Alternatives to Anomalo

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

Mask, generate, subset, virtualize, and automate your test data with the DATPROF Test Data Management Suite. Our solution helps managing Personally Identifiable Information and/or too large databases. Long waiting times for test data refreshes are a thing of the past.

In 30 minutes, you can monitor your entire warehouse. Automated warehouse-to-BI lineage can identify downstream impacts. Trust can be lost in seconds and regained in months. With modern data-era observability, you can have peace of mind. It can be difficult to get the coverage you need with code-based tests. They take hours to create and maintain. Metaplane allows you to add hundreds of tests in minutes. Foundational tests (e.g. We support foundational tests (e.g. row counts, freshness and schema drift), more complicated tests (distribution shifts, nullness shiftings, enum modifications), custom SQL, as well as everything in between. Manual thresholds can take a while to set and quickly become outdated as your data changes. Our anomaly detection algorithms use historical metadata to detect outliers. To minimize alert fatigue, monitor what is important, while also taking into account seasonality, trends and feedback from your team. You can also override manual thresholds.

Digna is a solution powered by AI that addresses the challenges of data quality management in modern times. It is domain agnostic and can be used in a variety of sectors, including finance and healthcare. Digna prioritizes privacy and ensures compliance with stringent regulations. It's also built to scale and grow with your data infrastructure. Digna is flexible enough to be installed on-premises or in the cloud, and it aligns with your organization's needs and security policies. Digna is at the forefront of data quality solutions. Its user-friendly design, combined with powerful AI analytics, makes Digna an ideal solution for businesses looking to improve data quality. Digna's seamless integration, real time monitoring, and adaptability make it more than just a tool. It is a partner on your journey to impeccable data quality.

A low-code no-code approach to data quality. SaaS offers flexibility, affordability, ease-of-integration, and efficient support. High standards for encryption, identity management and role-based access control. Data governance and compliance standards. Advanced ML models for detecting row-value data anomalies. The models will adapt to the business and data requirements of users. You can add any number of data sources, records, or attributes. For unpredictable volume spikes, well-equipped. Support streaming and batch processing. Data is continuously monitored to provide real-time notification, with no impact on pipeline performance. Easy boarding, integration, investigation. Telmai is a platform that allows Data Teams to detect and investigate anomalies in real-time. No-code on-boarding. Connect to your data source, and select alerting channels. Telmai will automatically learn data and alert you if there are unexpected drifts.

Cleanlab Studio is a single framework that handles all analytics and machine-learning tasks. It includes the entire data quality pipeline and data-centric AI. The automated pipeline takes care of all your ML tasks: data preprocessing and foundation model tuning, hyperparameters tuning, model selection. ML models can be used to diagnose data problems, and then re-trained using your corrected dataset. Explore the heatmap of all suggested corrections in your dataset. Cleanlab Studio offers all of this and more free of charge as soon as your dataset is uploaded. Cleanlab Studio is pre-loaded with a number of demo datasets and project examples. You can view them in your account once you sign in.

Automate the automatic coverage of thousands of tables using ML-based anomaly detection. 50+ custom metrics are also available. Monitoring of metadata and data. Comprehensive mapping of all dependencies between assets from ingestion to reporting. Collaboration between data consumers and data engineers is enhanced and productivity is increased. Sifflet integrates seamlessly with your data sources and preferred tools. It can run on AWS and Google Cloud Platform as well as Microsoft Azure. Keep an eye on your data's health and notify the team if quality criteria are not being met. In a matter of seconds, you can set up the basic coverage of all your tables. You can set the frequency, criticality, and even custom notifications. Use ML-based rules for any anomaly in your data. There is no need to create a new configuration. Each rule is unique because it learns from historical data as well as user feedback. A library of 50+ templates can be used to complement the automated rules.

Trackingplan is an observability tool for marketing that saves time for analysts and tag specialists. It monitors web and app traffic continuously to alert you to problems before they have a significant effect. It also helps you debug the problem to identify its origin and ensure a quick resolution. Trackingplan is plug-and-play: it's easy to install and auto-detects your analytics, marketing, and product stacks. It starts monitoring after a few days. Trackingplan also provides a summary report every morning at the start of your working day on the status of all your websites and apps. This includes everything from traffic tagged with campaigns to the behavior data sent to marketing pixels.

