Analytics360 – Next Gen Analytics Accelerator!


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.

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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.

Value Proposition

Cloud Computinng2 IcnMultiple domain and business cases understanding
Cloud Computinng2 IcnExpertise in hardware and software (immersive technologies & application development)
Cloud Computinng2 IcnExpertise in IoT, Data Science, Digital Twin, AI & ML
Cloud Computinng IcnIoT experience enablement through mobile apps and AR/VR
Cloud ComputingProprietary digital accelerators in the form of Analytics360, PraEdge, MFGSuite & Anuva

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Frequently Asked Questions

What Is Renewable Energy Analytics and How can Businesses Benefit?

Energy analytics is essentially the process of applying advanced analytical models to energy data to deliver insights. Renewable energy analytics is essentially energy analytics done for the renewable sector. Renewable energy analytics software can help the businesses with below:

  • Provide valuable insights into your energy data,
  • Improve energy efficiency and reduce energy costs,
  • Streamline energy consumption of your critical assets,
  • Helps in saving costs by optimizing energy consumption

What is Golden Batch in Manufacturing?

Batch processes have always been difficult to control and analyse because each and every batch in a process manufacturing ecosystem is unique. But batch analytics software can help. Manufacturers use batch analytics software to compare batches to help uncover potential problems in real time. The most ideal batch produced is then referred as the ‘golden’ batch. Golden batch analytics thus helps in determining what is working well and what needs to be improved. Golden batch analytics software aids in predicting quality parameters, identifying variables that are affecting the process, and detecting faults early in the process.

Why is Discrete Analytics Important in Manufacturing?

For the machine shop floor, high availability and performance are essential to deliver orders early. The key to optimizing the assembly flow is discrete analytics that helps in gaining visibility into the data and analytics of a complex process. For manufacturers seeking to improve capacity and throughput in discrete manufacturing, they’ll need metrics on all the moving parts of the assembly line using discrete analytics:

  • What is the availability of machines throughout the line?
  • Why is that machine down?
  • What is our capacity and throughput?
  • Where is the bottleneck?
  • What is our scrap rate?
  • Are we on track to hit our production target deliver finished product on time?

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