Data & Intelligence

Definition, at a corporate level, of a Data Governance federated-global model and proposal of a data-mesh architecture, allowing the standardization of data management and democratization of information, enabling information sharing by eliminating silos. This Strategy will be implemented in four regions where the client operates.

The client required the creation of an MLOps System to manage their existing algorithms. Its implementation improved their management of ML models and Data Science pipelines, automated workflows, shortened time to market, and proposed new open-source technologies that allowed them to achieve the flexibility requirement, since they may be replaced in the future by any cloud-native storage.

Following the client's necessity for a fast and automated API to feed data from Snowflake to other internal applications, NTT DATA, within 3 months, was able to provide an operational API with low maintenance costs in Azure Functions. Allowing for future scaling and increasing the client’s value, through innovation in data processes and cost load reduction.

To tackle the customer's challenges, NTT DATA upgraded their D&I Tool and the customer can now use it as self-service by creating and analyzing their own data. Moreover, a Data Governance Model was implemented, a corporate solution to gather, consolidate and provide organized data. As a result, end-user adoption rates improved by 80% for each focused business area while license and maintenance costs decreased.

In response to our banking client's lack of a Data Platform, NTT DATA delivered a cloud-native, IA-oriented data platform on Microsoft Azure cloud, providing advanced analytical services leveraging in-house accelerators to simplify deployments of new use cases in order to make the information available to the different business units.

In one month, NTT DATA was able to deliver a solution by leveraging AWS that gave the client immediate cost-saving benefits, more reliable and accurate analytics as well as improved service quality, and the ability to debug issues down to a granular level.

We modernized the expensive and high-effort maintenance legacy data platform of the client through the development of a migration strategy and roadmap to Snowflake. NTT DATA developed the architecture and approach, and migrated/implemented 10+ analytics systems to the new common Entereprise Data Warehouse architecture on Snowflake. Now we manage the entire Snowflake ecosystem ongoing Roadmap for migration of additional 50+ systems to snowflake (in-flight).

Our client was looking forward to starting the digital transformation process leveraging Microsoft tools, therefore, we integrated Power BI services in the Microsoft ecosystem, building end-to-end cloud solutions and merging more than +5 different source systems to Azure Data Factory.

Due to recent energy market developments, the client required a set of analytical improvements focused on energy production and consumption, by selling and buying energy in the market in an optimized way. The processing time for each energy buy offer and sale offer simulation scenario has been reduced by more than 10 times thanks to NTT DATA's solution, allowing for faster operations on the Iberian Peninsula's Day-Ahead Energy Market.

By leveraging the AI Labs methodology, different Cloud capabilities were explored for our banking client. This exploration would shape the overall understanding of the AI Cloud market, allowing our client to leverage state-of-the-art tools and techniques that Cloud providers are continuously incorporating into their portfolios and concentrate its talent and funding on the quest for the most adequate ways to solve the business challenges.

The project aims to improve patients' overall quality of life by creating a microservice that integrates six predictive models, each of which is related to a different pathology. The purpose is to examine the benefits and drawbacks of a use case in terms of scalability and performance in a practical manner.

In this paper, we will explain what Decision Intelligence is, its definition, growth forecasts, domestic and international trends, challenges to be solved and their effects, as well as the concept of application areas. We will also consider the future of decision making brought about by Decision Intelligence, with a particular focus on automation through the application of AI, and the impact of the expanded scope of AI application on business operations.

To get a good grasp of the latest few-shot learning techniques for NLP, we investigated PLM-based few-shot applications provided in real-world services. In the theoretical perspective, NLP few-shot approaches in fundamental research were also taken into consideration.

This document introduces initiatives about well-being in Japan and abroad, followed by the reason why well-being should be relevant to us. Then, we will show the processing model to support individual well-being that NTT Data designs and introduce technical frameworks for the purpose and client analyzing AI of NTT Data.

The AI Labs offer a 3-block approach for ideating fast AI initiatives that tackle the business needs and technological hurdles in an innovative way, involving a wide range of critical roles under a common working methodology for the development of D&I projects, and finally, leveraging cloud capabilities that can be embedded throughout the entire AI Lifecycle that enable to scale Digital Intelligent Services.

To materialize your previous D&I initiatives and boost the percentage of disruptive AI prototypes, we provide our Rapid Prototyping Studio, which consists of a controlled experimentation environment where in a few iterations designs and products can be refined and validated so that only the right products are released.

In this guide, we present the need of prototyping AI projects, expose the game changer technologies that allow the rapid and agile prototyping of this kind of projects and introduce our very own AI Prototyping Tech Stack.

We bring to our clients the “AI FOR BUSINESS VALUE GENERATION”. A guide to address business challenges, elevate business models to the next step and lead market disruption in this new environment by harnessing D&I capabilities.

Within AI governance, we identify the following three key domains to support the definition of solid AI-Driven Organization foundations: 1. AI Strategy, 2. AI Organization, and 3. AI Operational Lifecycle.

We look forward to supporting organizations to start ahead at defining the Trustworthy AI journey and transform the challenges into a new competitive advantage by harnessing our Responsible AI-driven solutions and methodologies with an end-to-end approach aligned with the provisions of the new AI regulation.

The finance sector (and banking in particular) is one of the industries with the greatest possibility for both potential and real applications of AI. Its capacity to generate information, its heavy volume of transactions and the quantitative character of industrial activity mean it is fertile land for cultivating all the possible benefits of AI methods.