Enterprises can manage their data quality lifecycle proactively through contextual data checks, anomaly detection, and remediation. Expose anomalies, metadata and help teams take corrective action. Automate remediation workflows for quick and efficient error resolution. Maintain high data-quality and prevent errors from impacting business decisions. The SLA chart gives an overview of SLA. It includes the total number SLA monitoring performed and any violations. This chart will help you identify data areas that require further investigation or improvements.

DQOps is a data quality monitoring platform for data teams that helps detect and address quality issues before they impact your business. Track data quality KPIs on data quality dashboards and reach a 100% data quality score. DQOps helps monitor data warehouses and data lakes on the most popular data platforms. DQOps offers a built-in list of predefined data quality checks verifying key data quality dimensions. The extensibility of the platform allows you to modify existing checks or add custom, business-specific checks as needed. The DQOps platform easily integrates with DevOps environments and allows data quality definitions to be stored in a source repository along with the data pipeline code.

You can prevent data outages by identifying data quality issues and fixing them before they reach production. In less than a day, you can increase your test coverage for data pipelines from 0 to 100%. Automatic regression testing across billions upon billions of rows allows you to determine the impact of every code change. Automate change management, improve data literacy and compliance, and reduce incident response times. Don't be taken by surprise by data incidents. Automated anomaly detection allows you to be the first to know about them. Datafold's ML model, which can be easily adjusted by Datafold, adapts to seasonality or trend patterns in your data to create dynamic thresholds. You can save hours trying to understand data. The Data Catalog makes it easy to search for relevant data, fields, or explore distributions with an intuitive UI. Interactive full-text search, data profiling and consolidation of metadata all in one place.

We are a leader for Data Quality & ML Models for enterprises adopting a modern data management ecosystem, multi-cloud and ML. Algorithms for tracking Data Anomalies in Azure GCP and AWS databases. Measure and monitor data issues across all cloud database management tools, data silos and data silos using a single centralized tool. Quality is in the eyes of the beholder. Data issues can have different implications depending where you are in the enterprise. Qualdo was the first to organize all data quality issues from the perspective of multiple enterprise stakeholders and present a unified view. Use powerful auto-resolution algorithms for tracking and isolating critical data issues. Use robust reports and alerts for managing your enterprise regulatory compliance.

Get a clear view of your data assets: popularity, usage, and schema coverage. Get important insights into your data assets, such as popularity and utilization. Find and filter data based on tags and descriptions in metadata. Get valuable insights about your data assets, such as popularity, usage, quality, and schema cover. Drive data governance and ownership throughout your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Lineage maps are automatically generated at the field level to help understand the entire data ecosystem. Anomaly detection is based on your data and seasonality patterns. It uses automatic backfilling from historical data. Machine learning thresholds are trained for each data segment and not just metadata.

Our solutions monitor data 24 hours a day to detect any potential problems in the quality of data and its integration into other internal systems. This ensures that your bottom line will not be affected in any way. Verify that your data is accurate before it is transferred or shared among your systems. You'll be notified if something is not right and the data pipeline will be halted until it's resolved. Our data solutions are tailored to your industry and region to ensure regulatory compliance. Our customers are empowered to gain greater control of their data sets when we show them how easy it is to measure and meet data goals and requirements by leveraging our user interface.

Your one-touch copilot to data quality, spend, performance and usage. Your data team will not be the last one to learn about bottlenecks or broken analytics. We detect anomalies and alert users immediately. Eliminate downtimes and improve data quality. You'll be the one to know when performance starts to trend in the wrong direction. We help you make the connection between data usage, resource allocation and cost. Reduce costs and allocate resources efficiently. We can slice and dice spending by warehouse, user and query. You will be notified when spending starts to trend in the wrong direction. Get insights into the impact of underutilized data on your business. Revefi is constantly on the lookout for waste and will alert you to opportunities that can be better rationalized. With automated data monitoring built into your data warehouse, you can say goodbye to manual data checking. You can solve problems and find the root causes within minutes, before they impact your downstream users.

