The tremendous impact of IoT is seen as the next big technology revolution. This radical technology will help humans and machines interact with each other, without the need for a computer interface. Think of a situation where you are commuting home from work, and tell the A/C at home to cool the room before you arrive Or imagine the refrigerator examining the contents within it and alerting you when the butter is finished and needs to be restocked. That’s the power of IoT for you.
Our Experience with IoT and Big Data
While the above scenarios were for a single household, IoT has an even greater role to play in enterprise level settings (think global pharma industry, healthcare, IT to name a few). We anticipate huge volume of machine generated data to emerge from IoT. This machine data (or sensor data) needs to be collected and analyzed in a strategic way to ensure insightful decision making. This is precisely where IoT will blend with Big Data.
By employing Big Data, organizations not only can collect humongous volume of data, but also gain from an intelligence platform, where one can gather, manage and analyze this scale of data in a cost-effective and scalable way.
Two key tools that help in reliable and time-efficient Big Data Analytics include Hadoop and MongoDB. Hadoop is an Open Source framework that facilitates distributed processing of large data sets using a simple programming model. MongoDB is an Open Source distributed database from NoSQL. The systems differ in their underlying architecture. While Hadoop is focused on Data Analytics, MongoDB is helpful for efficient storage and retrieval of data.
Our Analysis of MongoDB and Hadoop
These are some of the functional differences we see between the two systems.
| MONGO DB | Hadoop |
| Online | Offline |
| Real-time | Consumes processing time |
| Low latency | High latency |
| High availability | Availability is lower priority |
| Light-weight analytical workloads | More powerful analytical functions |
Hadoop provides immense value in the below use cases:
• Risk Modeling
• Churn Analysis
• Recommendation
• Engine
• Ad Targeting
• Transaction Analysis
• Trade Surveillance
• Network Failure
• Prediction
• Search Quality
• Data Lake
On the other hand, MongoDB is useful in implementing the below:
• 360 Degree View of the Customer
• Mobile & Social Apps
• Fraud Detection
• User Data Management
• Content Management & Delivery
• Reference Data
• Product Catalogs
• Machine to Machine Apps
• Data Hub
Use Cases at CIGNEX
CIGNEX has helped clients achieve multiple levels of efficiency, time saving and cost optimization. Let’s look at two scenarios where CIGNEX employed the power of Big Data Analytics tools to great effect.
1. Letting Agriculture Embrace Technology
A US based GPS provider was looking to integrate technology to streamline farm operations, enhance efficiency, improve everyday planning and decision making and ensure better operational strategy. CIGNEX employed Open Source technology and leveraged the data storage prowess of MongoDB. We enabled the following features on the field activity system:
a. The company was able to track valuable data points from farm land and livestock by combining GPS navigation and carrying out subsequent data analytics
b. Once the data was collected in an easy to comprehend format, it could be shared with key decision makers like farm owners and farm managers with real time information
c. It also ensured seamless interaction and communication between field and farm offices and between multiple vehicles operating in the same field
d. Some of the insights and actionable data included reporting of water management, mapping, accounting and much more
The below were the main benefits derived from the Open Source system:
a. Increased operational efficiency resulting in tremendous cost savings
b. Simplified data management and analysis
c. Improved agricultural performance and productivity
2. Delivering superior workflow to enhance efficiency
A US based GPS provider with solutions footprint in agriculture, scientific instrumentation, transportation, mobile resource management and fleet management utilized CIGNEX’ technology offerings. At the heart of the problem statement was the business need to improve workflows between key stakeholders at different locations to monitor & optimize time, costs, materials, staff and resources. Using a combination of Open Source tools and MongoDB, the following features were integrated into the workflow management system:
a. Integration with machine control & site positioning software using RFID tagging and GPS navigation
b. Connected site to office by enabling real time analysis and reporting of resources
c. Real time and precise fleet tracking
As a result the customer secured the following business benefits:
a. 99% uptime through 24 X 7 tech support
b. Reliable and robust disaster recovery strategy to ensure business continuity
c. Use of MongoDB ensured elastic scaling which allowed 30% cut in infrastructure investments
Connect with CIGNEX to know more on how you can use Big Data to generate real time actionable insights, so that even the smallest window of opportunity can be immediately tracked and reported to the decision makers within your organization.
