Build and who should do the work vs. Who are prooblems constituents of the system, and how can they best have their say in what the system will do? How do you shoehorn a major system upgrade into the working lives of the operations staff who are already stretched too thin in the first upsgream So the PMO springs into action.
A project manager is assigned. Defined and documented company procedures are brought to bear on the problem. Formal system requirements are gathered, the budget is identified, and a project is dosnstream into motion.
What I would propose, however, is downstdeam we need a new mindset about the systems themselves. Too often those formal project methodologies are based on a witb approach to problems that are very different.
There are fundamentally different business processes and problems addressed xystem upstream versus downstream systems that must be taken into account.
In insurance, policy management and producer management are the poster prblems for the concept of systems of upstream downstream problems with solutions system and sources of truth. They are self-contained; they own the data and processes in each of their domains. Whatever operational processes have been defined for these systems determine how data is entered and validated, how it will be stored, and how other systems might or might not use it.
And on that subject, upstream systems tend to be built to reflect their own needs and processes. If the data in the upstream system is needed elsewhere, well, knock yourself outsourcing it, interpreting it, Upstream Downstream Problems With Solutions Technology and using it. The system will be self-sufficient, but it is not often architected to play well with. The thing to remember about upstream systems is that these systems cannot be wrong!
Whatever upstream downstream problems with solutions system tell us is regarded as the truth, at least until we discover. Downstream systems are systems that are dependent on data from upstream systems. They upstream downstream problems with solutions system data from the upstream system, provide additional processing, perhaps perform rule-based calculations on it, and, in upstream downstream problems with solutions system, interpret upstream system data to provide additional business value.
They are the systems of record for the data upstream downstream problems with solutions system analytics they create, but they are not the owners of the data they consume. And of course, a downstream system can be an upstream system for other downstream systems.
An example of a downstream system in the insurance or financial services world would be an incentive compensation management. An ICM system consumes data from the policy management solugions producer management systems in order to calculate commissions and bonuses for the producers for the policies they service.
Without transactions and policy data from the policy management system and producer data from the producer management systems, an ICM system is unable to perform its tasks. An ICM system will be the source of truth for the commissions and bonuses it calculates, but it is dependent on data that it does not own or control to generate its results.
Downstream systems depend on upstream systems for the raw materials they use to do their jobs. When upstream systems have an error brought to their attention, they often overwrite the data to bring it in line with the new reality.
When legacy systems were designed and implemented decades ago, versioning and date-effectivity of data were far too expensive from a storage and aystem point of view. So that attribute was mistyped? But what if the downstream Upstream Downstream Problems With Solutions Example system has already consumed a prior version upstream downstream problems with solutions system that row of data, performed its magic, and generated results from it?
In the ICM system, that result might have caused commission checks for an agent hierarchy to be generated based on contract rules applied to it, rules that use the attributes of that row of data to decide who gets the commission and for how. An obvious example of the impact of dynamic, upstream data is the identification of the agent-of-record for a policy.
We thought Agent Larry was servicing this policy, but he retired and sold his book of business to Agent Mary last month and we only discovered it today. So calculations based on Larry being agent-of-record are now wrong.
The commissions we generated for Larry in the last month must be reversed and recalculated for Mary instead. So we find ourselves with at least two problems that arise out of changes to upstream data: how do we learn about them downstream, and what provision must be made to account for the recalculated results that might be generated based on those changes?
Determining the answer to these questions is a fundamental difference between defining problejs for upstream vs. Upstream systems are always right at this moment, at leastso what you are looking for if you modernize is good workflows and reporting from a new system, and a vendor that treats you right if you decide to buy an off-the-shelf application. Downstream systems have a different problem because they have to manage potentially dynamic data across its life cycle.
But dynamic data is an issue that must be addressed in defining and implementing the. In defining requirements for a downstream system, operational processes are as much a first-class problem as the data processing requirements are.
Changes to upstream data must be communicated downstream in a reliable and robust way. And dealing with the impact of changed calculations must be built into upstream downstream problems with solutions system processing and reporting requirements of the downstream systems along with the base formulas and rules themselves. Taking a static view of upstream data can introduce risk to downstream system implementation projects.
The math is easy, but the data driving it can be dynamic, and that reality must be recognized and addressed in defining system requirements and mindsets when looking at downstream systems. Founder of BobTrak, Inc. As the insurer vs.
This site uses Akismet to reduce spam. Learn how your comment data is processed. Want InsNerds to cover your conference? Downstream Systems Downstream systems are systems that are dependent on data from upstream systems. Downstream System Interactions with Upstream Systems Downstream systems depend on upstream systems for the raw materials they use to do their jobs.
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05.05.2021 at 17:55:35 For another memorable adventure into the swamp have been 3 elemental they often offer Upstream Downstream Problems With Solutions System a surprising amount.