Avoid the Pitfalls in Ambitious Data Analytics Projects

Ambitious data analytics projects contain many unknowns and can cause surprises. With a strong methodical approach through the project, you are able to avoid the most important pitfalls, such as lack of well-functioning solutions and budget overspend.

When conducting an ambitious data analytics project, the business idea is the focal point. In essence, the assignment is to develop the data model and algorithm, which can bring the answers that releases the potential of the business idea. Therefore, the business idea must be analyzed from the viewpoint of a business data scientist. This gives a first indication of whether there is sufficient data to support the business idea.  Afterwards, there will be one or multiple proof of concepts and pilots to clarify possible solutions, and this has to have participation from experienced domain experts in data projects.

Close cooperation and effective synergies in the project team are central elements to succeed in ambitious data analytics projects. Often, this is a significant challenge as many different highly specialized competences participate in the project, and they may not have a full understanding of each other's contribution required to fully succeed. The trick is to get the project members to speak the same language. It means having them complementing each other with the contribution from their field and reach a common understanding of the business idea and the possibilities, limitations, and challenges to be solved.

Not all business ideas have a sufficient data foundation for the project to be implemented. Also, the project may - with advantage - develop in another direction than originally thought. Even with sufficient data, the project might develop in another direction as better and more profitable ideas appear in the process. However, there might be reasons to stop the project. This may be due to IT limitations and practical issues as well as more profitable ideas may appear along the way.


The most important outcome of a pilot project is to get an indication of whether the data foundation provides the level of information aimed for. 

Start with a well-defined and limited area and adjust the project purpose, data, and scope as the pilot project progresses, based on the knowledge gained.

Avoid Surprises in the Ambitious Data Analytics Project

If the project takes a different turn than first planned, it shouldn't come as a big surprise, nor for top management. This is, however, easier said than done. With a strong and methodical approach to data analytics projects, it's possible to avoid surprises when implementing an ambitious data analytics project.

Use Rooftop's Predictive Analytics Model as methodological tool. This supports the project work during the development of the data model and algorithms. The project team is lead into the mandatory steps in Rooftop's Predictive Analytics Model, which must be executed when developing data founded business solutions. By applying the model, it also ensures the necessary data quality so that the company can rely on the results.

Prioritize conceptualizing the business idea from a business data scientist perspective. The first step is to examine the existing research within the field, which can support the thesis in the business case. Next, a business data scientist maps the requirements and limitations that exist to give the first indication of what the data analytics project can contribute with. Subsequently, considerations are to be taken on the data available and whether initiatives to establish more appropriate data is required. The thorough conceptualization of the business idea makes it possible to evaluate whether the company has or can obtain the required data, hence continue the data project or not. Perhaps the company determines that establishing the data set is too costly. 

The Right Team Makes the Difference

Take advantage of pilot projects. The main purpose of a pilot project is to get an indication of whether the data can deliver the information that the company aims for. Start with a well-defined and limited area. Depending on the complexity of the assignment, it may be relevant with several pilot projects to uncover the algorithms or the way to structure the data before it can be included in the analysis. Pilot projects can, first and foremost, verify if the data contains the needed level of information. It's also an effective way to work agile as you can adjust the project purpose, scope, and schedule in line with the knowledge gained during the project. It creates transparency and a foundation for the management to assess whether the business case can be fulfilled even though the assignment may have been adjusted since the beginning of the project.

Assemble the team for the data project with care. It makes a difference in terms of whether the project generates a good usable solution and how quickly it happens. Ensure a strong representation from the business and a project manager, who can facilitate the necessary cooperation between the different highly specialized competences. The business should preferably be represented by the field(s) from where data is applied and the solution is to be implemented. A business data scientist shouldn't be mistaken for an IT competence and must participate from the beginning of the project.

Designate the change agent - the employee with the largest share in the project. It will strengthen the project team's understanding of the assignment and the related opportunities, which arise along the way. Also, employees tend to trust the new product and thereby accept the new ways of working when a change agent is engaged. It's also instrumental in increasing the business readiness so that the lead time, from when the solution is completed until the solution is applied within the business, becomes shorter.

Choose a project manager with field insight and data knowledge to bridge project participants as they are far from speaking the same language. The team members are highly specialized in their respective disciplines and have limited knowledge of each other's disciplines and how those are relevant in solving the assignment. The sooner common ground is obtained for the assignment and an understanding of who has knowledge of what, the faster the project team start becoming productive and generates a solution of high quality. The synergies count, and this is central to the project's schedule, budget, and when the company can expect the coming product or service to generate future revenue.


Are you facing challenges in your data project, or encouraged to know more on how to prevent potential pitfalls in ambitious data analytics projects, contact Giovanni Mellace.

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Rooftop Analytics has deep experience in managing data projects that include solution development based on data. We follow a strong methodical approach in data clarification, data model development, and programming of algorithms. We are able to think through the project before start, and therefore we can identify the real cost and time horizon of the implementation.


Rooftop Analytics offers services necessary for a successful execution of ambitious data analytics projects.

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