Positioning software or a messaging solution often ends up being a chase for the proper platform. New platforms are announced regularly - often as the solution of the day. Businesses are hungry for such solutions because they’re under increasing pressure to manage growing data volumes and data formats which are produced by a growing number of applications and operated on an ever larger number of different infrastructures. More often than not, structures like that are called “data platforms”.
A data management puzzle
Looking closer, it is soon noticeable that many such data platforms are just insular solutions (or point solutions) which in fact are not able to manage more than one part of the data management puzzle. They only manage a specific type of data - such as on-premise data, virtualized data or some types of application data - or they are just not able to provide comprehensive and up-to-date data management possibilities. To judge whether a data platform is a true data management platform, it is essential to define the basic requirements businesses demand from such solutions.
Five basic principles to enable proper data management.
Commvault – our partner for data management – has defined five basic principles for proper data management. A solution must facilitate data comprehension, linking, mobilizing, regulating and, ultimately, utilization. These basic principles will enable you to implement an integrated data management strategy for the proper protection of your data as well as to activate their maximum business value. This list is not complete.
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Six capabilities which define a modern data platform
1. Open data access – data must be usable through standard protocols such as ReSt, file system, storage or APIs, not just a proprietary GUI or just some connector. A modern data platform must be independent and sustainable to make sure a business can be confident it will be able to access its data anytime and anywhere.
2. Virtual data consolidation – it is normal for data to be scattered across different locations, infrastructures, access methods and silos. It is practically impossible to merge all data within a company in one location, one format or one silo. A modern data platform must merge individual locations and formats virtually, through consistent management, processing and navigation. It must make data portable and thus moveable between different infrastructures.
3. Deep and adaptable data indexing - metadata constitute the foundation for intelligent data control. Without intelligent control we are forced to shift data blocks or containers around – a wasteful and not very effective strategy. Metadata handling of a modern data platform must go far beyond simple ACLs; it must reach into contents, recognize different aspects, classify them and process natural language. It also must accept specific programmatic-dynamic attributes because a modern data platform must be able to become smarter through its data.
4. Comprehensive Data Security – it’s about more than just encryption; a modern data platform must be able to authenticate and authorize individual data objects to prevent data leaks or data loss. Data security includes dealing with complex and changing authorizations, functions, and responsibilities. It must be able to seamlessly integrate into existing directory or security services of a business.
5. Lifecycle data services – such services offer the opportunity for significant savings, risk reduction and operational simplification more than any other options. A modern data platform must be able to orchestrate and automate data copy management, compliance, and governance across all infrastructures, application types, formats, containers and locations – even SaaS environments. It provides the natural software layer to control underlying memory resources and can optimize hardware utilization as well as data lifecycle performance. Thus, costs will be reduced and redundancies, copying, tiering, and security will be modified according to data profiles, access and usage.
A modern data platform must not be linked to specific hardware for the implementation of required lifecycle and copy data services.
6. Data value creation – it’s the job of any platform to bring its user as much benefit as possible -benefits that go much further than data analytics or simple data visualization. Rather, it’s more about adjusting information to user requirements. GPS coordinates, for example, could be used to align the activities of external service providers with security aspects or personalized patient data with insurance information. The higher the accuracy of a modern data platform’s fine-tuning of information and user requirements, the higher the ability to provide real benefits.
It is the aim of a modern data platform to bring the greatest possible benefits for its users. To create such benefits a data platform must comprehend data – on a deep and granular level. Without such a level of data comprehension it is effectively impossible for a modern data platform to do its job – that is: protecting and activating data, simply, reliably, securely and cost-efficiently.
(Source: Commvault, 06/2017)
More information at https://www.commvault.com/solutions/by-topic/gain-efficiency