Improve Demand Forecasting

Improve Demand Forecasting

The Goal: Your business teams need to accurately forecast demand for your products and services. Accurate forecasting is fundamental to supply chain management, as well as to sales and operations planning for the entire organization. 

 

The Requirements: Your forecasting models, especially those used for predicting short-term demand, need to have enough detail and granularity on the purchasing patterns of the various segments within the market. You can improve the accuracy of your demand forecasts by employing different models and drawing data from a variety of sources, including from partners, customers and suppliers.

 

The Challenge: Data prized by demand forecasters is highly sensitive because its derived from important value-producing activity. Predictive analytics require vast amounts of diverse raw datasets, rather than aggregates or summaries. Gathering data from multiple owners for demand forecasting requires that each owner can control how and by whom the data is used. 

 

The Problems:  Conventional systems can be configured to allow users to expose or change data owned by another party.  If data Owner A shares raw data with Owner B, using a conventional system, then Owner B can use the combined data sets in violation of Owner A’s rules.  Legal agreements cannot be relied upon to prevent data misuse. Most data sharing projects get stuck as a result.  Reliance on 3rd party services do not provide the capability for self-service analytics, and do not provide the data quality and transparency required to ensure the analytics are generating accurate results.

 

The Myngl Concordance Solution: Secure Analytic Containers work differently than conventional systems which allow users to view or change data from another owner. Concordance decentralizes security, governance and compliance controls to each data owner separately and simultaneously. Users, admins, even Myngl staff, are blocked from direct source data access to change or override owners’ rules. This removes vulnerabilities and prevents data risks, misuse, and breaches.

 

Concordance Analytic Containers provide analysts with a secure analytic sandbox utilizing open-source Zeppelin Notebooks, SQL, Python, TensorFlow and other powerful tools.  Analytic jobs are separately authorized by data owners to make use of restricted data. Automated scheduling is orchestrated via microservices and results are segregated from source data. This allows analytic apps to use data elements which are hidden from direct Subscriber view.  Metering data usage for monetization is automated.

 


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