Data science is often the key to improving business processes, providing more accurate results, and making better decisions. But how do you ensure your data science project will succeed?
5 steps to take before embarking on a data science project
1. Assess where you are now
Your first step should be an assessment of your current state – your goals, gaps in knowledge, technology stocks that are already in place, etc. Without identifying these elements upfront, it will be difficult to know which parts of the data science process must be carried out now versus those that can wait until later or even be handled by someone else. And if you try to do everything at once with inadequate resources or an insufficient understanding of what’s already available, you’ll waste time and money.
The big picture view allows you to identify your current strengths as well as the areas where you need help. Look at what is working well now so that you can use it in your next steps, or look for key people on whom you might be able to rely for guidance or expertise. On the other hand, if something isn’t working now but could with a few improvements, this gives you information about which elements of your data science project should be focused on first. This step also includes looking into how others are using data science. What are they doing that’s successful? Are there any new technology tools that they’re using that could help make things simpler or more efficient?
2. Get clear on your expectations
Creating a data science project that delivers on what you need is more effective than putting together a plan where certain outcomes are assumed without being stated. In other words, it’s important to define success for this project from the outset.
In some cases, your expectations may have changed over time or been influenced by factors outside of your immediate control – competitors’ actions or marketplace trends come to mind. If existing conditions have changed since you started planning the data science project, updating your goals will allow you to make sure they still fit within the current environment.
Sometimes you’ll realize that prior assumptions were invalid and certain elements no longer necessary; if so, update your goals accordingly. But in other cases, establishing clear objectives will highlight gaps in knowledge or technology that need to be addressed. If this is the case, it should help you better define the scope of the data science project.
3. Find a strong team
You can’t do everything yourself, even if it feels like it at times – so build a team. Of course, everyone has different skills and perspectives, but in general, there are two types of teams that work well together: one with members who have complementary skills and another with members who all have the same capabilities but just need to learn from each other. Members don’t have to have all of the same skill sets either – some people will be stronger in one area than another (e.g., machine learning versus visualization). Just make sure your team’s strengths cover all of the elements that need to be included in your data science project. 4. Establish a clear scope
As mentioned above, an assessment of what’s already there can uncover areas where you’ll be able to speed up work or apply existing knowledge and resources instead of starting from scratch each time. It should also help determine the ideal length for the project – in other words, how much time you have available. And if you’ve done this exercise well, it should become apparent whether the goals are realistic within this time frame (e.g., setting an unrealistic deadline will impact quality).
The scope for your data science project also includes identifying which previously used approaches might be applicable again; after all, something that worked before may still do so now. This is especially important when it comes to software tools, since there may have been a new release with greater functionality that would be useful.
5. Define your analytics goals
While you’re looking at what’s already available and how to use the information that’s been collected, don’t forget about re-evaluating your goals – they can also change based on new circumstances or technology. Plus, some data science projects have taken a while to get going so it might be helpful to do this step again in light of previous work done.
It’s always hard to determine whether a certain goal is attainable or not before actually performing an analysis – for example, finding out more about your customers’ interests will help you create better products and services in the future. But if you don’t know how to measure success, it can be hard to determine when you’ve reached your destination; and without a plan for how you’ll achieve this goal, it’s easy to get off course or fail altogether.
Conclusion:
A data science project is a great way to take your company further, and especially if you’ve been collecting data for some time now, it’s the perfect opportunity to put this information to good use. But just like any project of this nature, preparation is a key. Define clear goals and objectives before starting work on the analysis itself; that way, you’re sure to have a positive outcome.
