If you are reading this, there is a high probability that you have a problem and you think that data can be a solution for it. Another option is that the data is your problem — you have lots of it but it is not clean, comes from many sources, and is unorganized and unreliable. The first one relates to Data Science projects and the second one to Data Engineering projects. Some projects combine both fields. Data Engineering projects are more exact than Data Science projects, that’s why you may have the following concerns:
- You don’t see the value data can bring to your business
- Not achieving wanted results
- Too long development associated with the high cost
What can you do and how can we help you to overcome those concerns?
You don’t see the value data can bring to your business
Today, when everyone talks about data and its benefits, very few companies make use of it. With information surrounding us at every point, we sometimes fail to recognize its potential. Often, as a company, you might feel overwhelmed by it and it might seem like clutter. Having an optimal view of data could help you visualize future potentials and opportunities that it brings. On the other hand, a lot of information comes from within and having your business generate data is just a starting point. Our job is to help you turn it into value.
Here are some examples of the value you can get from it. If you are not yet utilizing data why not start with simple reports or more sophisticated interactive dashboards that offer you to view and understand your data. Data visualization plays an important role in discovering trends and when paired with your domain knowledge, and business intuition it helps you to make informed decisions based on hard data. Having a visual representation of your data allows you to convey information easier, but having insights and metrics play a leading role in forming your strategic decisions.
In case you already have a reporting tool why not enrich those reports with predictions. While reports help you to understand your data it is still up to your gut feeling to estimate what the future holds. Instead of just knowing the statistics for the past, with predictions you get a view into the future. You will be more confident in making decisions if they are backed up by mathematical models and machine learning predictions. Recognition of either customer and client patterns, or patterns and behaviour inside your company, presents an opportunity to make more precise future plans, set clear objectives, and predict trends or threats.
Not achieving wanted results
Setting high goals is good, but we also have to be realistic. Start with the main issue that’s bothering you and leave other “nice to haves” aside at the beginning. Applying the “SMART” principle when setting goals is a good start and that way you could create a great basis for what you want to achieve. You have to think about those objectives and if the data you want to use can accomplish them or if a data scientist or engineer could help you in creating a solution that will cover them. A clear strategy will help you form a sustainable and accurate solution that will serve you in the future.
Often, the main concern of data science projects is if they’ll generate desired results. Companies are worried that if they invest time, money, and energy into something, that ROI will not be at the level they want. What if you set unrealistic goals, or if the data you wanted to implement into the solution won’t so easily bring the wanted value.
When talking about desired results it is good, but not required, to have some benchmark solution that is used for tracking the performance. It can be a solution that you already use to solve the problem but you want something better, or it can be some simple but not optimal way of solving the issue. There are ways of maximizing and optimizing what you already have. Or a whole new solution could be created to deliver precise and meaningful insights and results. Don’t worry, you don’t need to have it implemented, we can implement it for you.
Too long development associated with the high cost
You may be concerned about the long development time and associated costs before you get some value from the solution. Getting from point A to point B takes time and other resources so it’s a valid concern if you have issues you need fixing as soon as possible. If you invest in something, surely you want the results to be speedy but optimal at the same time. You might be hesitant at first to delve into such projects since it’s either an unfamiliar or unsure territory for you, but having solid and trustworthy support will solve that.
We address those concerns by starting with an MVP (prototype) that solves the “core issue” in short sprints and fast feedback loops. It is probably not the optimal, or fully automated, nor fully integrated solution but it serves as an outline of the final product and when compared to a benchmark solution shows possibilities and realistic expectations. When you are satisfied with the MVP performance only then the full development process can start. Further development has the goal of improving the prototype’s performance and integrating it into your process.
All of the concerns for starting a data science project are valid and something to take into consideration when embarking on such a venture. You have to weigh in what are your goals and what do you want to invest in the process. A reliable partner is what you’ll need to make this journey smoother and in the end, effective.