Analytics 360

Overview

Analytics360 accelerator helps companies realise the benefits of analytics and data science at pace and affordably. It delivers a vision, advanced analytics capability and a technical roadmap to drive out long term value from analytics that demonstrates the value of advanced analytics. We have leveraged our Artificial Intelligence and Machine Learning expertise to develop a platform for rapid deployment and digital product development.

Analytics360 is a cloud-based platform that operationalizes machine learning models allowing users to deploy and reuse at scale. Organizations are rapidly adopting advanced analytics to implement data-driven business decisions. As a result, the demand for data science experts is growing. However, there is a huge gap between the demand and supply. The annual demand for data scientists has grown by 12%, surpassing the annual supply of 7% of data science graduates, which will result in a shortage of approximately 250,000 data scientists by 2024. In such a situation, it is better to leverage services of an advanced analytics company who is an expert in Data Engineering, Data Science and Data Visualization.

Components of Analytics360

Analytics360 Info2 Making Your Value Chain copy

Model Training

The two main approaches to model training are batch and real-time.

1. Batch Training

This is the most commonly used model training process, where a ML algorithm is trained in batches (or a batch) on the available data. Once this data is updated or modified, the model can be trained again if needed. This is mostly used in process industries.

2. Real-time Training

Real-time training involves a continuous process of taking in new data and updating the model’s parameters (e.g., the coefficients) to improve its predictive power. This can be achieved with streaming mechanism such as Spark Structured Streaming using StreamingLinearRegressionWithSGD.

Model Deployment

Under Model Deployment, we integrate a machine learning model into an existing production environment to make practical business decisions based on data. Depending on the clients requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data science process. Analytics360 has been developed to be able to accommodate simple and complex use cases with ease.

Model Validation and Model Monitoring

Once a model is deployed into production and providing utility to the business, it is important to validate the performance and continually monitor how well the model is performing. There are several aspects of performance to consider, and each parameter has its own metrics, thresholds, measurements that will have an impact on the overall life cycle of the model. Upon monitoring, the model can further be trained or retrained to improve its performance.

It is important to realize that models change and over time, their performance will decline. Any model that makes a prediction of a possible future event and identifies the root cause leading to that event is bound to change the system on which it is making predictions. This not only changes the data but also results in the model losing accuracy in its predictive power. What is furthermore important is knowing what model performance measurements matter to your business, how quickly the performance of the model is changing, and where to set the threshold to trigger the model retraining process.

Renewable Energy Analytics using Analytics360

  • Apollo™ is a suite of enterprise applications specifically designed to generate actionable closed loop insights for the renewable energy industry.
  • Apollo™ is powered by patented digital twin technology, proven ML models, IoT based KPIs and discover, advanced decision – making engine and closed loop insights.
  • Apollo offers a combined 450+ Key Indicators & 150+ Key Insights into your assets.

Golden Batch Analytics using Analytics360 for Process Manufacturing

  • Analysis for target based and peer-based Batch Production Performance Rating.
  • Analytics on cycle time optimization helps by providing near real-time metrics for the duration of each phase in the process, allowing to determine the amount of dead time in every batch.
  • Analysis for critical process parameters, or recurring issues within boundaries golden reference profile for batch processes.
  • Analysis to identify a golden batch reference profile reference to ideal process segment(s) for calculating statistical process control limits, setting boundaries, and monitoring variables to identify deviations using PCA – Principal Component Analysis.

Discrete360 Analytics using Analytics360 for Discrete Manufacturing

  • Live dashboards for production shop.
  • Production planning, energy, maintenance & quality dashboards.
  • Stakeholder dashboards.
  • Accommodate all types of cyclic/ discrete manufacturing process like Injection Molding, Machine Tools/Machine Centres.
  • Packaging, Stamping and Pressing.

Value Proposition

Cloud Computinng2 Icn

Multiple domain and business cases understanding

Cloud Computinng2 Icn

Expertise in hardware and software (immersive technologies & application development)

Cloud Computinng2 Icn

Expertise in IoT, Data Science, Digital Twin, AI & ML

Cloud Computinng Icn

IoT experience enablement through mobile apps and AR/VR

Cloud Computing

Proprietary digital accelerators in the form of Analytics360, PraEdge, MFGSuite & Anuva

Let’s Engineer Your Digital Journey Today

Get in touch