The WNS Analytics Decision Engine (WADE)SM is a predictive analytics solution framework to inspire and enable an organization to scale the analytical maturity curve and become fact-based in the way it thinks and acts to achieve its long-term growth targets.
In the journey towards analytical maturity, organizations today face six main challenges:
Data Access: Organizations generate considerable data, which tends to exist in silos, isolated by its sources. Data technologies, many a times, don’t ‘talk’ to analytics technologies. Quite often, the barriers to data access are either cultural or technological ─ relying on system integration to create an enterprise data warehouse, which struggles to provide intelligent data synthesis
Quantity:Organizational inability to capture and present the data from different sources at the required degree of granularity and regularity. Companies need to refine their current big data and analytics strategy in the face of emergence of newer sources of voluminous data such as Web chat, social media, video / audio, Weblogs and so on
Quality: Data varies in complexity and cleanliness, and the skills to clean and synthesize are limited in organizations. The organization may lack a ‘single version of the truth’ in its business metrics. The organization may also not have the ability to hire and retain analytics assets and resources
Insight Generation: Pockets of expertise exist with multiple ad-hoc project-based efforts throughout the system with no standard best practices to enable an enterprise-scaled solution
Execution of Insights: The effort towards assimilating data and drawing actionable insights often curtails organizations from acting on them, limiting their ability to make informed business decisions
Speed-to-Harvest Insight: Freshness of data is very critical in a dynamic market to react to competition and changing consumer preferences, often the process of mining and assimilating data overshoots its ‘best before’ date
Research Reuse: Most companies that use custom research are sitting on a goldmine of expensive research conducted earlier. WADESM and Infotools transform this data into a user-friendly and well-organized knowledge bank, which significantly reduces the need to commission new research each time.
The WADESM solution enables organizations to harness and leverage data sources existing outside their enterprise platform (like custom research, syndicated research, industry reports, other macro data sources and downstream data from transaction logs) securely on the cloud and set up a mature data and analytics infrastructure, customized business solutions and insights exchange through an analytical center of excellence.
In summary, WADESM helps companies build a comprehensive analytics decision framework to provide insights that drive intelligent growth.