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Introduction
IoT and big data are an intimate pair. Global data had grown and become big even before IoT devices started generating their own data.With Gartner’s prediction of 25 billion devices getting connected by 2020, data is expected to grow exponentially every year. Diverse data is generated from different sources such as sensors, wearables, and appliances. While thedata from RDBMS is structured, other types of data from images, emails, and sensors tend to be unstructured.
Internet of things data to top 1.6 zettabytes by 2020, according to a recent forecast from ABI Research
Gartner, Inc. forecasts that 4.9 billion connected things will be in use in 2015, up by 30 percent from 2014, and will reach 25 billion by 2020
According to Aureus Analytics, the world’s data volume is expected to grow 40 percent per year, and 50 times by 2020
Gartner forecasts that about one in five vehicles on the road worldwide will have some form of wireless network connection by 2020, amounting to more than 250 million connected vehicles
TECH analysis Research predicts that wearable units will be more than triple to 175 million by 2020
The data coming in could be uncertain and imprecise. Using big data, a rank could be assigned to specific data chunks based on the level of uncertainty, which could then be factored into the system for each case to handle veracity.
Management and analysis of unwieldy deluge of big data involving both structured and unstructured elements can aid in the strategic decision making in any organization. Enterprises can leverage IoT technology by adopting, processing, and analyzing massive amounts of data generated by connected devices.
The key advantage of big data is that it holds real and useful business insights that can be easily monetized. An organization’s success lies in unearthing these business insights through data analytics technologies.
Big data is characterized by 4 Vs – Volume, Velocity, Variety, and Veracity
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Use Case First
High-LevelMDM View
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Technology Stack
Agile Can Be the
Right Way
Synapt Data Lake
Enterprises are often looking for tools that can improve their business by reducing cost, improving efficiency, and increasing revenue. Synapt Data Lake aids enterprises by developing insights based on their needs and proposing these insights to them, thereby reducing churn and increasing end user retention and loyalty. With the huge relevant data collected by the IoT platform, predict the efficiency of machines, automate the servicing of appliances before they are worn out thereby utilizing manpower for solving more complex problems. With data analytics and patterns, the customers’ behavior can also be predicted for better decision-making.
The Synapt Data Lake has a data streaming engine, which takes the data from different sources and processes structured, unstructured, semi-structured and real-time data. The 6 vertical technical pillars forms the core framework and the data from NO SQL DB is used for Analytics and Reporting.
Big data provides solutions across broad categories and any solution for a use case would fit into one or more of these solution categories.
Prodapt does a due diligence by going through a rough sketch of the use case, understanding the source and type of data in Master Data Management (MDM), implementation of the solution whether on premise or on cloud and then selecting the technology applicable with these parameters. As this is an evolving industry, agile approach is followed.
Prodapt's Approach for Big Data Use Cases
Technology Stack Solution Categories
Historical Trend Analysis
Synapt Data Lake
Analytics and Reporting
Big Data Store
Data Streaming EngineStructured, Unstructured, Semi-structured,
Real-time data
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NO SQL DBReal-time Batch Analysis
Complex Event Processing
Forecasting & Prediction
Enterprise View Consolidation
Mass Personalization & Recommendations
Gather information targeting end users, resulting in increased customer satisfaction and e˜ective sales
Mass personalization:
Storage for the variety of data obfuscating meaningful information, which can be unearthed by data mining and analytics
Easy & secure storage:
Expedites the time to market by building new vertical analytics in the existing infrastructure
Extensible solution:
For analytics, fraud identiÿcation, alerts, etc.
Real-time retrieval of big data:
Integration of predictive analysis and BI tools gives meaningful information to the customers for making the right decisions
Delighted customers:
Helps generate service revenues by monetizing the valuable intelligence gained from big data
Generate service revenues
No need for pre-planned expensive hardware that is usually under- or over-utilized
Scalable solution on commodity hardware:
Includes not only RDBMS data but also unstructured information sources such as logs, system messages, and sensor data by providing real-time or near real-time analytic view of entire enterprise in context
Consolidated enterprise view:
Benefits
ABOUT PRODAPTProdapt is a leading global IT services and operations company focused on telecommunications and IoT. Headquartered in Chennai, India, Prodapt has additional locations in the US, Europe, and South Africa. Prodapt is part of the 120-year-old Indian business conglomerate, the Jhaver Group. The Group employs over 16,500 people across 64 countries. Prodapt is an ISO 9001:2008, ISO 27001:2015, SSAE16, and CMMI Level 3 certified company.
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