Role of ERP & Data Accuracy in Mergers and Acquisitions

Mergers and Acquisitions have become an inherent part of the global enterprise strategies and looking at the pace of acquisitions, mergers and de-mergers that are happening these days, it is important that organizations come up with detailed frameworks and leverage them to speedily complete the process of acquiring, merging or separation of entities. This will not only reduce the cost of activity but will also make sue that we cover all aspects during the process with no surprises left for later stages. ERP has a large role to play in these mega activities as it is seen that lot of issues can be reduced if ERP can be leveraged correctly. Obviously ERP’s role will be governed by the consistency, accuracy and completeness of data as the data in it will be a core factor to take multiple decisions in the process.

The benchmarks for divesting a company or merging with a newly acquired company have climbed in last couple of years to 6 - 8 weeks. Along-with this, internal re-organization exercises like creation of new regional business, moving production from one plant to another or compliance related changes also are eligible to be part of M&A activities in terms of their similarity of sub activities. These also have quite stringent benchmarks these days and are possible to achieve only with the help of ERP.

To execute the above talked business objectives, a comprehensive global data strategy is critical and must be part of the overall enterprise strategy. The scope of the data strategy must be coordinated and owned by a data management team that operates as a business cross-functional team. To ensure business sustainability and growth, the ERP system needs to be able to support data process changes in a focused, fast, and flexible manner (the front end) and must include the swift re-alignment of data and data hierarchies to reflect internally or externally driven changes to the business context. Also the introduction and scale of shared services will need to be enabled and even accelerated to provide a slim, cost-efficient, and service-driven back end. Business modernization, operational efficiency, and successful mergers and acquisitions all rely on accessible, timely, high quality data.

ERP can help you to define, measure and monitor the integration of new businesses with parent business and it is important that you come out with a framework which leverage your ERP, your ETL tool and the business logic for smooth integration of data integration. One of the popular frameworks is as below:

To measure the success of mergers and acquisitions transactions, it is imperative to set up clear criteria related to the data dimension. Otherwise, the business case will be incomplete. The cultural change is critical for any merger or acquisition. A tremendous effort will be required in this area to ensure a successful change management. Therefore, introducing and executing a proven data excellence framework supported by a robust platform is a mandatory accelerator to deliver an efficient data integration and to support a business driven data governance while the organization focuses on the cultural challenge. 53PSVV3Q4S2M

7 Best Practices for ERP Data Conversion or Migration

I will not talk about the process of data conversion or data migration during an ERP project but would like to share the best practices that will help us achieve a better data quality index in your new system. To start-with, we need to keep our focus clear on what are our success factors or how will we measure that the job has been done completely. Per my view, we have four major success factors or key metrics which are required to be measured at the end and these are:

• Data Accuracy
• Data Consistency
• Completeness of Data
• Validity of Data

One of my earlier posts provides details on how to measure these metrics. Coming back to the best practices, let me list them down below:

1. Complete your pre-work: The ingredients of the pre-work are:
• Identification of source legacy systems & source business units
• Find out which system is best to provide data on what as there might be duplicate data in systems
• Identification of data owners, data cleaners and data verifiers
• Chart out a dedicated project plan for data conversion (data migration)within the ERP project plan with a dedicated owner for this activity
• Decide on the size and period of data that needs to be converted with business logic (Tip: The thumb rule is to have last 2 years of transactional data and the open transactions in addition of all master data)
2. Workout Sessions: Plan extensive work out sessions between legacy system owners, consulting vendor team and the data owners to nail down different opinions and approaches and come out with a common approach, format of capturing data and segregation of manual as well as automated steps in data conversion
3. Clean and Re-clean: No data can be clean enough to be converted to the new system and it is highly recommended to clean the data in terms of not only removing special characters, junk characters etc. but also similar looking names of customers, suppliers, employees, buyers etc.
4. Plan about the deltas: After you extract the data from legacy systems and do your activities like cleaning, segregating and finally conversion, there will be some time when the transactions are still being done in legacy systems and data is still being created in there which is not part of your data dump. Plan in advance to handle this situation and how would you transfer that small deltas from different systems to the new system
5. Sequence out the data loads: Identify the dependencies of one data on the other and plan the sequence of conversion accordingly. For example, employees need to converted first and then buyers can be converted. This will not only save time and effort but will also make some automatic validation in the system to improve on data accuracy
6. Test the cycles twice: Before actual conversion or migration of data, it is strongly recommended to test the conversion run at least twice and validate the data correctness, completeness, consistency and validity as these simulations will bring out potential issues which can ironed out for the final data conversion run to production system. Also, include the load testing of data conversion as part of test runs
7. Verify the Data: Plan to have random verifiers from the business user fraternity to verify their data in addition of the verification done by the identified data verifiers. This will ensure successful conversion of data

The most important part of the data conversion or migration is regular communication and co-ordination between separate teams (specially if we have multiple legacy source systems) and I have seen in my experience that following the simple rule of sharing ideas, works miracles in data conversion.

SCM Strategy: Improve your data quality index to improve supply chain performance

Doing more with less or same resources is the mantra for everyone these days and supply chain is also in the same boat. World over, there is a huge pressure to improve the performance of the supply chain of organizations and large investments have been earmarked for deploying state of the art or what do we call them as best of breed systems to measure the supply chain performance and efficiency in real-time. These investments are, in spite of the ongoing recession or low demand phase where revenues have shrunk and margins have taken a nose dive. The only reason is that the organizations have the belief that there is enough room for improvements in this area and these investments will help them realize long term benefits.

All these big ticket IT systems largely work on the principle of improving the processes, automating the transactions, reducing the cycle time and improve the collaboration between departments of the organizations as well as with suppliers and customers. The improvement targets are fixed after measuring the as-is values of the decided SCM metrics and then a time-bound plan with well defined strategy is put in place to achieve the best in class supply chain performance.

This post focuses on the last part where these systems measure the SCM metrics and accordingly a plan is made to improve those. We always spent huge money on deploying these systems but the focus on the quality of data is not on similar scale. Lot of times, it is seen that the SCM Metrics had a wrong as-is value because the data quality was not correct. What happens due to this is obvious – the targets either will be conservative or aggressive. Both of these situations defeat the purpose of the whole exercise...