Data Science Success – 3 Tips to ConsiderRyan Swanstrom
Previously, we looked at some Challenges of Data Science Projects. They can be difficult, and they do not alway succeed. Below are 3 techniques to help your next project become a data science success.
1. Start with a Plan
If you don’t know where you are going, you might end up someplace else.Yogi Berra
This quote by Yogi is humorous, but it is true. Without a plan, who knows where you will end up. Data science is no different. Planning is important. Monica Rogati, one of the early pioneers of data science, has put together a Data Science Hierarchy of Needs. AI and deep learning are at the top. Everyone wants to start there. That is the cool stuff that makes the news and gets the attention. However, there is a large foundation that exists underneath AI. As an organization, you need to determine where you are on the pyramid and work your way towards the top.
2. Focus on Educating
Becoming data-driven often requires a bit of a mindset shift. Everyone at your organization does not need to become a mathematician, but people do need to gain an understanding and appreciation for decision making with data. Much of the education comes from experiencing the change and getting comfortable with the decisions aided by data.
3. Expect It will take Time
It can be tempting to go for the quick win. These don’t always work well. And, they can end up being quick losses which damage the potential for data science in the future. If successful, they can set incorrect expectations for future data science projects. Not all projects will be quick, easy, and successful. Many successful projects take time. Also, the education mentioned above will not happen immediately. People need to see examples of how the data is making things better before they truly start to believe.
Dwayne Gefferie has a great quote about this. He then recommends starting multiple projects to increase collaboration.
“Of course, starting a one-off AI project that doesn’t impact the way you do business, is not going to cut it. “Dwayne Gefferie, The Cold Start Problem with Artificial Intelligence
Data Science Success Starts with a Data Strategy
Before you start your next data science project, consider creating a Data Strategy. It should include a future vision and a plan to get there.
Thank you for reading this far.
What are some techniques you have used to increase success in your data science projects? Please leave a comment and let me know.