Successfully complete your Cloud migration by introspecting the source system

 

Successfully complete your  Cloud migration  by introspecting the source system

Data migration to the Cloud is a perilous undertaking that requires rigorous organization. Maybe a little more...     We are convinced that the key to the success of these migrations is to really know the data and processes that are at source. In short, to make a real inventory and correct the countless problems before “moving”!   Because the lack of knowledge and understanding of the source platform can seriously compromise or even cause a migration project to fail. We'll explain that to you.

All in the  Cloud  !

Gartner recently reported that the public cloud market has exploded, with growth of 41.4% in 2022 followed by growth of more than 20% in 2023, reaching almost $600 billion!   Cloud plays a role in business growth: to meet the demands of current business trends, 46% of companies had planned to intensify their digital transformation efforts between 2022 and 2023. And when we talk about transformation, it's about for most Cloud migrations.   Businesses no longer have a choice, but the obstacles are numerous.

The winding path to  successful migration

For businesses, the prospect of data migration can be quite scary: They say that the process will affect all aspects of the operation, starting with the business. A very distressing prospect.   The people responsible for this migration are made clear that it is also necessary to respect the budget and the timetable, which they know is very hypothetical.   These injunctions are so difficult to reconcile that many migrations are not implemented. Rightly so, because many migration projects fail.   “Only 16% of migrations were completed on time and under budget” (Scientific Research OpenAccess).   “More than 80% of migrations fail to deliver on time and/or go over budget” (ItToday).

But why do Cloud migrations  fail  ?

There are a number of factors that contribute to migration failures.  Each migration is obviously unique, because the datasets and architecture of each company are different, the means implemented too, and therefore the reasons for failures necessarily are. But certain pitfalls are generalizable.

No real introspection of the source system   There is a first common denominator to all migration failures: the lack of understanding of the data and flows within the source architecture.   Without granular information on the entire assets that actually need to be migrated, a number of problems arise: Teams can be significantly disrupted by missing or incomplete data after migration, and worse, inaccurate data. There may be regulatory violations if certain types of data are moved inappropriately. Transferring irrelevant data/processes can extend the migration window, thereby increasing costs and delays.  Problems that should have been solved at source are solved within the new environment, which takes time and resources away from real value-added projects.

Our approach Uses of data:  the analysis of uses of data must be defined to know which business direction will be affected, and through which tools if necessary. Define useful data pipelines: when data that actually has a use is coupled to its source via “data lineage”, we manage to discriminate for the entire system in source “the wheat from the chaff”, and it becomes possible to carry out extensive decommissioning ahead of the migration. Often, 50% of the system has no use.    With the data and processes actually useful in the source system, it becomes possible to define the effort required to migrate, as well as a realistic timeline. Then, it is possible to prioritize the migration according to business impacts.

The incompleteness of migrations   To migrate, you will need to choose a data transfer tool to the Cloud which will allow the data and processes to be moved to the target. The market offers a plethora of them.     Simply rewriting the code in the target language will generate countless hyper-damaging regressions. Indeed, proprietary data transformation tools (ETL/ELT) often handle a large part of the transformations. Schedulers schedule flows, etc. If the migration must be “As Is”, it must therefore include the code, but also ETL jobs and scheduling, taking care of dependencies, articulation with dataviz tools, etc. It's infinitely more complex. And it's never treated exhaustively.

Our approach The translation of the code is therefore a very limited proportion of the effort! Our vocation is to automatically address the completeness of the technical stack in source to carry out an “As Is” migration, well not completely, since we will have taken care to purify and optimize the system in source!   To do this, we install probes and parsers on all storage, processing, scheduling and data exposure technologies, with analyzes replayed continuously. Based on the information collected, the migration must be carried out extremely quickly, by automating all of the processes so that the business can very quickly use the target platform with confidence.   Then, we will compare the source system (once "cleaned / optimized") with the target system to guarantee ISO functionality.

Conclusion    Cloud migrations often fail because they are conducted in a largely manual and empirical manner. As a result, they drag on over time and rarely achieve the expected results. We offer a methodology in 2 actions allowing you to reach the target calmly, within tight deadlines and within the announced budget. To do this, our strong capacity for introspection of legacy systems implemented by many large account clients has allowed us to create an authentic Cloud migration catalyst.