5 Critical Phases Of Today’s Analytical Workflow

With the increasing prevalence of modern business intelligence models, businesses have no other option but to embrace a self-service-based workflow through utilizing different IT techniques. This transition will often call for choosing a modern business analytics platform that upholds the different business goals. Since the modern business analytical workflow involves different interrelated phases, both the IT department and the business at large have to choose a platform that enhances the quality of each phase.

While IT is meant to enable the workflow, the end user of the platform is the one who initiates the workflow. For businesses to make the most out of their workflow analysis, there is a need for collaboration between your IT department and business professionals. To widely deploy and adopt a streamlined workflow, you have to choose the right platform while paying attention to the value that it adds to your individual workflow processes.

Here are some of the critical phases in today’s analytical workflow.

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Access And View

As your business transitions from using the traditional IT-driven approach to a modern approach that is self-driven, it is essential that you identify and develop a set of trusted analytics content and data sources. From this data, all end users can get to formulate their analysis. With the right workflow analytics platform, you can develop and manage a centralized environment from which you can formulate the proper assessment procedures.

The Interaction Phase

Simply put, the interaction phase acts as an extension of the access and view phase of your analytics workflow. It offers the necessary information needed by consumers to perform the needed analysis while sticking to the set guidelines by content publishers. A great platform should make the interaction phase seamless for the consumer. It should ensure that they understand the requirements and can do the analysis with minimal errors.

Analyze And Discover

Bottlenecks are inevitable in the analysis process as the user interacts with workflow platforms. This is because dashboards might have a limit in terms of the guided experience that they offer. However, the questions that the user has will still have to be answered in order to complete the analysis. This will call for an autonomous framework through which users can ask the right questions and get relevant answers.

In some instances, a workflow analysis platform might not have the capabilities required to answer the questions. In such cases, inter-platform integration should come into play. The ideal platform should allow business professionals to access the required answers from other platforms without having to switch to another module or product. The ability to analyze and discover data is critical to ensuring a smooth workflow.

The Share Phase

The conventional business intelligence approach was to share files through printing or exporting the data to the receiver’s inbox. However, it is inevitable for data to get outdated under this approach as the previously shared data will not contain any information that will be derived afterward. Sharing of data has now evolved into a modern analytics approach.

To quench the information that users thirst for, a great platform should allow data to be shared in real time and also encourage collaboration to smoothen data analysis and reduce the gap between lead time and cycle time, according to Kanbanize. When evaluating the different platforms, pay attention to their sharing capabilities from the perspective of the consumer.

Promote And Govern

According to Computer Weekly, data and analytics governance are an integral part of improving the quality of the workflow analysis. A great tool should embrace flexibility in terms of placing appropriate governance models in place and allowing room for adjustment with time. Additionally, the platform should be flexible enough to encourage scalability within the business. This is especially important for organizations that might prefer traditionally using the modern platform before gradually embracing the self-driven aspects of a modern analytical platform.

Analytical Workflow – Conclusion

The transition from using conventional business intelligence platforms to modern self-driven ones is a necessary cost in order for your business to embrace the impact of data in delivering quality services. A modern platform encourages self-service and the use of trusted data in the analysis.

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