The Journey to Solving Data Science Problems

Data science requires a deep understanding of your business and specific tools. Without this knowledge, you won’t be able to create the workflow that takes you on the journey to solving your data science problems. It also prevents you from wasting time on solutions that your business didn’t need. There is a specific sequence of steps that are required to solve any data science problem.

What Problem Does Your Business Have?

Instead of looking at data science as a technology problem, businesses must start looking at things from a value perspective. What value does data science bring to your business? Businesses get value from data science by using these technologies to solve problems. It starts with defining a vision for your company data science can help you achieve. What is that vision? How can acquiring and manipulating data help you achieve this mission? It is crucial to have answers to these questions as data science and machine learning are not fun most of the time. In reality, you will spend most of your time during these projects on acquiring and cleaning data. Only about 5% of your time is spent on machine learning and integration into your projects.

Assembling Your Team

As with most journeys in business, it all starts with having a great team to help you achieve your goals. Your team should be comprised of top-level executives that are defining the mission for the company. The most important thing to remember is that data is an enabler for your organization. It enables your organization to make better decisions by having more detailed insights that come from data. Having a team that enables you to gather, clean, and process data effectively is crucial to this journey. You also need to be mindful of budgets and your access to infrastructure. While data science software is mostly open-source, cloud servers are not free. Part of assembling your team is also choosing the tools your team will use to work with this data science project.

Gathering the Data

The data you gather will make or break your data science project. The data you gather also has to be processed and clean effectively to enable you to get the right results. Improperly cleaned data will give you bad results, and it is why these steps are usually what your company will spend 95% of its time on. This step involves taking raw data and cleaning it to present it in the best format for your team.

Experimentation

Once data has been gathered and sufficiently cleaned, you are now ready to run experiments on this data. That is where the strength of your data science team comes in. The better your people, the more creative solutions and ideas they’ll be able to come up with to get the best answers from your data. It is also the time where the tools you chose can help you or make things worse for you. Tool selection is a crucial part of data science, and you will really feel it when it comes to running experiments. Tools that are hard and unwieldy will cause you to take a lot longer to complete the same tasks.

Implementing Your Solution

Finally, every data science project ends with the implementation of your machine learning solution. However, it usually doesn’t end there, as you will spend a lot more time tuning and improving your solution. The reality is that data science projects never really end. They get better as the data and algorithms improve. That is how the journey from an idea to a finished project is in data science. Once you understand this workflow, it will speed up and improve your projects immensely.