Data Science Maturity Scan

Data Science is a growing necessity for future-proof companies. Identify in which areas you excel and what your key focus points should be. We created a maturity scan that addresses data, Data Science applications, business analytics, tooling, infrastructure, team, and leadership. Fill out the questionnaire and you will receive a rich report with recommendations on […]

Why Data Science Projects Fail

why data science projects fail

More than 85% of Data Science projects fail, according to Gartner. Every step in the Intellerts’ 8-step model has pitfalls. Avoiding them will dramatically increase the chance of project success. Data Science projects do not often fail because of operational issues. Typically, tactical and strategic aspects dictate the success of a Data Science project. These […]

Tell the story – Communicate your insights

Tell the story

Tell the story. The final step of the 8-step model is to bring together and communicate all of your insights. This is when compelling presentations and dashboards are created. EVALUATION FIRST Whether a project is following a short-cycled lean approach or a more traditional project methodology, you must evaluate your work before you tell your […]

Data Visualization – Feed the eyes

Data visualization

Why do we visualize data? The most basic answer is that it’s very hard to read data encoded in a database or dataset. Visualization makes use of the basics of human perception to intuitively present the data. Even if you never made a graph, you’ve probably already created a data visualization. A mere table is […]

Model the data

modeling data

‘Model the data’ is the main aim of any Data Science project. Now, we can finally put all the work in the previous step from our 8-step Data Science method to good use. You may assume this step is the most time-consuming but, for most projects, this is not the case. That is because Data […]

Mining and combining

Mining and combining Data

Once the relevant data sources are gathered, the preparation, integration and exploration of the data can begin. This is the most time-consuming step in the framework. According to Forrester, one-third of analysts spend more than 40% of their time vetting and validating data. When preparing your data, the first step is to assess the content […]

Data Understanding

Data Understanding

Once the data sources have been reviewed, you must select the best-qualified data sources. To achieve this, you must gather and assess these sources to understand the exact content, structure, and quality of the data. When the data sources contain personal data, the data must be anonymized. Data quality and data management are not sexy […]

Data Landscaping

Data Landscaping

Once you establish a clear understanding of the problem, you can start to explore the relevant data sources. Exploring the data landscape is often an underestimated task. This is mainly because companies lack a good understanding of their data structure. In our current age of digitization, data is growing at an astonishing rate and AI […]

The Art of Asking Questions

The failure of many data science projects is often fixed from the start. This is because the problem is not clearly defined. Albert Einstein fully recognized this issue and there are a few reasons why this is so important. FIRST, everyone in the organization must have the same understanding of the problem. If you can […]


8 steps in Data Science

OUR 8-STEP DATA SCIENCE MODEL We have defined an 8-step Data Science model, providing a framework for managing your data science projects. In this section, we explain how you can organize your work to help you use data analytics to streamline your Data Science initiatives. This 8-step model works at every stage of your Data […]