One of the most challenging and complex aspects of any digital transformation is data migration. In the last decade, the importance of data in business has shifted considerably. It has evolved from a departmental role to a critical facilitator of digital transformation. The core of modern enterprises of all sizes is data centricity. Building a genuinely data-driven business require large-scale data transfer and modernization. However, this data transformation process entails much more than simply moving the data estate to the cloud.
Therefore, data migration should move up on the companies’ lists of project priorities.
However, most businesses overlook the need for data migration work as a prerequisite for a successful ERP implementation/digital transformation. Many businesses are unaware of the numerous difficulties or stages that are part of the digital transformation journey. It is an iterative process to migrate data. Some people wrongly believe that data can be transferred using (auto-magical) “lift and shift” technologies, or that someone else on the team will design and conduct the heavy lifting.
Data is frequently added too late in the project cycle when data readiness is not a well-planned pillar of the project. This results in the early discovery of system problems. Project delays and costly design/build/test rework cycles can cause late modifications to the new system. A possible impact is “dirty data.” When you fail to plan and implement data adequately in advance usually “trash in, garbage out” happens. Business intelligence, data analytics, the internet of things (IoT), and other large-scale IT efforts have potential. However, if a company has corrupted, inconsistent, or wrong data, these technological tools are useless.
Here comes another term in data transformation, namely “data cleansing.”
Data cleaning necessitates more extensive pre-work or may require external validation. Therefore, it is usually done before the actual data migration. Usually, skipping data cleansing phase is uncommon, even though it should start before software selection.
Data transformation is a systematic, programmed method for transforming large amounts of data according to established business and technical standards. The project team will be able to incorporate significant volumes of changed live data into the newly developed system for effective/realistic testing. Moreover, transformation is meant to be done iteratively and frequently. This entails not only comprehending the technical elements but also obtaining the necessary authorization for the mapping as to who has access to what data.
Even when digital transformation is now an essence in the business world, some companies still put off confirming their data until user acceptability testing.
The main reason here is the lack of a “data readiness track” to ensure that any meaningful data is available for testing. A significant drawback of data testing late in the journey is that is unlikely to complete it before going live. In addition, downstream systems, reports, and analytics will not be executed or tested with master and transactional data.
Since the system integrator is usually primarily responsible for the main ERP system, it cannot guarantee that downstream systems and reporting can correctly process the outputs. This can lead to an unpleasant surprise when an executive realises the poor state of data in their new dashboards. Data readiness preparation might help companies avoid unpleasant surprises after project’s completion.
And finally, what happens with the privacy and security concerns around data migration?
Data migration, data privacy, and data security all need data mapping and cataloging efforts. A company must first understand what data it has and how it travels and is utilized within the business to safeguard it. If teams adopt data privacy and security in time, the data transfer team can benefit from it. When a company is considering a data privacy or security program (highly recommended for any size company), they should look for ways to combine part of their work with data migration efforts. Furthermore, a migration team can work with (and understand) the relational benefits of doing so – a company can save a lot of time, resources, and expenses.