Data + AI
creating smarter businesses
Improving your business intelligence
Artificial Intelligence represents the pinnacle of the digital transformation journey for companies. It elevates the potential for innovation by enabling automation in operational decision-making with unparalleled precision. This drives significant change across a wide range of industries, enhancing operational efficiency while unlocking new opportunities for products and business models.
AI has the potential to completely transform the landscape, demanding a broad and strategic vision across multiple levels:
Strategic plan
A comprehensive understanding of the market, trends, competitive landscape, brand and product positioning, innovation management, and available financial and human resources.
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Operational plan
Identifying opportunities for efficiency gains and enhancing customer experience, with a focus on financial returns, improved brand perception, and strengthening organizational culture.
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Technology plan
Developing the infrastructure, solution architecture, tools, processes, and people needed for a digital transformation that leverages data to boost competitiveness.
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Cultural plan
Embedding data-driven innovation and decision-making into the organization’s DNA, fostering cross-functional collaboration and a unified language across teams.
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Implementation plan
Actions focused on team composition, cross-level interaction, employee upskilling, tool selection, balancing short- and long-term goals, and driving continuous experimentation.
Data Science
Methods, processes and systems for extracting insights and knowledge from complex data sets, involving the application of statistics, machine learning and data analytics to understand patterns, make informed decisions and solve problems.
Data Architecture
Organized set of principles, processes and technologies that defines how data is collected, stored, processed and used in a system or organization, aiming to guarantee the integrity, security, efficiency and governance of data management, facilitating decision-making and supporting operations.
Monitoring + Maintenance
Bring more value to your customer and become attractive to users of other solutions by connecting to other tools on the market.
MLOps
Practice of integrating machine learning operations into the software development lifecycle, ensuring efficiency, consistency, and governance when deploying and managing models in production, enabling continuous and reliable delivery of machine learning-based solutions.
NLP
Natural Language Processing (NLP) focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate text in a manner similar to human communication.
Data Mesh
Data architecture to decentralize and distribute responsibility for data across the organization, by proposing that individual teams assume ownership and governance of their own data domains. This strategy aims to improve scalability, flexibility, and agility in data management in large organizations.
Data Engineering
Process of collecting, transforming and storing large volumes of data for analysis and insights, ranging from ingestion to delivery of data ready for analysis.
Machine Learning (ML)
A subset of artificial intelligence that enables systems to automatically learn and improve from past experiences without explicit programming. It relies on algorithms that analyze data, identify patterns, and make decisions, allowing computers to perform specific tasks without direct instructions.
Data Apps
Applications with a primary focus on manipulating and utilizing data, designed to efficiently collect, process, analyze, or visualize data. Data Apps can include interactive dashboards, data visualization tools, or applications specifically for data-driven insights.
Generative AI
A category of artificial intelligence that uses models to generate new and original content. These models are trained on large data sets and can then create text, images, or any other content.
Computational Vision
Development and application of algorithms and computational techniques to enable computer systems to understand and interpret visual data, replicating human capabilities and involving pattern recognition, image analysis and interpretation of visual content.
Industries
The potential applications of AI across various business segments are virtually limitless. Below, we outline some possibilities relevant to your industries:
Finances
Risk management: proactively monitoring and managing financial risks, including market risk, credit risk, operational risk, regulatory risk and ESG-related risks.
Fraud detection: analysis of suspicious transactions and behavior to identify and prevent financial fraud.
Customer support: chatbots and virtual assistants for customer service, investment advice and technical support.
Data processing: automation of manual processes for collecting, organizing and analyzing financial data, such as balance sheets and income statements.
Market forecasting: predicting trends and movements in the financial market using machine learning algorithms.
Investment analysis: investment recommendations based on analysis of financial data, investment history, market information and alternative sources.
Portfolio management: optimization of investment portfolios and resource allocation based on optimization algorithms.
Credit analysis: credit assessment for loans and financing using ML techniques to analyze financial history and customer behavior.
