Data is everywhere. Therefore, the problem businesses often face is not how to get more data, but how to use data that they already have. In practical terms, how can you use data science to extract actionable insights that will boost your team’s performance and grow revenue? Let’s analyze this question in detail.
First of all, developing a strategy for your data usage should start with questions about your larger goals. What do you want to achieve in the first place? The end goal of any business is to generate more revenue, but how best to get there? To harness the power of data science, you need a measurable goal.
Let’s say you’re working in a marketing department. You’ve just wrapped up a successful campaign and would like to build on that success with the next one. You measure your success by the engagement ratio, including click-throughs, social media shares, and sign-ups, because those metrics directly influence sales. So your goal should be to boost the relevant statistics — your outreach numbers, including engagement. You now have a tangible, measurable goal to start with.
After you define a clear goal, it’s time to look at data sources that might be relevant to your project. You can look at both internal and external sources for useful data. In our example, previous campaigns and statistics would constitute internal sources, and reports on users’ engagement divided by demographics and market, other companies’ marketing campaigns, and social media data might be good external sources.
The key here is to define one or more clear, measurable goals first. Only then should you look at relevant data sources. This approach is much easier than just trying to figure out what you can do with all the data you have. Even better, making sure you articulate your goals first will have a measurable impact on your business.
Having defined a goal and identified potential data sources, it’s time to start building a data science strategy. Whether that takes the form of a simple document or a set of reports, what’s crucial is building a framework for all of the following aspects.
As outlined above, ask yourself what the measurable goal you want to achieve, and what data should you use to do so. For example, you could decide that you want to use past campaigns and users’ statistics to achieve 30 percent engagement growth over the next three months.
You also need to address your project infrastructure, which raises several questions. What technology stack do you want to use? Do you want your data science team to build everything in-house, or would it be more effective to buy an off-the-shelf product for your needs? Perhaps you want to hire a software house to mine data for you instead. Where and how will the data be stored? What will the end solution look like? Do you simply want actionable insights in the form of a report with clear instructions on what to do, or do you want a solution in the form of a dashboard that you can re-use later on with new data? Answers to these questions will depend on your specific goals, but you need to make sure you think through them.
This is a technical step that involves deciding on what methods you want to use. How much do you care about transparency? Do you prefer a better black-box solution or a fully explainable but likely less effective scripted solution? This decision will influence the extent to which you will be able to employ AI processes or whether you will have to stick to statistical inference. Scripted solutions take more time to code, but you can understand every element of them. Conversely, the individual steps of current AI solutions are harder to fully explain. This distinction may change in the next few years, however, due to the growing explainable AI movement, which advocates for greater transparency for AI processes.
Who’s on the data science team? Who will work on delivering the end goal? The more technical the solution, the more and better data scientists you need. On the other hand, people from other parts of your organization (e.g., marketers who know your product) are equally important to steer developers in the right direction regarding the most crucial goals for the business.
Figuring out all the details can be hard or impossible in some cases.The unknown performance of machine learning models, especially if your solution relies heavily on automation and not just statistics, makes certainty difficult. Still, having a broad outline of all the components involved will allow you to estimate costs and potential gains.
At this stage, if budget allows, you might want to hire an external expert who can help you with the process of developing a strategy. Not only can someone outside of the company help spot potential difficulties, but they may also suggest a fresh approach to your problems. Alternatively, to keep costs down, a senior data scientist on your team who has particularly good business acumen will likely be able to build a good data science strategy as well. This person should work closely with you to align your goals with the data and figure out what’s possible technologically.
Obviously, this process will take a great deal of work. Nevertheless, a clear, coherent data science strategy is crucial for understanding how long it will take to build your solution, estimate the costs, and then compare them to how much revenue the solution would bring. As always, doing more work planning on the front end will ultimately save you time and resources in the long run.
The final step in obtaining data-driven insights is to actually build the solution, be that a dashboard, web app, or a report with instructions about how to achieve the goal. At this point, you have to decide whether you want an in-house or outsourced team to do the building.
Building the solution in-house has the advantage of working with people fully focused on your business. Doing so also eliminates the need to discuss calendar availabilities or time zones. More importantly, an in-house team will ensure that the final product is secure and integrates well with the rest of your stack.
On the other hand, hiring good data scientists to permanently join your company can be hard, especially if you’re looking for experienced people. Unless you have an existing data science team that you plan to enlarge for this project, hiring a new group from scratch can take months. If your project is time sensitive, going with a software house that specializes in handling data science problems is often easier and faster. But if you deal with very sensitive data, the solution has to be on premises, or your industry is heavily regulated, hiring a permanent team will be better for the long term.
All in all, data science and machine learning are great tools to add on top of your existing technologies. They can allow you to find patterns and insights that a human analyst is likely to miss. With custom-made solutions, you will be able to explore and understand your data in a way suitable for growing your business.