IoT Analytics is emerging area. It's basically use of sensor data to extract useful and meaningful actionable intelligence.

Challenge of IoT Analytics lies in it's layered approach which involves signal processing, feature extraction , machine learning and building recommendation engine. Besides most of it have to be done locally close to sensors which is known as edge Analytics. Cloud Analytics in Public cloud is used for the purpose of mostly mobility and API integration with ERP and management system.

In Zreyas we have been doing extremely deep cloud and edge based IoT Analytics since 2012. We understand sensor data and how to leaverage them to solve practical problem. We also know how to use edge Analytics effectively for it.

We have worked with Electrial ( Voltage, current, power factor , energy) , mechanical ( vibration, magnetic field , gyroscope), ambients ( temperature, pressure, humidity) and environmental ( SOx, ppm, NOx, COx) signals.

Today, marketing and advertising campaign is totally data driven. Thanks to explosion of mobile apps, email campaign and web tracking, almost every company is having enormous data from saleforce or Google AdWords or their mobile app commerce site.

Aggregating all the data source and then building a quasi real time Predictive Analytic model for new sale, upsale, cross-sale, new prospects, target campaign and then extract a recommendation system for designing marketing campaign is quite a challenging task. However due to emergence of automation in Machine Learning ( known as Auto ML ) and Paas cloud for Analytics implementation either in Amazon and google, such infrastructure for target Marketing and advertisement can be built quite easily and effortleesly. No longer you need to spend hundreds of thousands of dollars to build big data analytics for advanced digital campaign system.

We approach this problem in three stages. In first stage we will scrutinize your data and requirements to do a feasibility analysis. In stage two, we will build data model in either Python or R so that it is easily integrable with Amazon or Google Cloud. In third phase we will do a real time integration with Amazon Lambda or Google cloud.

Today all the CSPs are getting into OTT service layers. On top almost all the OEM vendors are providing IPDR Analytics. However down the line, Predictive Analytics required for fault diagnosis in OSS layer or link that to Customer churn problems in BSS layer is increasingly difficult. Even large CSPs can't do effective data integration. This is more acute for small operators. Zreyas can help them after analyzing the requirements.

Today, marketing and advertising campaign is totally data driven. Thanks to explosion of mobile apps, email campaign and web tracking, almost every company is having enormous data from saleforce or Google AdWords or their mobile app commerce site. Aggregating all the data source and then building a quasi real time Predictive Analytic model for new sale, upsale, cross-sale, new prospects, target campaign and then extract a recommendation system for designing marketing campaign is quite a challenging task. However due to emergence of automation in Machine Learning ( known as Auto ML ) and Paas cloud for Analytics implementation either in Amazon and google, such infrastructure for target Marketing and advertisement can be built quite easily and effortleesly. No longer you need to spend hundreds of thousands of dollars to build big data analytics for advanced digital campaign system. We approach this problem in three stages. In first stage we will scrutinize your data and requirements to do a feasibility analysis. In stage two, we will build data model in either Python or R so that it is easily integrable with Amazon or Google Cloud. In third phase we will do a real time integration with Amazon Lambda or Google cloud.

Today, marketing and advertising campaign is totally data driven. Thanks to explosion of mobile apps, email campaign and web tracking, almost every company is having enormous data from saleforce or Google AdWords or their mobile app commerce site. Aggregating all the data source and then building a quasi real time Predictive Analytic model for new sale, upsale, cross-sale, new prospects, target campaign and then extract a recommendation system for designing marketing campaign is quite a challenging task. However due to emergence of automation in Machine Learning ( known as Auto ML ) and Paas cloud for Analytics implementation either in Amazon and google, such infrastructure for target Marketing and advertisement can be built quite easily and effortleesly. No longer you need to spend hundreds of thousands of dollars to build big data analytics for advanced digital campaign system. We approach this problem in three stages. In first stage we will scrutinize your data and requirements to do a feasibility analysis. In stage two, we will build data model in either Python or R so that it is easily integrable with Amazon or Google Cloud. In third phase we will do a real time integration with Amazon Lambda or Google cloud.

Today, marketing and advertising campaign is totally data driven. Thanks to explosion of mobile apps, email campaign and web tracking, almost every company is having enormous data from saleforce or Google AdWords or their mobile app commerce site. Aggregating all the data source and then building a quasi real time Predictive Analytic model for new sale, upsale, cross-sale, new prospects, target campaign and then extract a recommendation system for designing marketing campaign is quite a challenging task. However due to emergence of automation in Machine Learning ( known as Auto ML ) and Paas cloud for Analytics implementation either in Amazon and google, such infrastructure for target Marketing and advertisement can be built quite easily and effortleesly. No longer you need to spend hundreds of thousands of dollars to build big data analytics for advanced digital campaign system. We approach this problem in three stages. In first stage we will scrutinize your data and requirements to do a feasibility analysis. In stage two, we will build data model in either Python or R so that it is easily integrable with Amazon or Google Cloud. In third phase we will do a real time integration with Amazon Lambda or Google cloud.

Aggregating all the data source and then building a quasi real time Predictive Analytic model for new sale, upsale, cross-sale, new prospects, target campaign and then extract a recommendation system for designing marketing campaign is quite a challenging task. However due to emergence of automation in Machine Learning ( known as Auto ML ) and Paas cloud for Analytics implementation either in Amazon and google, such infrastructure for target Marketing and advertisement can be built quite easily and effortleesly. No longer you need to spend hundreds of thousands of dollars to build big data analytics for advanced digital campaign system. We approach this problem in three stages. In first stage we will scrutinize your data and requirements to do a feasibility analysis. In stage two, we will build data model in either Python or R so that it is easily integrable with Amazon or Google Cloud. In third phase we will do a real time integration with Amazon Lambda or Google cloud.