Advanced data analytics refers to the use of sophisticated techniques like machine learning, predictive modeling, and AI to analyze data and derive more complex insights.

Refers to the framework and policies implemented to manage and oversee the use of data within artificial intelligence (AI) and machine learning applications. This involves establishing standards for data quality, privacy, security, and ethical use, as well as defining roles and responsibilities for data stewardship. Effective data governance is essential for maintaining compliance with regulations and building trust in AI systems.

Refers to the practices and technologies used to manage data specifically for artificial intelligence (AI) applications and machine learning models. This encompasses data collection, storage, processing, and governance, focusing on the quality, relevance, and accessibility of data required to train and deploy AI systems effectively. Proper data management is critical for maximizing the performance of AI algorithms and achieving reliable results.

Anomaly detection refers to identifying patterns in data t hat deviate from the expected behavior, helpingorganizations catch errors, fraud, or unexpected trends. Typically, anomaly detection uses AI-driven algorithms that monitor data flows for out liers. These algorithms learn from historical data, continuously improving their accuracy. Anomalies can trigger alerts for manual review or automated corrective actions.

For instance, in a financial institution, anomaly detection could identify suspicious transactions, such as a sudden spike in wit hdrawals, prompting further investigation.

Asset data refers to the information related to an organization’s physical or digital assets, including details such as location, specifications, condition, value, and ownership. This data is critical for managing and optimizing asset performance, lifecycle management, and compliance with regulatory requirements. Accurate asset data supports informed decision-making and effective resource allocation.

Batch processing is a method where data is processed in groups or batches at scheduled intervals, rather than in real-time. This approach is ideal for handling large datasets or repetitive tasks that do not require immediate processing. Batch processing typically operates within an ELT (Extract, Load, Transform) or ETL (Extract, Transform, Load) architecture. The data is first extracted from various sources and then loaded into a staging area (such as a data warehouse), where transformations like filtering, sorting, or aggregating are applied before the data is moved to its final destination. This framework allows for high scalability and performance, making it well-suited for processing large volumes of data without disrupting real-time operations. Batch processing can also be scheduled to run during off-peak hours to minimize the impact on system performance. An example of batch processing would be a retail company processing daily sales transactions. Throughout the day, sales data is collected but not immediately processed. At night, the data from all stores is processed in a batch, where it is cleaned, aggregated, and transformed into reports for business analysis the following day.

Business intelligence (BI) refers to the technologies, practices, and applications used to collect, analyze, and present business data. The goal of BI is to support better decision-making by providing insights into operational performance, market trends, and customer behavior. BI encompasses various tools and processes, including data mining, reporting, dashboards, and data visualization.

Continuous integration refers to the practice of automatically integrating and validating new data as soon as it is available so that data remains consistent and up to date.

Correlation data analysis examines the relationships between two or more variables to determine how changes in one variable impact others.

The policies, procedures, and technologies that regulate who can view or use information within an organization. This means sensitive data is only accessible to authorized users, protecting against unauthorized access and data breaches. Effective data access control helps maintain data confidentiality, integrity, and compliance with legal and regulatory requirements.

A term meaning that data is readily available to all authorized users within an organization, regardless of where it is stored. It involves providing secure, easy access to data without compromising security or governance.

The process of gathering and summarizing data from various sources into a single, unified dataset for analysis.

Data analysis reporting is the process of presenting analyzed data in a structured format, such as charts or dashboards, to communicate insights to stakeholders. Semarchy makes it easier to understand your data with its Intelligent Data Hub and reporting tools that allow users to create customized dashboards and reports, improving visibility into data performance. Learn more about business intelligence capabilities.

Data analytics is the process of examining datasets to draw insights and make informed decisions. It involves techniques ranging from basic statistics to complex AI-driven analysis. Semarchy provides built-in analytics tools that allow users to analyze and visualize data directly within the platform, supporting real-time decision-making. See how Semarchy customers are using our business intelligence (BI) and analytics capabilities, listen to why enabling real-time data and analytics is so important, or explore the relationship between data analytics and data governance.

A data catalog is a centralized repository of an organization’s data assets, providing metadata about data sources, structure, and use cases to facilitate data discovery and management. Semarchy includes an advanced data catalog, supporting data discovery, governance, and compliance so users can easily access and manage their data assets. Learn more about what a data catalog is and explore an overview of Semarchy’s solution.

