Data Visualization

Data Visualization Services

Extract, Transform & Load

We combine your data from multiple systems into a single database, data warehouse or data lake through data pipeline establishment that can handle continuous streams of data in real time or in aggregated batches.

Self Service Dashboards

We help you leverage the packaged BI tools and empower your business users to easily build their own dashboards with no-code approach that can run high-performance queries against large volumes of data.

Open-Source JavaScript Charts

We design lightweight, robust yet economical JavaScript charting solutions that can be easily embedded into your web and mobile applications with full freedom of customizations and non-standard designs.

Data Warehouse

We build a central data repository of information that can be easily analyzed for more informed decisions with automated data pipelines that connects transactional systems, relational DBs and third-party sources.

Data Mining

We find the patterns, trends, anomalies that can help you draw predictive business conclusions by exploring large blocks of information, identify logical co-relations, verify associations and sequential examples.

Analytics Portals

We develop data driven analytics platforms with the blend of cloud and open-source technologies, obtained by the injection of OLTP & OLAP capabilities in the cloud native portals driven by user experience.

Pillars of Data Visualization

Value

Data needs to be accurate and actionable to provide value to the business.

Structure

Data requires to have a hierarchy, segmentation, relationships, compliance

Connectivity

Data shall flow into and across your different processes and enterprise systems

Coverage

Data shall cover your business entities, divisions, locations

Quality

Data requires validation, correction, transformation, and classification

Customer FAQs & Our Responses

We haven’t set up the right data infrastructure. How can you implement Business Intelligence from the ground up?

Our data consultants analyze your existing architecture and gather details on ideal scalability, performance, and security, then help you adopt the suitable foundational shift following below practices.

Build organization analytical data storage (database, data warehouses,
data marts) following CAP theorem
Devise an analytical data storage schema based on both your organization data and BI demands
Discuss with business stakeholders and decide the KPIs, Metrics and Formulas
Set up data visualization tools on top of analytical data storage
Develop periodic reports and self-service dashboards

Our organization data is not cleaned and formatted. How do you normalize the data and make trustworthy insights?

We believe in Garbage In, Garbage Out! Hence, we begin the engagement with data normalization activities like data de-duplication, update incorrect information, erasing irrelevant information and fix syntactical errors to improve the accuracy of datasets before implementing data driven insights.

How do you ensure the integrity of the data is not compromised?

We believe compromising on data integrity & security can lead to data manipulation & poor decision-making. Hence, we add the ethical practices below to maintain your data’s reliability, consistency, and accuracy.

Implementing an immutable log & monitoring mechanism
Locking, securing & encrypting the sensitive information
Automating the backup and disaster recovery controls
Validating the data quality & compatibility using scripts
Organizing the files, storage & systems for easy auditing

What is the modern data stack you used recently?

Data Visualization – PowerBI, Tableau, Cognos, D3JS, Highcharts
Serverless data platforms – AWS, Azure, GCP
Messaging platforms – Apache Kafka
Streaming processing and analytics solutions – Apache Kafka Streaming, Apache Flume, Apache Storm, Apache Spark
Alerting Platforms – Splunk, Graphite
Containerized data solutions – Kubernetes