Data Glossary

A Dashboard is a user interface that uses visuals to represent data in an organized and easy-to-read way. Well-designed dashboards allow us to keep track of KPIs and data trends for better decision-making. Dashboards might be used to monitor business performance, customer service metrics, financial performance, or any other data we want to keep an eye on.

Data Accuracy refers to whether the stored data values are correct, ensuring that we can use records as a reliable source of information. Data accuracy is essential for informed decision-making and data-driven insights. Poor data accuracy can lead to incorrect conclusions, which in turn can cause costly errors.

When we talk about Data as a Service (DaaS), we refer to a data management strategy that leverages the cloud to make data available on demand across all departments. DaaS enables data storage, integration, processing, and analytics to take place over the network and, as a result, benefits us with speed, scalability, and flexibility.

We use a Data Catalog as an organization’s data assets inventory to help technical and non-technical users locate relevant information. Data Catalogs can help facilitate better decision-making by providing a single source of truth for organizational data. Not only do Data Catalogs provide an organized inventory of all the data assets, but they can also offer insights into how data is being used and which pieces of it are most valuable to different teams and stakeholders.

Data Cleaning is a process that aims to fix or remove incorrect and/or incomplete data from a dataset to maintain high levels of data quality. Without the cleaning processes, data collection and combination often result in duplicated, mislabeled, or even corrupted records.

When we talk about Data Collection, we refer to the process of gathering and measuring data in an organized manner, enabling us to elicit insights into phenomena and forecast future trends. Regardless of the field, accurate data collection is essential for reliable results. For example, in medicine, data collection can help us uncover correlations between diseases and their causes. In marketing, data collection can provide valuable insights into consumer behavior.

A Data Consumer role refers to a person or a system that uses or processes data in some way, including analyzing data to make decisions, displaying data to users, or using data to train a machine learning model.

When we talk about Data Democratization, we refer to a powerful concept that aims to make digital information accessible to everyone – including individuals without a technical background. Data Democratization is about giving people access to data, insights, and tools without relying on a third party like system administrators, data stewards, or IT departments.

Data Governance is an essential part of managing data quality, security, and privacy. It describes the system we use to manage information, as well as the actions taken with that information, including the availability, usability, integrity, and security of data in an organization.

Data Ingestion is the first step in collecting data from various sources. At this stage, we can understand the size and complexity of the data, influencing how we use the data later on in terms of access, use, and analysis. The goal of data ingestion is to collect data into any system requiring a particular structure or format for downstream data use.

Data Integration is the process of consolidating data from multiple sources into a single location to achieve a unified view of data for improved decision-making. Based on their data pipelines needs, companies usually use one of two data integration approaches: ETL (extract, transform, load) or ELT (extract, load, transform).

A Data Lake is a storage repository designed to store both structured and unstructured data in any form. This means we can use the data in raw and unprocessed forms. How does a data lake benefit us? Simply put, storing data from any source, at any size, speed, and structure, makes more robust and diverse queries, data science use cases, and new information discoveries possible. Unlike storing data in individual databases, using a data lake is cheaper, more flexible, scalable, easy to use, and provides superior data quality.

Data Lakehouse is a storage architecture that combines the cost-effectiveness of a data lake with a data warehouse’s analytic and structural benefits. A data lakehouse enables us to use the same large data sets for different types of machine learning and business intelligence workloads.

Data Lineage visually describes data flow over time, detailing its origin, iterations, and destination. The process allows us to track day-to-day use and error resolution operations while maintaining the accuracy and reliability of the data and compliance with regulatory requirements.

Data Masking is disguising sensitive data to enable us to use data accurately without exposing private information. There are many common data masking techniques, including:

● Nulling – Returning data values as blank or replaced with placeholders.

● Anagramming – Shuffling characters or digit order for each entry.

● Substitution – Replacing each value with a randomly selected value.

● Encryption – Translating sensitive exported data into a cipher that requires a password or key.

When we talk about Data Mining, we refer to extracting information from massive data sets to identify patterns and trends for use. It typically involves business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Through data mining, we can assess valuable information necessary to mitigate risk effectively, anticipate demands, monitor operational performance, and acquire new customers.

Data Monitoring refers to oversight mechanisms helping us to ensure that we use accurate, valid, and consistent data while maintaining security. It typically involves a manual or automated reporting process, with the ability to notify administrators of important events.

Data orchestration, a relatively new discipline in computer engineering, aims to match the right data with the right purpose. It does so by automating processes related to managing data, including collecting, combining, and preparing data for analysis.

With a Data Pipeline, we can move data from one place to another, typically through some kind of data transformation, including filtering, masking, and aggregations, to ensure both data integration and standardization.

We use a Data Platform as a central repository that combines and utilizes the features and capabilities of several big data applications. It supports us in the acquisition, storage, preparation, delivery, and the governance of our data while also ensuring high levels of security.

Data Platform as a Service (DPaaS) enables companies to collect, manage, monitor, analyze and present data via a centralized platform. When using a DPaaS, we ensure strict governance, privacy, and security features for data protection and integrity.

Data Quality tells us about the condition of a particular set of data, including its completeness, accuracy, consistency, timeliness, validity, and uniqueness. Data quality activities can involve data integration, cleaning, rationalization, and validation.

Data Security involves procedures and specific controls to protect data from accidental data exposure, unauthorized access, or data loss. We apply various techniques to mitigate security threats, including data encryption, data erasure, data resilience, or data masking.

When we talk about a Data Source, we refer to the original location from which a piece or a set of data comes. It can be a database, a data warehouse, a flat file, an XML file, or any other readable format.

Data Storytelling is all about creating a compelling narrative from data analysis results, aiming to translate complex ideas into actionable insights tailored to a specific audience. Among the most critical aspects of data storytelling are narrative and visualizations. 

We use Data Visualization to represent information graphically, highlight patterns and trends and transform complex data into a more accessible and understandable form, such as a chart or graph.

We use a Data Warehouse as a central repository for storing data from multiple sources within an organization, allowing us to make better decisions based on a single source of truth. Unlike data lakes that contain a vast amount of raw data, data warehouses store structured, processed data ready for strategic data analysis.

A Database Management System (DBMS) is a software system that serves as an interface for interacting with a database. It allows end-users to access their databases to organize and access the information as needed, thus ensuring data security and integrity.