The 5 mistakes to avoid in your data analysis project

How many data analysis projects are aborted? How many analysis tools end up on business shelves collecting dust each year? Even if there are no statistics, we all have unfortunate projects in mind that will have only brought the company costs and energy expenses that are ultimately useless.

However, this is not inevitable and it is possible to conduct effective approaches that create values around data analysis. As long as you don't fall into certain pitfalls.

Mistake #1

Forgetting the most important thing, namely the goals of the project

Have you asked yourself why you wanted to set up a data analysis project? What challenges should the project serve, whether strategic, operational or organizational issues, the “why? ” of the project should not be forgotten. It is he who gives meaning to the project and will make it possible to mobilize and rally the teams around the project.

Mistake #2

Focusing too much on tools

We tend to immediately focus on the implementation, in particular on the tools necessary to carry out the project. By following this shortcut you risk moving away from the objectives of your data analysis project, of missing the real issues, of hiding the integration of the project into your processes and your organization, and finally of orienting yourself towards tools that will not be in line with your needs. And potentially to make investments that are disproportionate to what you really need.

Mistake #3

Not taking into account people and uses

Who will bring intelligence to your data? Certainly not tools or algorithms. You can expect very significant productivity gains for your teams thanks to modern analysis tools and algorithms, but it is their combination with the intelligence, experience of your teams and a relevant integration into your organization's processes that will create value.

Mistake #4

Approach the data analysis project in a monolithic way and want to cover everything immediately

How many projects are collapsing under the weight of their excessive size. You should have ambitious goals, but when implementing them, use iterative and agile approaches. Divide the goals into sub-goals that are achievable and that allow you to achieve results in a reasonable amount of time.

Mistake #5

Do not think of the data project in terms of business processes

For the project to succeed, it is essential that the profession takes ownership of it. It is therefore important to articulate your project around your business organization and your continuous improvement processes.

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