Organisations across all industries , and of all shapes and sizes , are looking to digitise in order to transform the way they work . Whether by driving greater efficiencies in existing operations , or developing entirely new projects , digital transformation projects are increasing businesses ’ dependence on hundreds – or in some cases thousands – of applications which drive every imaginable process , from sales and marketing , to product development .
The significant growth in applications has created some massive challenges for infrastructure management , further adding to the management headache .
Organisations frequently respond to this challenge by managing infrastructure to the lowest common denominator . This approach often reduces the management burden and , in some cases , even lowers initial costs , as management frequently represents up to 300 per cent of an asset ’ s cost .
In the long term , however , this approach tends to be painfully inefficient and expensive . Managing all application data in the same way , no matter what its data protection , security or performance requirements , leads to an overprovision of capacity and performance , or trapped silos of infrastructure . Not only does this lead to inefficient use of resources , it also compromises how an application is delivered , maintained and grown over its lifecycle .
Application DNA Every application has its own ‘ DNA ’, a genetic make-up , which accounts for its performance characteristics , and how it ought to be secured , protected and maintained in relation to other applications . However , managing applications according to their bespoke requirements adds significantly to the management overhead .
An application ’ s requirements can change within a single organisation according to the business operation . For instance , in financial services , the draconian penalties imposed when data cannot be retrieved will take priority over performance , with the processing speed of a data archive inconsequential in many cases . However , performance ( or latency to be more precise is much more precious attribute for a financial institution which leverage data analytics as part of its front-office activity , and whereby an improved processing time could provide advantages in terms of time to market or time to revenue .
Some organisations are adopting a policy based management process to help avoid managing applications to the lowest common denominator . Managing policies ensures that every application can be managed according to its bespoke characteristics , as the policies automate and standardise the management of applications with the same requirements .
Every application has its own ‘ DNA ’, a genetic make-up , which accounts for its performance characteristics .
For instance , if a retailer wants to change the back up of credit card details from hourly to every 10 minutes , the amendment can be made to all applications within a policy rather than to each application individually . This enables faster orchestration and drives standardisation . Furthermore , it creates a line of demarcation between application owners and consumers , and the infrastructure teams that deliver , maintain and support the shared infrastructure .