Data scientists have had to put up with sluggish machine learning and challenges in providing truly predictive analytics. But with no other options, moving data from a database to the machine learning software and then back to the database has been the only option these data scientists have had until recently.
In-database machine learning is where data analytics is headed and it’s making a huge difference in our ability to provide truly predictive analytics and make data actionable at the time we receive it.
Let’s look at some ways that various industries are applying in-database machine learning and the impact it is having.
In-database machine learning is ideal for a variety of industries and it’s the future of data analytics. The speed of integrated AI and machine learning within a database makes the impossible simple. Here’s a look at how several industries are applying this useful technology.
Detecting fraud the moment it happens and blocking the transaction can help protect consumers’ finances. But to do this, you must have transactional machine learning that can recognize normal purchases compared to suspicious ones based on a variety of data points, such as physical location, IP address, purchase history, time of day and more.
But in-database machine learning is doing more than just detecting fraud. It can also help customers identify ideal investment opportunities based on risk and existing portfolio investments.
And for lenders, it’s helping them determine the likelihood that a potential lender will default on a loan to help the lender make better informed decisions.
Manufacturers are using machine learning to identify product defects before the products make it past the line. That way, they limit their liability and expenses by not having faulty products on the market.
Another large impact of machine learning in manufacturing is detecting maintenance needs for equipment. One equipment outage could cost a manufacturing company tens of thousands of dollars as they clear the line to fix the problem. Reducing these outages is transforming the industry.
Plus, machine learning can aid in the supply chain as manufacturers work to limit inventory while maintaining adequate supply to manufacture their goods. But up until recently, this was a process that a human had to do and it was somewhat flawed.
Telecommunications can be a challenging industry because of the fluctuations in necessary capacity. But machine learning can aid these companies in predicting these fluctuations to prepare accordingly.
The ability to analyze incredible volumes of data in a matter of milliseconds is what is making in-database machine learning so powerful for the telecommunications industry. And it’s helping these companies retain their customer base through understanding customer behavior.
A/B testing has been a powerful tool for many years to help marketers determine the best messaging, images, placements on a page and more. But in those tests, there were only two variants you could test on.
With machine learning, we’re able to understand consumer behavior in a much deeper way. And instead of just A/B testing, ad companies can use multi-variant testing to determine the best possible scenario to reach customers in a meaningful way.
AdTech companies can now understand and predict consumer engagement patterns to deliver a unique experience targeted toward that user’s needs.
Transitioning technology platforms is never easy and can require some downtime and additional IT resources. And while that’s no one’s idea of a good time, the impacts this transition can have can be enormous and well worth the transition.
A few databases now offer this cutting-edge technology. BangDB is a NoSQL database with in-database machine learning that’s changing how data scientists view data analytics.
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