Automates data cleansing & consolidates data into a clean, trustworthy data layer to feed downstream reporting. Manages suppliers' data requests. Monitors data workflow to identify bottlenecks and resolve problems. To prove quality assurance for each data row, creates an audit trail. You can customize validation and governance to fit your organization. Data analysts can focus on their insights by reducing data prep time by 60% The central KPI Dashboard provides key metrics about your data pipeline. This allows you to identify bottlenecks and resolve issues, as well as improve performance. Flexible rules engine allow users to create validation and testing that are tailored to their needs. It's easy to integrate gather360 into your existing tools or use it for setting up your cloud infrastructure.

Waaila, a comprehensive application that monitors data quality, is supported by hundreds of analysts around the world. It helps to avoid disasters caused by poor data quality. Validate your data to take control of your analytics. They must be precise to maximize their potential, therefore validation and monitoring are essential. It is crucial that the data be accurate and reliable in order to serve its true purpose and allow for business growth. Marketing strategies that are more effective will be more efficient if they are of higher quality. You can rely on the accuracy and quality of your data to make data-driven decisions that will lead to the best results. Automated validation saves time and energy and delivers better results. Rapid attack discovery helps to prevent huge impacts and opens up new opportunities. The application management and navigation are simple, which allows for quick data validation and efficient processes that enable the issue to be quickly identified and solved.

Data can disrupt markets and open new frontiers, but it only works when it is trusted and understood. Stakeholders in your organization can collaborate to achieve data excellence by leveraging our AI/ML-enhanced cloud-based solution, which is built with 25 years worth of best practices. With hundreds of pre-built reports and embedded best practices, you can quickly identify and remediate data quality issues. Cleanse data before or during data migration. Track data quality in real time with customizable data intelligence dashboards. Monitor data objects continuously and automatically initiate remediation workflows to direct them to the correct data owners. To accelerate future data initiatives, consolidate data on a single cloud-based platform. All data stakeholders working together in one system will reduce effort and improve results.

TYPO is a data-quality solution that corrects errors at the point of entry to information systems. Typo uses AI to detect errors at the point of entry, rather than reactive tools that try to fix data errors after they have been saved. This allows for immediate corrections before they are stored and propagated into downstream systems and reports. Typo can be used in web apps, mobile apps, devices, and data integration tools. Typo can inspect data in motion as it enters an enterprise or at rest after storage. Typo provides complete oversight of data origins, points of entry and exit into information systems, including devices and APIs. The user is notified when an error is detected and given the chance to correct it. Typo uses machine learning algorithms for detecting errors. It is not necessary to implement and maintain data rules.

Deliver tangible strategic value, quickly. With AI-driven automation, you can ensure end-to-end support of data quality requirements across users and data types. 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. Facilitate collaboration between IT and business stakeholders and empower business users. All use cases and all workloads require management of the quality of multicloud and on-premises data. Integrates human tasks into the workflow. Business users can review, correct, or approve exceptions during the automated process. To uncover relationships and detect problems, profile data is used to perform iterative analysis of data. AI-driven insights can automate the most important tasks and simplify data discovery to increase productivity.

Empower enterprise data teams with the ability to prevent costly outages before they happen. With efficient time-bound queries, you can quickly scale data quality checks throughout enterprise data pipelines without compromising performance. Utilizing AI models that are specific to DQ, you can monitor and identify data anomalies without having to manually set thresholds. Lightup's solution provides you with the highest level of data quality so you can make confident decisions. Data quality intelligence will help you make confident decisions. Dashboards that are flexible and powerful provide transparency on data quality and trends. Lightup's built in connectors allow you to connect seamlessly to any data source within your data stack. Replace manual, resource-intensive data quality checks with automated ones to streamline workflows.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

Crux is used by the most powerful people to increase external data integration, transformation and observability, without increasing their headcount. Our cloud-native data technology accelerates the preparation, observation, and delivery of any external dataset. We can guarantee you receive high-quality data at the right time, in the right format, and in the right location. Automated schema detection, delivery schedule inference and lifecycle management are all tools that can be used to quickly build pipelines from any external source of data. A private catalog of linked and matched data products will increase your organization's discoverability. To quickly combine data from multiple sources and accelerate analytics, enrich, validate, and transform any data set, you can enrich, validate, or transform it.

