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Saama Technologies: Comprehensive Big Data Solutions

Saama Technologies: Comprehensive Big Data Solutions

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Suresh Katta,Founder & CEO

Suresh Katta

Founder & CEO

Today, the proliferation of data in terms of volume, variety and velocity has reached an unprecedented level. With the world's data volume expected to grow 40 percent per year, organizations are presently focused on deriving value from big data analytics projects. In such a situation, it is very essential to get the right data and analytics to derive desirable outcomes. In order to be data-driven, firms are working with raw data from both external and internal sources, and CIOs are seeking the right infrastructure for their respective organizations to store data, analyze them, and the appropriate data science expertise to generate insights. Since its inception in 1997, Saama, a company offering big data solutions is delivering its analytics advantage to clients. Saama has been turning raw data into actionable insights to enable enterprise leaders make timely and reliable business decisions. "Our analytics acceleration engine delivers rapid speed to value and specificity of distinct, strategic business outcomes for our customers," says Suresh Katta,
Founder and CEO, Saama.

Transcending beyond traditional Business Intelligence (BI) analytics, Saama Fluid Analytics Engine offers a backbone of core technologies upon which solutions catering to specific industry needs can be built within three months. The platform democratizes data science and enhances development of data solutions that help businesses convert traditional BI predictive insights into advanced analytics prescriptive actions. The Fluid Analytics software also abides by a declarative approach to separate design and runtime aspects and offer extensibility with loose coupling. This enables users to seamlessly combine existing and new data resources of their company with the Fluid Analytics Engine runtime orchestration environment, solution accelerators data models, and data science expertise. As a result, company specific-results can be realized at a rapid pace.

Besides, the tool integrates with external systems using prebuilt connectors to unstructured text, databases, flat files, APIs, Web Services, and ETL. Its loosely coupled architecture provides maximum flexibility to use different technologies at each layer and also leverages the existing customer investments. The prebuilt machine learning non-algebraic and algebraic models come with advanced visualizations and data management. This approach eliminates the requirement for expensive analytics engines and enables the most valuable data science resources to focus on solving core business problems.
Alongside, the solution maximizes existing customer infrastructure, allowing users to focus on the white space between critical business questions that need to be answered and the existing capabilities.

Saama has vivid experience in multiple projects like visualization, MDM, Hadoop, cloud, and other advanced analytics solutions, in industries such as life sciences, insurance, healthcare, financial services, CPG, high-tech and media. In an instance, Saama built a customized Patient Experience Insights Solution for the regional hospital management organization, to fulfill the client's data and analytical needs. Saama's comprehensive, strategic big data solution helped the customer to identify the key strategic insights, priority patient concerns, and optimal strategy for each hospital's unique demographic base. The solution, built upon Saama Analytics Framework, uses big data analytics, natural language processing (NLP), and scoring algorithms to develop an innovative Patient Experience Insights Solution to improve the patient experience.

Saama has recently enhanced Fluid Analytics, for Life Sciences industry-specific orchestration stack, to bring its proven services methodology and technology for rapidly orchestrating and analyze large volumes of data to the life sciences industry. Moving ahead, the company is planning to elevate its existing solutions to solve the problems of disparate data in various sectors.