business experience. We can also help train your talent in the AI and analytics skills needed to continue to deliver value.  

The success of any analytics system will be measured on its impact. Often, these are not bolt-on solutions, so it’s important that businesses get the strategy, execution and follow through right.  

A data engineering partner can help guide this process and ensure the vision really will deliver what is required. After all, the connected society expects companies to use the data that they gather to provide a better customer experience. The dystopian AI-led future that many fear may be irrational, but companies that don’t see the effectiveness of predictive analytics could find their business nightmares becoming a reality.  

Data has become the platform on which successful customer engagement is built, and the connected society expects companies to both use and understand the information. Predictive analytics takes this expectation to the next level. 

The good news is that Apexon’s digital engineers are well-equipped to solve the toughest challenges that companies face. Submit the form below to find how we can help alleviate your digital pain points. 

Alternatively, contact us, and we will ensure that the right person is given all the information they need to solve your problem.. 

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