The quality of business data is crucial to the quality of the actionable insights that can be derived from the data. In our experience, many companies overlook the value in ensuring that their enterprise data is accurate, up-to-date, and well maintained.
We'll take a quick look at some practical ways that your business can improve the quality of data and the benefits that may result.
Process Standardization (or lack thereof) and its impact on ERP business records
Often Enterprise Resource Planning (ERP) systems are implemented either without formal user training, or with training that is directed towards only a handful of employees, or with training that is so general that it does not address the specific needs of the business and its various departments. By under-investing in the human-side of a business technology platform as complex as an ERP system, enterprises are in essence setting the conditions for employees to develop their own unique ways to process transactions and 'get things done' in the platform. As each user develops their own shortcuts/methods to process transactions, data may get recorded in the back-end ERP database tables in slightly different ways, which may have ramifications down the line.
Let's take a look at a real-world example of this data divergence problem in the Aviation / Aerospace industry where platforms such as Quantum Control, Pentagon, AvSight, Corridor, Ramco, etc. are sometimes implemented under the conditions described above. A common use case for an Aviation ERP system is to procure material in support of MRO (maintenance-repair-overhaul) work taking place on key material assemblies of an engine for example. A user or department may begin to cut Purchase Orders (PO's) to buy material / parts for a specific repair order, but they may do so in a way that results in the PO not being linked to the corresponding Work Order (WO) for which the parts are being procured. This is usually the result of an employee doing the ERP activity (procurement) in a way that inadvertently or unknowingly skips the necessary steps to associate the PO to the WO. As a result, the back-end database system does not create the DB key entry to link the two transactional records together - i.e. the PO database table record with the Work Order database table record, as would have happened if the procurement had been initiated in the way and with the data that the ERP system was expecting. Subsequently as a result, when the data or IT team builds a report or develops a SQL query to determine Work Order-driven demand, material that was procured without the linkages will not factor in properly to demand planning decisions that are to be based on WO job projections due to this lack of database record linkage.
By contrast, some ERP system implementations are done with rules so rigid or time consuming that employees may encounter transactions that are difficult to process. In such cases, employees or teams may bypass the ERP system altogether. For example, a potential new customer may engage the Sales team to quote a new Part Number that has not been mastered in the ERP system. If the system rules that govern parts mastering are too rigid or too extensive, the Sales team may develop off-system methods of quoting (such as relying on in-house spreadsheets) and as a result the "demand" signal from the market (i.e. the quote for a part not currently mastered in the ERP platform) is never actually recorded. Over time, this can result in a suboptimal product mix being stocked and the resultant missed potential sales and customer acquisition that the new part number would have generated. A better approach would be to set up a small initial set of required fields to allow the part mastering to take place (or for the non-verified part to be added to the system), with a data exception or new parts added report to be circulated on a scheduled basis so that the appropriate team can complete the full metadata set for the newly created part(s). This will ensure that business can get done, but that data quality in the long run is also preserved.
How to Improve Data Quality
The keys to tackling the process standardization / data divergence problem are:
1) Develop a fundamental understanding of how things are done today in the organization through departmental interviews and following teams as they process transactions or complete important steps in the ERP system.
2) Analyze the "as is" process above carefully to determine potential non-alignment with best practices or with how the ERP system expects the process to be done, as the latter will likely result in data quality issues while the former can help the company improve its operations.
3) After a "to be" process has been mapped and constructed, and the corresponding training / standard operating procedure documentation (playbook) has been created, a company needs to invest time and resources into training employees (or up-skilling as necessary) in the "new way of doing things". It often helps to carefully explain why the change is necessary, as in addition to being new, it may involve additional steps or work on the part of people and teams. This is where the "change management" part of business consulting comes into play as an outside consultant is often better able to have change accepted within the organization.
4) Build exception reports to catch data quality escapes and quickly determine their source and remediation actions. Good data reporting can often be counterintuitively about identifying poor data as its generated.
5) ERP business rules should be constructed based on careful analysis and understanding of the processes involved, while at the same time balancing the need to standardize with the need to provide flexibility. This is the area where experience and a team-approach involving departmental SME's can be vital.
There are many other business, people, and technical aspects to data quality that an enterprise small or large will need to be aware of and develop solutions for, but the practical steps above should help provide a solid foundation from which to improve the fundamental digital asset (data) of modern business, the asset upon which sound decision making relies.
Contact our team at clients@fintechtx.com to discuss how to start your journey towards establishing solid data and business process practices within your organization. We have a wide variety of experience in ERP and non-ERP systems to help your teams get moving in the right direction.
