Big Data Analytics Solutions & Services for data-driven businesses

Datamatics Big Data & Analytics practice helps build intelligent solutions for data-driven businesses. Datamatics offers data management, business intelligence, data visualization, advanced analytics and artificial intelligence solutions with ‘Intelligence-First’ principle at the core and advocates true ‘Data Democracy’.

Datamatics Big Data & Analytics practice enables companies to work with high volume multi-structured data including, structured, semi-structured, and unstructured by using Big Data and Analytics solutions.

With domain expertise in Banking & Financial Services, Insurance, Finance & Accounting, and Research & Analytics across industries, the company enables businesses to store, manage, and monetize data with actionable insights. With strategic partnerships with vendors in the cloud and data storage, and data management space, Datamatics offers solutions across the data lifecycle.

Datamatics Big Data & Analytics practice enables businesses augment large and diverse data sets to discover specific patterns, correlate data points, and uncover hidden insights. With the most prevalent use in consumer behaviour analytics, predictive analytics, and Artificial Intelligence / Machine Learning enabled forensic data research, Datamatics enables businesses to scale the next level.

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What is Big Data?

Big Data refers to extremely high volume datasets that require special storage platforms and processing tools. Given the extremely huge data size and data variations by the minute, Big Data can be analysed over a time series to discover trends as well as unearth patterns and associations.

What is Big Data Analytics?

Big Data Analytics is the use of specific advanced analytics techniques on big data or extremely high volume dataset to generate business insights. It starts with data collection through pre-defined templates and forms, followed by data curation, data processing, through data analysis. It allows to process structured, unstructured, and semi-structured data, join the dots to see the bigger picture, and to take data-driven, informed decisions.

How is Big Data Analytics facilitated with Data Lakes?

Data Lakes are huge, centralized data reservoirs, which allow to store structured, unstructured, and semi-structured data at scale in As-Is format and from where different analytics programs, machine learning sequences, visualizations, and dashboards can be launched for better business decision making.

How is Big Data Analytics leveraged with Visualization tools, such as TruBI?

Big Data Analytics taps into huge data reservoirs to generate business insights. Visualization tools, such as TruBI, allow users to work with high data volumes and thousands of concurrent users to graphically represent data and bring out the underlying data story. The interactive visual elements allow users to understand the trends and patterns in the data and pinpoint the outliers.

How is Big Data Analytics leveraged further with Artificial Intelligence / Machine Learning (TruAI)?

Big Data and Big Data Analytics provide the vast datasets and analyses for TruAI, in specific, or AI / ML algorithms, in general, to learn from and generate sharper insights, which are not evident from slicing-dicing and analytics.

Big Data Analytics and AI/ML are symbiotic. AI/ML learns from Big Data and Big Data Analytics to get better at unsupervised learning and decision making. While Big Data Analytics leverages AI/ML to offer vivid analyses and better decision making power.

The combination of Big Data Analytics and AI/ML can be used holistically in all data-intensive sectors, such as Banking, Financial Services, and Insurance (BFSI), Healthcare, Communication, Media, and Entertainment (CME), Education Technology, Manufacturing & Supply Chain Management, Energy & Natural Resources, and Government & Public Sector Units.

How does Big Data Analytics over the Cloud benefit data-intensive practices, such as Research & Analytics?

Big Data Analytics over the Cloud offers the most benefits for data-intensive practices, such as Research & Analytics. It offers a scalable digital platform for parallel data processing, real-time analytics, stream data processing, context-sensitive analysis, inter-geography collaboration, and operational cost reduction.