The modern data workspace. All your data assets, from data tables to reports, will be instantly discoverable. The combination of powerful search algorithms and easy browsing makes it easy to find the right asset. Atlan automatically generates data quality profiles that make it easy to detect bad data. We have you covered, from automatic variable type detection and frequency distribution to missing values or outlier detection. Atlan takes the hassle out of managing and governing your data ecosystem. Atlan's bots analyze SQL query history to automatically construct data lineage. They also auto-detect PII information. This allows you to create dynamic access policies and best-in-class governance. Our Excel-like query builder allows anyone to query multiple data lakes, warehouses, and DBs. Native integrations with tools such as Tableau and Jupyter make data collaboration possible.

NetOwl NameMatcher was the winner of the MITRE Multicultural Name Matching Challenge. It offers the fastest, most accurate, and scalable name match possible. NetOwl solves complex fuzzy name matching problems by using a machine learning-based approach. Traditional name matching methods such as Soundex edit distance and rule-based methods have problems with precision (false positivities) and recall (false negativities) when it comes to addressing the various fuzzy name matching challenges. NetOwl uses a machine learning-based probabilistic approach that is empirically driven to solve name matching problems. It automatically derives intelligent, probabilistic names matching rules from large-scale, real world, multi-ethnicity variant data. NetOwl uses different matching models that are optimized for each entity type (e.g., person or organization, place). NetOwl also performs automatic detection of name ethnicity.

The Easy Way to Remove Duplicate Salesforce Records DataGroomr uses Machine Learning to automatically detect duplicate Salesforce records. Duplicate Salesforce records are automatically loaded into a queue so users can compare them side-by-side and decide which values to keep, add new values, or merge. DataGroomr provides everything you need to locate, merge, and get rid off dupes. DataGroomr's Machine Learning algorithms take care of the rest. You can merge duplicate records in one click or en masse from within the app. You can select field values to create a master record, or you can use inline editing for new values. You don't want to see duplicates across the entire organization. You can define your own data by industry, region, or any Salesforce field. The import wizard allows you to merge, deduplicate and append records while importing Salesforce. Automated duplication reports and mass merging tasks can be set up at a time that suits your schedule.

Find opportunities to increase revenue, improve the effectiveness of functional teams, and provide great customer experiences. Poor CRM data is a problem that must be fixed immediately. Learn how CRM data can hinder sales and what you should do about it. Higher quality data can help you generate up to 70% more revenue. You can control the frequency of assessment, which allows you to instantly gauge the impact of a list load and current data quality practices. You can compare your data quality to the industry average by aggregating analysis of duplicate, invalid, and malformed data across CRM objects. This step-by-step guide will show you where to focus your cleanup efforts, how long each task takes, and which tools you can use to restore quality. What you don't know could be dangerous. Get your Data Trust Score, and a detailed report about the quality and health your customer data.

This machine learning monitoring tool allows you to easily monitor and troubleshoot large model volumes. Data scientists can avoid false alarms and dead ends by using an unrivaled explainability accuracy and unique analyses that aren't available anywhere else. This allows them to quickly and effectively address critical problems. So that your business runs at its best, machine learning models are optimized. TruEra's explainability engine is the result of years of dedicated research and development. It is significantly more accurate that current tools. TruEra's enterprise-class AI explainability tech is unrivalled. The core diagnostic engine is built on six years of research by Carnegie Mellon University. It outperforms all competitors. The platform performs sophisticated sensitivity analyses quickly, allowing data scientists, business users, risk and compliance teams to understand how and why a model makes predictions.

