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Indusa (now Synoptek)

Intelligent Business Processing using Dynamics 365 for Operations and Azure Machine Learning


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Imagine if you could predict what your customers are going to buy on their next shopping spree. Or have insight into how frequently a particular item is purchased. While automated business processing has long resulted in efficient business outcomes, trends in intelligent business processing is what is defining the future of technology. Leveraging the best of Dynamics 365 for Operations and Azure Machine Learning can help in bringing consistency in your business processes and improving productivity in the long run.

 

Keeping Pace in a Dynamic World

The variety, volume, and velocity at which data is being generated today is massive. Adopting the right set of technologies that have the capability to work with such humongous amounts of data is a challenge many enterprises are struggling with. As the business environment and customer needs change continuously, Azure Machine Learning provides all the answers you need (in real-time and at a fraction of the cost). Instead of depending on a system that makes rule-based decisions, you can now make critical business decisions using advanced analytics and big data technologies in the cloud, while leveraging various data sources, including public data and the computing power of the cloud.

  • Built-in Algorithms: With every successful new application being built as an intelligent application, Machine Learning is soon going to be the foundation of every app. Every technology, gadget, and app will soon be managed by algorithms and data, making Azure a major contributor in defining the next generation of applications and business processes. Azure Machine Learning helps you stay competitive, enabling you to step up to the growing business opportunities enabled by algorithms.
  • Intelligence Personified: The new era of intelligent applications is a combination of radical new hardware, massive amounts of data, and unprecedented advances in deep neural networks. Azure, through the use of intelligent business analytics, is set to reinvent, digitize, and eliminate a large number of redundant, time-consuming, and non-essential processes. Gartner predicts, by 2020, Machine Learning will eliminate 80% of business processes and products from a decade earlier.
  • Easy to Deploy: Being cloud-based, Azure Machine Learning is easier to deploy and far more affordable than traditional on-premises enterprise solutions. You can quickly analyze large, complex data, and deliver faster, more accurate insights without the high cost of deploying and maintaining complex machine learning systems. Integration of Azure with Dynamics 365 for Operations further makes predictive analytics easier, more affordable, and an integral part of business processes.

 

How Azure Machine Learning Does It

So how does Azure do this? With enterprises all over the world using Dynamics 365 for Operations to manage their day-to-day operations, Azure uses the data stored in the Dynamics database to perform intelligent processing; data from a Dynamics database is put into a SQL database for transformation and processing, which is then sent to Azure Machine Learning for prediction. The prediction results are then received back in the analytics database and the reprocessed data is then made available in your Dynamics application.

Azure builds predictive models using simple and inexpensive tools that can be developed, tested, deployed and consumed without any up-front investment. These models aid in several critical decision points, helping you stay engaged and continuously develop newer models to answer deeper questions about your business. The flexibility of deploying a predictive model and the ease with which it can be modified using Azure makes for intelligent business processing within a matter of a few hours.

 

Predictive Forecasting for Success

The robust amalgamation of big data, algorithmic intelligence, and cloud hosted intelligence is causing a paradigm shift in the way businesses function (and customers consume products and services).  For example, Indusa implemented a predictive demand forecasting model for a manufacturing client in Ohio. Using a time-series algorithm, Indusa sourced data from their ERP and CRM, as well as other databases, took into account seasonality, and used machine learning to accurately predict sales and revenue.

With Dynamics 365 for Operations on the Azure platform, you no longer have to manage bulky infrastructure or implement complex code. The cloud based system can scale and generate new models on the fly, delivering faster, more accurate results. Make good use of the increasing amounts of data (decreasing costs of processing data), create better machine learning models, and enable predictive forecasting to achieve success.

 

To learn more about the symbiotic relationship between Dynamics 365 for Operations and Azure, read our ebook on the latest release of Dynamics AX - Dynamics 365 for Operations.

 

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