data-management – Aliferousanalytics

Aliferous Analytics Data Management Program

To help you manage corporate data throughout its life cycle, we implement a comprehensive data management program, which includes the following elements:

Data Governance

Drawing up data governance standards and policies to ensure data availability, integration, quality, security, proper usage, etc.
Evaluating the existing data governance standards and policies.

Data Architecture

Designing data architecture to govern how data is captured, integrated, stored, analyzed, and used.
Auditing data architecture to align it with the enterprise strategy.

Data Integration

Consolidating data from disparate data sources with extract, transform, load (ETL) or extract, load, transform (ELT) processes and data virtualization.

Data Quality Management

Data cleansing activities, data enrichment, and regular data quality assurance.

Data Storage

Designing, implementing and supporting storage solutions for datasets of varying scale and format.

Reference & Master Data Management

Enabling data consistency and quality across transactional and business intelligence systems with data profiling, data deduplication and standardization, etc.

Metadata Management

Designing and populating metadata repositories with metadata to ensure localization of a data asset, data lineage, etc.

Data Warehousing, Analytics, & Reporting

Designing and implementing the BI and data analytics infrastructure to ensure maximized data value.

Data Security

Setting up data security practices and regular BI and DWH risk assessment to prevent unauthorized data access and inappropriate data usage.

Data Migration & Backup

Moving your data from one system to another ensures efficiency and security with preliminary data assessment, data migration automation, and data completeness evaluation.

Data Quality Standards We Target

When establishing data management for customers, Aliferous Analytics guarantees the following data quality characteristics:

Consistency

No data contradictions within one data store and across different data stores.

Accuracy

The information your data contains is reliable and error-free.

Completeness

Data is sufficient for answering your business questions.

Auditability

Data is accessible, and it is possible to trace the introduced changes.

Uniqueness

Data represents reality within a reasonable period of time or in accordance with the corporate standards.

Completeness

A data record with specific details appears only once in a database, no data duplicates are reported.

Cooperation Models We Offer

Aliferous Analytics helps you launch a full-scale data management program for intelligent decision-making by:

Defining data management objectives.
Evaluating the maturity level of your data management.
Drawing up data governance policies and standards.
Designing architecture and selecting software for a technical solution(s).
Developing and stabilizing a tech solution for each data management program element.
Launching the tech solution(s).

Pricing model: Time & Material, Time & Material with a cap – you receive the end-of-the-month invoice based on the hours or efforts reported per month (under the stated upper limit in the case of T&M with a cap).

Data management as a service is a pay-as-you-go cloud service that provides:

End-to-end cloud data infrastructure configuration, management, and optimization.
Automated data backup to safeguard your data assets.
Easy and secure data access.
Reduced data management infrastructure costs.

Pricing model: Monthly subscription fee – you pay a fixed price based on a variety of factors (data volume, number of data sources, data architecture complexity, data quality requirements, etc.). You can choose to pay the cloud provider’s fees either directly or via Aliferous Analytics (we work with AWS, Azure, GCP).

The Benefits of Aliferous Analytics Big Data Services

With practical experience in 30+ domains, we speak your language, understand your unique challenges, and offer pragmatic solutions that fit your processes.

We use our DevOps and Agile expertise to build efficient development processes, apply feasible test automation, and rightsize cloud resources to reduce cloud fees.

We set up automated data governance and reporting procedures to eliminate manual work for your IT and BI teams and reduce the risk of human errors.

Enjoy the complete clarity of your big data dashboards: we build easy-to-read reports and responsive interfaces that easily adapt to users’ needs (e.g., sleek visuals for C-level presentations, in-depth data exploration for analysts).

We establish robust big data quality management processes that ensure your data is always accurate, consistent, and complete to serve as a trustworthy source for analytics.

We combine best-fit algorithms and create tailored data sets for model training, apply cross-validation to fine-tune hyperparameters and enable self-learning for ML engines to deliver consistently accurate AI output.

Enterprise Data Management Sourcing Models

Sourcing Model	All In-House	A Mix Of An In-House Team & Outsourced Consultancy	Data Management Is Fully Or Partially Outsourced
Pros	Maximum control over data management infrastructure and resources. A data management team is fully aware of the company’s internal structure and processes.	Outsourced consultancy provides expert guidance and mentors in challenging activities. Closes gaps in specific technical skills. Optimized costs due to quick scaling of resources.	Quick ramp-up of projects and operations. Access to experienced specialists and best practices. Vendor takes responsibility for staffing and management.
Cons	Slow hiring and training process. High internal staffing costs. Requires ongoing employee development.	Vendor selection risks. Coordination and communication challenges. Dependency on external consultants.	Higher dependency on external vendors. Less direct control over processes. Potential security and compliance concerns.