User churn, demand forecasting, and supply chain optimization are a few of the many ways that a company may want to utilize Artificial Intelligence/Machine Learning (AI/ML). The idea of building out a robust machine learning platform that can increase revenue, retain customers, and reduce expenditures sounds great to most executives. In order to build an effective AI/ML platform, you need reliable, clean data. A commonly used phrase is, “Garbage data in, garbage insights out.”Making sure that you are not working with “dirty” data is one of, if not the most important parts of building out a successful AI/ML platform. I am going to discuss creating a data strategy which is the first step in making sure that your data is prepared for any current or future AI/ML projects.
Among other things, “dirty” data is a broad term that encompasses errors such as duplicates, punctuation mistakes, or data that might be accurate but is stored in the wrong place(this is common in companies with siloed data). According to an article written by Forbes, “Poor data quality costs the US economy approximately $3.1 trillion annually.” If you/your company has any current or future plans to build an AI/ML platform, it is highly recommended that you develop a data strategy first.
A data strategy should encompass the way you would like to structure your data, organize it, house it, and secure it(to name a few). Before thinking about what data is going to be used, think about what you’d like your desired outcome to be. Understanding what your needs, challenges, and priorities are will help you better understand effective KPIs, allow for better benchmarking/progress checks throughout your project, and ultimately help ensure that you select the correct data set(s) for your project.
Creating a data strategy is a critical component to an AI/ML project. With that said, you should not focus on data clean up until your data strategy is in place. A dynamic, successful, and impactful AI/ML platform is the result of the hard work and time you put in to making sure your strategy is in place and your data is clean. For this reason, my next blog is going to focus solely on data clean up, and what steps should be taken once you have a strategy and are ready to start preparing data for your ML/AI project.
Is this something that interests you? Want to learn more? Let’s connect to discuss how we can help as we have a ton of experience in this space. Please feel free to send us a note at email@example.com and we can find some time to have a conversation.