Machine Learning (ML) is an increasingly critical field, and there is a competitive advantage to those first adopters. Yet, many companies are buying ML technologies and developing strategies before they have taken the appropriate step back to evaluate whether their data is ready or not.
ML is going to continue to expand the value it provides to organizations, but in order to access any value through ML, you first need to make sure you have a solid data foundation. The quality and quantity of data directly impact the accuracy and effectiveness of machine learning models.
Setting Up Your Data Foundation Will Enable Machine Learning Initiatives
Machine learning models require large amounts of high-quality data to be trained effectively. The better the data, the more accurate and effective the model will be. If the data is inaccurate, incomplete, or inconsistent, it can negatively impact the accuracy and effectiveness of the machine learning model. It is critical to ensure the quality of the data before using it for machine learning.
A solid data foundation ensures that the machine learning models have access to accurate, comprehensive, and relevant data. This results in better insights and predictions, which can lead to better decision-making and improved business outcomes. Additionally, a strong data foundation can help reduce bias in machine learning models. For example, if the data used to train a model is biased towards a particular group, the model will be biased as well. Ensuring a diverse and representative dataset can help mitigate these biases.
Machine learning models will rely on your data foundation to continue learning and improving over time. By feeding new data into the model, it can adapt and adjust its predictions to reflect new trends and patterns.
Common Machine Learning Terminology
Algorithms are math computations that have been written by humans to continuously take input and adjust — in other words, that’s what the “learning” part is all about within Machine Learning. Algorithms represent the brains of that approach.
A model is also provided by a human. The model defines the relationship between the input data, which we call features, and the thing that we’re trying to predict, which is called the labelled data. The data that is labelled is evidence. Labelled data could be the square footage of a house, or the total sales in a day. It’s the evidence that gets collected and submitted. The models then train with these datasets and the more data you give a model, the better it gets at predicting over time.
Training is just a way of explaining how the model adjusts from all of that incoming data. Training continues to a point where humans achieve their target level of confidence that the model has adjusted and improved enough to a point where the model can make accurate predictions in real life. So in other words, the model is applied for inference to brand new completely unlabelled data in the real world, and the results are working. Faces are getting recognized because the model has been trained with thousands of pictures of your face, and in real life, most of your expressions have been seen by the model before and Facebook gets it right, “most of the time”, which is a confidence target that Facebook is happy with.
And finally there is big data. This is the single most important thing that ML platforms and applications need to produce results. Without an immense amount of data provided, ML algorithms would not be able to produce accurate and confident results. Having raw, cleansed semi structured data — and having a LOT of it — is mandatory into getting the most out of machine learning tasks.
The Phases of Machine Learning
There is a standard process that machine learning uses to enable your organization to use Machine Learning for initiatives like better customer experiences, stronger personalization, segmentation, churn prediction, and better analytics. Again, this process cannot take place without a solid data foundation in place, which can be established through the use of a Customer Data Platform.
There are three phases to the Machine Learning process.
Phase 1: The first phase involves processing the input data until we are satisfied that we have a prepared dataset, or a solid data foundation, as mentioned above. It starts with designating the raw data sources to be used, understanding what it will take to cleanse that data, and then using technology that can work with massive volumes rapidly to inspect, automate the cleansing, and producing a clean, relevant result. This phase is often called Data Wrangling and it represents the bulk of a Data Scientists time and efforts. The importance of cleansing data cannot be understated — and that’s because if the input data to the next Phase is not properly formatted or doesn’t have the right context, the learning will be flawed and the model will not produce accurate results.
Phase 2: In Phase two, all of the hard work that’s been done to cleanse the data pays off because now is the time where our machine learning algorithms come into play. In Phase 2, our cleansed datasets now act as “Test Sets” for the algorithms and the training of the model can now begin.
Just like before, these Test Sets are continuously provided and so the training happens repeatedly. The more test sets of cleansed data that we provide in this phase, the better the model can learn. Humans check the results of this training throughout the process, seeing how the model reacts to new test data and confirming that the model has become reliable to predict answers to the business question.
Phase 3: When a target level of confidence is successfully reached on a consistent basis, we move into Phase 3 of the process, where the model is deployed into production and the Machine Learning can make a positive impact to our business environment by working with technologies to respond to data in real time and provide predictive solutions that help the business.
Data Supply Chain’s Role in Machine Learning – Collection and Standardization
The data supply chain refers to the process of collecting, processing, and transforming data into a solid data foundation that can be used by machine learning algorithms to make predictions or decisions. The role of the data supply chain in machine learning is critical, as the accuracy and effectiveness of machine learning models depend heavily on the quality and quantity of data that is fed into them.
Step 1: Collection
The first step in the data supply chain is data collection, which involves gathering data from various sources, such as databases, sensors, social media platforms, or other external sources. The quality of the collected data is crucial, as the accuracy of the machine learning models depends on the quality of the data used to train them. Therefore, it is essential to collect relevant and reliable data that can represent the real-world scenarios or problems the machine learning models are meant to address.
Preparing your customer data for meaningful ML projects can be a daunting task with the sheer number of disparate data sources and data silos that might exist in your organization. In order to build an accurate model, select data to use that is likely to be predictive of your target: the outcome which you hope the model will predict based on other input data. This goes beyond reacting to abandoned carts or recommending a favorite category, but rather is a truly revolutionary capability (not just taking last actions and extrapolating or averaging) to actually predict the future.
Step 2: Standardization and Normalization
Once the data is collected, it needs to be standardized and transformed into a format that can be used by machine learning algorithms. Standardization involves cleaning and transforming the data to ensure that it is in a consistent and uniform format, regardless of the source or type of data. This step may involve removing duplicate or irrelevant data, filling in missing values, and converting the data into a standardized format, such as CSV or JSON, that can be processed by machine learning algorithms.
Standardization is important because machine learning algorithms require consistent and uniform data to produce accurate results. If the data is not standardized, it can cause errors or biases in the machine learning models. For example, if the data contains missing values or inconsistent formats, it can cause the machine learning algorithms to produce inaccurate or unreliable predictions.
This is the step in the ML process is where analysts and data scientists typically spend most of their time on analysis projects: cleaning and normalizing dirty data. Often times this requires data scientists to make decisions on data they don’t quite understand, like what to do with missing data or incomplete data, as well as outliers.
In a matter of minutes, those of you working on these ML initiatives can test and verify that the data is clean and meets expectations prior to writing a single query.
The Significance of the Data Foundation Goes Beyond ML
While it is absolutely imperative to have a strong data foundation to unlock the benefits of Machine Learning, your data foundation provides value in numerous ways beyond ML.
A strong data foundation allows your company to make informed decisions based on accurate and reliable data. This reduces the risk of making decisions based on incomplete or incorrect information, which can lead to costly mistakes. By having accurate and comprehensive customer data, you can tailor your products, services, and communications across all channels to meet the specific needs and preferences of individual customers.
A thoughtfully established data foundation will increase efficiencies in your organization, as well, allowing you to automate repetitive tasks, and free up employee time to focus on higher-value tasks. This leads to increased efficiency and productivity across the board, which translates into cost savings and increased profitability.
It will also enable the quick identification of trends and patterns in your data, allowing you to respond more quickly to changes in your market, launch new products and services more quickly, and make more informed strategic decisions.
A Customer Data Platform like Tealium, that is vendor agnostic and has over 1300 integrations, makes the establishment of a reliable and scalable data foundation a possibility. For more information on how to use a CDP to kickstart your Machine Learning efforts, request a free demo today.