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ADVANCED ANALYTICS AND DATA MANAGEMENT
Advanced analysis with machine learning to extract information, make predictions, and make decisions.
As advanced economies shift from a physical production base to an intangible asset base, more and more organizations are harnessing the power of data analytics and making significant investments in the process
Advanced Analytics involves the sophisticated analysis of data to gain insights, while Data Management focuses on the collection, storage, and proper management of this data. Both are critical components of an organization’s data strategy, enabling it to make informed decisions and gain a competitive advantage
Our offer includes
Based on our experience, integrating specialized teams for data management, analytics, models, and optimization algorithms, we consider that the primary and most crucial objectives in each project are:
- Planning and design of a central repository: which allows the integration and making available, in the first instance, the information from each project that serves as a single source for the generation of reports, as well as for the creation of advanced analytics and predictive models.
- Construction, data refinement and Elastic architecture: allowing gradual growth according to the needs of the business and each project.
- Integration and automation: to mitigate human errors and manipulation of information by users (capture recommendations).
- Deployment, operation and optimization: optimize times in generating reports and ensure that human resources dedicate time to analysis and generation of new insights for each project and in each area.
- Enabling models and new technologies: recommend and, where possible, enable new generation technologies that allow the growth, value and development of each project as innovative (Advanced Analytics, ML, Precision).
Data and Analytics Management
- Teippo consulting services to configure data collection, data ingestion, data curation and transformation, virtualization and storage for a customer scenario
- Develop and create data virtualization, metadata management and catalog services on behalf of the client
- Teippo can manage security, integrate a governance model, cost control and democratize data and user provisioning
Technical Components
- Data Fabic Integration Models
- Data Governance And Analysis
- AWS, GCP, Azure Datalake y Data Factory
- AWS, GCP, Azure Event Hub, Event Grid
- Storage services (Any DB, Datalake, SQL, NoSQL)
- Related On-Prem, Hybrid and Cloud infraestructure landing zone in customer tenants (Pre-packaged, software-defined)
- DWHI Datawarehouse and ETL Methodologies
Potential and Evolution of Complexity
- Ability to design, expand, develop and evolve predefined data ingestion structures for specific customer and industry scenarios
- Practice of a Cloud Center of Excellence
Data Management Objectives
Ensure the accuracy and reliability of the data
Improve data security
Provide easy access and data recovery
Maintain data consistency across all systems
Key components of data management
Data architecture and database administration: this leads to the structuring and categorization of databases to ensure that they meet the needs of the organization, have scalability as needed, and operate efficiently.
Data quality management: focuses on maintaining and ensuring the accuracy, accuracy and reliability of the data. This includes activities such as data cleaning, validation and profiling.
Data governance: Defines the processes and guidelines to ensure the availability, usability and security of data in an organization. This includes policies, standards and the assignment of data-related roles and responsibilities.
Data security
Data security: Ensures that data is protected against unauthorized access, violations and other potential risks. This includes encryption, access controls, regular audits and other security measures.
Data integration
Data integration: Involves unifying data from multiple sources and providing users with a consolidated view. ETL (Extraction, Transformation, Loading) processes play a crucial role in this regard.
Data storage
Data storage: This aspect is related to the electronic preservation of an organization's information to perform analysis and generate reports.
Master data management
Data consolidation: Here, it's about creating a single and authoritative view of the data entities that are shared throughout the organization.
Data backup and recovery
Data protection: We ensure that the organization can recover data in case of loss due to problems such as system failures, violations or disasters.
Data analysis and business intelligence
Data analysis: We use data to gain insights, identify trends and support informed business decision-making.
Data lifecycle management
Monitor the flow of data throughout its life cycle, from creation to retirement.
Data compliance and policy management
Regulated compliance: We ensure that data management complies with relevant legal, regulatory and policy requirements.
Some data analysis applications
Business intelligence: We empower companies to make informed decisions based on past and present data.
Finance: Investment risk assessment, fraud detection and optimization of stock trading strategies.
E-commerce: Personalize customer experiences, optimize supply chains and predict sales.
Sports: Improve player performance, predict game results and optimize team strategies.
Public sector: Improve public policies, optimize resource allocation and improve service delivery.
Entertainment: Recommend content to users, optimize advertising strategies and predict blockbusters.
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