The process of organizing data into categories based on specific criteria, such as sensitivity, importance, or purpose. This practice facilitates better data management, security, and compliance by allowing organizations to apply appropriate handling and protection measures to different types of data.

Data cleansing is often seen as the second stage of data quality management, focusing on correcting or removing data errors, inconsistencies, and duplicates. It involves standardizing data formats, validating data against predefined rules, and merging duplicate records. Learn more about data quality best practices.

Removing duplicate entries from a dataset to ensure each data entity is unique and reduce storage needs. View our infographic on how to achieve “golden records” with Semarchy.

The process of identifying patterns, relationships, and insights in data, often as a first step in analysis.

Data encryption converts data into a secure format to prevent unauthorized access and increase data privacy and security.

Enhances existing data by adding external information or additional context to improve its value.

Data governance refers to the overall management of data availability, usability, integrity, and security within an organization. This discipline involves establishing policies, standards, and procedures to manage data effectively while defining roles and responsibilities for data stewardship. Effective data governance helps organizations maintain compliance with regulations, improve data quality, and promote accountability in data management. Semarchy xDM provides robust data governance features that facilitate the establishment of data policies and practices. These capabilities include tools for data lineage tracking, access control, and quality monitoring, allowing organizations to maintain oversight of their data assets. By integrating data governance within its platform, Semarchy promotes a culture of data stewardship, supporting organizations in achieving their data management goals. For more information about how Semarchy supports data governance within its platform, you can explore the xDM solution or data governance case studies.

Data integration is the process of combining data from different sources to provide a unified view. It involves extracting, transforming, and loading data into a single system for consistency across platforms. In Semarchy, data integration is a core function of Semarchy Data Platform, allowing users to bring together data from multiple systems, transform it as needed, and make it accessible for analysis and reporting. Semarchy supports real-time and batch integration, enabling seamless data flow across departments and systems. Learn more about our data integration software and tools or explore Semarchy’s integration documentation.

Data intelligence refers to the insights gained from analyzing data, often involving advanced techniques like AI and machine learning. It helps organizations understand their data better and make data-driven decisions. This is done by examining details about the categories, quality, origins, custodianship, modifications, and interconnections of data. Simply put, data intelligence is the who, what, where, when, and how of your data. Semarchy provides an end-to-end data intelligence solution for every asset, initiative, and stakeholder. Learn more about bit by downloading our data intelligence eBook or explore our what is data intelligence blog.

Data lake management involves organizing, storing, and governing large volumes of raw data within a data lake — a centralized repository for structured and unstructured data. Effective management ensures that data remains accessible and usable for analysis.

Data lifecycle management (DLM) refers to the comprehensive process of managing data throughout its entire lifecycle, from creation and storage to usage, archiving, and deletion. This approach involves implementing policies and practices that govern data handling so that data remains accurate, secure, and compliant with regulatory requirements at every stage.

Data lineage tracks the movement and transformation of data throughout its lifecycle, from source to destination. It allows organizations to trace the origin, changes, and usage of data, ensuring transparency and compliance. With Semarchy xDI, data lineage is an integrated feature, offering a detailed view of how data flows up and downstream through the system. This helps users ensure data quality, maintain governance, and comply with regulations.

Data management is the practice of organizing, storing, and maintaining both internal and external data into a single master or “golden record” so it is accurate, accessible, and secure. This includes everything from data storage to governance and quality management. Data management is the backbone of the Semarchy Data Platform. It provides a comprehensive solution for managing master data so that all organizational data is clean, consistent, and available for use. Explore Semarchy master data management or read through our ultimate master data management (MDM) guide.

Data migration is the process of moving data from one system or format to another, often during system upgrades or consolidations. This can involve transferring large volumes of data while maintaining integrity and accuracy. In Semarchy, data migration is facilitated by the platform’s integration and transformation capabilities, allowing data to move seamlessly between systems while maintaining quality and compliance. Learn more about migrating your data to the cloud or mastering your Systems, Applications, and Products in Data Processing (SAP) data migration strategy.

The process of defining how data is structured, stored, and accessed. It involves creating diagrams or schemas that describe data relationships, rules, and constraints so that data is used correctly across systems.

Data orchestration refers to the automated process of coordinating and managing data flows across various systems and applications. This practice involves integrating data from multiple sources, transforming it as needed, and delivering it to the appropriate destinations for analysis or operational use. Data orchestration streamlines workflows, enhances data accessibility, and improves the overall efficiency of data management.