Only Data Observability platform that allows complete control over enterprise data systems. Comprehensive, cross-sectional visibility of complex, interconnected data systems. Synthesizes signals across workloads and data quality, security, infrastructure, and security. Data processing and operational efficiency are improved. Automates data quality monitoring from start to finish for rapidly changing and mutable datasets. Acceldata offers a single window to identify, predict, and fix data problems. Complete data issues can be fixed in real-time. You can observe the flow of business data from one pane of glass. Find anomalies in interconnected data pipelines.

DataMatch Enterprise™, a highly visual data cleansing tool, is specifically designed to address customer and contact data quality problems. The platform uses a variety of proprietary and standard algorithms to identify phonetic and domain-specific variations. You can create scalable configurations that allow for deduplication, record linkage, suppression enhancement, extraction, standardization, standardization, and standardization of customer and business data. This will maximize the impact of your data throughout the enterprise.

Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.

iCEDQ, a DataOps platform that allows monitoring and testing, is a DataOps platform. iCEDQ is an agile rules engine that automates ETL Testing, Data Migration Testing and Big Data Testing. It increases productivity and reduces project timelines for testing data warehouses and ETL projects. Identify data problems in your Data Warehouse, Big Data, and Data Migration Projects. The iCEDQ platform can transform your ETL or Data Warehouse Testing landscape. It automates it from end to end, allowing the user to focus on analyzing the issues and fixing them. The first edition of iCEDQ was designed to validate and test any volume of data with our in-memory engine. It can perform complex validation using SQL and Groovy. It is optimized for Data Warehouse Testing. It scales based upon the number of cores on a server and is 5X faster that the standard edition.

Data is used to inform every aspect of a plant or facility. It is the basis for most operational processes, business decisions, and environmental events. This data is often blamed for failures, whether it's operator error, bad sensor, safety or environmental events or poor analytics. APERIO can help solve these problems. Data integrity is a critical element of Industry 4.0. It is the foundation on which more advanced applications such as predictive models and process optimization are built. APERIO DataWise provides reliable, trusted data. Automate the quality of PI data and digital twins at scale. Validated data is required across the enterprise in order to improve asset reliability. Empowering the operator to take better decisions. Detect threats to operational data in order to ensure operational resilience. Monitor & report sustainability metrics accurately.

Automate every stage in your systematic literature review to produce evidence-based research more quickly and accurately. DistillerSR automates literature collection, triage and assessment with AI and intelligent workflows. DistillerSR makes managing any project, no matter how large or small, easier. It can also be configured to produce transparent, audit-ready, and compliant literature reviews. DistillerSR integrates with data providers such as PubMed, automatic reviews updates, and AI-powered duplicative detection and removal. This makes it easier to search more efficiently. Automatically import new published references, keeping literature reviews current. You can detect duplicate citations and remove them, preventing bias and skew caused by studies that were included more than once. DistillerSR can reduce your screening burden by 60% DistillerSR makes it easier to get started on the later stages of your review faster and more accurately.

Data visibility and control for security, compliance, privacy, and governance. BigID's platform includes a foundational data discovery platform combining data classification and cataloging for finding personal, sensitive and high value data - plus a modular array of add on apps for solving discrete problems in privacy, security and governance. Automate scans, discovery, classification, workflows, and more on the data you need - and find all PI, PII, sensitive, and critical data across unstructured and structured data, on-prem and in the cloud. 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.

This platform is the ultimate tool for expanding your business with data-driven decisions. Every step of your supply chain journey can be improved. Our no-code platform improves the quality and quantity of master data to provide you with a range of optimization solutions, including inventory planning and distribution network. Trusted data-driven analytics can help you prevent critical issues from occurring and make crucial real-time adjustments. You can build trust in your data and manage it with confidence. Our platform is user-friendly and provides financial institutions with data-driven insights that are transparent and easy to use to improve their financial processes. We provide eXplainableAI to business professionals so they can create advanced financial models and improve their decision-making. Rulex Academy will help you learn how to analyze your data, create workflows, understand algorithms, and optimize complex processes using our interactive online training courses.

