Data Science

The era of big data has seen companies gathering increasing amounts of data. While this data is the company’s lifeblood, containing an enormous amount of wisdom, companies don’t always find it easy to extract that wisdom to automate processes and optimise decisions. This is where data science comes in.

What is Data Science?

While there is no single accepted definition for data science, from a technical perspective, data science is an umbrella term covering a range of advanced techniques, such as algorithms, statistics, and machine learning, that generate insights from your data.

However, data science can be divided into two parts based on the famous 80/20 dilemma. 80% of the time taken for data science is for data wrangling, including data preparation and cleaning (see big data & data engineering). In this section, we’re going to look at the last 20% where advanced techniques like machine learning are applied to get the most insights out of your data.

Why do you need Data Science?

How could you improve your business with data science? Here are some examples that our customers have experienced:

  • Fraud detection
  • Process improvement
  • Recommendation engines
  • Image recognition
  • Customer segmentation
  • Improved customer experience

The options that data science offers are endless. However, while data science can add a lot of value to your company, there are some pitfalls that technology alone cannot solve. That’s why we use a specific data science approach to detect potential value in advance.

How can you implement Data Science?

When implementing a data science project, we recommend that you start by defining your business case. In other words, what do you want to do and achieve with this project?

The next step is to start prototyping your solution, evaluating after each iteration to monitor the value already achieved. We find that the first evaluation usually gives you a good idea about what outcomes are and aren’t feasible. We usually follow a value-first approach which defines success, value, and possible gains expected in the following iteration. These results are used to (re-)evaluate your GO-Live, improve planned changes, and occasionally even pause the project.

How Cubis can help you with Data Science?

There are numerous ways that we can help you with data science. We usually start by validating and refining the business case, before moving towards the first iteration within our value-first approach. We are also happy to help increase your data science capacity within your existing teams.