Every day, people across the globe generate over 2.5 quintillion bytes of data. For the last decade, data analytics has helped industries better understand their consumers. The technology to gather and analyze this data has continued to evolve rapidly over the last 10 years. Now, enterprises can achieve a new level of insight through advanced predictive analytics.
As it stands, every industry and every facet within each industry can benefit in some way from the use of predictive analytics. With the ability to collect, organize and analyze big data faster and more efficiently than prior systems, an enterprise can finally make use of some of the industrial data they may have been collecting for years.
Thanks to advancements in artificial intelligence (AI) and analytics software, companies can transform their past and present data into future insights. From the sales front and the customer side to production, engineering, even finance and marketing – every department can improve through enterprise-wide digital integration.
In this ever-changing landscape, predictive analytics can allow an enterprise to not only succeed but also thrive. Let’s explore what this new application of data entails for a business, and what skills that business needs to implement this modern technology.
Though industrial digital transformation can take time, predictive analysis stands as a key trend for enterprises to pick up first. In addition, COVID-19 very well may have sped up this need to go digital, no longer allowing enterprises to take their time with digitalization.
The reality is that the pandemic showed cracks in various industries’ supply chains, as well as how they’re able to communicate and engage with their customers. Predictive analytics proves to be a perfect solution.
In the past, corrective maintenance and preventive analytics were the tried and true methods. For example, preventive maintenance helped mitigate loss from equipment lifecycle failure. According to a 2015 study from the ARC Advisory Group, that only accounts for about 18% of equipment failures. This leaves 82% of equipment failures left to random causes that predictive maintenance software can help minimize.
Using modeling techniques and statistics from data gathered by IoT industrial sensors, predictive maintenance analytics can determine each piece of equipment’s future performance. This valuable insight saves capital, time and can even improve worker safety. By looking at past and current data, predictive analytics determines which patterns are most likely to present again, which reduces risk while improving operational efficiency.
Sifting through massive swathes of data manually isn’t feasible, cost-effective or conducive to an enterprise’s productivity. Instead, predictive analytics allows companies to locate patterns within the data, both for potential opportunities and risks.
Reduce Risk While Improving Operations
Using digital twin technology, a business’ complete infrastructure can be modeled digitally. The business can then make use of predictive analysis tools to identify and better understand relationships between different behavior variables.
Then, that company can use its digital model to present a variety of conditions and assess the risk or opportunity that stems from these conditions. For example, a predictive model of a hotel can project how many guests they’ll have on any given night to maximize occupancy and optimize revenue. Alternatively, the predictive analytics of an airline can evaluate and set ticket prices.
No matter the industry, this valuable digital transformation enables enterprises to better manage resources, forecast their inventory, keep up with supply and demand, and, overall, function more efficiently.
Cybersecurity is becoming a growing concern, especially for companies that don’t have a firm digital infrastructure in place. Predictive analytics’ pattern detection capabilities can help lessen or prevent criminal behavior.
With so much data available to the software, it can spot any inconsistencies or abnormalities that could indicate fraud, persistent cyber threats and zero-day vulnerabilities. This comes at a time when enterprises need it most, especially those that may still have those technological vulnerabilities.
Optimize Marketing Campaigns
The opportunities for predictive analytics when it comes to the customer are some of the most important for an enterprise to focus on. In this consumer-centric world, an approach that also centralizes itself around the consumer is essential. Predictive tools can help organize and track shopper purchases, customer feedback and find patterns that present as cross-sell opportunities.
The world is plugged in more than ever – by the numbers, over 4 billion people use the internet. Over 70% of US Americans own a smartphone, where they can access the web with a few taps even when they’re on the go. Tools that help a business attract, grow, and retain loyal consumers are an integral part of this new digital age, and customers are waiting for it.
What is predictive analytics used for?
Predictive analytics is the process of using data to predict future trends. It is the use of statistical techniques to identify patterns in data and to predict future outcomes using those patterns.
What is the best tool for predictive analytics?
The best tool for predictive analytics is the human brain. While data analytics can be an excellent, almost fully-automated type of machine learning software, humans must define the questions that they want predictive analysis to answer. Without identifying a clear business objective, the tool is only used to gather big data.
What companies use predictive analytics?
Predictive analytics is used by many companies, including Amazon, Netflix, Apple and Google. An example of one of these companies using such a tool is when Netflix uses it to recommend movies and TV shows to its users.
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