1. Deciding On Key Metrics
Before embarking on a BI project, it’s important to decide on the metrics that are meaningful to your business. For instance, if you’re analyzing advertising and customer data to uncover your best customer acquisition data, conversion rates and customer spending data are incredibly valuable. Eliminating extraneous data will simplify your visualizations and allow executives to focus on relevant metrics to make decisions that may change the course of a division or company. Some questions to consider when evaluating which metrics to focus on:- What is the end goal for the data collected?
- How will this data help achieve my business goals?
- Am I data mining or conducting predictive analysis?

2. Avoiding Common Data Modeling Mistakes
In order to gain actionable insights from all the data you’ve collected, you need a way to model the data correctly. This means avoiding common errors such as ignoring small data sources, failing to account for how calculated fields could affect your model and implementing poor naming standards. Without taking these potential errors into account, you can end up with data models that are cumbersome and confusing. Take the time to plan the goals of your analytics, especially when merging data from many different sources. Ensuring calculated fields are consistent, creating proper dimensional hierarchies that allow users to drill down into the data, and other data modeling best practices will make it far easier to model complex data.3. Creating Dashboards that Work
We’ve written before about the principles of designing better dashboards, so we won’t go into too much detail here. Suffice to say, visualization is at the core of how we interact with data, particularly for non-technical users.
4. Choose the Correct Tool
When you’re evaluating business intelligence tools, there are many options to choose from. On the surface, they may seem similar in many ways, however, behind the curtains they are quite different. Many tools either focus entirely on visualization, lack the power to handle large datasets pulling from disparate sources or are unintuitive, placing additional demands on IT staff. Look for a solution that offers a single-stack capabilities and is powerful enough to handle even the most complex data while still being intuitive enough for less technical users. Rather than requiring separate tools for data preparation and visualization, our single-stack approach wraps an analytical database, built-in ETL, and a robust data analysis and visualization suite all into one, so you won’t need any other BI tool.
Tags: Best Practices | Dashboard Design