Data Governance & Quality

Data Governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate data governance.

Data Quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is “fit for its intended uses in operations, decision making and planning”. Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People’s views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. When this is the case, data governance is used to form agreed upon definitions and standards for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality.

Deliver tangible strategic value, quickly. Ensure end-to-end support for growing data quality needs across users and data types with AI-driven automation.

✓ Empower business users and facilitate collaboration between IT and business stakeholders.

✓ Manage the quality of multi-cloud and on-premises data for all use cases and for all workloads.

✓ Incorporates human tasks into the workflow, allowing business users to review, correct, and approve exceptions throughout the automated process.

✓ Profile data and perform iterative data analysis to uncover relationships and better detect problems.

✓ Use AI-driven insights to automate the most critical tasks and streamline data discovery to increase productivity and effectiveness.

✓ Enable business users to build and test logical business rules without relying on IT.

✓ Accelerate projects with a comprehensive set of pre-built business rules and accelerators; reuse common data quality rules across any data from any source to save time and resources.

✓ Ensure delivery of high-quality information with data standardization, validation, enrichment, de-duplication, and consolidation capabilities.

Empower collaboration to fuel business initiatives with trusted, governed data. Your teams need consistent, trusted data to support data-driven decision making. Make sure they have it with integrated, automated, intelligent data governance at scale.

✓ Easily identify stakeholders and facilitate knowledge transfer across communities so teams can learn from each other.

✓ Ensure that teams can quickly find, access, and understand the data they need to uncover analytics insights with a carefully curated data marketplace.

✓ Use governed data to fuel key initiatives (such as improving customer experience) and deliver consistent, trusted results across your organization.

✓ Build governance and data privacy into your processes and projects from the start to support compliance with regulations like GDPR and CCPA.

✓ Develop a common data dictionary to provide a consistent source of business context across multiple tools.

v Generate data quality measurements based on business definitions, then measure and monitor over time.

✓ Create end-to-end business flows to visualize and expose impacts, dependencies, duplication, and more. Automatically tie business semantics to technical metadata.

✓ Customize key fields to ensure that data governance communities can execute their program properly

Confidently engage with your customers using verified and enriched contact data. Data as a Service (DaaS) helps organizations of all sizes verify and enrich their data so they can confidently engage with their customers. With customer experience and engagement a top focus across all industries, ensure that messages and products make it to their intended targets via postal mail, email, or phone.

Quickly identify, fix, and monitor data quality problems in your cloud and on-premises business applications. Get up and running faster with comprehensive self-service capabilities that you can use across all cloud and on-premises sources.

Informatica Cloud Data Quality’s intelligent self-service approach enables you to cleanse, standardize, and enrich all data using an extensive set of prebuilt data quality rules that includes address verification.

✓ Profile data and perform iterative data analysis to understand the nature of your data and better detect problems.

✓ Ensure delivery of high-quality information with data cleansing and standardization, verification, and enrichment capabilities.

✓ Accelerate projects with a comprehensive set of pre-built business rules and accelerators; reuse common data quality rules across any data from any source to save time and resources.

✓ Analyze the level of duplication across all records in a data set and consolidate duplicates into a single, preferred record.

✓ Enrich and standardize any data from multi-cloud and on-premises sources for all use cases and for all workloads across the enterprise.

✓ Automate generation of data quality measurements based on business rule definitions associated to a term or a critical data element.

✓ Continuously monitor and track data quality across source systems over time.

✓ Leverage a modular, agile approach to implementing data quality for faster integration and flexible deployment.

Deliver fit-for-purpose big data with scalable, role-based data quality. Manage all your big data on Spark or Hadoop in the cloud or in on-premises environments to ensure it is trusted and relevant.

Cleanse, standardize, and enrich all data—big and small—using an extensive set of prebuilt data quality rules including address verification.

✓ Deploy pre-built data quality rules so you can easily handle the scale of big data to improve quality across the enterprise.

✓ Understand the nature of your data and identify the relationships between various data objects.

✓ Use relevant, accurate, clean, and valid data to operationalize your machine learning models.

✓ Data standardization, validation, enrichment, de-duplication, and consolidation ensure delivery of high-quality information.

✓ Empowers business users and facilitates collaboration between IT and business stakeholders.

✓ Use AI-driven insights to automate the most critical tasks and streamline data discovery to increase productivity and effectiveness.

Discover and inventory data assets across your organization. A machine learning-based data catalog that lets you classify and organize data assets across any environment to maximize data value and reuse, and provides a metadata system of record for the enterprise.

✓ Automatic scanning to discover and catalog assets across on-premises, cloud, and big data platforms; across BI tools, ETL, and third-party metadata catalogs; and across structured and unstructured data types.

✓ Automate data curation with AI-powered domain discovery, data similarity, and business term associations and recommendations.

✓ Enable more self-service with simple, click-through data provisioning to deliver your data to desired targets.

✓ Tap into shared data knowledge with certifications, ratings and reviews, a Q&A platform, and change notifications.

✓ Get complete tracking of data movement, from high-level system views to granular column/metric-level lineage, and detailed impact analysis.

✓ Fully understand the quality levels of key data assets by viewing data quality rules, scorecards, metric groups, and profiling stats.

✓ Quickly identify related tables, views, domains, and reports. See intelligent data recommendations based on column similarity and inferred domains.

✓ Discover and understand data in context based on lineage, certifications, peer reviews, and intelligent metadata—all within the native Tableau user interface.