Oracle ERP data conversion theory (1)

In the Oracle ERP Method system, AIMApplication Implementation Method falls under the scope of Implementation management. From the AIM process, we can see that the system timeline is divided into six phases:

◆ Definition stage (Definition)
◆ Operations Analysis in the business Analysis stage)
◆ Solution Design)
◆ Build the system stage)
◆ Transition during system switching)
◆ Production in the formal operation phase)

Note: The legend is from the official AIM 3.1.0 documentation.
The implementation of information systems takes precedence over planning, while planning and data must be implemented first. comprehensive, accurate, and timely data is the prerequisite for normal system operation. High-quality data is a huge driving force for the launch. The Data Conversion to be discussed in this article spans the four stages of requirement definition, solution design, system establishment, and system switching on the timeline. In traditional AIM, the main task of data conversion in the demand definition phase is to define data conversion requirements and develop solutions based on requirements. The main transaction of data conversion in the design phase is to customize the conversion standards, prepare the environment; establish the data conversion work in the system stage mainly to design and test the imported program and verification script, and prepare for the next import; in the system switching stage, the main task of data conversion is to install the import program and import tool in the formal environment, finally import the collected data, and finally verify the data.
However, the methodology focuses more on the overall framework from the theoretical perspective. Each process in each stage is an "optional" step. How to do it and how to do it well is not mentioned above. Data collection is highly demanding, time-consuming, and difficult. From the beginning of definition, data collection should begin in a certain emergency and order. I will summarize my experience here for my colleagues' reference. Due to the limited level, mistakes and omissions are inevitable. You are welcome to discuss and correct them.

I. Data 

First, let's look at the data. Generally, the data of an enterprise when implementing ERP includes three parts, which are called Basic data. The base data is the data required during system setup. For example, accounting subject, tax rate, inventory organization, sub-stock, goods location, etc. The second part is called static data, which is used in actual operations after the system goes online. For example, customers/suppliers, employees, bank accounts, material information, and process routes. Basic data and static data can also be called static data. The other part is called dynamic data, dynamic Data is the time point data required for system initialization, such as the account balance, supplier balance, customer balance, inventory amount at the beginning of the period, sales/purchase orders not closed, and tickets not closed. When formulating the ERP launch plan, we need to comprehensively consider the data in these three aspects and collect the data in a certain emergency and order.
Basic data is usually in a fixed format, with high requirements on data quality and high timeliness. Therefore, data collection may need to be prepared in advance at the requirement definition stage. After the basic data is complete, we can test the integrity of the system settings through the diagnostic tools provided by the system.
Static data is usually relatively simple. Since static data is basically not changed after it is entered into the system, it is sufficient to ensure that the data is complete and accurate. In fact, static data often exists in the current business of an enterprise. On the one hand, it is easy to collect, and on the other hand, it is universal within the enterprise, if the original material code is improved or re-edited, the uniqueness, uniformity, practicability and ease of use must be taken into account.
Dynamic Data is divided into initial data and daily data by time. The most familiar initial data is the initial value of the storage period and the opening balance of the financial account. qualified enterprises should check the initial data. The daily data includes unpaid sales orders, unpaid procurement orders, and unpaid tickets. To prepare this part of data, enterprises should define the days of the outstanding documents collected before going online. These documents only need to count the number of outstanding documents, that is, the total number of orders minus the number of deliveries, the total number of tickets minus the number of completed warehouse receiving orders can be summarized according to certain conditions. For example, a supplier may have multiple unpaid purchase orders, you can collect statistics by supplier into an outstanding purchase order table and import it to the system. The subsequent documents will be used as the daily operations of the system, and will be input to the system at any time as needed during the launch process. Unsettled documents should be settled as much as possible before going online to reduce the difficulty of manual and system switching and reduce the workload for future reconciliation.

Ii. Data collection processBytes

In the process of data collection, we should follow a certain sequence and certain collection methods. Qualified enterprises should perform several rounds of data collection and analysis to avoid data modification after the formal system is imported.
1) understand and confirm the characteristics and data volume of the enterprise's industry
Different industries have different requirements for data. For example, the assessment system of medical systems for suppliers is different from that of general enterprises, the size of the same data size also determines how you can process the imported data. For example, customers of companies with long distribution channels may only manage the imported data to agents; short-channel enterprises may manage wholesalers and retailers. We can use a manual method to import a small amount of data, but the data volume is large. We have to use a program to import it to help us.
2) discuss and confirm data collection projects, sources, and detailed plans
The collected items usually determine which data needs to be collected based on the system's requirements on data and the special nature of the enterprise industry. It also determines where the data comes from, whether it is migrated from the old information system or obtained from the archive data, detailed plans include evaluation of data collection workload, lead time, and owner.
3) sort out data collection solutions and tables
The collection scheme is the data requirements of enterprises and the collection strategy for these requirements, and is the guiding principles in the data collection process. The data collection table is reasonably designed according to the data collection scheme, detailed data collection instructions should be provided for data tables.
4) Customers collect data
Data collection usually takes a lot of time and resources from the beginning to the end. to ensure data accuracy, in addition to answering any questions in real time, the customer's collection process is usually sampled and analyzed in stages, and the monitoring data is moving towards our goal from the very beginning.

5) organize the format of the data imported into the system
According to the completed data collection table, the ing system needs to perform format conversion and adjustment. So that the system can be imported or input in different ways.
6) confirm data
Multiple parties confirm the input data.