Migration Solutions
The following are solutions enabled by our SAP migration software. Using domain knowledge that has been discovered, captured, and managed within a single repository, the following “Push of the Button” solutions are enabled.
Migration Validation
SAP Migrations can be both extremely simple and exceedingly complex at the same time. For certain fields it’s as easy as moving the information from one field to another. At other times, the migration can become complex. Most migration tools assume the domain knowledge is possessed by the user of their designer tool. Unfortunately the necessary domain knowledge is often spread across an organization among people, processes, models and documents. This assumption about domain knowledge leads to the need to migrate the information numerous times prior to actual success.
A better and more efficient manner would be to have both the Domain Knowledge of the source and target system stored in a repository and to resolve these issues prior to movement of the information. Our software can collect and validate the migration processes as well as manage their completeness within the target system. The capability can define the missing information and inconsistencies within an existing information set long before the movement of data. Architel’s Solutions can, with the correct E.T.L. capability, write scripts that can automatically with a “Push of a Button” create the actual migration scripts.
Semantic Query
Over the years we have encoded much of the information which we store in data bases and data warehouses. The ability to access this information is constrained by the ability of those creating requests of the information to having detailed knowledge about the manner in which the information is stored. This detailed knowledge consists of the physical location of the data, the format of the data, the often cryptic names of the fields in which the data is stored, as well as how the data is actually used within different applications. This knowledge is referred to as Domain Knowledge.
If we could eliminate or seriously diminish the amount of Domain Knowledge required to access and query data to provide meaningful information we could make this information more actionable for the organization. With our software, the information within the knowledge-base has been decoded to allow for semantic queries. The user no longer has to worry about the way the information is stored or the implicit relationships between the data values. The methodology in our software has made these relationships explicit in the repository and directly linked to the business concepts that provide the semantics of the data. A semantic query based on business terms can be generated and with the “Push of a Button” the results delivered back to the user in an understandable manner.
Semantic Validation
Using a popular Service Oriented Architecture (SOA) based product, our software is able to configure a web service component that would evaluate the semantic correctness of data values passed within the SOA architecture and, based on the result, process the messages differently.
As an example, the discovery and capture of the domain knowledge for a particular datastore determined that the encoded values within a table in the database were constrained by the values of other fields within the database. For instance, if the value was a “B” then the inventory quantity value had to be “0” and the status had to be “5”. Using our software, the relationship and rules that connect each of these data elements could be captured and placed in the repository. Prior to the web service accessing the information, the web service would call the Domain Knowledge Repository function to retrieve the rules. The query would create a “Push of a Button” result that would configure the web service to check the values based on the constraint and relationship. If the value of the field was “B” and the quantity values was not “0”, then the web service would send the information to a human interface process to clean the data. If the results of the information sent to the web server were accurate, the data would pass the semantic validation to the next process step. Note that the values could all be valid from a data perspective, but not valid from a semantic perspective.
Constraint Query
Many times the information which is of interest to an organization is the number of times a certain condition occurs within a set of data. Normally, programmers would write a small program that would determine based on the criteria how many records match. If however, the conditions change at a fairly steady rate and the number of records that match the criteria is important to the process or the quality of the process, it would be more cost effective to write a program that could define and store criteria as editable constraints and, based on the constraint definition, provide a report each time of the number of data occurrences that match the criteria.
Our software can do just that. Using our software Manager, conditions and criteria which must be satisfied to meet the objective of the selection can be defined and managed. Then at anytime, with the “Push of a Button”, you can analyze any data set to determine how many records meet the required conditions.
