The pragmatic reality for the vast majority of enterprises is that their IT, and thus their data and services, will span multiple data centres and computing clouds.
This will accelerate fragmentation of data and systems that have to be seamlessly tied together, that is, “integrated,” to yield their full potential.
Despite the many compelling benefits promised by public cloud computing, most enterprises can’t move all their data into a public cloud for legitimate reasons; whether data sets are too big to move in bulk or because of other preventative regulatory, privacy or security requirements. And, even in cases where those constraints are not prominent, the switching costs for legacy systems often do not have a good ROI.
As a result, the predominant IT pattern for many years to come will be a hybrid, comprising a mix of traditional IT along with a growing collection of multiple private and public clouds, often referred to as hybrid cloud.
As this multi-cloud world grows, the next big question for enterprises becomes: Are their clouds integrated in the most strategic manner for their business – not only fiscally, but strategically – to put valuable data and systems fully to use?
This increasingly heterogeneous computing environment demands a range of integration approaches including data integration, application integration, message-based integration, and API-based integration to provide optimal access to data and systems wherever they reside.
We are already seeing this.
According to an IBM study, Growing up Hybrid: Accelerating digital transformation, two-thirds of organizations implementing hybrid cloud report they’re already gaining competitive advantage from their hybrid environments and are nearly three times as likely to use it to assemble data assets or monetize data.
Cognitive services on the cloud are giving businesses insights into all aspects of business like never before – from the supply chain to client management, to marketing.
When businesses have data residing on systems and services across multiple clouds, the approach to implementing and overseeing data analytics is not always clear or simple.
This makes meticulous management of data in hybrid cloud environments especially crucial. After all, no matter how good a cognitive algorithm may be, the quality of its training and analysis are bounded by the data it can access wherever that data resides.
Banks, for example, keep customers’ sensitive financial data in private clouds where they can best control the information. But if a bank offers mobile banking for their customers, it will need to access data from mobile apps that may be served from a public cloud.
The complex mix of data generated from customers’ mobile banking activity, like average deposit amounts, time spent on page, and money transfer trends can be analyzed to improve the customer experience.
Thus, if the customer usually transfers “$500 for rent to Bob” each month, the app can learn that habit and open with that transaction as the first option in following visits to save the customer time.
Like Bob’s rent each month, the next logical step in managing multi-cloud environments with data analytics is automating tasks to save time and money.
IBM is already working with clients to provide tools to break down the walls traditionally associated with running multiple cloud infrastructures, reducing the manual processing, and providing them with greater control.
By bringing together and integrating more systems, data, and cognitive capabilities with an open cloud platform, we are giving clients the control they demand. In doing so, companies have a more complete view of what is happening with their company
As the journey to the public cloud evolves, it is critical to assist with the grand challenges: Maximizing current investments, centring a strategy around data and leveraging advanced applications in the cloud like cognitive applications.
Mark Noppe, is vice-president of the cloud business unit of IBM Canada
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