Identify if your data is ready for a machine learning model. A step-by-step guide for businesses to do data preparation and what to expect in the process.
There have been numerous studies on the introduction of artificial intelligence (AI) in business practices on a global scale. Many of these studies focus on ROI or how implementing AI can affect a company’s organizational culture.
Machine Learning (ML) a distinct branch of artificial intelligence, has claimed a lot of the spotlight as of recently, and its trending popularity doesn’t seem to be declining anytime soon.
This may be due to the fact that the positive effects of a well-trained Machine Learning system have helped companies grow, scale, and delegate their human resources more effectively.
If you’ve done the research on ML as a tool for your business, the next step is to determine if your data is ready for a Machine Learning Model.
In this article, we’ll guide you through 4 steps to get your data ready for a Machine Learning Model that will set your business or organization up for long-term success.
The first and most important step in the process is clearly defining your business goals and how a Machine Learning Model will help you reach them.
Some common goals include optimizing operational efficiency, increasing customer engagement, gaining a unique competitive edge, reducing risk or liability, and/or decreasing the cost of resources.
Whatever your business goals are, make a determination to approach your Machine Learning Model with the intention of achieving that primary and those secondary goals.
Also, understand that Machine Learning is an ongoing process.
The more effectively you can provide the right data inputs to your model, the faster and more effectively you can train your model to make a positive impact on your business and to help you reach your goals.
Providing great data inputs comes from understanding what kind of outputs you would like to generate. Understanding where you are starting and where you want to end up is key for success in every step. This leads us to our next step, Gathering Data.
Gathering data can be one of the most time-consuming pieces of prepping your data for a Machine Learning Model (directly behind Step 3: Cleaning your Data).
Before you continue with this step, you will need to decide whether or not you have enough data to move forward at this time.
Big data models can be very expensive, especially for small and medium-sized businesses (SMBs). Although they are not required for an ML model, it’s still important to assess whether you have enough training data to set your model up successfully before proceeding forward.
Depending on how you and your team have been tracking and storing data over time, you may find that aggregating data requires additional work from multiple team members, from different platforms, and across many departments.
As a quick example, let’s say that your company does eCommerce and your goal is to increase your customer engagement and decrease wasted ad spend.
The customer data you have tracked is past purchases, total payment, date of purchase, and time of purchase. You will need to extract every pertinent metric from billing and marketing to quantify data points within your customer's profile.
In addition to these metrics around shopping carts and payment, you also have similar products or product category groupings on your eCommerce site. You will need to gather this information as well as the associated prices of the items on your site from your product team(s).
Finally, you have advertising and marketing metrics from Google Analytics, GoogleAds, Facebook Ads, and LinkedIn Ads about your customer demographics, visits, conversions, and buying behavior. Perhaps you need to retrieve this information from your marketing team and/or an outside agency or 3rd party vendor.
Now that you have determined what data you need and the sources of your data, you must compile all of the different data that you’d like to input into your Machine Learning Model in one location.
This leads us to the next part of gathering your data which is, formatting your data.
Data formatting is the practice of monetizing your data for consistency and computer processing. An example is putting it all in an organized spreadsheet where the data holds very specific value in a field and can be easily translated to a computer.
You can gather and format your data yourself, use an in-house resource, hire a data scientist, or employ another third party vendor.
This is the most laborious step in prepping your data for machine learning and it can take about 60% of a Data Scientist’s job on a project.
In order to make this step run as smoothly as possible, it’s critical that you gather and format your data effectively and hand over the best possible data set to the person who will be scrubbing your data and preparing it for input.
A clean, optimized data set, is the foundation on which your entire machine learning model will be built. It helps make early determinations for how it will continue to learn with new inputs and how it will produce data-driven insights both internally to a company and/or externally to a customer.
Let’s continue with the example of an eCommerce store. You’re looking to increase engagement and decrease wasted ad spend. You’ll want to make sure that every metric around a product and transaction is included in your compiled data set.
This can include but is not limited to:
Your data scientist will need to intelligently organize this information to make it uniform and digestible. They will assign certain levels of importance to each data element and define how it corresponds to other data elements in the given data set.
At the same time, your data scientist will be scrubbing the data, looking for outliers (data points that fall outside of the expected range), omitted data, and invalid data.
This is important because depending on the criticality of the data point, your data scientist will need to make an intelligent determination as to whether to assign an average value to the missing field, keep it ‘null,’ or delete the data record altogether.
Finally, your data scientist is responsible for creatively defining the data relationships, factoring in both numerical and non-numerical data and creating clusters of data.
If completed successfully, your data should be clean and ready to be handed over to the implementation team. This is where we move to the next step, Splitting Your Data.
Splitting your data is the equivalent of setting up a proper science experiment with a control group and a testing group.
In order to see how effectively your machine learning algorithm is learning and to evaluate the effectiveness of your model overall and make improvements, you need to split your data into two sets.
When you set up your data sets, make sure that they are mutually exclusive from one another. In other words, ensure that neither of the data sets has overlapping subsets.
To come full circle to our eCommerce example, let’s say you split your scrubbed and cleaned data right down the middle. Half of your buyer data and all of its subsets goes into your input pile for your machine learning algorithm (your training set).
The other half of your data becomes your evaluation set to ensure that your predictions on this data are in line with the outcomes of your machine learning data.
You are ultimately testing to ensure that both data sets net-net the same or close to the same results. Your ML model should be intuitively pushing out the same end results that an intelligent human in your organization would be.
If not, you’ll want to re-evaluate what data you are feeding your model and the pathways you’ve created for the model to produce outcomes and predictions that make the most sense for your business.
How you prepare your data will have the single greatest influence on the effectiveness of your ML model and the value it brings to your business. When you prepare your data you have a lot of different options.
Technology’s advancement has led to a drastic increase in the amount of data available worldwide, and the vast majority of it (up to 80%) remains unstructured.
This leaves room for massive innovation in the data analytics space.
AI and advanced analytics have helped businesses take a large quantum of data, analyze it, and create informed business decisions on a massive scale faster and cheaper through consistency and top-down communication.
You can employ a data scientist in-house or a third party vendor. There are also a lot of tools out there to help you get started.
If you make the investment upfront, you can rest assured that you’ve put your business in the best possible position to face digital transformation head-on with the best methods and tools offered.
For more tips and assistance on preparing your data for a machine learning model, see our 8 Question Quiz: Will Your Data Provide a Competitive Advantage. If you have questions about your data and talk to a Stratorsoft data expert.