Top 5 Tips for Adopting Enterprise Machine Learning – IndustryWeek
When you first got your business off the ground, you may or may not have paid much attention to the technologies that would be available to you in the years to come—like machine learning. Machine learning was the stuff of science fiction just decades ago; now it’s practically everywhere.
So, what is machine learning? Simply put, machine learning is a subset of artificial intelligence in which computer algorithms learn from large datasets in order to make more accurate predictions over time. Obviously, it’s a lot more complicated than that, but it poses numerous benefits to business owners—assuming it’s used the right way. Here are five tips for successfully adopting machine learning technologies in your day-to-day operations.
Identify the Problem(s) You’re Trying to Solve
It goes without saying that machine learning is not a toy. Nor is it a silver bullet. It’s a tool that can help you tackle all kinds of challenging problems to take your business to the next level. Before you invest in any new technology, machine learning or otherwise, think long and hard about which problems are causing you the most grief, such as:
- Repetitive, manual tasks
- Expensive operating costs
- The inability to manage tons of data
- Lackluster customer engagement
- Customer churn
Not the most exhaustive list in the world, but these are key pain points that affect lots of business owners. Let’s tackle customer churn, for example. According to the Harvard Business Review, acquiring new customers costs five to 25 times more than retaining existing customers. Pretty devastating, right? Especially when the competition is heating up in your industry. The good news is that machine learning can help you determine what’s causing customer churn by offering eye-opening insights into why customers might be leaving based on existing customer data. These insights allow you to make quick decisions in order to order to increase retention, and thus increase annual profits.
That’s a lot more concrete than saying “We need machine learning because it’s the hottest new trend in the industry.”
Educate Your Team About Machine Learning and Its Benefits
Inevitably, someone on your team is going to ask: “What is machine learning?” and “What can machine learning do for our business?” By no means is this an easy conversation to have, but you must take an optimistic, level-headed approach. When you talk to the other executives, think about how you might sell one of your own products or services. But at the same time don’t mistake machine learning for a product or a service: it’s a science and a tool.
During the conversation, communicate both your company’s key pain points (like the ones listed in tip #1) and the benefits of machine learning applications for your company. Overwhelming them with fancy details about the technology itself will mean almost nothing if they don’t understand the benefits of those features. Sell them on the fact that machine learning can help reduce operating costs, improve efficiency, and boost productivity by quantifiable amounts for a specific task.
Assuming you don’t have a data scientist or a machine learning engineer on your staff, you don’t want to go in over your head trying to apply machine learning to everything you think is broken—you aren’t Tim Taylor. Instead, start with a small dataset pulled from a much larger dataset, such as your company’s customer data. We understand that the more data you have, the better, but this is all about learning how to harness the power of machine learning in order to understand the impact it can have on your business.
This is also a great opportunity for you to determine whether machine learning is the right tool for the job. Machine learning is an amazing science that’s capable of solving so many of the world’s greatest challenges, but again, you can’t throw it at everything and expect it to eliminate all of your pain points. It never hurts to consult an expert or a representative from the company helping you get set up.
Treat Data As the Lifeblood of Your Business
Whether you implement machine learning technologies or not, data is one of the most valuable assets an enterprise can have. This isn’t something you haven’t heard before, but it’s important to emphasize anyway—especially when you’re adopting emerging technologies like machine learning. And with machine learning, data cannot be treated like an afterthought. Not to mention that machine learning is essentially worthless without data.
No matter what industry you’re in, data availability and quality are key to making well-informed decisions for the benefit of your business.
Treat ML As An Ally, Not a Replacement
Say it with us: “Machine learning will help, not replace, human workers.” To some, this seems like a foreign concept, but they’re ignoring one of AI and machine learning’s greatest contributions: collaborative intelligence. Machine learning technologies enable us to make a significant impact on our ever-changing world. Even if you don’t feel like you’re changing the world, machine learning can make a world of difference for your staff, your customers, and your business as a whole. And that’s what matters most; that’s probably why you’re reading this post in the first place.
You came to transform your business by leveraging machine learning technologies. By collaborating with these technologies, you can have tremendous success in doing so. Just remember to follow our adoption tips, have realistic expectations, and be optimistic.
Learn more about SparkCognition’s AI solutions here.
German semiconductor producer Infineon Technologies AG warned Tuesday that microchip supply bottlenecks could continue into 2022, in a blow to the automobile industry.
“We predict that the imbalance between supply and demand will continue for a few quarters yet, with the risk that it lasts into 2022,” said Infineon CEO Reinhard Ploss in a virtual press conference.
He added that the “bottlenecks” are a particular problem for the Munich-based company in areas where they do not produce the chips themselves but buy them from subcontractors in order to equip microcontrollers for cars or smart appliances.
The automobile sector remains plagued by “severe delivery problems,” amid a rise in demand driven by a recent boom in electric vehicles, said Ploss.
Infineon, which plans to finish construction on a new chip production site in Austria later this year, has profited in early 2021 from a booming semiconductor market.
While the surge in demand for electronic devices during the pandemic has helped chipmakers, it has also led to a semiconductor supply crunch in the automobile industry, where chips are a key element in modern vehicles.
The shortage of chips has pushed many automakers to idle production lines for brief periods when they temporarily run out of supplies.
According to Infineon marketing director Helmut Gassel, the shortage affected the production of around 2.5 million cars in the first quarter of 2021.
Copyright Agence France-Presse, 2021