1. Data integration
Data integration is a process of bringing together data from different sources to obtain a unified, more valuable view of it so that companies can make better, faster decisions.

a. Data asset
The term “data assets” refers to sets of data, information, or digital resources that an organization considers valuable and critical to its operations or strategic objectives. These data assets can include a wide variety of data types, such as customer data, financial data, inventory data, transaction records, employee information, and any other type of information that is essential for operations and decision making. of a company or organization.

b. Data engineering
Data engineering is a discipline that bases its approach on designing, building and maintaining data processing systems for the storage and processing of large amounts of both structured and unstructured data.

c. Data Cleaning
Data cleansing, also known as data scrubbing, is the process of identifying and correcting errors, inconsistencies, and problems in data sets. This process is essential to guarantee the quality of the data and the reliability of the information found in a database, information system or data set in general. Data cleansing involves a number of tasks, which may include:

• • • • Detection and correction of typographical and spelling errors.
      • Elimination of duplicates.
      • Data standardization.
      • Data validation.
      • Handling missing values.
      • Referential integrity verification.

Data cleaning is a critical step in the data management process, as inaccurate or dirty data can lead to erroneous decisions and problems in analysis.

2. Data quality
Data quality refers to the extent to which data is accurate, reliable, consistent, and suitable for its intended purpose in an organization. It is essential to ensure that the data used for decision-making, analysis, operations and other processes is of high quality and accurately reflects reality. Data quality involves several key aspects, including:

• • • Accuracy.
    • Integrity.
    • Coherence.
    • Relevance.
    • Present.
    • Reliability.

Improving data quality is essential for an organization to make informed decisions and obtain accurate results from analysis and processes. Data quality management involves implementing policies, processes and technologies to continuously maintain and improve data quality over time.

a. Data enrichment
Data enrichment is a process by which existing data is added or enhanced with additional, more detailed or relevant information. The primary goal of data enrichment is to improve the quality and usefulness of data, which can help organizations make more informed decisions, better understand their customers, and improve the accuracy of their analyzes and models.

b. Data Protection
Data protection refers to measures and practices designed to ensure the security, privacy and integrity of personal or sensitive information. This is essential to protect the confidential information of individuals and organizations from potential threats and abuse.

Some key aspects of data protection include:

• • • • Privacy.
      • Security of the information.
      • Legal compliance.
      • Consent management.
      • Data retention and deletion.
      • Monitoring and auditing.
      • Incident response.

c. Data validation
Data validation is a process that involves verifying the accuracy and integrity of data entered or stored in a system or database. The main goal of data validation is to ensure that the data is consistent, reliable, and meets certain predefined criteria or rules. This process is essential to maintain data quality and prevent errors that could affect operations and decision making.

Here are some common techniques and approaches in data validation:

• • • • Format check.
      • Numerical validation.
      • Length validation.
      • Pattern validation.
      • Validation of business rules.

Data validation is essential to ensure data quality and avoid issues such as incorrect or inconsistent data that can impact the accuracy of reporting, decision making, and the efficiency of business processes.

3. Data governance
Data governance is a set of processes, policies, standards and practices that are implemented in an organization to ensure effective management, quality, security and compliance of data throughout the enterprise. The primary goal of data governance is to establish a robust framework that allows an organization to make the most of its data while minimizing risks and ensuring the integrity and confidentiality of the information.

a. Data catalog
A data catalog is a tool or system that acts as a centralized repository of information about data within an organization. Its primary purpose is to provide an organized and detailed view of available data assets, making them easy to discover, access and manage.

The data catalog plays a crucial role in data management and data governance by providing visibility and control over an organization’s data assets.

b. Data lineage
Data lineage is a concept that refers to tracing and documenting the provenance and changes that a data set has undergone throughout its lifecycle. In other words, data lineage shows the complete history of a data item, from its origin to its current state, including all the transformations and processes it has undergone.

c. Data policy and workflow
Data policies and data workflows are two essential components of data management in an organization. Together, they help define how data is handled, stored, protected, and used consistently and efficiently.

d. Data Policy
A data policy is a set of guidelines, rules and principles that establish how data should be managed and used in an organization. These policies are created to ensure data quality, privacy, security, regulatory compliance, and decision-making based on trusted data.

e. Data Workflow
A data workflow, also known as a data process, describes the sequence of steps and tasks that are followed to move, transform, and use data in an organization. These workflows are essential to ensure that data is processed efficiently and effectively from its source to its final destination. Some key elements of a data workflow include:

• • • • Extraction.
      • Transformation.
      • Burden.
      • Programming and automation.
      • Monitoring and management.

Both data policies and data workflows are essential for effective data management in an organization. Policies establish the framework for how data should be treated, while workflows enable the practical implementation of those policies in the daily life of the organization.

4. Data status
“Data state” refers to the current condition of data within an organization or system at a specific time. Describes whether the data is accurate, up-to-date, complete, consistent, and available for its intended use. Data health is a critical indicator of the quality and usefulness of the information an organization uses to make decisions, perform analysis, and conduct operations.

a. Business results
“Business results” refer to the achievements, metrics and data that an organization obtains in the course of its business operations. These business results can vary depending on the industry, type of business, and specific objectives of the organization, but in general, they are used to evaluate the performance and success of the company in financial and operational terms.

b. Data Preparation and Data API
“Data Preparation” and “Data APIs” are two important aspects of managing and effectively using data in an organization. Both concepts are described here:

Data Preparation: Data preparation is the process of cleaning, transforming and organizing data so that it is in a suitable format and usable for analysis, reporting or other applications.

Data API: A data API, or data application programming interface, is a set of rules and protocols that allow computer applications and systems to communicate with each other and share data in a structured way.

c. Data Literacy
“Data literacy” refers to a person’s ability to understand, analyze, and use data effectively. It involves the ability to read, interpret, and communicate data-driven information critically and accurately. In a world where data plays an increasingly important role in decision-making, data literacy has become a critical skill both personally and professionally.