8 Reasons Why most Data Analytics projects fail
Experiments and studies conducted by Gartner revealed that nearly 85% of the data analytics projects fail. While some consider this to be the sheer outcome of the size and complexities related to the seam, but the real picture is totally different. It is not the technology but the implementation of the technology that leads to failures.
Cutting the chords, our Data Analytics consultants have outlined a few reasons why a data analytics project could probably fail.
Reasons why a Data Analytics project could fail
Insignificant data creates a lot of trouble later on
Be it the industry leaders or subject matter experts, the wave of data analytics has captured the attention of all. Everyone wants to make the most out of this technology and hence, captures all the organizational data. But this data also needs to be properly put into an environment for data modeling & analysis – like Data Lake or Data Warehouse. Siloed data leads to conflicts and lack of data quality, integrity, and data governance often disrupts the success.
Picking the wrong KPIs will affect you adversely
Let your business drive robust Analytics projects and not the other way around. No matter how vast your empire is or how many problems you have in hand, you need to work on problems that actually affect the day to day business operations; implies focusing on the right KPIs. If you are in an empty room, you are in the wrong one.
Poor management does not handle things well
Change is disruptive and not everybody likes it. Timing, Scope, Quality, Budget are significant components of any data analytics project. During the initial days of the implementation process, people would be hostile towards the change and be the cause of the failure. If the Data analytics team uses Agile in your model development, then it is preferable to keep sprints to at least 2 weeks and roll-out an MVP. This will help everyone on track and focused.
Unrealistic expectations are never met, and you should know that
True that data analytics could be a game-changer. But expecting too much would not be ideal. The best way to start is to remain focused, pick smaller goals, and scale as the project grows. Keep your goal of 1-5% KPI improvement and aim to deliver 10%. This way you can achieve more than expected.
Lack of communication is an enemy, you must not ignore it
It is crucial to have a simple and straightforward channel for communication while working on data analytics or data science models. The stakeholders must be in touch with the data scientists to see how things work. Often, the lack of interaction accounts for failure to understand the issue.
Blurred vision doesn’t take you anywhere
When working on a data analytics project, it is important to be clear about your business problems. The data required, the tools to be used, and the methodology to be adopted needs to be clear. Missing either of them would lead to failures.
The Wrong team cannot get things done right
To make a project successful, you must have the best minds. Lack of knowledge and hands-on skills to work over the project is the prime cause of failures. Make sure your staff is equipped with the technology and has the knack to leverage the technology.
No planning related to ROI? It’ll haunt
Before starting out 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 benefit the company, then it is as useful as any other project in your organization.
To Sum up it all
In the end, it is the results that matter the most. However, data analytics is not only the result but the cumulative output of each of the factors mentioned above. Along with adopting the change, make sure you adapt to it as well.