We’ve dedicated the whole copy to the benefits of AI and Machine Learning for business. All of them boil down to one thing: making better decisions.
Often these decisions are related to product or service facing problems, such as reducing churn, launching new product lines, or improving marketing strategies. Other times, bringing Machine Learning and AI into the organization can improve efficiency by replacing human intensive and repetitive tasks.
If you are already convinced that ML and AI are much more than just buzzwords, you may wonder how you can launch a successful Artificial Intelligence and Machine Learning project in your company. It’s not an easy task but we’ve put together a few steps you can follow to reduce the probability of wasting resources and funds, and generate more value for your company.
Step 1: Map out Your Main Challenges
This step is the cornerstone for developing a machine learning strategy for business. It determines how you address all steps that follow – from the types of data you collect to the metrics you choose to measure.
After all, you need to know what your pain points are before you try to remove them.
Many small businesses have too many problems they would like to see fixed by Artificial Intelligence and Machine Learning. Going after all of them isn’t feasible when it comes to resources and budget. It’s best to start with something small – a simplified version of the most pressing issue – and then expand on it later.
If the business challenges you are dealing with seem too big, try breaking them down into smaller parts. This process will enable you to analyze the different aspects your problem composes of and find how you can solve the problem.
If you find yourself getting stuck, there are a ton of Machine Learning ideas for small businesses. We don’t advise directly copying another project. However, exploring what other companies in your industry do within the ML field might serve as inspiration.
Step 2: Understand the Possibilities of Machine Learning
Once you’ve settled on the problem your business is going to tackle, take a bit of time to understand what ML and AI entail. To understand what these fields can do for you, you first need to understand the specific capabilities that are available to you.
This is especially important for managers that will work closely with data science teams later on. There are a lot of great resources available on basic concepts of Machine Learning and AI.
Step 3: Collect Data (or Use Existing)
The third step in starting with Artificial Intelligence and Machine Learning for business is collecting relevant and comprehensive data. The problem you’ve defined in step one will guide you on this step but there is no magic formula for how much data is enough.
The volume will depend on the complexity of the problem and the ML algorithm that will be used later on in the project. The types of data you collect will have a direct impact on the performance of the algorithm, as this data is its so-called learning material.
Incorporate control factors and noise factors in your data to improve its quality and, later on, the robustness of the algorithm. Don’t shy away from near-real or real-time data if the problem calls for it, but don’t feel the need to include it. Such types of data aren’t always superior.
Focus on the types of data that will best represent your problem. For example, if you are trying to predict customer churn, the physical location data of your clients might not be as valuable to you.
Most importantly, don’t forget the data you already have in your company. Chances are your company’s day to day operations already generate big amounts of data that can be utilized. This can range from obvious sources such as a customer service database to website analytics for your company’s domain.
Step 4: Put Your Data to the Test
Once all necessary data has been collected, look for trends, outliers, exceptions, incorrect, inconsistent, missing, or skewed information. This might sound similar to the next step – data preparation – but the main difference is that this step is much more analytical in nature.
During the data exploration stage, you are looking to ensure that your data doesn’t contain any biases that might influence your findings down the road. Without proper data exploration, you might end up feeding incorrect data to your Machine Learning algorithms and getting undesired results. After all, bad data leads to bad results, even with a perfect algorithm.
For example, if you are trying to build an algorithm for unbiased hiring, that data must contain an equal number of data points for female and male candidates. Otherwise, the model will be trained with a bias towards the majority.
Any data exploration process should look at the following in the data:
- Outliers. Values too large or too small compared to the average of the data sets.
- Similar variance. The variance in data variables needs to be homogenous.
- Normally distributed data. Think of the traditional bell curve.
- No missing data.
- Correlation between variables. Change in several variables within a dataset will affect other variables.
- Independent datasets. Different datasets are not dependent on each other.
This process relies heavily on common techniques within statistical analysis and data visualization. While tedious and math-heavy, this step will also help you decide which model or algorithm is best to use for your project, and help you develop a Machine Learning strategy for your business.
Step 5: Massage Your Data (Data Preparation)
Data preparation is a crucial part of an ML project. It is also the most time-consuming. Together with the previous step, this data preparation might take up as much as 79% of your Machine Learning journey.
However, this step ensures that your data is formatted consistently and in a way that best fits your model. The more data sources you use for your AI and Machine Learning project, the more anomalies you might discover and the more work the data will need.
Data preparation includes but it is not limited to:
- data cleansing
- labeling the data
- dealing with missing data
- dealing with inconsistent data
- data flattening
- data imbalancing
Feature extraction might also be a part of data preparation. It’s particularly useful when you have to deal with big volumes of data with a lot of variables, which requires a lot of computing power to process. Feature extraction techniques reduce the dimensionality of data by combining two or more variables into features without losing the valuable information they hold. It also eliminates any redundant data you might have.
Step 6: Train Your Model to Tell the Future
This is the step where you select, train, and validate a Machine Learning model, or Machine Learning systems as they are also known. Data modeling is essentially a process where your algorithm tries to understand the relationships within your data. Here is where the amount of data and the quality of it comes into play.
Once the algorithm is trained, it should be introduced to new data sets and generate insights and predictions based on the data. These insights are what will drive the answers to the problem you determined in step one.
There is, unfortunately, no one set blueprint you can follow to determine which model fits your business problem. The common approach is to try different algorithms and compare their performance.
Some good metrics to use for performance measurement are low bias, meaning the results a model produces fit well with historical data. Another one is low variance, where the results of the analysis aren’t skewed too much by outlier variables.
Step 7: Evaluate the Process
After going through all the other six steps, make sure you are using your time efficiently. That means you shouldn’t spend too much time trying to pick the perfect ML algorithm but rather go through rapid tests to ensure compatibility with your business problem.
First, select a part of your data that will be used for testing your fully trained algorithm. This selection of data needs to be new for the algorithm. If you are testing multiple algorithms within AI and Machine Learning for business, use the same test data for all of them.
The performance metrics you pick will depend entirely on your business problem and the algorithms your team works with. Most algorithms have standard performance metrics you can use. For example, classification algorithms will be measured against classification accuracy.
Other Machine Learning Ideas for Small Businesses
Remember, ML and AI applications don’t stop at your product and service. They can also make it easier to run your business.
Study your business processes and identify which internal business processes can be turned over to ML. Look for processes that are repeatable, time-consuming and manual. Typically, any simple processes that require a review of data can be automated with the help of AI and Machine Learning.
As we mentioned in the beginning, starting with an ML project in your business can be daunting. But the key is starting small. Furthermore, don’t feel pressured to follow each step one at a time. You and your Machine Learning team may jump around the steps, or do multiple at once.