Internet of Things
IoT services focus on Smart Sensors, M2M, Cloud, Analytics and Mobility. The convergence of these technologies presents a new paradigm in the way products and services are conceived and delivered to customers. We have developed a deep understanding of various initiatives in the IoT space from an Engineering Product Design aspect.
Devices, Sensors, Gateways
Industrial design for devices and sensors – enclosure design, branding and mechanical detailing
Device, sensor, gateway hardware, system and application development
Communications integration and testing(cellular, satellite, LoRaWAN, BT, Zigbee, wifi, etc)
Cloud platform
Connectivity, device management
Big data, network management
Analysis and prediction models
Hosted services
Applications
Service/workflow design
Mobile/web-based app development
User experience and UI design
DATA ANALYSIS AND AI/ML SERVICES
A “parallel universe” of new data, and new information sources are fast emerging all around us as a result of technological advancements and the changes they have caused in practical daily life. Regardless of how it is defined, the phenomena of big data is becoming more prevalent, relevant, and widespread.
Big Data has a tonne of value potential, including new perspectives, better knowledge of issues, and numerous chances to anticipate and even influence the future. Discovering and utilising that potential is primarily accomplished through data science. Data Science offers methods for utilising and managing Big Data, including strategies to recognise trends, identify links, and make sense of astonishingly diverse data and images.
Organizations now, have far more chances than ever before to capitalise on their most precious asset—information—to generate new value and gain a competitive edge. Big Data helps organisations create more efficient, high-quality, and tailored goods and services that increase consumer satisfaction and revenue. Big Data analytics open up new research directions for scientific endeavours, potentially yielding richer findings and more insightful conclusions than were previously possible. Big Data analytics frequently combine organised and unstructured data with real-time feeds and queries, creating new opportunities for innovation and understanding.
Types of analytics:
- Diagnostic Analysis
- Predictive Analysis
- Preventive Analysis
- Textual Analysis
- Statistical Analysis
Data Analysis Applications:
Field | Data Analysis Type | How Data Analysis can be utilized? | Method |
Education | Descriptive Data Analytics Model | Enhancing the use of the online educational platform | Analyzing data captured from online educational platforms can ease educators remote leaning experience. |
Healthcare | Diagnostic and Predictive Data Analytics Models | Vaccine distribution | Machine learning models to prioritize the citizens’ need and urgency to the vaccine. |
Banking | Diagnostic and Predictive Data Analytics Models | Risk Assessment | Use predictive and diagnostic data analytics algorithms to examine real-time data to determine a customer’s creditworthiness. Thus, creating a customer portfolio that is acceptable and customising services to meet client needs. Consequentially increasing consumer satisfaction and loyalty while improving banks’ bottom lines. |
Agriculture | Diagnostic and Predictive Data Analytics Models | Precision farming | By finding methods to specifically adapt planting, fertilising, irrigation, and pesticide applications to the geographical and temporal micro-climates that occur on each farm, precision agriculture seeks to boost agricultural management efficiency. |
Logistics | Diagnostic and Predictive Data Analytics Models | Delivery | Logistics delivery firms are able to determine the finest shipping routes, the quickest turnaround times, and the most economical modes of transportation by using data analytics applications. |
Retail | Diagnostic and Predictive Data Analytics Models | Smart retail | Big data can be used by retailers for a variety of tasks, such as managing inventories, making product suggestions, monitoring customer demographics, and monitoring and managing the effects of product recalls. |