Enterprises must move away from traditional Business Intelligence (BI) and shift to Decision Intelligence (DI). In this digital age, organizations have massive amounts of data, and they make increasingly complex business decisions to effectively steer their business. The process of translating the data into insights, utilizing it for effective decision-making and creating executable action plans to derive the outcomes is not easy. Traditionally, BI would help to an extent, however, it is not an efficient and complete solution considering the evolution of business models and digital ecosystems.
To stay competitive, businesses must adopt a decision intelligence (DI) strategy, taking advantage of Digital Twin (DT) and AI/ML technologies to become information- and knowledge-driven organizations.
Globally known Standards Development Organizations (SDOs), such as the TM (TeleManagement) Forum, have taken the initiative of providing guidelines for Communication Service Providers (CSPs) in the domain of DI by initiating the Digital Twin for Decision Intelligence (DT4DI) collaboration project.
Many TM Forum members, including TCS, are actively involved in defining and developing a DI framework that integrates DT, AI, and other technologies to help CSPs make faster and more accurate decisions. This framework is evolving, and it may soon become an industry standard.
Why adopt DT4DI
Data-driven decision-making is still an aspirational goal for many CSPs, and there are many challenges in achieving this. DT platforms leverage AI and advanced analytics to create virtual models of the business ecosystem including customers, products, processes, resources, and the like, and are strongly emerging as a viable way to enhance DI through simulation.
With the growing emphasis on DT and AI solutions, DT4DI has a potential to help CSPs take real time accurate data-driven decisions by improving forecasting, predictive ability, and simulations. Adopting DT4DI solutions and practices will enable organizations to:
A whitepaper, ontology, reference architecture, and maturity model have been published as part of the collaboration project with the TMF leveraging TCS TwinX™. Recently, a point of view was also presented on how TCS TwinX™ aids DI via risk-free business experimentation in the digital ecosystems use case, overcoming the complexity of dynamic multi-player, multi-decision scenarios involving various decisioning approaches.