Considering Embedded BI
The way in which organizations apply analytics is in constant flux. Technology and integration advancements make access to analytics and BI applications much more flexible, leading to greater adoption of embedded analytics. Companies want to embed their analytics within their day-to-day applications to make analytics access more seamless within their daily operations.
The way in which organizations apply analytics is in constant flux. Technology and integration advancements make access to analytics and BI applications much more flexible, leading to greater adoption of embedded analytics. Companies want to embed their analytics within their day-to-day applications to make analytics access more seamless within their daily operations. One of the reasons behind this is the ability to grant access to more people without being limited by BI expertise. Additionally, companies want to empower their employees to act upon issues as they occur, instead of having to rely on accessing multiple applications and searching for answers.
For organizations transitioning towards embedded analytics, there are a number of considerations required, some of which were addressed in a recent Webinar titled 7 Considerations of Embedded Analytics in conjunction with Pentaho. These considerations (which include looking more broadly at data integration, understanding potential big data challenges, and ensuring closed-loop processes to make information actionable) provide general guidelines as well as some of the technical requirements for embedded BI adoption. Many businesses adopt this type of analytical approach as a way to deliver BI access to a broader array of business users without requiring high levels of training to go with it. More accessibility and easy access to data translate into more effective decision making overall.
On the other side of the argument, organizations need to realize that when they choose an embedded approach, they may be limiting data access to specific data sources and not creating a broader approach to decision-making across the organization. Embedded BI is most intuitive when limiting analytics views and interactions to the questions being addressed within the transactional/operational applications being used. This means that two types of BI may be required – the first being a more holistic approach to information challenges within the organization, and the second requiring more targeted analytics addressed through embedded analytics.
Overall, there are definite benefits to leveraging an embedded BI approach to analytics. At the same time, organizations need to realize that considerations for embedded BI adoption require looking at the analytical needs of the organization more broadly. Users adopting embedded analytics might also need access to additional data sources and other ways of interacting with BI meaning that although embedded analytics can provide added value, it may not be the only analytics use required for more effective decision making.
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