We assure seamless and successful application migrations from financial, operational and regulatory perspective
When migrating data from one system to another or any upgradation, a core consideration is the existing database of the application system. Criteria for successful data migration for banks includes seamless user experience, adequate backend experience, and no downtime.
A complex activity, data migration requires a separate approach, project, plan, budget, and team right from the start. A robust plan has to be created for conversion of the existing database at field and entity levels for upgrade or migration. The approach will determine whether all the data is loaded in one go or in small batches at regular intervals.
Data transfer is a complex and potentially risky process that could get marred in a range of problems. This is the high visibility and high impact area as any issue has impact across entire existing user base and results in immediate need to address it. The tech team executing the whole process should be well versed in the problems that may come up during data migration and should have the capability to find solutions.
Migrating data from one database environment to another is a multi-step and time-consuming process involving a plethora of activities such as planning, data profiling, testing, and reconciliation. If you are unsure what reconciliation is, it focuses on ensuring balancing the source and destination systems. The projects might be ranging from a run-of-the-mill database upgrade to a full-fledged transfer of the enterprise system to the cloud.
A complex and potentially risky undertaking, data migration often becomes a cause of great anxiety for organizations. Here are three typical challenges they face:
When separate systems are engaged as source, destination and intermediate staging database, there springs up a daunting task of comparison and reconciliation. The team needs instant and accurate system reports to detect, track, and correct discrepancies in data as there is no room for error.
As the systems are built and run over a period of time, some data inconsistencies are bound to develop over time. During the course of data migration, these inconsistencies need to be properly handled, which turns out to be a real challenge for the team executing the task.
The tech team has to pre-orient various possible scenarios that might occur during various phases of migration. They also have to be prepared for last minute fixes which may arise out of nowhere. They may also need to test each part of the data transferred to fix issues in a streamlined manner.
Our overall approach towards data migration depends on the kind of business you run and your goals. As your business will undergo such an implementation once or twice in a lifetime, it is imperative you get it right.
Our team comprising seasoned data migration experts review data migration strategy document as well as reconciliation reports created by product vendor to understand the migration strategy. We also define the data migration test strategy, validation approach, timelines, checklist, detailed activities, roles & responsibilities, defect management approach and status reporting.
We believe in a calibrated approach towards test planning. During the planning and design phases, we conduct mock runs of the data transfer to make sure the process will eventually achieve the desired outcome. A common strategy we follow is to segregate the data into subsets and pull over one category at a time, followed by a test.
Factoring in the design, the data to be transferred, and the target database environment, we define timelines and other project concerns. Our team uses various techniques to accelerate migration timelines and boost efficacy of the process. Continual reviews along with tests help us to take corrective action whenever required.
Before setting off with data migration, you need to figure out what you are migrating and how it will be held within the target database. Understanding data format and volume is critical. Data mapping verification includes the review of source and target systems, the symmetry between them, and the data quality.
When data is migrated, data sample verification at different critical points and system entity levels assumes a key role. It becomes important to ensure that records are transferring as intended from the source system to the target system. Giving verification a miss and going for the migration straightaway is only going to create huge problems.
Pro-active checks on data samples and reconciliation are likely to alter the mode of interaction with the database environments in the source and target systems as well as the desired goals. Re-active checks, on the other hand, hinge towards the corrective actions, for instance, adjusting parameters for sustaining the desired system state specs.
Mock run or dress rehearsal is an exercise of data conversion/migration while deploying the agreed upon data mapping and transformation rules. It is run over an agreed upon set of objects and volume of data with a time- and scope-bound approach. Mock runs can be run several times to identify potential issues.
Our team compares target data with source data to ascertain that the migration architecture is transferring data successfully and accurately. Technically, we are capable of executing a bi- or a tri-party reconciliation underlining system data sanctity. The process helps pick up issues like run time failures like broken transaction or network dropouts.
Tools we use for mock runs can generate customized test reports for various data states. Our professionals are also adept to each step of the cutover process, item by item, and closely coordinate with one another until the application system goes live.
The job involves data mapping and format verification from source to target system. We conduct Verification of records, reconciliation and financial reconciliation using source extract reports and target system reports; Account level monetary, non-monetary and historical data; posting of cycle-to-date transactions on target system; Iinterest accrual after Day 0 Batch; System balancing, and critical reports.
Other tasks include Authorization routing from source to target system; Collecting monetary data at the system level and product level; Account cycling and statement processing; Report generation; and Application screen & database validation.