Predictive analytics uses historical data to forecast future outcomes. Today, the AI-powered technology—which also uses statistical modeling to find valuable insights in big data—is analyzing more information than humanly possible in a wide variety of industries. Businesses are applying predictive analytics to data ranging from machine uptime and customer behavior to weather and patents. With the help of AI and machine learning, companies can formulate predictions about maintenance, consumer demand, global warming, or innovation days, months, and years into the future. This data-backed prediction for what’s coming next allows business leaders in these fields and many others to make strategic business decisions and manage risk.
COVID-19 accelerated many companies’ digital transformation, making predictive analytics an essential strategy for remaining competitive across industries. The predictive analytics industry is forecasted to grow from a revenue of $8.12 billion in 2020 to more than $39 billion by 2028. For individuals, teams, and organizations focusing on the innovation lifecycle, many predictive analytics trends within this acceleration are highly relevant.
Cloud Based Analytics
Historically, datasets were stored onsite at the business that owned and analyzed them. The speed, security, and flexibility of cloud computing make insight-rich data even more valuable. Cloud-based predictive analytics delivers a faster “time to value” than previous technologies and enables collaboration across teams, regions, and disciplines. For innovative companies, this data analytics trend decreases time to market, a distinct competitive advantage in many situations.
Real Time Insights
While predictive analytics is, rather obviously, a look into what will likely happen next, the technology was previously limited by the inability to use the most up-to-date data. As technology improves, businesses can garner real-time insights from data as it’s collected. This powerful technological advancement allows businesses to react more quickly than ever before to changes in the landscape around them. Take, for example, patent landscapes. This in-depth look at a space is incredibly informative. In the past, assembling this kind of information took so long it was likely out of date before it was done. With AI processing new data instantaneously, it’s easier to make informed decisions and test hypotheses.
Data Fabric
Most organizations have mountains of data; not all of it is particularly helpful in predicting what’s next due to the inherent difficulties of data collection and management. Using data fabric to “knit” related data points together into a more valuable collection of information helps businesses uncover insights more efficiently. The ability to unify and reuse previously exhausted data can push engineers in new, innovative directions.
Real Value
It can be difficult to measure direct returns on any new technology. As predictive analytics accelerate and mature into standard business practice, it will become increasingly important for companies to prove how investing in data analytics delivers real value. How an organization uses its data can attract investors and M&A activity. While selling data and insights derived directly from it is always an option, there are opportunities for businesses to approach this trend with creativity and innovation.