Best On-Premise Data Quality Software

(Bank CFO) “I don’t have confidence and trust in our data. We keep discovering hidden risks”. Since 70% of data initiatives fail due to unreliable data (Gartner research), are you risking your reputation by trusting the accuracy of your data that you share with your business stakeholders and partners? Data Trust Scores must be measured in Data Lakes, warehouses, and throughout the pipeline, to ensure the data is trustworthy and fit for use. It typically takes 4-6 weeks of manual effort just to set a file or table for validation. Then, the rules have to be constantly updated as the data evolves. The only scalable option is to automate data validation rules discovery and rules maintenance. DataBuck is an autonomous, self-learning, Data Observability, Quality, Trustability and Data Matching tool. It reduces effort by 90% and errors by 70%. "What took my team of 10 Engineers 2 years to do, DataBuck could complete it in less than 8 hours." (VP, Enterprise Data Office, a US bank)

Use Semarchy unified data platform to experience xDM. Discover, govern, enrich, enlighten and manage data. You can easily transform data into insights with xDM and rapidly deliver data-rich applications with automated master data management. Its business-centric interfaces provide for rapid creation and adoption of data-rich applications, while automation rapidly generates applications to your specific requirements. Use the agile platform to quickly expand or evolve data applications.

Utilizing the data that's spread across your organization shouldn't be so difficult! With Zuar Runner you can automate the flow of data from hundreds of potential sources into a single destination. Collect, transform, model, warehouse, report, monitor and distribute: it's all managed by Zuar Runner. Pull data from Amazon/AWS products, Google products, Microsoft products, Avionte, Backblaze, BioTrackTHC, Box, Centro, Citrix, Coupa, DigitalOcean, Dropbox, CSV, Eventbrite, Facebook Ads, FTP, Firebase, Fullstory, GitHub, Hadoop, Hubic, Hubspot, IMAP, Jenzabar, Jira, JSON, Koofr, LeafLogix, Mailchimp, MariaDB, Marketo, MEGA, Metrc, OneDrive, MongoDB, MySQL, Netsuite, OpenDrive, Oracle, Paycom, pCloud, Pipedrive, PostgreSQL, put.io, Quickbooks, RingCentral, Salesforce, Seafile, Shopify, Skybox, Snowflake, Sugar CRM, SugarSync, Tableau, Tamarac, Tardigrade, Treez, Wurk, XML Tables, Yandex Disk, Zendesk, Zoho, and more!

QuerySurge leverages AI to automate the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Apps/ERPs with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Hadoop & NoSQL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise App/ERP Testing QuerySurge Features - Projects: Multi-project support - AI: automatically create datas validation tests based on data mappings - Smart Query Wizards: Create tests visually, without writing SQL - Data Quality at Speed: Automate the launch, execution, comparison & see results quickly - Test across 200+ platforms: Data Warehouses, Hadoop & NoSQL lakes, databases, flat files, XML, JSON, BI Reports - DevOps for Data & Continuous Testing: RESTful API with 60+ calls & integration with all mainstream solutions - Data Analytics & Data Intelligence:  Analytics dashboard & reports

Design, debug, run and troubleshoot data transformations and jobflows in a developer-friendly visual designer. Orchestrate data workloads that require tasks to be carried out in the right sequence, orchestrate multiple systems with the transparency of visual workflows. Deploy data workloads easily into a robust enterprise runtime environment. In cloud or on-premise. Make data available to people, applications and storage under a single unified platform. Manage your data workloads and related processes together in a single platform. No task is too complex. We’ve built CloverDX on years of experience with large enterprise projects. Developer-friendly open architecture and flexibility lets you package and hide the complexity for non-technical users. Manage the entire lifecycle of a data pipeline from design, deployment to evolution and testing. Get things done fast with the help of our in-house customer success teams.

Adopting data-centric AI has never been easier with automated data quality profiling and synthetic data generation. We help data scientists to unlock data's full potential. YData Fabric empowers users to easily understand and manage data assets, synthetic data for fast data access, and pipelines for iterative and scalable flows. Better data, and more reliable models delivered at scale. Automate data profiling for simple and fast exploratory data analysis. Upload and connect to your datasets through an easily configurable interface. Generate synthetic data that mimics the statistical properties and behavior of the real data. Protect your sensitive data, augment your datasets, and improve the efficiency of your models by replacing real data or enriching it with synthetic data. Refine and improve processes with pipelines, consume the data, clean it, transform your data, and work its quality to boost machine learning models' performance.

