Data Science

The increasing amounts of data that organisations are gathering contains a lot of vital information for their future. However, organisations don’t always find it easy to extract this information, leading to sub-optimal decision making. This is where data science becomes useful.

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What is Data Science?

Data science covers a range of advanced techniques, such as statistics, machine learning, and algorithms that generate insights from your data. While 80% of the time spent on data science is for data wrangling, including data preparation and cleaning, this section looks at the 20% of the time spent on advanced techniques like machine learning to get the most insights from your data.

Your organisation needs Data Science

The benefits of data science experienced by our customers include:

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

Data science offers an almost endless array of options. However, there are some pitfalls that require more than just technology which is why we use a data science approach to detect potential value in advance.

Implementing Data Science

What do you want to achieve with data science? This important question needs to be asked before implementing any data science project. After your goals are clear, the next step is prototyping your solution, complete with an evaluation after each iteration to monitor the achieved value.

This first evaluation gives you a good idea about what is and isn’t feasible. By using a value-first approach, we define success, value, and possible gains expected in the following iteration. These results are used to (re-)evaluate your GO-Live, improve your planned changes, and pause the project if necessary.

How can Lytix help you with Data Science?

What help do you need with your data science? While Lytix usually starts by validating and refining the business case before implementing the first iteration within our value-first approach, we can also work within your existing teams to increase your data science capacity.