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Programming Console

Information Management

Infobahn considers the following foundational aspects of Information Management as critical for any organization:


  • Data Management

  • Big Data & Handling of Structured/Unstructured Data 

  • Business Intelligence & Data Visualization

  • Data Integration

  • Advanced Analytics


Data Management

The overall data strategy and architecture is being pushed to become more “service” oriented and include more reusable data objects The ability to “prioritize” down to a specific need for an “analytics” question places a new lens on data quality and how it is addressed. Data Governance and Metadata Management is driven by the business demanding more and more access to data that they can “explore” and look around in for patterns. Data security and privacy is becoming more imperative with the landscape of modern data platforms. Infobahn can help you with your data management challenges.



Enterprise Data Management
Data Warehouse Design & Architecture
Data Quality Solutions
Data Governance
Metadata Management
Data Security & Privacy


Big Data

The amount of data used by the federal government continues to grow. New information channels such as Open Government, social networking and media, mobile devices, and private sector data collected to fulfill regulatory responsibilities, have led to Big Data presenting a powerful opportunity for the government to achieve cost optimization as well as leverage advanced analytics to discover the hidden insights. Big Data provides agencies and organizations with the ability to reduce costs and drive competitive advantage by collecting and analyzing previously unusable data to generate significant results and capitalize on actionable outcomes.


Infobahn will help your organization address challenges around Big Data and address the following questions:

  • What levels of availability and reliability are possible in mission-critical applications with large data volumes?

  • How can your current IT skill sets best be leveraged in evolving the infrastructure to include Big Data?

  • How can security and privacy concerns be factored into the design of a Big Data environment to reduce vulnerability to threats?

  • What data governance is appropriate when analysis is distributed, needs change, and definitions and schemas evolve over time?

  • How can non-traditional unstructured data be integrated with data stored in traditional transactional systems?

  • With a complex environment, how do we develop a strategy for integration and deployment?

  • With so much data, what are the right questions to gain additional insight that adds value?

  • How do we protect the quality of unstructured data, and with an increasing volume of data, what data do we keep?


Big Data Strategy & Architecture

Big Data Development

Big Data - Data Integration using Sqoop, Flume, Kafka, Pig, Hive, Spark

Big Data Analytics

Big Data Integration with Business Intelligence Tools

Data Security & Privacy


Business Intelligence & Data Visualization

Infobahn's Business Intelligence (BI) and Data Visualization (DV) services help you create the right metrics and obtain the appropriate visualization for the same. We offer services around creating dashboards and reports including their underlying data and semantic layer. Our data visualization service simplifies the presentation of your data and helps your numbers tell a better story. New patterns and relationships become visible and help you communicate a clear and coherent story to your audience. Business Intelligence (BI) and Data Visualization (DV) address these challenges effectively by tracking metrics and making data more contextual by helping visualize it and benchmark it.



Extend existing enterprise software implementations with BI platforms

Establish appropriate data management foundation for BI solution

Implement BI tools on Big Data

Prototype Dashboards & Scorecards with key metrics

Develop Ad hoc Reporting framework including Reporting & BI security

Develop Mobile BI solutions


Data Integration

We have expertise in the latest ETL tools including Big Data tools like Flume & Kafka to build real time data pipelines that allow us to rapidly build high quality jobs significantly reducing the build and maintenance costs for our clients. We leverage the efficiencies of a standardized Data Integration Framework applicable for both Relational Databases & Big Data that allows us to rapidly develop data integration solutions.



Data Integration Strategy and Roadmap
Data Quality Audit and Risk Assessment
Data Integration Development on various tools i.e. Informatica, AbInitio, DataStage
Data Integration on Big Data stack i.e. Sqoop, Flume, Kafka, HIVE, Spark


Decades of investment in large data warehouses and data marts have left organizations facing an avalanche of collected data and an aging infrastructure. The recent economic crisis, the increased flux in the regulatory environment and cost pressures are exasperating the need for decisions and actions rooted in insights from advanced analytics on previously untapped data sources, such as sensors, video, text documents, images, and social media. Federal clients are facing a growing number of challenges with the proliferation of Big Data while looking for Advanced Analytics capabilities and a Big Data perspective to tackle these new data needs.

Infobahn works with organizations and incorporates analytics into every aspect of their business to help our clients deliver on their mission and leverage our breadth and depth of knowledge in Analytics. We are passionate about leveraging analytics to empower data driven impacts for our clients. Our analytics advisors deliver quick results and use methodologies and solutions designed to scale over time.

Infobahn’s solutions provide the agility required for analytics and ensure that business decisions derived from the analytics solution can be trusted. This approach allows data sources and analytical models to be flexibly added as required by power users and data scientists. However, flexibility does not imply unmanaged data. Policies, standards, and procedures are used to ensure that data sources are trustworthy, timely, and meet minimal data standards to make the data source useful for analysis.

As models and solution prototypes mature and are deemed enterprise worthy, governance controls are tightened with more rigorous standards and policies around data standardization, automated transformation, metadata for both data and analytic models, and validation for production.



Strategy Advanced Algorithms and Modeling Techniques
Structured and Unstructured Information Management
Reporting and Visualization
Data Mining & Predictive Analytics
Machine Learning
Advanced Analytics Platform Evaluation
Advanced Analytics on Big Data
Risk & Fraud Analytics

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