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Business Intelligence (BI) has quickly moved from nice-to-have technology to need-to-have technology. As evidence of the value continues to show itself in the form of quantifiable increases to the bottom line, companies are scrambling to implement BI tools in the most advantageous ways to edge out their competition.
BI tools are evolving at a rapid pace, bringing new capabilities to market quicker than companies can effectively implement them. Organizations that focus on strategic deployments of BI solutions and who devise an aggressive method to identify new and innovative ways to use the technology will have a huge advantage.
Embedded BI will become prominent as the need for an increased number of users across organizations will continue to rise. Users outside of IT and data analytics teams need access to enable data-driven decision making but are typically more comfortable pulling that information from within systems they already use such as ERP or CRM. Embedded BI enables a user to pull data without leaving the application they are accustomed to working in throughout their day.
Look for providers of BI and complimentary applications to work together to provide integrations, connectors and open APIs to enable embedded dashboards and other embedded data analytics.
Real-time and anytime analytics have been some of the most useful aspects of BI thanks to their valuable and timely insights for making urgent data-driven decisions, but that will give way to a new ability to analyze data before it ever hits a data store. Capturing and analyzing data as it is streamed from IoT devices and sensors is providing the ability to make even faster decisions.
For instance, imagine a financial organization that is able to react to real-time streaming analytics vs. waiting for the data to come to rest in a data store. In the case of fraudulent activity, the difference between responding instantaneously vs. minutes or hours later could be the difference between stopping thousands more security attacks. Organizations will be able to affect change during an instance vs. reacting after the damage has been done.
Proactive Analysis Based on Machine Learning
By utilizing machine learning, BI tools will be able to proactively prepare analytics that users want to see. Over time, the tool will monitor usage and learn common patterns related to data requests. Based on the learned information, reporting will eventually be conducted without initiation from the user. The user will automatically receive the information that they need based on historical requests. These proactive events could include producing reports from the system or setting up dashboards that represent the information that has been shown to be critical to the user.
The type of advanced analytics and functionalities mentioned above are coming quickly to fruition as more and more industries are identifying use-cases for these advanced features. Be aware of the possibilities and plan in advance for the most effective and productive scenarios where your business could prosper.