Empower your business with Sneak Peek Tech to help you manage corporate data throughout its life cycle , we implement a comprehensive data management program.
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, Sneak Peek Tech 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.
Timeliness
Data represents reality within a reasonable period of time or in accordance with the corporate standards.
Uniqueness
A data record with specific details appears only once in a database, no data duplicates are reported.
Cooperation Models We Offer
Data Management Software Implementation
Sneak Peek 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 (DMaaS)
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 Sneak Peek Tech (we work with AWS, Azure, GCP).
The Benefits of Sneak Peek Tech Big Data Services
Industry-Centric Approach
Managed Data Analysis
We use our DevOps and Agile expertise to build efficient development processes, apply feasible test automation, and rightsize cloud resources to reduce cloud fees.
High Degree Of Automation
User-friendly UI
Clean Data For Reliable Insights
95%+ AI/ML Model Accuracy
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 the 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, mentors in most challenging activities (e.g., creating data governance policies) and closes up the gaps in specific tech skills.
- Optimized costs due to quick up- and down-scaling of data management professionals.
- Quick ramp-up of data management projects.
- Implementation of data management best practices.
- In case of full outsourcing, a vendor has full responsibility for the data management initiative and all related risks.
Cons
- Lengthy rollout of data management projects due to hiring and training in-house specialists.
- High risks of consultancy vendor selection.
- Challenges in the coordination of in-house and outsourced resources.
- High vendor risks.
Data Management Tools and Technologies
Data integration
Cloud data storage
Data warehouse technologies
Big data
Data visualization
Programming languages
