Data Migrations (DM): Conceptualization VII (Data Import Layer)

Data Migrations Series

The data requirements for the Data Migration (DM) and Data Quality (DQ) are driven by the processes implemented in the target system(s). Therefore, a good knowledge of these requirements can decrease the effort needed for these two subprojects considerably. The needed knowledge basis starts with the entities and their attributes, the dependencies existing between them and the various rules that apply, and ends with the parametrization requirements, respectively the architecture(s) that can be used to import the data.

The DM process starts with defining the entities in scope and their attributes, respectively identifying the corresponding entities and attributes from the legacy systems. The attributes not having a correspondent in the legacy system need to be provided by the business and integrated in the DM logic. In addition, it’s needed to consider also the attributes needed by the business and not available in the target system, some of them more likely available in the legacy systems. For such attributes is needed either to misuse an attribute from the target or to extend the target system.

For each entity is created a data mapping that basically documents the data transformations needed for migrating the data. In the process is needed to consider also attributes’ data types, the (standard) formatting, their domain of definition, as well the various rules that apply. Their implementation belongs into the DM layer from which the data are exported in a standard format as needed by the target system.

Exporting the data from the DM layer directly into the target system’s tables has in theory the lowest overhead even if the rejected records are difficult to track, the rejections resulting only from records’ ‘validation against database’s schema. For this approach to work, one must have a good knowledge of the database schema and of the business rules implemented into the target system.

To solve the issue with errors’ logging, systems have a further layer on top of the database model, which also allow running data validation against target system’s business rules. Modern import frameworks allow loading the data via a set of standard files with a predefined structure. The data can be thus imported manually or via load jobs into the system a log with the issues being generated in the process. Some frameworks allow even the manual editing of failed records, respectively to import the data. Unfortunately, calling the layer from the DM layer is not possible from a database, though this would bring seldom a benefit. Some third-party tools attempt to improve the import functionality by calling the target system’s import layer.

The import files must be generated from the DM layer in the required structure with the appropriate formatting. The challenge however resides in identifying all the attributes that should make scope of the load. It’s an iterative process which sometimes is backed by try-and-error heuristics. Unless target system’s validation rules are known beforehand, the rules need to be discovered in this process, which can prove time-consuming. The discoveries need to be integrated also in the DM and from here results the big number of changes that need to be performed.

Given the dependencies existing between entities the files need to be generated and loaded in a predefined order. These dependencies are reflected also in the data processing and the validation rules considered in the DM layer.

A quality checkpoint can be implemented between the export from the DM layer and import to enforce the four-eyes principle. It’s normally the last opportunity for trapping the eventual issues. A further quality check is performed after import by validating on whether the data were imported as expected.

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ERP Implementations: It’s all about Partnership I

Unless the organization (customer) implementing an ERP system has a strong IT team and the knowledge required for the implementation is available already in-house, the resources need to be acquired from the market, and probably the right thing to do is to identify a certified implementer (partner) which can fill the knowledge and skillset gaps, respectively which can help splitting the risks associated with such an implementation.

In theory, the customer provides knowledge about its processes, while the partner comes with expertise about the system to be implemented and further technologies, industry best practices, project methodologies, etc. Further on, the mix is leveraged to harness the knowledge and reach project’s objectives. 

In praxis however finding an implementer which can act as partner might be more challenging than expected. This because the implementer needs to understand customer’s business and where it’s heading, bridge the gap between functional requirements and system’s functionality, advise on areas of improvement, prepare the customer for the project and lead the customer through the changes, respectively establish a basis for the future. Some of the implications are seldom made explicit even if they are implied by what is needed by the project. 

Technology is seldom the issue in an ERP implementation, the challenges residing in handing the change and the logistics required. There are so many aspects to be considered and handled, and this can be challenging for any implementer no matter how long has been on the market or how experienced the resources are. Somebody needs to lead the change and the customer seldom has the knowledge to handle the change. In some cases, the implementer must make the customer aware of the implications, while in others needs to take the initiative and lead the change, though the customer needs to play along, which can be challenging also. 

Many aspects need to be handled at management level from a strategical point of view on customer’s side. It starts with assuring that the most important aspects of the business where considered, that the goals and objectives are clear, that the proper environment is created, and ends with the timely decision-making, with assuring that the resources are available when needed, that the needed organization structures and roles are in place, that the required knowledge is available before, during and after implementation, that the potential brought by the ERP system is harnessed for the years to come. 

A partnership allows in theory splitting the implementation risks as ERP implementations have a high rate of failure. Quite often the outcomes of such projects don’t meet the expectations, the systems being in extremis unusable or a bottleneck for the organization. Ideally one should work with the partner(s) and attempt solving the issues, split eventually the incurred cost overruns, find a middle way. Most of the times it’s recommended to find a solution together rather than coming to a litigation. 

Given the complex dependencies existing between the various parts of the project, the causes that lead to poor implementations are difficult to prove, as there are almost always grey areas. Moreover, the litigations can require a considerable time and resources to settle. These can be just extreme situations, and as long one has a good partner, there’s no need to think that far. On the other side, even if undesirable, one must be prepared also for such outcomes, even if the countermeasures may involve an additional effort. Therefore, one must address such issues in contracts by establishing the areas of accountability/responsibilities for each party, document adequately the requirements and further (important) communication, make sure that the deliverables have the expected quality, etc.

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