Discover & Plan

Establish a clear roadmap for data centralization, ensuring alignment with business needs and data quality.

Step 1: Define Data Strategy & Business Objectives

Actions:

Identify key business objectives (e.g., reducing storage costs, improving analytics, enhancing AI capabilities).

Assess current data sources (CRMs, ERPs, marketing platforms, IoT, financial systems, etc.).

Establish KPIs to measure success (e.g., storage cost savings, data retrieval speed, AI model accuracy).

Outcome:

A clear roadmap for data centralization tailored to business goals.

Step 2: Assess and Cleanse Existing Data

Actions:

Conduct a data audit to identify redundant, outdated, or inconsistent records.

Apply data deduplication techniques using Databricks Auto Loader & Delta Lake.

Standardize naming conventions, data formats, and schemas across sources.

Outcome:

High-quality, structured data ready for consolidation.

Step 3: Choose the Right Data Architecture (Lakehouse Approach)

Actions:

Adopt a Data Lakehouse Model (Databricks) to merge structured and unstructured data.

Implement Delta Lake for versioning, ACID transactions, and schema enforcement.

Select a cloud provider (AWS, Azure, GCP) to host the centralized data warehouse.

Outcome:

A scalable, cloud-based lakehouse architecture optimized for analytics.

Build & Integrate

Implement scalable pipelines, optimize data storage, and enable analytics.

Step 4: Implement a Scalable Data Ingestion Pipeline

Actions:

Use Databricks Auto Loader to ingest data from multiple sources (databases, APIs, streaming data, IoT).

Enable real-time data streaming using Apache Kafka or Delta Live Tables.

Establish batch processing pipelines for periodic data ingestion.

Outcome:

Automated data flow from disparate sources into a single unified system.

Step 5: Optimize Data Storage to Reduce Costs

Actions:

Tiered Storage: Store frequently accessed data in high-performance tiers and move historical data to lower-cost options (AWS S3, Azure
Blob Storage).

Data Compression: Use Parquet format to reduce storage footprint.

Lifecycle Policies: Automate archiving and deletion of outdated data.

Outcome:

Significant cost savings with intelligent storage management.

Step 6: Ensure Security, Compliance & Governance

Actions:

Implement RBAC (Role-Based Access Control) and attribute-based security.

Use Unity Catalog for centralized data governance and audit tracking.

Ensure GDPR, CCPA, and SOC 2 compliance with automated policy enforcement.

Outcome:

Secure and compliant data infrastructure with controlled access.

Optimize & Scale

Strengthen security, governance, and scalability for long-term success.

Step 7: Implement a Scalable Data Ingestion Pipeline

Actions:

Use Databricks Auto Loader to ingest data from multiple sources (databases, APIs, streaming data, IoT).

Enable real-time data streaming using Apache Kafka or Delta Live Tables.

Establish batch processing pipelines for periodic data ingestion.

Outcome:

Automated data flow from disparate sources into a single unified system.

Step 8: Optimize Data Storage to Reduce Costs

Actions:

Tiered Storage: Store frequently accessed data in high-performance tiers and move historical data to lower-cost options (AWS S3, Azure
Blob Storage).

Data Compression: Use Parquet format to reduce storage footprint.

Lifecycle Policies: Automate archiving and deletion of outdated data.

Outcome:

Significant cost savings with intelligent storage management.

Step 9: Ensure Security, Compliance & Governance

Actions:

Implement RBAC (Role-Based Access Control) and attribute-based security.

Use Unity Catalog for centralized data governance and audit tracking.

Ensure GDPR, CCPA, and SOC 2 compliance with automated policy enforcement.

Outcome:

Secure and compliant data infrastructure with controlled access.

Discover

Our process begins with a thorough exploration of your business needs. Through collaborative sessions, we identify challenges and outline a clear, actionable path forward.

Vision and Process Mapping

Capture and translate your business goals into actionable data strategies.

Functional Design

Develop a detailed design that aligns with user needs and business requirements.

Define Scope

Establish project scope tailored to your budget and timeline.

Advisory Report

Provide a comprehensive action plan with recommendations and next steps.

Design

After solidly understanding your objectives, we prepare a detailed implementation plan. This ensures that the digital solution perfectly meets your requirements.

Identifying Data Sources

Determine where your data resides and how it can be accessed.

Defining Data Structure

Outline how your data will be organised and managed.

Data Transformation and Storage

Plan how your data will be processed and stored for optimal use.

Build

Our expert team constructs the components planned in the design phase, using both reusable components and custom solutions to meet your specific needs.

Rapid Development

Deliver new working components of your data platform every fortnight, incorporating feedback and adjustments immediately.

Automatic Testing

Ensure stability and reliability through regular, automated testing during development.

Develop Data Pipelines and AI Models

Create robust data pipelines tailored to your architecture.

Develop AI Models

Create advanced AI models tailored to your use cases.

Highest Safety Standards

Adhere to the highest security standards to protect your data.

Test

Before deployment, we rigorously test every aspect of your data platform to ensure it operates correctly and meets the established requirements.

Data Verification

Ensure data is correctly transformed and loaded.

Application Testing

Test the applications and tools developed to confirm they function as intended.

Issue Resolution

Identify and resolve any issues before the project is rolled out.

Deploy

We ensure your data platform is ready for use and accessible to your team, providing the necessary training and support for a smooth transition.

Production Launch

Deploy the data platform and make it available to your employees.

User Documentation

Provide comprehensive documentation to help users navigate the new system.

Training and Support

Train your team to operate independently, with ongoing support available as needed.

Change Management

Guide your organisation through the transition to a data-driven approach.

Grow

Our commitment to your success continues post-deployment. We offer continuous support and development to ensure your data intelligence platform evolves with your needs.

Maintenance and Support

Provide comprehensive support and security to maintain your data platform.

Further Development

Continuously work on improvements and new features, ensuring your platform remains cutting-edge.

Service Level Agreement

Define clear agreements on availability and support to ensure ongoing service quality.

Review

We periodically review your data platform's performance to ensure it meets its objectives and continues to deliver value.

User Feedback

Collect feedback from users to identify areas for improvement.

Adjustments and Enhancements

Make necessary adjustments to optimise performance.

Follow-Up Steps

Establish follow-up actions to maximise the value of your data initiatives.

Methodology Driven Data Transformation

Discover

Vision and Process Mapping

Functional Design

Define Scope

Advisory Report

Design

Identifying Data Sources

Defining Data Structure

Data Transformation and Storage

Build

Rapid Development

Automatic Testing

Develop Data Pipelines and AI Models

Develop AI Models

Highest Safety Standards

Test

Data Verification

Application Testing

Issue Resolution

Deploy

Production Launch

User Documentation

Training and Support

Change Management

Grow

Maintenance and Support

Further Development

Service Level Agreement

Review

User Feedback

Adjustments and Enhancements

Follow-Up Steps

Methodology Driven Data Transformation

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