Predictive analysis applications have been part of the technological spirit of the time in what appears to be a very long time. In the last decade (at least), Gartner’s annual hype cycle has made the likelihood of increased adoption very clear. However, the concept of taking a proactive as opposed to a reactive attitude towards a business optimization strategy is something that is slowly taking hold in the boardroom.
When data researcher talks about predictive analytics, their focus is on the use of historical data to predict future events. This idea is nothing new, learning from the past to avoid making the same mistakes is part of a functioning society. But by building complex models that incorporate significant trends and assumptions, predictive analytics provides companies with vital insights into their customers, prospects, employees, competitors, and increases the supply chain.
Introducing artificial intelligence or more specifically machine learning in predictive analytics means the AI system can take analytics to the next level. In almost all scenarios, technology can make assumptions, test the waters and learn independently.
Now, let’s take a deeper dive into what predictive analytics brings to the business table and, importantly, how AI can deliver the competitive edge you seek.
Do we need AI-driven analysis?
As analysis systems become more advanced, they move on to what is known as a “prescriptive” model. This transition gives companies decision-making based on the action-oriented insights of the program.
AI is often the scary element in science fiction movies, technology that sees trends and “learns” to solve/eliminate what it sees as the problem. From a business point of view, however, it is far from a dystopian future to integrate a system that not only learns but also predicts a better path.
Let’s start with this basic truth; predictive analytics is already established in many business contexts.
In manufacturing, for example, analysis systems interpret existing data to predict demand or when significant maintenance needs to be planned. In retail, predictive analytics provides insight into customer preferences, buying behavior, and predicts increases in demand or inflows. Want to know why Netflix and Amazon Prime know what you (in theory) want to see next? Predictive analysis.
The applications for predictive analytics are already spread over a large surface area; weather forecast, disease detection and diagnosis, improvement of sports performance, risk management in the insurance and financial sector… all these use cases are data-driven. There is also a great demand for analytics to improve specific areas within a business function, such as HR or marketing.
So how does AI fit into predictive analytics? Spoiler alert: It’s all down to data.
Data is a valuable commodity, but only if you know how to exploit it. By integrating AI into predictive analytics, organizations can extract much more value from data. For example, AI-driven predictive analytics can be used in similar scenarios as stand-alone predictive analytics (minus AI, basically), but the AI element is what will deliver more sophisticated prescriptive results.
AI-driven predictive analytics is often critical, as updated information from different data sources needs to be processed for rapid decision-making. A road-based pilot scheme in India, for example, helps reduce the number of car accidents by warning motorists of potential dangers in a timely manner so that they can prevent collisions. This pilot relies on the AI element to be truly effective.
In addition, there are situations where historical data — as used in traditional prediction systems — does not actually help predict future behavior. Think about how the COVID-19 pandemic affected the demand for protective equipment. A predictive system would not be able to draw on any data that could have foreseen the sharp increase in demand that occurred.
As we noted above, enterprise applications for AI in predictive analytics cover a wide range of utility cases.
Exploiting AI along with analytics accelerates product lifecycles, improves resource allocation, and increases operational efficiency. In addition, the alignment of external and internal data sources with an AI system that can make assumptions, test them, and learn from the results means that organizations are not only improving their understanding of the company as it stands now, but future growth opportunities. as well.
However, there is a small fly in the ointment.
“Analytics” is a broad term that applies to different disciplines and solutions. And although it has become an essential part of the technical conversation, it is not a silver ball.
To put it simply, introducing AI systems into your existing analytics capabilities requires a solid data base. Businesses have access to vast amounts of data, but the ability to launch them for the benefit of the business is often hampered by increasing complexity, limited skills, and poorly equipped older infrastructure.
These identified pain points underscore why Infostretch’s recent acquisition of Gathi Analytics meets the urgent need we see for companies to not only revise but also modernize data environments, even more so if they want to deliver insight and deliver business impact. Data is power, and the companies that use this resource in the right way no doubt demonstrate the level of digital maturity that the affiliated community demands.
Think about the future
One question to consider is what companies need to know. Recognizing that they need to advance their analytical skills is only the first step, the crucial thing is what tools they need to get the job done right. This step often depends on which service providers can answer the question.
For example, decision-makers should consider the following:
The 5 Vs — Learn how to manage data volume, speed, variation, truth and value to make data a strategic asset. Data is constantly being generated, so companies need to understand how to deal with the deluge of data in order to extract maximum value. Our expertise extends deep into covering the business, financial and technical aspects of your business data to help you achieve your business goals.
Get AI-ready — If your end goal is an advanced analytics, your starting point should be with your data. Getting your data infrastructure and ecosystems in order first builds a strong foundation for future analytics capabilities. This is the core of computer technology, and the fact that digital engineering companies like ours are experiencing such high demand for these data modernization services is a strong indicator that artificial intelligence will be the next big business priority.
Turn data into action-oriented intelligence — Data visualization plays a key role in ensuring that insight becomes action. The time is over where data could only be deciphered by the IT department. By enabling smart data delivery applications, efficient visualization, and an interactive, intelligent dashboard, organizations put the power of data-driven decision-making in the hands of their business and operations managers.
