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Insights-as-a-Service can accelerate the speed and scale of data-driven intelligence

Tej Vakta

In disruptive times, big data and analytics are proving to be essential for firms attempting to generate actionable insights from massive structured and unstructured data sets. Now more than ever, the ability to effectively gain business insights through data can be a competitive edge.

However, it isn’t easy to mine diverse and complex data without a flexible and scalable platform. It’s no wonder “Insights as a Service” (IaaS) is picking up steam, particularly as interest in artificial intelligence (AI) gains ground. Our yearly publication, World Wealth Report 2019[1] also revealed that wealth managers rated “data and analytics innovation” as the leading factor that will impact the wealth management industry in the coming years. With data insights and AI bound to play significant role in next wave of transformation, IaaS, which includes cloud-based services, will further help firms leverage insights to achieve business goals. This market is particularly driven by the need for businesses to mine large amount of structured and unstructured data and gain intelligence to enable data-driven decisioning for achieving desired business outcomes.

As a result, many firms are experimenting with concepts such as virtualized data science labs and third-party insight exchanges to make AI accessible across the enterprise as a service. This model allows a user to request as little or as much information necessary to drive business intelligence.

IaaS can accelerate experimentation by meeting needs for:

  • A foundational platform
  • Data assessment
  • Data-science-models tailored to business needs

As an instance, Capgemini’s Augmented Advisor Intelligence (AAI) solution offers a structured approach to support ideation – from pilot through implementation, on site or in the cloud (Capgemini 890) – and foster quick experimentation.

  1. Strategy and ideation: An ideation engagement to build tailored strategy for AAI that aligns with enterprise vision and build blueprint that enables longer-term objectives of intelligent organization.
  2. Roadmap and business case: Identification and prioritization of AI opportunities and build business cases around AAI solution
  3. Accelerated pilot: An open experimentation engagement to identify and design a pilot Best Practice Recommendation (BPR) model for value-based segmentation of financial advisors (FA) leveraging our IaaS solution components
  4. AAI sandbox: Develop AAI Sandbox by leveraging a pre-built Capgemini 890 solution that allows for open experimentation of AAI components (compatibility model, FA model, BPR model) and associated user journey ideation, which can later be taken to maturity once their success is proven
  5. AAI insight exchange: Leverage the Capgemini 890 solution with pre-packaged analytical services or a pre-built catalog of model accelerators: API development and management accelerators, cloud infrastructure accelerators, security accelerators, application functionality, domain.
  6. AAI platform implementation: A fully integrated AAI platform on the cloud with a diverse set of internal and third-party data sources to augment and empower every interaction and run an intelligent organization.

As we shift in the data-centered future, IaaS services will become a precursor to insightful solutions. It is essential for firms to ask whether they are working on actionable insights or whether they are lagging behind.

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[1] Capgemini World Wealth Report, 2019; Capgemini Wealth Manager Survey, 2019.