Data Management

Data Management involves the process of collecting, storing, organizing, and maintaining the data created and collected by an organization. Effective data management ensures that data is accurate, available, and accessible, enabling businesses to make informed decisions, comply with regulations, and gain a competitive edge.

Why Data Management is Crucial ?

Data Quality

 Ensures that your data is accurate, consistent leading to better decision-making and improved business outcomes.

Compliance

Helps organizations meet regulatory requirements by managing data securely and ensuring privacy and protection.

Business Intelligence

Enables advanced analytics and reporting, turning raw data into valuable insights that drive business growth.

Strategic Opportunity for Market Capture in Cloud Data Management ?

Develop Comprehensive Cloud Data Management Solutions:

Invest in R&D to create advanced, and secure cloud data management solutions tailored to various industries such as finance and retail.

Leverage Partnerships and Alliances:

Form strategic alliances with leading cloud providers like AWS, Microsoft Azure, and Google Cloud to leverage their infrastructure and expand our service offerings.

Focus on Customer-Centric Services:

Offer customized cloud data management services that address specific client needs, including data migration, backup, disaster recovery, and compliance.

Invest in Talent and Skills Development:

Recruit and train a dedicated team of cloud experts to ensure we have the necessary skills to deliver top-notch cloud data management services.

Adopt a Multi-Cloud Approach:

Encourage a multi-cloud strategy to provide clients with flexibility, redundancy, and optimized performance across different cloud platforms

Cloud Data Management Services for Edge Data Centers

Setting up data management for a data center involves several key steps to ensure efficient, secure, and reliable handling of data.

Cloud Data Management Framework Coverage:

Data Governance

Building High Level Base Framework (Low Code, No Code)

Establish Processes for managing data assets

Create Metadata Management based on the data & domain

Data Access Control

Data backup & recovery policies

Data Quality

Cleansing, validation and normalization of data

Data Accuracy , Data Transformation

Data Consistency , Data Validation

Quality Monitoring and Improvement

Quality Reporting Dashboards

Data Security

Procedures & SOPs for data breaches, security incidents and other emergency responses

Business Continuity & Disaster Recovery Plans

Roles based data access

Authentication & Authorization Servers

Privilege Principles and Rules Engine

Monitoring Reports & Logs

Regulatory & Compliance

Data Retention Policies Configuration

Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) configuration based on data types

Back-up Testing and Restore Processes

Conduct Audits, Assessments and Compliance Reviews based on domains (Healthcare HIPAA, GDPR, etc.)

Compliance Violation Reports

Data Governance Models for Deployment

Models that can be deployed for managing data assets within Data Centers are portrayed below. We can offer a variety of services tailored to different business needs, ranging from infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS) and software-as-a-service (SaaS).

The choice of private/public cloud provider often depends on specific business requirements, existing technology stack, and geographic considerations

Private Cloud

AWS Outposts/ Private Link

Azure Private Cloud & Stack

GCP VMware Engine/ Anthos

Rackspace Private Cloud/Managed VMware Cloud

VMware Cloud Provider Program/VMware Cloud on Dell EMC

Private Cloud

AWS

Azure

GCP

VMware

Others : Alibaba Cloud, Salesforce(Heroku), Tencent Cloud

Other Service Offering : Data Analytics as a Service

Offer Data Services to move the domain data to an analytics layer and perform the below functions:

1. Data Ingestion on a selected cloud platforms

2. Data Storage on Data Lakes and Data Warehouses Supporting all kinds of structured and unstructured data

3. Data Processing layer for managing analytics workloads (AL/ML layers) along with Model management

4. Analytics (Prescriptive and Predictive) layers and Visualization (Power BI/Tableau/native visualization tools)

5. Security and compliance layer for data access control and monitoring