Business Innovation and Agility
A final benefit is the ability of smart data to drive innovation and agility for business operations. Smart data forms the base of a 100% cloud-based platform that runs the engine of the business across the various aspects of the sales and service lifecycle. The cloud-based model pushes businesses to the next level of innovation by reducing the complexity of running a business using legacy systems. All aspects including configuration, activation, fulfillment, billing, and ongoing servicing are streamlined with a digital platform built with insights from smart data. The transformation to a cloud-based platform helps OTT services and other digital media and entertainment organizations to further insulate them from ongoing market and industry disruptions and helps them adapt quickly to dynamic market conditions.
Best Practices for Using Data in Customer Engagement and Retention
There are several best practices that digital media and entertainment companies can follow to most effectively utilize data in their customer engagement and retention efforts during key phases in their digital transformation. Discovery Services Discovery services provide business intelligence (BI), analysis, and visualizations for decision makers in the organization. Provision of these services is the first step in becoming a data-driven organization. In enabling discovery services, a data reservoir is formed, and data collection from customers is initiated. Best practices in the discovery services phase: • Sensitive personal data collected from customers must be removed from data storage systems and AI models • A single reservoir of customer data has to be formed to provide consistency across business units and a seamless customer experience • Inbound data has to be cleaned and should be stored in this form
• Joins or merges with data from other sources should be done in reading the data • Collect and store only data that can be converted into smart data for use in intelligence-based actions
Prediction Services
Prediction services require the development and development of machine learning models.
Best practices in the prediction services phase: • Bias in the data must be effectively detected and removed • The inclusion of explainability , the ability of the model to justify the prediction it is generating, is strongly encouraged • Models should be accompanied by a confidence score or prediction interval that indicates the level of trust in the model and the ability to make decisions based on it • Recurring statistical evaluation of the quality of the model is strongly recommended as model quality can decay over time as business conditions change
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