You are a medium sized logistics company and most of your package deliveries are to corporate customers. To increase your profitability you are trying to optimize your business processes as well as increase customer satisfaction. And you are willing to step back and take a holistic approach without leaving any stone unturned. We try and answer some key questions:
1) Where should you start?
2) What should you do?
The first step is to get the lay of the land right. Competing value chains and that means business processes would soon be the only differentiators of your enterprise in the face of increasing commoditization of products and services. A holistic approach is needed, as several of your processes will be interdependent and optimizing one while ignoring the other is like working on one side of the equation while ignoring the other.
For example as a logistics company your business processes might
include setting up new customers, get customers package delivery request, drop off and pick up customers packages, sort packages, track packages and invoice customers. A lost package during sorting could result in issues in customer tracking process, package delivery and subsequently delayed payment from the customers. As these processes are interdependent a holistic view is critical, to observe the connecting dots.
The white boarding sessions by the Business Analyst, Data architect, and Integration architect need to be independent so that each serves its unique purpose. The following three artefacts must necessarily have the concurrence of business.
A CRUD matrix (Create, Read, Update or Delete) for the data elements should be owned by the data integration architect.
The Conceptual data model is the first step to holistically understand the data that your critical business processes are
relying on. It is important for the following reasons:
In a nutshell the conceptual data model will help you understand your key data entities and how they are related to each other.
Looking at this data, independent of its physical layouts allows you to identify and document relationships that are neither enforced at a data or application level, but are nevertheless critical for the functioning of the business.
Data integration solutions can be real time, batch or real-time / batch combination. Contrary to popular beliefs, simplicity in data integration requires greater effort, more planning and more collaborative efforts.
It requires an approach in design that evaluates various solutions for simplicity and picks the one that is easiest to develop and maintain. It requires an automated regression testing approach with a constant set of test data ensuring that changes don’t break what has been working before.