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Best Practices in Data Analytics


Today the average agency within the Federal Government manages dozens, if not hundreds, of data sources and services that drive their mission functions. These data sources are of variable pedigree, quality, and efficacy and are frequently developed in silos presenting substantial challenges. The nature of the data that the Government leverages is becoming increasingly complex with the increased use of geospatial data, large sets of unstructured data as well as streaming data from sources such as Internet-of-Things (IoT) devices. Regardless of these challenges, Government agencies must seek new ways to exploit data to support their missions and improve the return on investment for taxpayers. Effective Data Analytics is crucial to extracting maximum value of information.

Data Analytics is the discipline of identifying, extracting, cleansing, transforming, mining, and visualizing data for valuable information that helps leadership make critical decisions. The effective employment of Data Analytics provides agencies with high-value data regarding their organization, their partners, and their stakeholders. It also helps reduce costs by decreasing the need to build potentially redundant systems that contain data which already exists in authoritative sources but may not have been identified.


Our Advanced Data Analytics Framework provides an extremely agile approach to discovering, analyzing, and leveraging data – including innovative approaches for data analytics, predictive analytics, and sentiment analysis. Our comprehensive framework focuses on the following best practices:

  1. Goals and Metrics – Establishing goals prior to engaging in data analysis is essential to defining the right metrics and for keeping data analysis efforts on target. Similarly, knowing the target metrics keeps the team focused and helps avoid scope creep.

  2. Data Pedigree – Understanding the pedigree of your data sources is essential to ensure you are accessing the most authoritative and reliable data possible.

  3. Data Virtualization – Data Virtualization uses data in place rather than costly and error-prone file import/export. Using data virtualization wherever possible reduces the need for expensive and time-consuming data loading, thereby reducing costs and data latency.

  4. Service Level Agreements (SLA) – Establishing SLAs is an essential element in data analytics to ensure external and partner-owned data sources are maintained with appropriate levels of availability, reliability, data latency, and quality.

  5. Agile Analysis – Agile software engineering practices are ideal for data analytics because they promote early prototyping of data followed by increasing refinement. By presenting data to the users early they can help shape the discovery and analysis of additional data. The use of wireframes can drastically improve the efficacy and usefulness of visualizations.

  6. Self Service – Effective data analytics solutions provide the right data services and visualization capabilities which enable users to derive answers on demand.

  7. Microservices – Use of highly performant, rapidly developed microservices based on open standards such as Representational State Transfer (REST) and JavaScript Object Notation (JSON).

  8. Security – Data security is essential for protecting organizational assets and privacy and reducing organizational vulnerability to hacking attempts.

Our leading-edge framework delivers the following key features:

  1. Rapid delivery of initial data capabilities via our DevSecOps framework

  2. Lowest possible data latency with ideal pedigrees

  3. Accelerated delivery and deployment of data services

  4. Extensive application of open data standards such as microservices and service bus technology

  5. Expertise in advanced data analytics tools such as Cloudera Hadoop, Pentaho, and numerous open source visualization technologies

  6. Cross-database platform support including Oracle, Microsoft SQL Server, IBM DB-2, Amazon RDS, and most leading platforms

  7. Self-service Business Intelligence using leading-edge solutions including Tableau and QlikView

  8. Advanced analytics using SAS, Cognos BI, Business Objects, and the R programming language

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