Business Intelligence projects normally combine an extensive set of activities, from integrating data from different sources, processing and modeling the data and, finally, creating reports and providing insights to support company decisions. 

Understanding the problem 

As obvious as it may seem, one of the most important steps in establishing the project’s objectives is understanding the problem. Knowing the problem we want to solve is the first step towards starting to design the solution. 

Different from knowing that something is wrong and simply starting to look for solutions, it is only by understanding the problem that we can define the routes, identify the possible risks involved and the resources that can contribute to the solution. 

Defining the Key Performance Indicators (KPIs) 

Once the problem has been identified, it is possible to start defining which indicators can help to monitor progress. These indicators must be measurable and actionable, so that they show whether we are making progress or not. 

Defining SMART goals 

SMART objectives are an effective way of measuring the progress of a project through objectives that are Specific, Measurable, Achievable, Relevant, Timely. This means that the objectives defined using this methodology are clear and concise, we can monitor their performance, they are realistic, they are important for solving the problem, and they have a date for completion. 

Clear objectives will drive the success of your project, so it’s important to keep an active eye on them and make adjustments whenever necessary. 

One of the pillars of a Business Intelligence project is data quality. Neglecting data cleansing and quality control can lead to inaccurate insights and raise major concerns about data reliability. Ultimately, poor data quality can lead to misleading conclusions, missed opportunities and a drop in credibility among report users. 

Data quality involves identifying and correcting errors and inconsistencies in the data, including removing incorrect records, correcting text, and standardizing data types and formats. By implementing a data quality control process, in addition to the direct benefits of having reliable data and accurate insights, you also reduce the time spent on manual checks and corrections caused by untrustworthy data. 

Creating overly complex dashboards can confuse users and hinder effective decision-making. 

Just as important as the data being presented is the way in which it is presented. Confusing and overly complex dashboards are the villains of user adoption and have as much power as poor data in repelling users and losing their trust. In addition, users are likely to struggle to interpret the content of complex dashboards, reducing the efficiency of the decision-making process and limiting the potential of the data available. 

Power BI reports shouldn’t be complex and when it comes to dashboard design, less is usually more. Effective ways of achieving a clean, accessible, and easy-to-understand design are: 

• Using clear words and titles. 
• Be subtle and consistent in the use of colors. 
• Be careful with the spacing of visuals. 
• Don’t overload pages with too many visuals. 
• Use appropriate types of graphics for each analysis.