5 Reasons Why You Need Machine Learning in Software Applications
Evolution is seldom linear. As the world moves forward, new technologies emerge or evolving cultural values become mainstream. Suddenly, these changes can appear to become ubiquitous, often catching some people off guard. Malcolm Gladwell’s “The Tipping Point” articulates this evolution. Machine Learning in software applications is now hitting its stride, becoming mainstream. Suddenly, it now recognized as a “must have” technology in every software application.
For the purpose of this article, I am grouping “cognitive computing,” “predictive analytics” and “artificial intelligence” all into the single term of “machine learning.” I realize that this is a bit of an over-simplification, but for the most part, the concepts I’ll present apply to each of these technologies. Specifically, my focus for this article is on the importance of Machine Learning within the context of what to expect from within software applications. Those interested in reading a deeper dive on Artificial Intelligence should read this article.
The Profound Impact of Machine Learning
We live in a big data world. The Internet of Things (IoT) revolution has impacted all facets of our lives, from how we consume entertainment to how we purchase food to how we travel. The Industrial subset of the IoT is completely changing how products are made, processes are managed, and operational performance is optimized. This digital revolution will continue to morph as new devices and applications are launched, leveraging data with increasing frequency and complexity.
As one example, the path to innovation lies with how effectively data can be leveraged, to then gain new insights into future purchasing or consumption behaviors. Machine Learning lies at the core of how best to understand data, and then act upon it intelligently – ideally in an automated manner. The use of data will separate the winners from the losers in new product and service introduction.
Five Ways Machine Learning is Driving Software Effectiveness
The prognosticators of this market suggest there are at least five ways that Machine Learning within software applications is changing our lives – hopefully for the better!
Innovation
Continuing with the concept presented above, Machine Learning has become a critical component to drive innovation in software development. With the enormous amount of complexity now inherent within even the most mundane devices we take for granted – such as a watch – the calculations and data-based decision modeling that is going on in the background is stunning.
The best example that comes to mind is what helped Amazon become an industry leader. Their use of Machine Learning now prompts us to purchase follow up items that make a lot of sense. I can’t tell you how many times I have seen my shopping cart continue to expand with their suggestions.
Here is an article on sales innovation that explores other ways to drive this concept across sales teams.
This use of Machine Learning led Amazon to see what new products they should stock and offer for sale to their customers, a great way to accelerate innovation and expand their business.
Exploration
This concept is beyond “innovation,” pointing to exploring completely new opportunities based on what an analysis of the underlying data reveals. In these instances, there may not be an immediate problem or challenge seeking resolution.
Software that manages heating and ventilation systems might collect measurements on hourly, daily and monthly usage, which when tied to weather patterns, might reveal opportunities for new energy efficiency. Opportunities might be revealed in this example where no one had even considered that energy costs could be further reduced.
Software now exists to better manage sales teams through the collection and visualization of term performance and time spent performing various activities. This data, which is readily available with programs such as what Prodoscore offers, can then be matched with quota performance and overall company plan objectives.
This data, as isolated, might not reveal any new insights. However, by using Machine Learning and advanced analytics, new patterns will emerge. This intelligence, which can then be readily shared with others on the team, can be used to improve overall sales team effectiveness – without even realizing that future performance improvement was even possible!
Prototyping
Machine Learning excels in solving complex, data-rich situations where traditional approaches, often heavily reliant upon human judgment fall short. With the ever-increasing volume of data that is created every day, the complexity of understanding all that data grows exponentially. The result is an increasing reliance upon Machine Learning, as part of how software applications mine and analyze that data, to search for new models to solve known business issues.
A great example of this use of Machine Learning is what the airline industry has accomplished over the past few years. As a somewhat frequent traveler, one thing that was quite clear is the fact that I seldom have an empty seat next to me anymore. It seems like every flight is now 100 percent booked (even over-booked, as what we have seen in the news recently), yet I don’t hear about offers for seat vouchers all that often.
This is an example of how seat allocation and pricing models are being prototyped, through the use of Machine Learning, to then optimize passenger capacity on airplanes. This process is executed through the global travel booking software applications we have all come to know and love. What is impressive is how well these applications really do work.
Refinement
This use of Machine Learning is not quite as revolutionary as the above classifications. No new business models will be revealed with this use case. Nonetheless, continuous process improvement is an essential part of business efficiency and profitability. Here, Machine Learning while running in the background of operations procedures can point out new opportunities to further refine existing processes.
The “factory of the future” is an example that comes to mind here whereby maintenance schedules have evolved from “run till fail” to quite sophisticated “predictive” and even “prescriptive” strategies whereby Machine Learning can point to future issues before they happen, within an existing maintenance schedule, to then suggest proactive measures to avoid future unplanned downtime, as well as the specific actions to take in order to avoid asset failure.
Opportunistic
This last category is more of an “other” bucket, one that is focused on providing support to existing staff or applications. Think of this as a second chance to avoid making a costly or dangerous mistake. These types of Machine Learning use cases exist as a prevention strategy whereby a second qualification or testing process is introduced to a business process just to make sure nothing gets by.
Quality Assurance software is a perfect example. Random manual samples might still be the best strategy for say a pharmaceutical or food & beverage manufacturer to perform to ensure the highest quality standards. In addition, these companies can also run QA software that examines the samples, look for patterns to then see if the results are random, or, if another broader pattern could be revealed, suggesting a process change might be needed.
Hopefully, these examples and categories help better explain the incredibly pervasive nature Machine Learning has become as part of our lives – specifically within software applications. This is the reason why it is now a hot topic, discussed much by many different people operating in virtually every industry. Its use has become so dominant that it is hard to imagine not having this technology embedded within a software application.
If you are inquiring about the investment in new software, then you really should also investigate how Machine Learning plays a role in that field, such that any future application you consider purchasing is the best it can be. Best to be investing in a solution that will take you through the next decade.
