Welche Tools und Techniken gibt es zur Automatisierung der Datenerfassung und Datenherkunft?

What data catalogue does is it will categories around 60-80% percent of data depending how uniformly the data attributes are defined. There is always sifnificsnr work required to cover all the data in the catalogue and hence would require data stewards to analyse, categorise and stamp the data catalogue. Some edc systems like informatica EDC use AI to automatically look for attributes and classify them like an number similar to phone is classified as phone number or postal code but it may not cover everything

Data Discovery: Data catalogs enable users to easily search for and discover relevant data assets based on various criteria, such as name, description, tags, or metadata. This streamlines the data acquisition process by reducing the time and effort required to locate specific data. Data Understanding: Data catalogs provide detailed information about each data asset, including its structure, format, source, owner, and usage examples. This helps users understand the context and relevance of the data, facilitating informed decisions about data acquisition and usage.

Automating data acquisition and data lineage is important for maintaining data quality, traceability, and understanding the flow of data through your systems. The combination of tools, techniques, and best practices is often required to establish effective automation of data acquisition and data lineage. The choice of specific tools may depend on your technology stack, cloud provider, and specific requirements.

Data Ingestion: Data pipelines automate the process of extracting data from diverse sources, such as databases, APIs, cloud storage, or real-time streams. They can handle different data formats and protocols, ensuring seamless data acquisition. Data Transformation: Pipelines perform various transformations on the acquired data, including cleaning, filtering, aggregating, enriching, and converting it into a standardized format. This ensures data consistency and usability for downstream analysis. Data Loading: Pipelines automate the loading of transformed data into target systems, such as data warehouses, data lakes, or analytical databases. They can handle batch or real-time loading based on the requirements.

Traditionally data pipelines have been synonymous with ETL tools, providing a visual way to engage with data and create pipelines. Over time this has change to script based and at present t this is best done by a framework that wraps up subtasks like reading data , transformation it and loading data. An abstraction layer would provide SQL like framework to call these sub tasks and customize the same.

This is one of my favourite areas. I will talk about mainly with the context of NLP, Computer vision AI. AI dev teams need access to high quality labeled data in an iterative way. For this they need to ensure representative raw data is being collected and curated first. Once that is curated, there should be an efficient mechanism where that data(Images, videos, text, audio) can be labeled with minimal costs and time. Once that data is labeled it should be transformed in such a way that it is easy to consume by AI/ML dev pipeline. I would like highlight an example where our customer is offer waste intelligence on images containing garbage. Before their engine consumes it goes through a data pipeline of image segmentation annotation.

Na qualidade dos dados, é interessante você levantar indicadores e possíveis métricas para o conjunto de dados que você está utilizando, de modo que fique mais claro o nível aceitável para sua operação

Data Profiling: Tools that analyze data sources to understand their structure, content, and quality characteristics (e.g., completeness, accuracy, consistency). This helps identify potential issues before data is ingested into pipelines. Data Cleansing and Standardization: Tools that clean and standardize data by removing errors, filling in missing values, and transforming data into a consistent format. Data Monitoring and Anomaly Detection: Tools that continuously monitor data quality and alert users of anomalies or inconsistencies in data pipelines. Data Validation and Rule-Based Tools: Tools that validate data against predefined rules and business logic to ensure accuracy and consistency.

Data lineage visualization is a crucial aspect of automating data acquisition and lineage. It provides a visual representation of the flow of data from source to destination, making it easier to understand how data is transformed, where it's stored, and who is using it. This visibility is essential for data governance, impact analysis, troubleshooting, and regulatory compliance. Graphical Representation: Tools that generate interactive diagrams, graphs, or flowcharts to visually depict the lineage of data assets. These representations often include nodes for data sources, transformations, and destinations, connected by edges that represent data flows.

Data Quality: Data governance establishes standards and processes for data quality management, ensuring that acquired data is accurate, complete, and consistent. This includes defining data quality rules, implementing validation checks, and monitoring data quality metrics. Metadata Management: Data governance frameworks often include metadata management practices to capture and maintain information about data assets, such as their origin, lineage, definitions, and relationships. This metadata is essential for understanding data lineage and ensuring data consistency.