Data privacy refers to the protection of personal and sensitive information from unauthorized access, use, or disclosure. It encompasses the policies and practices organizations implement to safeguard data while complying with legal and regulatory requirements. Data privacy is crucial for maintaining trust with customers and stakeholders, as it addresses concerns related to individual rights and the ethical use of data.

Refers to the accuracy, completeness, consistency, and reliability of data for its intended use. High-quality data allows organizations to make better decisions, improve operational efficiency, and maintain regulatory compliance. Poor data quality can lead to errors, misinformed decisions, and increased operational risks. Data quality is a key component of the xDM platform. Semarchy integrates data quality management directly into its master data management (MDM) solution, offering automated tools for data cleansing, deduplication, validation, and enrichment. The platform verifies all data entering the system meets rigorous quality standards, providing businesses with clean, accurate, and trustworthy data for reporting and analytics. For more information about data quality in Semarchy, you can explore xDM documentation, review a data quality tutorial, or explore our blog on getting data quality right.

Data risk management involves identifying, assessing, and mitigating risks associated with data handling and storage within an organization. This process focuses on protecting data from potential threats such as data breaches, unauthorized access, and compliance violations. Effective data risk management promotes data integrity, confidentiality, and availability, helping organizations safeguard their valuable information assets.

Data synchronization is the process of maintaining consistency and accuracy of data across multiple systems, applications, or databases. When data is updated in one system, those changes are automatically reflected in all connected systems, keeping data aligned and preventing discrepancies.

Data transformation is the process of converting data from one format or structure into another to make it compatible with the target system or for analysis. This process can involve various operations such as cleansing, aggregating, filtering, or enriching data so it meets specific business requirements or analytical needs.

ETL (Extract, Transform, Load) is a data integration process that involves three key stages: extracting data from various source systems, transforming it into a suitable format or structure for analysis, and loading it into a target database or data warehouse. It is essential for consolidating data from different sources so it is clean, accurate, and ready for reporting and analysis.

The General Data Protection Regulation (GDPR) is a comprehensive data privacy law enacted by the European Union. This regulation governs how organizations collect, process, store, and share personal data, emphasizing the protection of individuals’ privacy rights. Key principles of GDPR include obtaining explicit consent for data processing, maintaining data accuracy, allowing individuals to access their data, and implementing measures for data security and breach notifications. Semarchy xDM includes features designed to assist organizations in achieving and maintaining GDPR compliance. It offers tools for managing personal data, including consent management, data lineage tracking, and automated data governance processes. These capabilities allow organizations to demonstrate compliance with GDPR requirements, such as maintaining accurate records of data processing activities and responding to data subject requests effectively. By integrating data privacy and security practices into its core functionalities, Semarchy supports organizations in navigating the complexities of GDPR while promoting efficient data management processes. For more information about GDPR compliance and how Semarchy can assist organizations in this area, you can visit our blog on GDPR master data management or our GDPR datasheet.

A golden record is a single, authoritative version of data that consolidates information from multiple sources into one complete and accurate dataset. This record serves as the trusted source of truth for critical business data, providing a comprehensive view that eliminates discrepancies and inconsistencies. Semarchy automates the creation and maintenance of golden records for master data domains. By integrating data from various systems and applying cleansing, deduplication, and enrichment processes, Semarchy produces a unified dataset that represents the most accurate and up-to-date information. This capability is vital for organizations looking to improve data quality, streamline operations, and enhance decision-making based on reliable insights.

Metadata management involves the processes and technologies used to collect, store, organize, and maintain metadata, which is data that describes other data. Effective metadata management enables organizations to understand their data assets better, facilitating data discovery, data governance, and compliance efforts. Semarchy xDM includes robust metadata management capabilities that help organizations document and manage their metadata across various data sources. This functionality provides a clear overview of data lineage, data definitions, and data quality metrics, promoting transparency and accessibility. By integrating metadata management within its master data management solution, Semarchy empowers users to gain valuable insights into their data landscape, improving data governance and compliance with regulatory requirements. Learn more about the differences between metadata and master data management.

Real-time data analytics refers to the process of analyzing data as it’s being generated, providing immediate insights.

Time series data analysis is the study of data points collected or recorded at specific time intervals to identify trends or patterns over time.

A comprehensive approach that involves managing all aspects of an organization’s data, offering a full, unified view across various data sources. The Semarchy Data Platform enables 360-degree data management by consolidating master data management, data intelligence, and data integration, providing visibility and control over data across departments.