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Requirements for a successful data analytics project

For a successful data analytics project, a solid data foundation is critical. To put it in the language of us at Datasense, you can only make sense of the data if it is available and accessible.

Here are some important aspects of the data situation that are considered prerequisites for a successful project:

1. Quality: data must be of high quality, meaning it should be as free as possible from errors, inaccuracies and inconsistencies. Poor data quality can lead to incorrect results and decisions.

2. Relevance: The data should be relevant to the problem or issue being addressed in the project. Irrelevant data could unnecessarily complicate the analysis and lead to inaccurate results. However, it is possible to add additional external data to a well-defined project. We often do it in our projects, especially in predictive analytics.

3. Quantity: Sufficient data points are necessary to achieve statistically meaningful results. Too few data points can lead to uncertainty and biased results. We already had to reject a use case for an industry customer because the data situation did not offer enough datapoints. From our point of view, it is better for all parties involved to cancel a project early enough or not to start it if the data quantity is not sufficient from the beginning.

4. Availability and integration: the required data must be available, ideally in a structured format suitable for analysis. Problems can arise when important data is missing or difficult to access. However, this problem is not insurmountable: modern technologies such as cloud or in-memory analytics enable successful and relatively inexpensive data engineering to concentrate the existing data (see points 1-3) in a data mart and thus make it available for the purpose of analysis.

5. Cleaning and pre-processing: After integrating the data and before the actual analysis can begin, the data must be cleaned and pre-processed. This includes removing duplicates, handling missing values, and converting data to the correct format.

6. Data protection and security: it is important to ensure that data collection and processing comply with applicable data protection regulations. Sensitive data should be adequately protected to avoid breaches.

7. Clear questions and goals: There should be a clear understanding of what the questions or objectives of the data analytics project are. This helps to focus the analysis and achieve meaningful results. In fact, this point should be at the beginning: only when we ask ourselves the right question can we use data analytics to look for the answer. The reason why we put this point only at the end is another: in our experience, the best projects have been created through open approach to data analytics. The philosophy - "I look at the data and find what it gives or can give, then I see which of my business challenges I can solve with it" - has worked best in our experience.

In summary, a successful data analytics initiative requires a combination of high-quality, relevant, sufficiently large, and timely data. Careful preparation, pre-processing, and exploration of the data are also essential to produce meaningful and reliable insights. It is often not possible to determine exactly what these are at the beginning of the project.


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