8 Reasons Why a Data Analytics Project Could Fail

Why a Data Analytics Project Could Fail

1. Non-essential data causes a lot of trouble later

Be it industry leaders or subject matter experts, the wave of data analytics has caught everyone’s attention. Everyone wants to make the most of this technology and, therefore, captures all organizational data. But for data modeling and analysis such as a data lake or data warehouse, this data needs to be properly inserted into an environment. Silence leads to data conflicts and a lack of data quality, integrity, and data governance often impedes success.

2. Choosing the wrong KPI will affect you negatively

Let your business run strong Analytics projects, not vice versa. No matter how big your empire is or how many problems you have on hand, you need to work hard on issues that actually affect daily routine business operations; This means focusing on the right KPI. If you are in an empty room, you are in the wrong place.

3. Poor management does not handle things nicely

Change is unfamiliar and not everyone likes it. Timing, scope, quality, budget are important parts of any data analytics project. During the early days of the implementation process, people will be hostile to change and lead to failure. If the data analytics team uses Agile in the development of your model, it would be better to keep the sprint for at least 2 weeks and roll out an MVP. This will help everyone stay on track and focused.

4. Unrealistic expectations are never met, and you should know that

It is true that data analytics can be a game-changer. But it is not ideal to expect too much from it. The best way to start is to stay focused, choose small goals, and scale as the project progresses. Keep your goal of 1-5% KPI improvement and aim to give 10%. This way you can achieve more than you expect.

5. Communication gap is an enemy, you should not ignore it

Having a simple and direct channel for communication is very important when you are working on a data analytics or data science model. Stakeholders should always be in contact with data scientists to see how things work. Often, the lack of interaction between the two is blamed for the failure to understand the problem.

6. Blurred vision doesn’t take you anywhere

When you are going to work on a data analytics project, it is necessary to be clear about your business problems. The data required, the tools used and the methodology to be adopted should be clear. Failure to do any of these will result in failure.

7. A wrong team can’t get things right

To make a project a success, you must have excellent brains. Lack of knowledge and practical skills to work on the project is the major cause of failures. Make sure that your staff is equipped with technology and has the ability to leverage technology.

8. No planning related to ROI? It’ll haunt

Before you begin to assess the system and implement the solution, you need to have a direct link between your data analytics strategy and ROI. If the project fails to connect to ROI or advantage the company, it is as useful as some other project in your company.