Data Terminology

Pentaho > Data Terminology

Analytics Data Ready Pipeline

Analytics data-ready pipelines gather data from multiple sources and systematically process it through stages of cleansing, transformation, and enrichment, ensuring the resulting data is structured, accurate, and optimized for efficient analysis and informed decision-making.

Example:

  • Retail analytics commenced with loading data from the ERP system into a replication layer and then transferring it to a staging layer.
  • From this staging layer, built a data warehouse (DWH) and subsequently created a data mart to enhance and simplify the analytics process.
  • For the ETL pipelines, Used Pentaho Data Integration (PDI) and employed PostgreSQL for constructing both the DWH and the data mart.

Streaming Data Pipeline

Streaming data pipelines move data from multiple sources to multiple target destinations in real time, capturing events as they are created and making them available for transformation, enrichment, and analysis.

Example:

  • Customers upload ESG-related details from various locations.
  • Upon uploading, the files trigger an ETL process that loads the data into a data warehouse (DWH) for real-time processing.
  • We use Power Automate to trigger the ETL process and Apache Hop for the data processing.

Streamlined Data Architecture

Streamlined data refers to data that has been optimized for efficiency and clarity.
It means that the data is well-organized,easy to access,and free from unnecessary complexity.

Example:

  • Automated the process flow using Power Automate to streamline data handling.
  • The data warehouse (DWH) is designed to accommodate both unstructured and semi-structured data, such as PDFs and JSON files.
  • This data is then converted into a structured format to facilitate easier analysis.

Intelligent Data Collection

Being intelligent about what, when and how much data you collect is the key to having enough of the right data available to solve the problem at hand.

Example:

  • Sales Performance Report: Analyze sales trends, identify best-selling products, and assess sales performance by store or region.
  • Customer Insights Report: Segment customers based on purchase history, analyze customer lifetime value, and identify trends in customer preferences.
  • Inventory Report: Monitor stock levels, identify fast-moving and slow-moving products, and optimize inventory replenishment.
  • Alerts and Notifications: Set up alerts for critical events such as low stock levels or unusual spikes in sales.

Data Cataloging

A data catalog helps data users identify which data assets are available and provides relevant context about that data, allowing them to assess the data for use. Data catalogs help you organize and evaluate information about your data, including:The source and current location of the data, the data’s lineage, the data’s classification.

Example:

  • Schedule jobs for regular updates to keep the data catalog current.
  • Track the flow of data from source to destination. This helps in understanding how data is transformed and used across different systems.
  • Implement a user-friendly interface where users can search for and view data assets.

Data Architecture

Data architecture is a discipline that documents an organization’s data assets, maps how data flows through IT systems and provides a blueprint for managing data.

Example:

  • Retail Domain Data Architecture:
  • Daily Operations : The retail company’s POS system records every sale and updates inventory in real-time.
  • This data is stored in the operational database and periodically extracted, transformed, and loaded into the data warehouse.
  • Customer Insights : The company analyzes customer purchase patterns using data from the data warehouse to create targeted marketing campaigns and personalize offers.
  • Inventory Management :By integrating data from the warehouse, the company forecasts inventory needs and optimizes stock levels to avoid shortages or overstock.
  • This structured approach to managing data ensures that the retail company can effectively utilize its data assets to enhance operations, improve customer experience, and drive strategic decisions.

Data Lakehouse

A data lakehouse is a data architecture that blends a data lake and data warehouse together. Data lakehouses enable machine learning, business intelligence, and predictive analytics, allowing organizations to leverage low-cost, flexible storage for all types of data-structured, unstructured, and semi-structured, while providing data structures and data management features.

Example:

  • Google Cloud approach has been to unify the core capabilities of enterprise data operations, data lakes,and data warehouses.
  • This implementation places BigQuery’s storage and compute power at the heart of the data lakehouse architecture.
  • You can then apply a unified governance approach and other warehouse-like capabilities using Dataplex and Analytics Hub.

Pipeline Observability

Pipeline observability refers to the ability to monitor, understand, and manage the performance and behavior of data pipelines or software pipelines throughout their lifecycle. This involves collecting and analyzing metrics, logs, traces, and other relevant data to ensure that the pipeline operates efficiently, detects issues, and facilitates troubleshooting and optimization.

