Data Analytics & IA.
From the Data&AI experience cluster, Axpe approaches data with a 360 view, focusing on turning data into business intelligence. To achieve this goal, the cluster focuses its talent and effort on two guiding threads: data management and artificial intelligence. The differential value that can be extracted from both threads is achieved by focusing on the most current technologies and models, so the knowledge and experience captured by Axpe is constantly evolving and adapts to continuous change. This is reflected in the clients, sharing and making them participants in the value chain.
Data management has 3 main capacity blocks, developed below:
- Data strategy: before being able to develop solutions that focus on the data life cycle, it is necessary to be clear about where we are and where we want to go. That is why a clear roadmap must be defined, include a detailed aspirational model, design how to operate the data from the beginning and provoke a controlled cultural change focused on preparing teams, attracting talent and specific training. This block of capabilities also includes governance and data quality, these lines of work being more transversal, both departmentally and extended throughout the data life cycle. Axpe accompanies its clients in the complete process of data governance, defining functions, responsibilities and processes, while ensuring compliance with data quality standards (availability, usability, reliability, relationship and description) and offering its experience in implementation of methodologies such as DAMA and use of tools.
- Architecture and data life cycle: Axpe offers end-to-end solutions for data platforms. The process begins with the definition of the architecture, following different types of proven implementation patterns or ad-hoc solutions. Next, the migration of the data is undertaken, from the identification of the sources, the ETL tasks and the Journey to the Cloud. In the final part of the data life cycle, the solution offered includes data consumption through visualization and Reporting. The different dashboards and reports that provide the necessary information to guide the client in making decisions are defined. Facilitating decision making is achieved through the mix of visualizations, self-service data discovery, and AI model output.
- Data Foundation: in order to extract added value from the data, the cluster makes use of knowledge graphs and synthetic data. Knowledge graphs are a way to structure data through nodes and relationships, in a way that simplifies queries and exposes patterns and behavior that would not be seen in a structured way. Synthetic data is data generated by AI that has the same statistical characteristics and the same behavior against models as the original data, being able to overcome problems such as data scarcity, privacy preservation and biases, while at the same time achieve cost savings.
The services of the cluster in terms of AI cover the following points:
- Advanced analytics: Axpe provides its clients with its know-how in modeling through AI. This modeling can automatically recognize patterns by reproducing the behavior of processes (descriptive modelling), predict the impact, evolution or what will happen in a hypothetical scenario (predictive modelling) or plan, optimize and recommend decisions (prescriptive modelling). Added to this, Axpe has extensive experience in applications within voice and text analytics and Computer Vision in various sectors.
- Industry 4.0: the link between the industry and Cutting Edge technologies is a point that Axpe takes into account. Solutions such as digital twins, with different degrees of application, 5G technology and Edge Computing allow us to address this perspective of industrial innovation and go one step ahead in concepts such as Smart Cities.
- Intelligent automation: in line with the advanced analytics part, artificial vision, natural language understanding, Machine Learning or process automation (RPA) are some of the current capabilities that allow simulating the actions of human beings and automating highly repetitive action processes. Applications of intelligent automation are natural language processing and document digitization.
- Customer Engagement: with the aim of improving service quality, optimizing resources for customer service and increasing customer satisfaction, the cluster also focuses its efforts on monitoring and analyzing user interactions. In addition, thanks to the use of conversational banking, it is possible to carry out risk management, fraud detection and save complicated navigations using the speech patterns of users.