Data Services

Data services are silos of software functions that attribute data characteristics to raw data that is saved in data storage volumes. Data services add value and layers of abstraction to data by enhancing its availability, validity, and resiliency and adding characteristics such as metadata that is not already present in the data. The data is hence made more useful to both programs and users through this process.

Data service functions take raw data as inputs and output data that can be used for a specified purpose. In data service management, such output data service function falls under three types:

• Procedural- The data is processed as part of analytics, data modelling, and artificial intelligence/machine learning (AI/ML).
• Organizational- Data here is structured, batched, and consolidated that is extracted from sources such as databases that are structured, data warehouses that are semi-structured, and data lakes that are unstructured.
• Transferable – The data is subject to movement from their point of origin through a network to a data endpoint such as a platform or application.

Data services aid in data aggregation from separate parts of architecture or in the generation of a data center repository, may deal with data in transit or storage, or even perform different analytics on big data sets.

Here are detailed applications of business data services:

• Data source abstraction – the data as a service platform enables the abstraction of physical data from consumers. This helps data providers to make changes to the data structure, format, persistence mechanism, and exchange protocols and not adversely impact consumers.
• Alignment with logical data models – data services enable alignment with logical data model elements such as account and profile details by maintaining consistency in data structure and data attribute behavior.
• Reuse – data services function as reusable building blocks of operation upon business data. These execute Create, Read, Update, and Delete (CRUD) operations on enterprise data and are reusable across various projects.
• Aggregation – the data as a service model enables the provider to utilize one or more data sources to build a business entity using both homogenous and heterogeneous data sources. This includes structures and unstructured and semi-structured data.
• Single point of interaction – Consumers can benefit from data services acting as a single point of interaction to data entities for consumers.
• Value-added features – Along with the main function of operating on data elements, data services could offer value-added services such as data caching, filtering, and subscription management.

Cloud data management services refer to the process of storing enterprise data at a data center that’s offsite that is usually owned and managed by a vendor specializing in public cloud infrastructure (AWS/Azure). This approach offers easy access that is location agnostic, professional support coupled with an automated backup strategy.

Here are the features of a reliable cloud data management service:

• Planning and forecasting – involve setting policies for data transfer from on-premises to cloud, one cloud storage class to another, and so on. This also includes the forecast of fees for retrieval and egress. 
• Policy-based data replication, data management, and data archiving – involve the work involved in the transfer and management of data.
• Fast, reliable Cloud data migration: includes the response to migration over a WAN (Wide Area Network), data security during migration, and so on.
• Data Lifecycle Management, Intelligent Tiering, and Intelligent Cloud Archiving: Management of ongoing data lifecycle in the cloud along with support for various cloud storage classes. 

Cloud data management services offer a wide variety of advantages that are aligned with the considerable benefits that the cloud provides. Here are some of those advantages:

• Security – it is a proven fact that modern cloud data management software usually offers enhanced data protection when compared to on-premise solutions. This is because the data loss risk from hardware damage or failure is greatly reduced. Also, the enterprises involved in cloud hosting implement advanced security measures for data protection.

• Scalability and savings – the biggest advantage of cloud data services is the ability to scale rapidly up or down. This applies to both compute power and storage that can be varied easily to match varying workloads.

• Governed access – enhanced security easily translates to better-governed data access that is regardless of location. Such access also encourages a highly collaborative work culture enabling working together and helping each other to share insights easily.

• Automated backups and disaster recovery – the cloud vendor handles the management and automation of data backups speeding up the disaster recovery process after emergencies. It also reduces the impact of any possible ransomware attacks.

• Enhanced data quality – A single source of truth for all data points leads to consistent and up-to-date data that is accessible for real-time data analytics and advanced machine learning applications.

• Automated updates – Cloud data management services usually offer the highest quality services and capabilities, running updates for applications when required automatically. This frees up time for enterprise teams to work round the clock while the vendor is at the task of updating.

• Sustainability – In today’s environmentally conscious world, cloud data management helps decrease the adverse impact of an enterprise on the environment. This includes a decreased carbon footprint caused by operations.

Data management has been undergoing accelerated development, starting from locally hosted systems of storage to more reliable and versatile cloud data management services. Staying competitive in the current market will require all companies handling data to make the digital transformation switch from on-premises to cloud data management services.

It is significant to note that cloud data management services include the management of data on an end-to-end lifecycle basis. This includes data creation to deletion along with the planned progression of data through every stage along its lifecycle. This helps mitigate the risks and costs associated with regulatory noncompliance, security breaches, legal complications, and more. There is also access to accurate and precise data in real-time, with clear-cut communication regarding the data migration and storage.

Cloud data management platforms have the following salient features:

• Support for data across multiple cloud ecosystems
• Delivery of services to be presented as microservices and being API-driven
• Use of modern elements such as containers and serverless operation for deployment that is scalable and faster.
• Inherently simple installation and set-up
• Easy management that includes patch management and automatic upgrades
• Granular pricing that is based on service utilization

At CastawayIT Cloud Management services, we ensure that your enterprise data needs come first. We take utmost care to implement the best Cloud Data Management services by:

• Offering accurate analytics across cloud infrastructure with a single source of truth for all users’ cloud accounts that ultimately save storage costs using an analytics-powered approach
• Speeding up cloud data migrations with marketing leading fast data migrations across clouds and even for on-premises. 
• Providing a forecast of cloud cost optimization by setting up various data lifecycle policies that are customized according to your enterprise leads to controlled costs.
• Enabling multi-cloud lifecycle management that is policy-based by progressively moving data objects according to policy across various storage classes in a transparent manner.

Our Cloud data management experts come with years of marketing leading experience in cloud service offerings and management.