A Roadmap for Building a Robust Data Analytics Environment

On Point3 minutes readJun 26th, 2013
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Organizations of all sizes rely on Hadoop implementations to derive business insights from their data. Our experience building large and complex data analytics for the Federal government and private companies shows that this “Haddop-la” effect is misguided and can potentially drive precious resources in the wrong direction.

Below are nine steps to build a flexible, scalable and reliable data analytics environment, tightly integrated to your existing data architecture. These steps ensure a roadmap for data analytics and provide critical business insights to drive efficiencies in any agency.

  1. Work with business leaders to understand and quantify your data value chain – To extract vital insight from your data, it is essential to work with stakeholders and understand the business value associated with it. The implementation of an encompassing enterprise data management strategy is required to achieve this.
  2. View data as an enterprise asset – No longer is technology the driving force in an IT environment. Information – the “I” – now drives future business decisions, not the technology. As data becomes a fundamental asset of your enterprise, agencies need to extract the most pertinent information out of existing data to create new value in their enterprise.
  3. Innovate through the creation of new data products and services – Convert data to information through the creation of data products. Data products provide users with the ultimate tool to improve current business processes, identify hidden efficiencies, and create new revenue channels.
  4. Retrain staff and/or acquire data scientist skills – In order to create powerful data products, data scientists – not just the typical IT staff – are necessary to ingest, analyze and predict the future. Data scientists are also versed in data model creation and pattern recognition.
  5. Integrate teams across big data, data warehousing, and business analysis – Data scientists need to integrate into your existing data environment by executing your data warehousing and business intelligence strategies.
  6. Revise information management strategies to incorporate big data – Take a step back here to reformulate your information management strategy to encompass new tools and technology that will help drive the analytic process.
  7. Develop new ways of capturing information e.g., mobile and streaming data – Providing a flexible and scalable environment to support mobile devices and the streaming of data from multiple sensors is critical. The “Internet of All Things” creates a massive amount of data and great data stream opportunities for your agency.
  8. Identify and leverage previously unused internal and external data – Data that has not been utilized for various reasons should be brought up and linked to new data sets. As the social matrix continues expanding, data obtained will help accelerate knowledge sharing and faster response times.
  9. Automating Knowledge Work – Use learning algorithms to search for essential information and find patterns of meaning at superhuman speeds. Retraining your work force to effectively use critical new pieces of data is also an essential step in maximizing a stellar data analytics environment.

Tags-   Data analytics Data products Date warehousing