Best Practices for Embedded Business Intelligence

Data and analytics world can be difficult to navigate, but having the right resources in place can help businesses gain the information they need to reach, and even surpass, their business objectives. The ability of a modern organization to make real-time decisions based on reliable analytical insights that are seamless, clear to understand, and user-friendly is critical to its long-term success.

Any organizational change brings with it difficulties, which are often exacerbated by a lack of familiarity. As a result, it is important for companies to comprehend the basic needs of their product’s end users. Embedded Business Intelligence can be customized to the needs and function of the end-user during the setup and customization process, ensuring that embedded analytics is seamlessly integrated into the end user’s daily workflow.

Here are a few best practices for embedded business intelligence that businesses can consider.

Data management plan

Data management should be part of every organization’s strategy. Organizations often fail to plan for modeling, effective data extraction, data stewardship, and aliasing, and all of which are critical aspects of data management. When it comes to executing the embedded business intelligence deployment, the time spent planning and establishing the parameters will immensely benefit the company. Data management may not be the most exciting aspect of the insight-to-action process, but it is a critical component.

Also Read: The Future of Business Intelligence and Analytics – Trends for 2021 and Beyond

Phased rollout

A staggered deployment process uses less resources and takes less time, and almost always saves money in the long run. A gradual embedded business intelligence rollout could involve distributing a beta version to a small group of users and collecting feedback. This input can be used to direct the product team and can also be used to generate testimonials, product reviews, case studies, and other promotional materials.

A piecemeal release strategy allows teams with limited resources to get to a complete application launch without losing momentum. While the concept of beta isn’t fresh, its value in product updates and feature releases is often ignored.

Clear view of goals

A clear understanding of the objectives will aid in determining which factors contribute to success. This is critical to the key goals of the embedded business intelligence implementation – minimizing the reporting backlog and increasing revenue. This can become much more critical as time passes because performance criteria are likely to change as business needs and implementations change.

Also Read: Every Strategic Move for a Data-driven Decision Is Vital

Data-driven culture

When more casual users become power users, and as companies increasingly depend on data to guide decisions around the board, making data literacy a primary target for training and implementation efforts benefits everyone in the organization. This emphasizes the importance of having pre-determined success factors when starting a project. The importance of setting realistic, attainable objectives cannot be overstated.

When it comes to data literacy, it’s critical for business leaders to equip their employees with the tools they need to avoid statistical fallacies, particularly when they’re convenient and tell them what they want to hear. While data has no prejudices or opinions of its own, humans may inadvertently cause them to affect their conclusions. Hence, being conscious of these tendencies is crucial.

Everyone can not only use the tools and reports but also understand why they’re so relevant if they understand the meaning and significance of the data passing through the whole BI deployment.