Example:

  • Pipeline observability refers to the ability to monitor, understand, and manage the performance and behavior of data pipelines or software pipelines throughout their lifecycle.
  • This involves collecting and analyzing metrics, logs, traces, and other relevant data to ensure that the pipeline operates efficiently, detects issues, and facilitates troubleshooting and optimization.

Data Pipeline

A data pipeline is a systematic and automated process for the efficient and reliable movement, transformation, and management of data from one point to another within a computing environment. It plays a crucial role in modern data-driven organizations by enabling the seamless flow of information across various stages of data processing.

Example:

  • Developed an automated data pipeline to streamline data flow from AWS S3, Enabled near real-time data analytics and enhancing customer insights for easier analysis.
  • Integrated IoT devices and converted unstructured and semi-structured data into structured formats to improve data integrity.
  • Experienced in using tools such as Pentaho Data Integration (DI), Apache Hop, Apache NiFi, Talend, SSIS, Informatica PowerCenter and more…

Data Intelligence

Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.

Example:

  • Data intelligence has thus evolved to answer these questions, and today supports a range of use cases.
  • Examples of Data Intelligence use cases include:
    Data governance,Cloud Transformation,Cloud Data Migration

Data Federation

Data federation is a data management technique that allows users to query and manipulate data across multiple, heterogeneous data sources as if they were a single, unified dataset. Instead of physically moving or copying data into a centralized location, data federation enables real-time access to distributed data sources through a virtual layer.

Example:

  • Customers upload ESG-related details from various locations.
  • Upon uploading, the files trigger an ETL process that loads the data into a data warehouse (DWH) for real-time processing.
  • We use Power Automate to trigger the ETL process and Apache Hop for the data processing.

Data Readiness

Data readiness refers to the state in which data is prepared and structured adequately for analysis, decision-making, or operational use.

Example:

  • Retail analytics commenced with loading data from the ERP system into a replication layer and then transferring it to a staging layer.
  • From this staging layer, built a data warehouse (DWH) and subsequently created a data mart to enhance and simplify the analytics process.
  • For the ETL pipelines, Used Pentaho Data Integration (PDI) and employed PostgreSQL for constructing both the DWH and the data mart.

Data Orchestration

Data orchestration refers to the process of coordinating and managing the flow of data across different systems, processes, and applications to ensure that it is efficiently collected, integrated, and utilized. It involves automating and optimizing how data is transferred, transformed, and synchronized between various data sources and destinations.

Example:

  • Data orchestration refers to the process of coordinating and managing the flow of data across different systems, processes, and applications to ensure that it is efficiently collected, integrated, and utilized.
  • It involves automating and optimizing how data is transferred, transformed, and synchronized between various data sources and destinations.

Data Cloudification

Process of moving data and data management functions to cloud-based platforms. This involves shifting from traditional on-premises data storage and management systems to cloud-based solutions.

Example:

  • Migrated data from PostgreSQL to Snowflake for cloud-based analytics and management.
  • Created scripts to generate COPY commands for retrieving table details, applied data type conversions, and loaded data into Snowflake using internal stages.
  • Utilized Apache NiFi to transfer data files into Snowflake internal stages.

Data Transfer

Data transfer refers to the process of moving data from one location to another.

Example:

  • Developed an automated data pipeline to streamline data flow from AWS S3, Enabled near real-time data analytics and enhancing customer insights for easier analysis.
  • Integrated IoT devices and converted unstructured and semi-structured data into structured formats to improve data integrity.
  • Experienced in using tools such as Pentaho Data Integration (DI), Apache Hop, Apache NiFi, Talend, SSIS, Informatica PowerCenter and more…

Data Processing

Method of collecting raw data and translating it into usable information.

Example:

  • Developed an automated data pipeline to streamline data flow from AWS S3, Enabled near real-time data analytics and enhancing customer insights for easier analysis.
  • Integrated IoT devices and converted unstructured and semi-structured data into structured formats to improve data integrity.
  • Experienced in using tools such as Pentaho Data Integration (DI), Apache Hop, Apache NiFi, Talend, SSIS, Informatica PowerCenter and more…

Data-driven Transformations

“Data-driven transformations” refer to changes or improvements in an organization or process that are guided by insights derived from data analysis.

