This is the last in our series of blogs on analytics and workflow. In our last blog we looked at the components of an analytic workflow system. In this blog we look at the design aspects of a workflow driven system and things to look for in a software solution.
Download our latest ‘Workflow & Analytics white paper‘ that covers the whole series.
Designing the workflow
Designing a company-wide analytic system that incorporates workflow requires a different approach to the way discrete analytic models are typically designed today. The starting point is the organisation’s business processes that we have covered in previous publications. Our white paper ‘Data Driven Planning’ describes 7 analytic models that every organisation needs to manage performance and describes how these relate to an organisation’s business and management processes.
With this in mind, an enterprise wide analytic system should focus on the effectiveness and efficiency of business processes. The workflow component should define the end-to-end interaction between departments that deliver the organisations core capabilities that cover revenue generation; production and delivery of products/services; how it manages customers; and the way in which it develops new products/services. It should also cover how those core business processes are supported by other functions that typically include IT, HR, and Finance.
To support this ‘closed-loop’ approach requires a technology solution that has workflow embedded within the product. Such a product is CorPeuM, which for the first time in a mainstream analytic system incorporates a powerful workflow capability. CorPeuM has all of the components outlined in our last blog, and allows the setting up of closed-loop solutions.
For more information on how CorPeuM’s workflow interacts with analytic models, download our paper ‘CorPeuM: Supporting Strategy Execution’.
Evaluating analytic solutions
There are many vendors offering analytic solutions, but very few support workflow as outlined in this paper. When looking for a system take a long look at what is being offered to automate the interaction between data and departments. In particular check that the workflow capabilities:
- Support the automatic transfer of data between multiple analytic models. Most enterprise-wide applications require multiple models to cover the complexity of an organisation. It’s therefore important that relevant key data can freely flow between them.
- Provides control over multiple models ensuring that users access the right parts at the right time. As covered in the above point, some users will need access to multiple models but the timing and parts that are’ open’ to change will need to be managed.
- Allows access to external systems, can sub-run processes on those systems which can then be picked up by the analytic models. For example, getting the analytic system to run a GL extract and then load it into the analytic model for processing. If this is left to manual procedures, the process will not be as efficient as it can be and leaves it open to integrity issues should those manual operations not be carried out.
- Supports the initiation of tasks within a workflow by dates, events, exceptions, and a combination of all three. For a closed-loop system this is essential.
- Provides administrators with a ‘real-time’ overview of tasks and their dependencies on a moving timeline. In this way, bottlenecks can be quickly identified.
- Provides automatic escalation procedures should a task fail or not be completed on time.
- Allow the manual re-configuration of the workflow being run so that administrators can intervene to sort out workflow issues, but without affecting other users.
- Allow the automatic re-configuration of the workflow. For example, a variance may require the workflow to go in a different direction than was expected, in which case, all affected users need to be notified.
- Finally, users need to know at all times what they need to do and by when. This could be delivered as a personalised ‘To do’ list that is automatically updated as the workflow changes.
The above is not an exhaustive list, but will quickly help you to identify whether the analytic system being proposed, has an adequate workflow capability.
In this paper we have put forward the case as to why workflow within analytics is necessary. A true workflow capability means that trends and variances identified will not be ‘forgotten’ and that actions to take advantage of them can be taken quickly and efficiently.
Workflow in analytics can transform systems from being static, silo based blocks of data, into a dynamic flow of information that automatically adapts itself to changes in the business landscape.
This is not a pipe dream for the future – but one that can be a reality today.
Well that brings us to the end of this series of blogs. You can download the complete series as a white paper by following this link. If you have any questions you would like to ask, or your are interested in finding out more about our own modern workflow-driven analytic solution, do get in contact by email to firstname.lastname@example.org.