Big Data and Analytics
Transforming Data into Decisions
“Big Data Leverages Analytics for Business Insights”
Big Data often helps the organizational or business decisions. The concept of Big Data and Analytics is the complex process of scrutinizing the huge data sets with varied permutations and combinations to discover the data structure, and data relations. Big Data Analytics helps the Organisations to interpret the customer pulse, customer feedback and contributes towards the business decisions. Organizations using Big Data Analytics, are dealing with terabytes of data that is received from different means like and processes to derive a logical equation that contributes to the strategic planning. Big Data overrules the capacity of traditional RDBMS available in the market. As the term says, it is the capacity of Big Data is very huge to manage or process.
Why Big Data is Important?
Volume, Velocity and the variety are main features of Big Data. Fast moving trends of internet operations, usage, Artificial Intelligence, Social Media, and Mobile World are contributing to the intricacy of Data. Various sources that contribute towards Big Data are Chats, Facebook, What’s App, Files, feedback posted on sites, customer reviews, feedback, and images posted on Social Media.
Generally the data in the form of chats, SM conversations, Facebook chats/images and customer feedbacks, views and reviews content is the available data that is in unstructured format. Leverage this unstructured data by applying AI and converting it into meaningful structured data that helps the Organizations to derive best business equations is the capacity of Big Data Analytics.
Benefits of Big Data
Customer Churn Analysis
Health Care Analysis
Big Data Platforms, Frameworks, Components and Tools
Big Data Architecture & Patterns
The Best Way to a solution is to “Split the Problem”. This Architecture helps in designing the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. Secured data flow can be viewed under Big Data Architecture in 6 layers that ensures data security.
Data Ingestion Layer
This layer is the first step for the data coming from variable sources to start its journey. Data here is prioritized and categorized which makes data flow smoothly in further layers.
Data Collector Layer
In this Layer, more focus is on the transportation of data from ingestion layer to rest of data pipeline. It is the Layer, where components are decoupled so that analytic capabilities may begin.
Data Processing Layer
In this primary layer, the focus is to specialize in the data pipeline processing system, or we can say the data we have collected in the previous layer is to be processed in this layer. Here we do some magic with the data to route them to a different destination, classify the data flow and it’s the first point where the analytic may take place.
Data Storage Layer
Storage becomes a challenge when the size of the data you are dealing with, becomes large. Several possible solutions can rescue from such problems. Finding a storage solution is very much important when the size of your data becomes large.
Data Query Layer
Main data analytics processing is done here. The primary focus is to gather the data value so that they are made to be more helpful for the next layer.
Data Visualization Layer
Visualization Layer is the most important layer and this is phase where the users can have the look and feel and important of Data. This provides the view for the end users of the actual VALUE of the Data synthesized.
Silver Touch-Big Data Analytics Services
Big Data Consulting
- Silver Touch assists our customers in defining their big data strategy and selecting appropriate technology tools and processes to achieve the strategic objectives.
- We offer vendor-neutral recommendations that are tailored to customer specific requirements, current technology landscape, preferences, objectives and budget.
Application Development, Infrastructure Set-Up and Systems Integration
- Silver Touch creates and delivers optimum and engaging features to drive user engagement on a single and secure platform. Make scaling easier by processing high-velocity and high-volume transactions and events more efficiently and in a faster time frame.
Maintenance & Support
- We have well versed off-shore team for handling the entire lifecycle of Big Data implementation- Installation, Integration, Configuration and Monitoring of Hadoop Clusters with optimum performance. We provide Big Data solution maintenance and support services addressing all the functional components including Data Provisioning, Data Management and Data Consumption.
- Big Data Analytics allows the business owners, experts and others involved in decision making to churn the unstructured data which was meaning less and a scrap, and by applying the technology landscapes, derive meaningful equations for business decisions.
Boost Your Business with Our Big Data Analytics Solutions
Data & Analytics Strategy
- Quantifiable business outcomes with an
- Agile and Data-Driven approach
- Augment Digital Assets with AI-enabled analytics tools
- 360-degree customer views
- Synthesize and Analyze Data from Ecosystem
- Empower Business Decisions
Big Data Visualization
- Interactive Dashboards
- Real-Time Illustrative Graphs
Frequently Asked Questions
The concept of Big Data and Analytics is the complex process of examining huge data sets with a variety of permutations and combinations to discover the structure of the data and the relationships between the data. Big Data Analytics assists organizations in interpreting customer pulse, customer feedback and contributes to business decisions. Big Data is beyond the capacity of conventional RDBMS available in the marketplace. As the term says, it’s the capacity of big data to manage or deal with.
Big data analytics is the use of advanced analytical techniques over vast and diverse data sets. It includes structured, semi-structured, and unstructured data from several sources and sizes, from terabytes to zettabytes.
- Here is the type of Big data analytics:
Predictive forecasting is an automated forecasting procedure that constantly adjusts forecasts to assist the company expeditiously in identifying new opportunities and risks and improve cost-effectiveness.
Prescriptive (Simulation & Optimization): Prescriptive analytical solutions use optimization technology to resolve complex decisions affecting millions of decision, constraint, and compromise variables.
Descriptive (Business Intelligence and data mining): Descriptive analysis summarizes or describes raw data and turns it into something that can be interpreted by humans.
Diagnostic analytics: Diagnostic analysis is a form of data analysis that relies on descriptive analysis to help you figure out why something happened in the past. Diagnosis is often called root cause analysis.
Variety, Velocity, and Volume they are major attributes of Big Data. Usually, the data in the form of chats, SM conversations, Facebook chats/images, and customer feedback, views, and reviews content is the vacant data that is in an unstructured format. Leveraging this unstructured data by enforced AI and converting it into worthwhile structured data that helps the Organizations to derive the best business equations is the capacity of Big Data Analytics.
– Sentiment Analysis
– Customer Churn Analysis
– Advertisement Analysis
– Predictive Analysis
– Weather Forecasting
– Health Care Analysis