Without a doubt, machine learning is a fascinating trend. But as is often the case with emerging technologies, the level of hype surrounding it long overtook the conversation. It is true that adding automation to the business process can enhance efficiency and performance. However, simply because it holds high potential to improve a business does not inherently mean that your business is ready for machine learning implementation.
In order to deliver the right results, your machine learning strategy has to be based on the right foundation. There are a number of questions that require answers before embarking on the implementation process.
How to Determine Whether Your Company is ready
Before embarking on the implementation of machine learning, consider the following points to assess your company’s readiness:
a. Does the company have sufficient data?
Training machine learning algorithms requires tons of data. Logically, the first question you need to answer has to do with the quantity of data at your disposal. Deploying a machine learning solution against a relatively small data set is an exercise in futility. Not only do you need to have enough data but it is also important to keep accumulating more.
b. What type of data does the company have?
Equally significant is the quality of the data at your disposal. You have likely heard of the acronym GIGO – Garbage in, Garbage out. If you were to feed your machine learning algorithms on garbage, don’t expect anything different when it comes to the output. It is only possible to get value from an analytical exercise if the data you use is clean and consistent.
c. Have I calculated the costs?
Machine learning implementation is a costly exercise. To begin with, having tons of data necessitates getting adequate secure storage for it. Furthermore, computational costs also increase with higher amounts of data. So does the frequency of analysis and the need for personnel to undertake the exercise.
How to Prepare for Machine Learning Implementation
Based on the answers you give for the above questions, there are a number of steps you can take to prepare for the implementation of machine learning applications. Consider the following steps below.
a. Establish a Reliable Collection Process for Good Quality Data
Everything about machine learning, even the smallest, most mundane task, requires data. But not just any data will do. Sure, you can scrape the internet for data and clean it. However, the fact that it is publicly accessible means that the rest of the world has access to it. What you need is original, top quality data. How can you get that?
- Consistent formatting – Machine learning applications cannot generalize. For example, they cannot tell when different spellings and variations of a name actually refer to the same person. Format your data consistently to ensure you get accurate predictions.
- Keep it updated – Make it a habit to discard old and irrelevant data sets to create space and optimize results.
b. Narrow Down on an Area of Business that Requires Improvement
There is no point in having all the data in the world if you cannot figure out how to use it. After all, the whole idea behind implementing a machine learning strategy is to solve an existing problem. It is not meant to uncover a new one. Where can you start?
- Catalogue current business processes – Identify procedures that are undertaken consistently and frequently. They could include activities such as loan approvals and processing. Collect as much data as possible on how the decisions are made.
- Focus on simple issues – Machine learning algorithms work best when you define and understand the problem at hand. Avoid vague problems like how to make customers happy and focus on something like how to identify a fraudulent transaction.
- Don’t apply machine learning to situations where simple business logic is sufficient. Remember that automation is useful for complex processes that require simplification. For example, you can use a machine learning algorithm for a procedure that follows a complex and possibly non-linear pattern.
With that in mind, you can now start identifying specific business areas that could benefit from the application of machine learning. These could include:
Process automation
The simplest and most straightforward application of machine learning is automation. Machine learning algorithms thrive at analyzing data for a specific task and consistently improving output. For this reason, they are ideal for automating routine processes. Think about processes that are rich in data or those that involve analyzing big data amounts.
Creating better products
To build a better product, it is important to understand the consumer better. What are their preferences? What are they talking about? Based on these questions and others that you formulate, you can tailor products to suit customer preferences better.
Does the Investment Have What It Takes To Generate Positive Returns?
As mentioned above, implementing machine learning comes at a great cost to companies. At the end of the day, every business move should generate a positive ROI, machine learning included. Some entrepreneurs jump on the bandwagon with the conviction that any machine learning boost is good for business. But that is not sound business logic as it is a by-product of the hype surrounding the space.
Taking time to calculate the ROI could help keep you from investing enormous amounts into a superfluous project. In order for any machine learning project to bring gains, it needs to meet the following basic criteria:
- It should be an opportunity to generate revenue – In order to establish whether it meets this requirement, ask yourself: Can the project solve a real business problem to create a new revenue stream or reduce wastage of resources?
- Train the application with quality data specific to your organization – Machine learning applications are only as good as the data used to train them. For an application to add value and solve a specific business problem, you have to collect sufficient amounts of relevant data related to the area you want to improve.
- The strategy used should be appropriate for proven machine learning technology – There are numerous proven machine learning approaches that a company can apply to create a unique product or service. To illustrate, Spotify has a popular feature known as Discover Weekly. This is an algorithm-based recommendation engine that creates a personalized playlist of 30 songs weekly. It uses individual users’ data to analyze their tastes in music, weigh this against current trends and use the information to analyze and classify new songs. That is a perfect example of using proven technology to create an innovative product.
Ensure that the project you have in mind meets all three of the above criteria and you can have a chance of getting a positive ROI. Based on this, you can now calculate potential returns and determine whether the investment is justifiable or not.
For example, if you want to automate a process, multiply the number of hours you save on the task by the hourly salaries you would have paid to get it done manually. These represent basic gains. Take it a step further and calculate the gains made from the saved hours.
Only after establishing that the process will generate a worthwhile ROI can you now start figuring out how machine learning implementation works.
Make Your Company Machine Learning-Ready
In spite of the bold, and at times outrageous, claims that you may have heard or read about with regard to machine learning, the technology is not magic. Therefore, it cannot miraculously transform poor performance to good. But by putting thoughtful consideration into the implementation process, your company has the potential to make significant gains. However, note that the investment does not always pay off immediately, and you might need to exercise patience.
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