An AI-powered data management platform that enables true data democratization. Integrates & centralizes all data sources, facilitates collaboration, and empowers organizations for innovation, driven by Insights. SCIKIQ is a holistic business data platform that simplifies data complexities from business users through a no-code, drag-and-drop user interface which allows businesses to focus on driving value from data, thereby enabling them to grow, and make faster and smarter decisions with confidence. Use box integration, connect any data source, and ingest any structured and unstructured data. Build for business users, ease of use, a simple no-code platform, and use drag and drop to manage your data. Self-learning platform. Cloud agnostic, environment agnostic. Build on top of any data environment. SCIKIQ architecture is designed specifically to address the challenges facing the complex hybrid data landscape.

Coginiti, the AI-enabled enterprise data workspace, empowers everyone to get consistent answers fast to any business question. Accelerating the analytic development lifecycle from development to certification, Coginiti makes it easy for you to search and find approved metrics for your use case. Coginiti integrates all the functionality you need to build, approve, version, and curate analytics across all business domains for reuse, all while adhering to your data governance policy and standards. Data and analytic teams in the insurance, financial services, healthcare, and retail/consumer package goods industries trust Coginiti’s collaborative data workspace to deliver value to their customers.

DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. The platform provides an efficient user interface to quickly add data sources, configure data quality checks, and manage issues. DQOps comes with over 150 built-in data quality checks, but you can also design custom checks to detect any business-relevant data quality issues. The platform supports incremental data quality monitoring to support analyzing data quality of very big tables. Track data quality KPI scores using our built-in or custom dashboards to show progress in improving data quality to business sponsors. DQOps is DevOps-friendly, allowing you to define data quality definitions in YAML files stored in Git, run data quality checks directly from your data pipelines, or automate any action with a Python Client. DQOps works locally or as a SaaS platform.

Decube is a data management platform that helps organizations manage their data observability, data catalog, and data governance needs. It provides end-to-end visibility into data and ensures its accuracy, consistency, and trustworthiness. Decube's platform includes data observability, a data catalog, and data governance components that work together to provide a comprehensive solution. The data observability tools enable real-time monitoring and detection of data incidents, while the data catalog provides a centralized repository for data assets, making it easier to manage and govern data usage and access. The data governance tools provide robust access controls, audit reports, and data lineage tracking to demonstrate compliance with regulatory requirements. Decube's platform is customizable and scalable, making it easy for organizations to tailor it to meet their specific data management needs and manage data across different systems, data sources, and departments.

iCEDQ is a DataOps platform for testing and monitoring. iCEDQ is an agile rules engine for automated ETL Testing, Data Migration Testing, and Big Data Testing. It improves the productivity and shortens project timelines of testing data warehouse and ETL projects with powerful features. Identify data issues in your Data Warehouse, Big Data and Data Migration Projects. Use the iCEDQ platform to completely transform your ETL and Data Warehouse Testing landscape by automating it end to end by letting the user focus on analyzing and fixing the issues. The very first edition of iCEDQ designed to test and validate any volume of data using our in-memory engine. It supports complex validation with the help of SQL and Groovy. It is designed for high-performance Data Warehouse Testing. It scales based on the number of cores on the server and is 5X faster than the standard edition.

BigID is data visibility and control for all types of data, everywhere. Reimagine data management for privacy, security, and governance across your entire data landscape. With BigID, you can automatically discover and manage personal and sensitive data – and take action for privacy, protection, and perspective. BigID uses advanced machine learning and data intelligence to help enterprises better manage and protect their customer & sensitive data, meet data privacy and protection regulations, and leverage unmatched coverage for all data across all data stores. 2

Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear in your data and before anyone else is impacted. Detect, root-cause, and resolve issues quickly – allowing everyone to feel confident in the data driving your business. Connect Anomalo to your Enterprise Data Warehouse and begin monitoring the tables you care about within minutes. Our advanced machine learning will automatically learn the historical structure and patterns of your data, allowing us to alert you to many issues without the need to create rules or set thresholds.‍ You can also fine-tune and direct our monitoring in a couple of clicks via Anomalo’s No Code UI. Detecting an issue is not enough. Anomalo’s alerts offer rich visualizations and statistical summaries of what’s happening to allow you to quickly understand the magnitude and implications of the problem.‍

Up to 20 percent of a company’s contacts contain bad data according to industry experts; resulting in returned mail, address correction fees, bounced emails, and wasted sales and marketing efforts. Use the Data Quality Suite to standardize, verify and correct all your contact data, postal address, email address, phone number, and name for effective communications and efficient business operations. Verify, standardize, & transliterate addresses for over 240 countries. Use intelligent recognition to identify 650,000+ ethnically-diverse first & last names. Authenticate phone numbers, and geo-data & ensure mobile numbers are live & callable. Validate domain, syntax, spelling, & even test SMTP for global email verification. The Data Quality Suite helps organizations of all sizes verify and maintain data so they can effectively communicate with their customers via postal mail, email, or phone.

Digna is an AI-powered anomaly detection solution designed to meet the challenges of modern data quality management. It's domain agnostic, meaning it seamlessly adapts to various sectors, from finance to healthcare. Digna prioritizes data privacy, ensuring compliance with stringent data regulations. Moreover, it's built to scale, growing alongside your data infrastructure. With the flexibility to choose cloud-based or on-premises installation, Digna aligns with your organizational needs and security policies. In conclusion, Digna stands at the forefront of modern data quality solutions. Its user-friendly interface, combined with powerful AI-driven analytics, makes it an ideal choice for businesses seeking to improve their data quality. With its seamless integration, real-time monitoring, and adaptability, Digna is not just a tool; it’s a partner in your journey towards impeccable data quality.

Talend Data Fabric’s suite of cloud services efficiently handles all your integration and integrity challenges — on-premises or in the cloud, any source, any endpoint. Deliver trusted data at the moment you need it — for every user, every time. Ingest and integrate data, applications, files, events and APIs from any source or endpoint to any location, on-premise and in the cloud, easier and faster with an intuitive interface and no coding. Embed quality into data management and guarantee ironclad regulatory compliance with a thoroughly collaborative, pervasive and cohesive approach to data governance. Make the most informed decisions based on high quality, trustworthy data derived from batch and real-time processing and bolstered with market-leading data cleaning and enrichment tools. Get more value from your data by making it available internally and externally. Extensive self-service capabilities make building APIs easy— improve customer engagement.

Rapidly transform high-volume, disconnected data into trusted and actionable business insights with scalable enterprise data quality. Trillium Quality is a versatile, powerful data quality solution that supports your rapidly changing business needs, data sources and enterprise infrastructures – including big data and cloud. Its data cleansing and standardization features automatically understand global data, such as customer, product and financial data, in any context – making pre-formatting and pre-processing unnecessary. Trillium Quality services deploy in batch or in real-time, on-premises or in the cloud, using the same rule sets and standards across an unlimited number of applications and systems. Open APIs let you seamlessly connect to custom and third-party applications, while controlling and managing data quality services centrally from one location.

Data is used in every aspect of a processing plant or facility, it is underlying most operational processes, most business decisions, and most environmental events. Failures are often attributed to this same data, in terms of operator error, bad sensors, safety or environmental events, or poor analytics. This is where APERIO can alleviate these problems. Data integrity is a key element of Industry 4.0; the foundation upon which more advanced applications, such as predictive models, process optimization, and custom AI tools are developed. APERIO DataWise is the industry-leading provider of reliable, trusted data. Automate the quality of your PI data or digital twins continuously and at scale. Ensure validated data across the enterprise to improve asset reliability. Empower the operator to make better decisions. Detect threats made to operational data to ensure operational resilience. Accurately monitor & report sustainability metrics.

Deliver tangible strategic value, quickly. Ensure end-to-end support for growing data quality needs across users and data types with AI-driven automation. No matter what type of initiative your organization is working on—from data migration to next-gen analytics—Informatica Data Quality has the flexibility you need to easily deploy data quality for all use cases. 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.