Data Analytics & AI Solutions
We focus your insights to power strategic decisions
A Business Intelligence, Data Analytics & AI Consulting Company
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 Consult, Design, Ingest, Transform and Store Data Ecosystems
Data consolidation continues to be a challenge to many companies. We consult, design, implement and support, Data Ingestion and Transformation, 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 on Azure Synapse, Azure Data Factory, Databricks, Microsoft Fabric, Purview and Microsoft Power BI implementation. We work with our clients to connect to different data sources, build secure data models and implement Business Intelligent Visualizations, Descriptive and Predictive Analytics with Self-Service Reporting capabilities..
Talk to us today to help you choose the kind of cloud analytics architecture based on your users, solution type, pricing and TCO. We help you unify your data estate and position your company for AI.
We specialize in Inventory Management Descriptive and Predictive Analytics, Human Resources, Sales & Marketing and Sustainability Solutions.
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.
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.