Example:

  • Predictive Modeling: The company develops predictive models to forecast future demand more accurately.
  • These models consider historical sales data, promotional events, and external factors like weather or local events.
  • Inventory Optimization: Based on the insights from the predictive models, the company adjusts their inventory levels.
  • They implement automated reorder points and optimize safety stock levels to minimize both stockouts and overstock situations.
  • Real-Time Monitoring: The company sets up real-time dashboards to monitor inventory levels, sales performance, and supply chain metrics.
  • This allows them to make quick adjustments based on current data.

Data Regulation

Data regulations describe policies and laws ensuring that processed data is shared or governed appropriately, where the right data assets go to the right place at the right time.

Example:

  • Consent Verification: Ensure that data extraction only includes information for which consent has been obtained.
  • Data Anonymization: Mask or anonymize sensitive information to protect individual identities, especially when preparing data for analysis or sharing with third parties.
  • Data Encryption: Encrypt data during transformation to ensure security and compliance with data protection standards.
  • Data Integrity Checks: Ensure data accuracy and completeness, aligning with data quality regulations.
  • Access Control: Restrict transformation access to authorized personnel only.

Data Harmonization

Data harmonization is the improvement of data quality and utilization through the use of machine learning capabilities

Example:

  • Names: Standardize the format for names (e.g., “John Doe” vs. “Doe, John”).
  • Emails: Ensure all email addresses are in lowercase and free of extra spaces
  • Phone Numbers: Format all phone numbers consistently (e.g., “(123) 456-7890” vs. “123-456-7890”).
  • Match Records: Use algorithms or manual checks to match records across sources based on common fields (e.g., email address or phone number)
  • Resolve Duplicates: If the same customer appears in multiple datasets, merge their records into a single entry.

Data Regulation

Data regulations describe policies and laws ensuring that processed data is shared or governed appropriately, where the right data assets go to the right place at the right time.

Example:

  • Data Encryption: Encrypt data during transformation to ensure security and compliance with data protection standards.
  • Data Integrity Checks: Ensure data accuracy and completeness, aligning with data quality regulations.
  • Access Control: Restrict transformation access to authorized personnel only.

Data Wrangling

Data wrangling, refers to the process of cleaning, transforming, and organizing raw data into a format that is suitable for analysis.

Example:

  • Leveraged AI-driven auto-suggestions to correct inaccurate data following pattern matching, lookup value matching, and user-defined rules, thereby improving data integrity and enabling users to cleanse data more efficiently.
  • Utilized Pentaho Data Integration (DI) for the ETL process and handled CSV file processing.

Serving AI & Real time analytics

The discipline that applies logic and mathematics to data to provide insights for making better decisions quickly.

Example:

  • Applied data science algorithms like K-Means and Apriori, and generated SPC/OPC charts to provide deeper insights, including customer analytics, churn analysis, and opportunities for cross-selling and up-selling.
  • For data processing, we utilized R programming and Pentaho Data Integration (PDI).

Batch Processing

Batch processing is the method computers use to periodically complete high-volume, repetitive data jobs.

Example:

  • Batch processing is often used in ETL to handle large volumes of data efficiently.
  • Extract: Every night, data is extracted from multiple source systems like point-of-sale systems, online sales platforms, and inventory management systems.
  • Batch processing in ETL helps manage large datasets, ensures data consistency, and optimizes resource usage by processing data in scheduled intervals rather than real-time.

Data Mining

Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis.

Example:

  • Retailers often use data mining techniques to analyze customer purchase history and identify patterns or associations.
  • For example, market basket analysis can reveal that customers who buy diapers are also likely to purchase baby food, leading to cross-selling opportunities.

Data Estate

Infrastructure that helps you manage all your data, no matter where or how it’s stored.

Example:

  • Data estate modernization involves modernizing legacy application for compatibility with new applications across the cloud platform for optimized delivery.

Data Engineering

Data engineering is the practice of designing and building systems for collecting, storing, and analysing data at scale.

Example:

  • A retail chain with multiple locations wants to improve its inventory management to better meet customer demand and reduce costs associated with overstock and stockouts.

Data Governance

Data governance means setting internal standards data policies that apply to how data is gathered, stored, processed, and disposed of. It governs who can access what kinds of data and what kinds of data are under governance.

