If CIOs can manage their entire data sets on widely accepted standards, then they will create a single data platform that ensures both information integrity and long-term stability.This is crucial if digital transformation initiatives are to succeed and avoid the many hidden costs of data management.Now that companies have achieved their quick wins, it is only through this consolidated view of their organisations and customers that they will be able to move to the next stage of digital transformation.See also: Consolidation in the security market The reason is simple.For example, CIOs need to know how easy it is for them to integrate data from different stores into their existing infrastructures.As they seek to use data from disparate applications, CIOs should be wary of hidden costs such as indirect access.This does not mean getting rid of No SQL-only or Graph databases in favour of relational ones.No SQL databases perform very valuable functions, but if CIOs are to enable businesses to create an integrated, a single view of customers, market opportunities, and operational efficiencies, consolidation has to be a priority.
Open Source will also help to ensure CIOs minimise the potential challenges and hidden costs set forth above.
This is the practice, common among very large database vendors, of charging customers for another vendor’s application to access and use data stored in their systems. CIOs are being told of huge cost savings and operational efficiencies in the Cloud, but setting up applications and transferring data to and from the Cloud can have its challenges.
Data consolidation should offer efficiency and greater flexibility to move between on premise, public, private, and hybrid Cloud with ease.
Therefore, in the coming year I predict data consolidation will become the watchword for smart CIOs.
This will see organisations integrate and streamline information from existing back-end and legacy IT systems to digital, customer facing applications.
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For example, unlike the relational SQL model, which aims to normalise data to eliminate redundancy and reduce storage, the No SQL data type does not encourage this approach.