Data Engineering and Governance

Accelerate the RoI of your data investments while achieving trust and compliance. 

Organizations need to be agile with evolving customer demands and preferences. Even when they are saddled with complex tech, data, and cloud infrastructure, enterprises need an efficient, reliable, and trustworthy data life cycle. How do organizations achieve agility? The foundation is set by the data engineering and governance practices for businesses including:

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1. A trustworthy data management practice and security controls enabling analytics and AI models

2. Realizing the value and actionable intelligence from the fast-growing enterprise data pool

3. Sustainable data preparation methodologies to access insights from structured and unstructured data

Best Data Engineering Practices – Need

Reliable data structures

Data quality – utmost need

Real-time data processing

Monitoring of data pipelines

Ensures data consistency

Our Data Engineering Services

Data Ingestion

Integration of data from multiple sources and handling of structured, semi-structured, and unstructured from stream and batch processing.

Data Storage ETL/ELT

Extraction, transformation, and storing of data in various relational, big data, and cloud storage depending on the format, volume, and velocity of data.

Data Governance –
What do we do?

Data Quality

Profiling, cleansing, matching, stewardship, and analysis to determine the right value of data. Knowledge-driven quality controls for data of any size or format.

Master Data Management

MDM implementations planned across multi-domains through scalable and reliable architecture.

Regulatory Compliance

Audit and Regulatory impact assessment partnering with your data and compliance teams.

Data Security

Smarter data security solutions with visibility, automation, and protection against emerging threats.

How We Do It?

Our experts at Saxon leverage a consultative approach to identify your data challenges and leverage data engineering, governance, and modernization expertise to evolve you into a modern data ecosystem.

Strategize

Gap analysis, current state, future state, and map solutions for the organization’s insights goals.

Re-engineer

Methodologies for data pipeline automation, reusability of data assets, and establishing pre-built cleansing routines and data preparation.

Modernize

Modernize data architecture and information consumption, and establish methodologies for data discovery, virtualization, and delivering data models at scale.

Monetize

Monetize data assets to empower stakeholders to leverage governed self-service data management and analytics tools for rapid insights.

Saxon’s End-to-end Solution​

Are you looking for a one-stop solution that can automate your data pipelines and perform a few auto-cleansing routines and create a dashboard in a day?

With in-built data engineering components, pre-built dashboards, and industry-specific AI and ML use cases, InsightBox operates with efficiency at the nucleus.

InsightBox addresses the needs across your data lifecycle by connecting your data sources, organizing and preparing data for insights, and simplifying dashboards and predictive insights. 

The platform is:
  • Cloud-agnostic – On-prem, Azure, AWS, or Google
  • Scalable with self-service capabilities powered by low-code
  • Holistic across the data engineering and insights to reduce cost, time, and data teams effort by around 50%
  • Highly secure