Increasingly, more companies are investing in business intelligence (BI) technology, which helps consolidate, store, and analyze diverse data sets to improve data analytics efficiency and accuracy. According to Precedence Research, the global BI software market is going to reach $63 billion by 2034, showing a CAGR of 7.26% from 2024.
Larger companies implement more complex BI solutions, namely enterprise business intelligence. These organization-wide solutions can pull data of different formats from hundreds of systems (including CRM, ERP, or HRM) and process vast information volumes without compromising data analytics speed. They can also feature self-service capabilities to members of all company departments, thus helping businesses foster data democratization.
In this article, enterprise BI experts from Itransition share three best practices to help your company ensure smooth enterprise BI adoption.
Table of Contents
1. Designing a scalable enterprise BI architecture
As mentioned previously, enterprise BI software processes vast amounts of data and serves a large number of users, which can increase dramatically when a company grows. 64% of businesses that participated in the 2024 AI & Information Management survey by AvePoint manage no less than a petabyte of data, while 41% of respondents manage at least 500 petabytes. If a BI solution cannot handle large data volumes and increased user queries efficiently, it can negatively affect user experience and decision-making speed.
To mitigate performance degradation risks, a company should develop and implement BI solutions that can scale easily to support its evolving needs. One of the best ways to ensure advanced BI solution scalability is to design an architecture that allows for smooth integration with new data sources, adding data repositories, and increasing a user base.
To establish a scalable enterprise BI architecture, BI implementation teams can follow a modular approach. As an option, an architect can break down a BI solution into a set of small software components (microservices), each serving its own purpose (data acquisition, data transformation, etc.). These services can be scaled independently based on user demand, even in real-time, which helps optimize computing resource utilization and prevent software outages.
BI implementation teams can also apply performance optimization techniques at different layers of the architecture, namely caching and load balancing, to ensure stable software performance. While the first helps lower data retrieval latency, the second can reduce network latency, which is essential for optimizing overall BI software response time.
2. Providing employees with self-service BI capabilities
As stated earlier, enterprise BI implementation can improve data democratization and foster a data-driven culture within a company. Nonetheless, this is only possible if a corporate BI solution provides robust self-service capabilities, which can make data analytics available for all employees.
These can include augmented analytics and reporting capabilities, enabling both data analysts and regular business users to run queries and create their own data reports and dashboards, allowing for more rapid and deep data exploration. These can also include natural language search capabilities. Instead of manually searching for the necessary data or generating a report, an employee can make a request in natural language, and the BI solution will provide relevant output.
Some BI platforms available on the market provide self-service functionalities, including Power BI, Tableau, Looker, and Qlik. Companies prioritizing data democratization can consider implementing any of these solutions. Alternatively, a company can develop a custom enterprise BI solution from scratch and equip it with the required self-service features.
3. Monitoring enterprise BI adoption among employees
Enterprise BI solution implementation cannot bring any meaningful business insights if a company fails to ensure high BI adoption among users. Tracking user adoption from the launch of an enterprise BI solution is critical, as it helps detect and address potential adoption issues promptly.
Centers of Excellence, BI specialists, or any other teams responsible for supervising a BI implementation program within a company can track user adoption continuously by using different turnkey or custom-built software. For example, Microsoft Power BI’s built-in tools allow teams to track the usage of data analytics reports, including how many people are using the reports and whether those numbers are growing or decreasing. Tableau, in turn, can help teams track the number of users who subscribe to specific dashboards and those who set data-driven alerts.
If a company detects any user adoption issues, it can take different measures to fix them. One of the possible ones is to provide employees with continuous BI training going beyond one-time onboarding training. Additionally, a company can share success stories of active BI users in a corporate intranet to show different teams how they can benefit from the technology and inspire them to use enterprise BI.
Final thoughts
Large companies seeking to improve the efficiency of data analytics should consider adopting enterprise BI. Being deployed enterprise-wide, BI solutions provide robust data visualization, analytics, and reporting capabilities to business users across all teams, fostering decision-making significantly.
To make enterprise BI implementation a success, it is recommended that a future-proof enterprise BI architecture be designed to ensure software scalability in the long run. Businesses should also provide employees with self-service BI capabilities to achieve data democratization and track user adoption to reduce the friction with new BI tools. To learn more valuable recommendations, you can turn to enterprise BI consultants. They can guide you at any stage of your BI project, whether it is strategy definition, BI technology selection and deployment, or user training.