Machine learning gives computers the ability to continuously analyze and improve their own performance without the need for direct human intervention. In order to further the automation of various business tasks, many organizations are looking to put the technology to use.
A recent survey done by MIT Technology Review and Google Cloud found that businesses are adopting machine learning at a rapid pace. According to the data collected, 60 percent of respondents indicated that they have already implemented machine learning strategies for their businesses. While a third of survey respondents confirmed that they were already at the mature stage of their implementation efforts.
Additionally, a recent survey by Tata Consultancy Services that reached out to 835 executives found that 84 percent of them believe that machine learning technology is now essential to maintain business competitiveness.
However, the way that machine learning is being discussed makes it sound like it is a magical solution to nearly any business problem. People who have not yet used it imagine that they can just press a button to get instant results. Although there are some amazing things that can be done with machine learning, in reality a successful machine learning strategy involves a significant amount of effort on the part of your organization.
Now that you understand the work that is required, here are 10 tips to follow to help you develop a successful machine learning strategy.
1. Rethink Your Corporate Culture
The skillsets of your team and corporate culture are extremely important as you define your machine learning strategy. Identify the roles of the future and anticipate how employees need to engage with the machines to build a corporate culture that embraces machine learning.
2. Focus on Logistics
In many cases, a successful machine learning strategy isn’t only about the algorithm. In fact, the logistics of how you plan to solve a particular problem with machine learning is often more important. For example, while adjusting the algorithm that you use might give you a slightly better result, adjusting how you collect the data that the algorithm uses could easily deliver a 100 percent improvement in your results.
3. Choose the Right Algorithm
The major types of machine learning are supervised, unsupervised, and reinforcement learning. Some of the factors that you’ll need to consider include accuracy, training time, linearity, number of parameters and features, and special cases. Make sure that you are using the algorithm that is best suited to the characteristics of the data. It will help you to get the most accurate results.
4. Be Open to Changes to Your Algorithms
Machine learning implementations require continual trial and error. Regardless of how well designed you believe your algorithms are, if the system interacts with humans, adjustments will need to be made periodically.
You should continuously monitor the effectiveness of your machine learning implementation in order to determine if there are changes and variables that could be adjusted it make it better or worse. This may sound obvious but many businesses aren’t doing this at all or they aren’t doing it very well.
5. Change Your Metrics
In order to reinforce the business case for your machine learning strategy, you need to develop metrics that can effectively measure the outcomes from your machine learning implementation. These metrics won’t be the same as the ones that you traditionally used for your business before machine learning.
6. Use Many Different Tools
To build a successful system, you’ll need to make use of a variety of different tools. On average, businesses use a minimum of 5-7 tools and oftentimes even more, according to Ted Dunning, Ph.D., the Chief Application Architect at enterprise Hadoop vendor MapR.
7. Plan for Capital Improvement Projects
At some point the technology needed to make continuous performance improvements in your machine learning strategy will need to be upgraded. Keep in mind that budgeting and transition planning can be tricky. Consider your long-term goals before choosing tools so that you don’t end up with technologies that are no longer compatible with the needs of your business.
8. Quantify the Business Value of Your Machine Learning Strategy
What is the business value of achieving the expected results of your machine learning implementation? What areas of your business can benefit most from automation? What are the productivity gains that you hope to make from implementing machine learning?
Without concrete answers to these questions, your machine learning strategy risks devolving into a science project rather than a solution that creates a competitive advantage for your business.
9. Expensive Doesn’t Mean Better Results
The amount of time and money that you dedicate to your machine learning implementation don’t automatically equate to its business value. The best way to position your business for success is to make sure that the strategy you develop is reliable and doesn’t require an excessive amount of your organization’s resources.
10. Anticipate Failure
Unlike previous IT strategy design paradigms, failure is a constant rather than the exception in a machine learning strategy. The impact of failures can easily outweigh success even with an algorithm that is 70% or 80% accurate, resulting in a significant loss of revenue or reputation for your business. To be successful, your organization needs to effectively manage algorithmic risks.
Build a Foundation for Success
Machine learning can potentially unlock new insights for your business. However, it isn’t a one-size-fits-all solution to every problem that you can just buy and set up. It will require some experimentation and alignment between IT and the other parts of your business in order to create a synergy that can truly move your business forward.
The first step to a successful machine learning implementation is combining the right enabling architecture, tools, and team. Set up a free consultation today and let’s discuss how we can design a machine learning strategy to help you achieve your business goals.
