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Captura de datos

Data Capture

In this phase, it is about "acquiring" the data by various means,

Collection methods include:

  • Manual Methods (when, for example, we collect "by hand" data from a patient in the Clinical History)
  • Automatic Methods (when through our daily actions,   computer systems record activities, consumption, times,) This collection can also be done through automatic devices. Another way of collecting data for information systems is webscraping techniques (automatic methods of capturing information on pages  web)



One of the most important challenges we currently face in the different companies we work with is the ability to "make systems talk," that is, integrate the different information systems they use for their business processes through data exposure and/or consumption. To achieve this, our team is skilled in:

  •  API development : building web services for the exposure/consumption of information from and to information systems.

  • Analysis: analyzing data to identify (together with clients) the business rules that need to be considered to cleanse data from different data sources.

  • Data dictionary: surveying fields and gathering information related to undocumented transactional databases.

  • Mapping: the process of identifying fields in transactional systems and/or different sources of information that are intended to be brought into analytics-oriented data storage models (DM/DWH/DLake).

Ancla 2

Data Preparation (ETLs)

  • DB Connection (SQL/NoSQL): connecting to data sources from different infrastructure locations.

  • Flat File Connection: connecting to machines for file consumption.

  • Extraction: designing/developing the necessary queries to extract data and move it to staging areas where required transformations will take place.

  • API Connection: consuming web services for data querying/extraction.

  • Servers: cloud/on-premises Flat File.

  • Profiling (preliminary data quality and cleansing): preliminary analysis of data quality.

  • Data Loading: arranging data in an analytics-oriented data architecture/model or required views for data consumption from visualization layers or advanced analytics models.

  • Transformations: implementing data transformation processes such as column elimination, creating new variables, data quality analysis procedures, implementing business rules for field selection, among others.

  • Data Quality: Consistency, integrity, validity, etc., and imputation rules.

  • Business Rules: Join conditions, record prioritization, etc.

  • Joining different sources 



  • Inserting transformed fields into databases using analytics-oriented models and architecture such as:

  • Files

  • DW/DL

  • Views


Data Models

Analytics Data Models (OLAP): Storage

Designing and implementing analytics-oriented data models such as:

  • Data Warehouse (DWH): data models for structured data.

  • DataMart: fact tables and dimensions in a star or snowflake model.

  • Datalake: storage models oriented towards analytics for unstructured data.


These models allow for querying from higher visualization layers or advanced analytics models, optimizing infrastructure and application performance.


Advanced Analytics

Designing and implementing advanced analytics models:

  • Descriptive

  • Diagnostic

  • Predictive

  • Prescriptive


  • Strategic

  • Operational/Monitoring


  • Financial

  • Purchasing

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