Customers want to be able to see the data when they need it, and they want to have a better digital assets and interactions with us. That's where they see the results of our transformation. They will not know why, but suddenly the website works faster, or that calls are being routed in a better way, and the truth is that they really don't need to know exactly where or how the data is is going from point A to point B and why it's taking longer or shorter times in order to still experience the benefits. By going through data transformation and implementing machine learning and AI models, what we really do is improve our customer service and by doing that we are able to grow our business and keep our clients happy.
Organizations should have thorough requirements at the very beginning of the data transformation process to ensure nothing is missed. You have to make sure that you don't skip any pieces when you're putting your requirements together. For example, if you have a project where you're bringing data from many different places, if you forget a couple of pieces, then they won’t be there when you need them, and it will be a lot more difficult and complex to bring those pieces in. Easier when you plan ahead and map out exactly what pieces and transformations you will need and how you will take each item from point A all the way through where it will live moving forward. This is actually a lot easier than bringing 80% of what you need, but forgetting 20% and having to figure out as you go. It is wise to spend the time before you start moving any data to really lay out clear requirements.
Data Transformation is a team effort that requires organization-wide support and a collaborative process in order to be successful.
The ability to translate complex technical ideas to internal stakeholders in simple and quickly understandable terms is a vital skill data scientists should continually nurture.
Just as technology and processes are constantly evolving and growing, it’s important for data scientists to cultivate a learner’s mindset, always exploring how to embrace and adapt to new changes.
About Vanessa Gonzalez
How to Become a Data Scientist in 8 StepsFind out everything you need to know about becoming a data scientist, and find out whether it’s the right career for you!
How to Become a Data Analyst in 2023: 5 Steps to Start Your CareerLearn how to become a data analyst and discover everything you need to know about launching your career, including the skills you need and how to learn them.
How Data Science is Changing SoccerWith the Fifa 2022 World Cup upon us, learn about the most widely used data science use-cases in soccer.
How Chelsea FC Uses Analytics to Drive Matchday SuccessGet behind the scenes at Chelsea FC with Federico Bettuzzi to see how data analytics informs tactical decision making.
Sports Analytics: How Different Sports Use Data AnalyticsDiscover how sports analytics works and how different sports use data to provide meaningful insights. Plus, discover what it takes to become a sports data analyst.
Inside the Generative AI Revolution
Martin Musiol talks about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, and what the future holds.