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5 min read

6 common pitfalls of analytics projects and how to avoid them

Published on
June 12, 2022
6 common pitfalls of analytics projects and how to avoid them

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What are the main reasons why analytics projects fail to return the expected investments? Our experienced Data Analysts listed the most common pitfalls in analytics projects and how you can avoid them.

1. Too broad project scope

One of the most common reasons why analytics projects may not succeed is that the project scope is too broad. The main challenge, in that case, is that the project cannot be properly shared in separate phases. This way, the situation equals not having the project scope at all.

If the project scope is too wide, it is difficult to prioritize the most important things to get from analytics. It is also challenging to estimate when the project should be finished and the level of acceptable costs for the project. Without defining these aspects, it is very unlikely that the project will be successful. The good rule of thumb is to do things but start with small steps. This could include, for example, fixing only the financial reporting at first and expanding after that to the other areas.

2. Re-inventing the wheel

Many companies start building integrations, data warehouses, and report templates from scratch even though they could save tons of money and time by leveraging pre-built solutions. For example, building your own data warehouse usually takes from weeks to even half-year and costs thousands of euros.

BI Book is an excellent example of a tool that provides its users with pre-built data warehouse, integrations, and report templates for over 50 systems. It enables deploying BI reporting in days or weeks instead of months and significantly reduces deployment costs. Read more about BI Book.

3. Heading to the project without a proper plan

One of the common pitfalls is to start the project without a proper plan and setting clear objectives on what is desired to achieve with the project. At the beginning of the project, it is crucial to understand what information would provide additional value for the business. That requires understanding the current business processes.

Having an idea of what kind of data would boost your business also creates the objectives and direction for the project. The objectives should focus on whether the desired metrics have been built and whether the end result is reliable. The analytics project is always a great chance to find innovations, so it is good to leave some room to play with the data. This way, you can find new valuable metrics that you could not even think of at the beginning of the project.

4. Waiting for the data quality to be perfect before starting the analytics project

It is a common misconception that the data quality should be fixed before starting an analytics project. Controversy, the analytics tools help you efficiently find the gaps in the data and the need for different metrics. The old processes often also have some bottlenecks, and analytics tools enable revealing those and renewing processes.

5. Implementing the project without the needed level of expertise

One of the common mistakes is to head to the analytics project without the required experience and level of expertise. It may seem the most cost-efficient solution initially to do things on your own, but it may be costly in the end. Data analytics is quite scalable, and similar elements repeat in projects.

The things that take a couple of hours from an experienced expert in data analytics often take several days from an inexperienced person. The high level of experience and expertise also significantly reduces the errors and costs of the project. In most cases, buying experienced help outside the organization may be the most cost-efficient solution concerning the price/quality ratio.

6. Thinking of the project as an IT project instead of a business project

An analytics project is not only about deploying the analytics tool. You do not benefit from the top-level analytics if your employees do not know how to use the tools. Even though some of the analytics tools are very easy to use, all of them still require some level of training. So make sure that the employees get the most out of the tools by resourcing time for the training.

Planning to start an analytics project?

BI Book analytics tool has helped hundreds of companies worldwide to deploy top-level analytics within days or weeks instead of months. BI Book provides its users with all the Power BI’s standard features + several extra features. Read more about the additional features and book 30 minutes free meeting with our Analytics Specialist to discuss more!