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Data Analytics & AI

Data Analytics is the process of organizing and analyzing data to enable data driven decision making. Data Analytics provide organizations with great potential to improve organizational performance through data driven decision making and strategy formulation. It also includes machine learning and predictive analytics.

We are a Data Analytics Company

We Consult, Design and Transform Data Ecosystems

Data consolidation continues to be a challenge to many companies. We consult, design, implement and support, Data Ingestion and Preparation, from your multiple sources, On-Premise data, Cloud data, SAAS data. Then we Model and Serve the data.

Analytics on Cloud Scale

Our professional Cloud Analytics Engineers have knowledge, deep expertise, and experiences in Azure Synapse, Azure Data Factory, Analysis Services. Microsoft Fabric and Microsoft Power BI implementation. We work with our clients to build Secure Data Models and consult on Enterprise Visualizations, Predictive Intelligence and Self-Service Reporting. 

What's NEW?

The Release of Microsoft Fabric

Our Data Analytics Services

1. Consulting

Data Strategies, Data Architecture Designs, Technology Recommendations and Approaches to the Analytics Solution to support the business needs

2. Data Integration

Integration from various Data Sources into a Cloud Analytics Platform. With more than 90+ built-in connectors to On-Premise Data, Cloud Data, gone are the days where multiple ETL pipelines that is run after office hours, is now replaced with a fully managed integration service. We consult, design, implement and support ETL and ELT processes to deliver data to your Data Warehouse.

3. Data Warehousing

Building and managing data warehouse to organize and store large volumes of structured and unstructured data. To data warehouse or To Not data warehouse, read our blog here.

4. Data Analysis and Visualization

We work with our customers to provide data analytics services, help them grow and differentiate against competition, future proof their business, do more with less. Make informed decisions.

5. Predictive Analytics

Developing, building and using statistical models to make predictions, forecast future trends and outcomes, based on historical data patterns.

6. Machine Learning Solutions

Implementing machine learning algorithms to automate processes, make predictions and uncover hidden patterns in data. Machine Learning is a subset of Artificial Intelligence (AI). 

7. Data Governance and Security

Establishing policies, procedures and technologies to ensure data quality, privacy and compliance with regulations.

8. Training and Support

Providing training programs and ongoing support to equip organizations to use analytic tools effectively and address any issues that arises.

Our Process

Our Process


We use CRISP-DM (Cross-Industry Standard Process for Data Mining), a robust framework that serves as a beacon for analytics projects, guiding us from inception to fruition with precision and effectiveness. It also serves as a beacon of guidance and structure in the ever-expanding realm of analytics.


CRISP-DM embodies a methodology that fosters collaboration, transparency, and strategic decision-making throughout the analytics lifecycle. Image Framework shown here is adapted from Wirth and Hipp (2000)

1. Business Understanding

Understand the business problem that is being addressed to design a data analytics solution


2. Data Understanding

Understanding the different data sources available and the different kinds of data contained in the sources, once the data analytics solution in Step 1 is ascertained.

3. Data Preparation

Converting the disparate data sources that are available to a Centralized Schema. In Predictive Analytics, it is a conversion to the Analytics Base Table.

4. Data Modelling

Designing and implementing data models to provide the necessary analytics for visualization. If it is predictive analytics, here different machine learning algorithms are from which the best model will be deployed.


5. Evaluation

Solutions are evaluated to see if business objectives are met. In predictive analytics, all the evaluation tasks to demonstrate that a prediction model will be able to make accurate predictions, is evaluated at this phase.


7. Deployment

Integration of the solution to the organization's environment and into the organization's processes.

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