Cross-sectional methodology

Big Data

Information security and backup control.

Data Mining (Extraction, Transformation, and Loading) - Automation.

• In Big Data, we ensure the integrity of information and its safeguarding during and after its lifecycle, through a controlled system of backups stored in the cloud.

• Mining, cleansing, segmentation, transformation, and automation of data loads.

• Web process automation and implementation of bots directed towards auditing and automatic execution of core processes.

Deployment of applications and clustering.

• In Big Data, Docker is an essential tool. It provides isolation and portability for our applications, simplifying scalability and efficient service management on servers. Additionally, it accelerates the development and deployment of solutions.

Storage, centralization, segmentation, and distribution of data.

• Centralization model through Enterprise Data Warehouse (EDW) and data distribution using the Snowflake architecture method. Data Governance.

Server Administration and Monitoring.

• Real-time analysis and monitoring with the aim of preventing failures and optimizing processing.

Information Processing

• Large-Scale Data Processing for Machine Learning and Analytics.

Data Science

• Understanding the fundamental problem that is intended to be resolved.

Business Understanding

Data Exploration

• Understanding patterns and biases in the data.

• Application of statistical and machine learning techniques.
• Generation of reports and summaries.

Data Mining

• Collecting additional data from diverse sources.

Modeling and Visualization

Data Engineering

• Data Adjustment Process, Creation of New Variables, and Indicators.

Data Cleaning

• Detecting and correcting deficiencies in the data.

Workforce

Planning

To detect on time the ideal number of resources to each period (short, medium and large time), that allow the achivement of objectives of level of the service based on the operational need.

• To detect on time the ideal number of resources to each period (short, medium and large time), that allow the achivement of objectives of level of the service based on the operational need.
• Reduce the uncertainty of the decisions that affect the future of the bussiness and the involved ones.

Forecasting

Reduce uncertainty about what may happen in the future by providing information close to reality that allows for decision-making on courses of action in both the present and the future.¿What is Forecasted? (1. Calls - Auxiliary - 2. Sales - Absenteeism - 3. Email - Agents - 4. Chats - Positions - 5. AHT - 6. Productive Hours - 7. Productive Minutes - 8. Various Events - 9. Capacity Reducers)

Real-time Analysis and Decision-Making

Scheduling

To ensure that the schedule is adhered to, the forecast is met, and the agreed-upon targets are achieved: Service Level, Occupancy, Efficiency, and Utilization. Additionally, ensure that necessary adjustments are applied if sizing variables deviate, thereby creating added value aligned with key WF requirements.

Conducting workforce planning and scheduling

• collecting and analyzing information, including historical data.
• Predicting future call volumes and establishing behavior patterns.
• Forecasting the future volume for each type of transaction

Business Intelligence

Data Modeling

Information Gathering

• Extraction of external information and proprietary applications.
• Loading of information through Pentaho - Python (Big Data)
• Data modeling through MySql - Visual Studio Code - DataGrip - Python libraries.

• Report Inventory
• Raw Data Inventory
• Relevant Indicators

Shipments and Tracking

• Periodic operational audits, quality control in information and KPIs.
• Scheduled updates hourly
• Daily monitoring by email

Parameterization

• Development of the front-end and back-end with a team of specialized professionals in the construction of dashboards to be used by both our internal and external clients.

Data Literacy

In Business Intelligence, we focus on measuring indicators for continuous improvement with dashboards geared towards decision-making, maintaining cross-cutting standards in visualizations.

Percona Monitoring

MONITOREO (RAM Y CONSULTAS)

INTEGRACIÓN ENTRE AREAS

Storage in Hadoop Distributed File System (HDFS) format

IMAGENES PARA LANZAMIENTO DE CONTENEDRORES

APACHE SPARK

At GroupCOS, Apache Spark stands out as a fundamental piece of our strategy. This distributed processing platform allows us to effectively manage and analyze large volumes of data. With Spark, we perform real-time analysis, apply advanced machine learning, process data in the form of graphs, and optimize our server resources. These capabilities allow us to offer our clients highly efficient and customized data analysis solutions that respond to the increasing demands of today's market.

RESPALDO DE LA INFORMACION

Active Containers