Example:

  • A data steward ensures that product descriptions, prices, and promotions are accurate across the website, mobile app, and in-store systems.
  • Automated data validation tools check for discrepancies in inventory counts, flagging any mismatches between online listings and physical stock levels.
  • Classify customer data by segmentation (e.g., frequent shoppers, high-value customers) to tailor marketing efforts and promotions.
  • Analyze sales data to identify trends and adjust inventory levels or promotional strategies based on customer purchasing patterns.

Data Consultation

providing expert advice and solutions to help organizations manage, analyze, and leverage their data effectively

Example:

  • Discussing with Customers on their business Insights to improve the data management and analytics capabilities to enhance customer experience, streamline operations, and support data-driven decision-making.

 

Data Fabric

Architectural approach that provides a unified and integrated data management framework, allowing organizations to seamlessly access, integrate, and manage data across various sources and environments

Example:

  • Use a data fabric solution that integrates data from in-store POS systems, online sales databases, inventory management, and CRM systems into a single, accessible layer.

Golden Records

Single data point that provides all of the important information about a customer, client, or resource with total accuracy.

Example:

  • in a CRM system, each customer might be assigned a unique Customer ID.
  • This ID, when used, could retrieve comprehensive details about the customer, such as their name, contact information, purchase history, preferences, and interactions with customer support.
  • The accuracy and completeness of the data associated with this ID ensure that all essential information about the customer is available instantly..

Data Integration

process of combining data from different sources to provide a unified view and facilitate analysis

Example:

  • By integrating data from various sources, the retail organization gains a holistic view of its operations, leading to improved decision-making, more efficient inventory management, and enhanced customer experiences.
  • The ability to analyze data from multiple channels in a unified way helps the company better understand trends, optimize processes, and ultimately drive growth.

Big Data Integration

Big data integration refers to gathering and collecting data from multiple data sources like IoT devices, social media, customer and business systems to create a single, robust data set for running analytics and business intelligence efforts.

Example:

  • Analysis of sensor data and supply chain information helps streamline store operations.
  • For example, identifying peak times can help in staffing decisions, while supply chain insights can improve order fulfillment and reduce delays.

Data Observability

Ability to monitor, analyze, and understand the state and behavior of data as it flows

Example:

  • Created a dashboard to track data synchronization across three layers, showcasing the synchronization status for each table to ensure data consistency and integrity.
  • Included email alerts for success and failure notifications.

Data Quality Management

Data Quality Management (DQM) refers to the processes and practices used to ensure that data is accurate, complete, reliable, and timely. The goal is to maintain high-quality data throughout its lifecycle, which can significantly impact decision-making, operational efficiency, and overall business success.

Example:

  • ETL Process for Integrating Sales Data from Multiple Sources, During extraction, the system assesses the quality of data sources, checking for issues like missing fields or inconsistencies in data formats.

Data Support

Providing the support from the collection, recording and recovery of your data

Example:

  • Data support analyst provides guidance, assistance, coordination and follow-up on customer inquiries related to computer operating systems, hardware, software applications, asset management, audio/visual, internally developed applications, mobile devices, network connectivity, etc,

Data Warehousing

A data warehouse is an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis as well custom reporting. A data warehouse can store both current and historical data in one place and is designed to give a long-range view of data over time, making it a primary component of business intelligence.

Example:

  • Developed a pipeline to extract and load data from 12 distinct locations into the data warehouse.
  • Processed nearly 1 million rows daily across various layers, including ODS (Operational Data Store), staging, and the data warehouse.
  • Constructed data marts specifically for analytical purposes.

Enterprise Data Strategy

A data strategy is the collection of tools, processes, and policies that define how an organization will collect, store, manage, analyze, and share its data. You can think of it as the foundational strategy that drives all the other strategies related to data, including data management and data governance frameworks.

Example:

  • Enhance Customer Experience: By analyzing customer data, they can personalize marketing campaigns, recommend products, and improve service.
  • Optimize Inventory Management: Data insights help in forecasting demand, managing stock levels, and reducing excess inventory.
  • Boost Sales Performance: By understanding sales trends and customer preferences, TrendyThreads can tailor promotions and optimize pricing strategies.