A recent BARC study says that data visualization has become a trend in developing Business Intelligence (BI) dashboards. Developing BI dashboards is essential for businesses for many reasons. Flawless data visualization helps business managers to make the right decisions in crucial junctures. As a result, the business starts flourishing economically.
Businesses can understand their strengths and weaknesses using advanced analytics. However, the decision-making accuracy largely depends on the effectiveness of the Business Intelligence system. Data teams work together to develop and manage the BI dashboards. For keeping the dashboard accurate in analyzing complex raw data, the BI team has to adapt to a few standard practices.
So, what are the best practices for the BI teams working with the data teams? Find a guide in the following section of this article.
Deciding the Priorities
BI and data teams should list the priorities for working together. When two departments work on a project, communication issues may happen in a few cases. Poor communication leads to confusion, and thus the jobs become messed up. Due to such a messy approach, the BI dashboard does not accurately show critical data in a structured manner.
For seamless communication, two teams should create a list of their priorities. Comparing those priorities will help the teams to build a bridge of seamless communication. The two teams will work according to their priorities. Therefore, the business analytics dashboard will become more accurate and productive for the end-users.
A Detailed Conversation on Usability
The data team wants a user-friendly dashboard. But, user-friendliness is a relative term. The data team may have specific requirements for the features and options. So, it is essential the BI team must understand those requirements of the data team.
The best way of understanding the requirements of the data team is by initiating conversations. Taking the feedback of the existing BI platform is also essential to make it better. The BI team should ask some important questions to the data team. What are those questions? Find them in the following.
- Does the system offer seamless support in decision-making?
- Does the data team conduct additional research to make a decision?
- Are the analytics reports easy to understand?
- Do you want more interactive reports for better decision-making?
The BI team should ask these questions to the data team. Finding the right answer will help the developers to create a more useful and user-friendly business intelligence dashboard.
Building Trust to Enhance Accuracy
Both BI and the data team work together to make a business intelligence dashboard useful for decision-making. Both teams want to develop quality output. Since the decision-making process of a business is related to a BI dashboard, accuracy has become a significant concern for every business.
Developing trust and good communication between BI and data teams will make the BI and analytics platform more accurate. The developers have to trust the feedback received from the data team. On the other hand, the data team should trust the BI team’s skill for rendering accurate services.
Cost-effectiveness
The ultimate aim of a business is to increase revenue and reduce expenses. No business wants to make a hefty expense for building and managing a business intelligence dashboard. Moreover, they want top-notch services from the dashboard. Meeting both these requirements is a challenge. Establishing seamless communication between BI and the data team will ensure a cost-effective BI system.
Data Storage
A BI system helps a business to manage and regulate crucial business assessment data through a virtual platform. For the digital transformation, the BI and data teams should decide on data storage. Keeping data on the cloud is the best way of managing a BI system. However, BI and data teams can also choose hybrid and on-premises data storage for the BI platform.
Keeping these common practices in mind will help a business develop a more powerful and accurate business data intelligence system.