End-to-end DataOps built upon an agile platform that protects and improves your data assets. Arena is the leading augmented data management platform. Our active data catalog allows for self-service data enrichment to control complex data environments. You can create custom workflows to increase the reliability and accuracy of each data set. Machine-learning can be used to identify and align master assets for better data decisions. Superior security is assured with complete lineage, including detailed visualizations and masking. Data management is easy with Arena. Arena can catalog your data from any location. Our extensible connections allow for analytics across all your preferred tools. Overcome data sprawl challenges with our software. Our software is designed to drive business and analytics success, while also providing the controls and extensibility required in today's multicloud data complexity.

DQLabs has over a decade of data-related experience for Fortune 100 clients. This includes data integration, data governance and data visualization. The platform comes with all the features necessary to enable autonomous execution without any configuration or manual intervention. This AI-powered tool can automate every aspect of scaling, governance, and automation. It can also be integrated and compatible with other tools within the data ecosystem. Machine Learning and AI make it possible to make decisions in all aspects data management. No more ETL, workflows and rules. The platform automatically learns and reconfigures rules as the business strategy changes and requires new data patterns and trends.

Standardize your data across multiple formats and inputs by extracting, normalizing, and standardizing it. Normalize all information, both business and personal, structured or unstructured. The supervised machine learning neural networks-based techniques are used to precisely understand the structure and variations in different types of information. Data is automatically parsed using these techniques. Spectrum Quality is well-suited for global clients who require multi-level data standardization, transliteration, and translation for multiple languages and culturally relevant terms. This includes those in Arabic and Chinese, Japanese, Korean, and Japanese. Our text-processing technology allows information extraction from any input text in natural languages and assigns categories to unstructured texts. You can use pre-trained models and machine-learning based algorithms to extract entities and further train your models to customize your models to identify specific entities of any type or domain.

A platform for data management powered by AI that allows data democratization. Insights drives innovation by integrating and centralizing all data sources, facilitating collaboration, and empowering organizations for innovation. SCIKIQ, a holistic business platform, simplifies the data complexities of business users through a drag-and-drop user interface. This allows businesses to concentrate on driving value out of data, allowing them to grow and make better decisions. You can connect any data source and use box integration to ingest both structured and unstructured data. Built for business users, easy to use, no-code platform, drag and drop data management. Self-learning platform. Cloud agnostic, environment agnostic. You can build on top of any data environment. The SCIKIQ architecture was specifically designed to address the complex hybrid data landscape.

Data plays an important role in many areas of a company, including sales, marketing, and finance. Data must be managed throughout its life cycle to get the most out of it. Uniserv's core philosophy and the products it offers is data quality. Our customized solutions make your customer master information the success factor for your company. The Data Quality Service Hub ensures that customer data quality is high at all levels of your company, including international. We can correct your address information according international standards and based upon first-class reference data. We can also check bank data, telephone numbers, and email addresses at different levels. We can search your data for redundant items based on your business rules. These items can be consolidated automatically using prescribed rules or manually sorted for reprocessing.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

Vertify's Data Quality Analysis provides a preview of marketing's greatest asset, its contact database. The Synthio – by Vertify Data Quality Analysis provides a preview of marketing's greatest asset: the contact database. The DQA will provide you with an overview of your email addresses and tell you how many of your contacts have moved to a new company. It also allows you to see the potential number of contacts you might be missing in your marketing database. Synthio by Vertify integrates with top CRM and MAP systems for data cleansing, enrichment and origination.

Monitor your data health, and monitor your pipeline performance. Get unified visibility for all pipelines that use cloud-native tools such as Apache Spark, Snowflake and BigQuery. A platform for Data Engineers that provides observability. Data engineering is becoming more complex as business stakeholders demand it. Databand can help you catch-up. More pipelines, more complexity. Data engineers are working with more complex infrastructure and pushing for faster release speeds. It is more difficult to understand why a process failed, why it is running late, and how changes impact the quality of data outputs. Data consumers are frustrated by inconsistent results, model performance, delays in data delivery, and other issues. A lack of transparency and trust in data delivery can lead to confusion about the exact source of the data. Pipeline logs, data quality metrics, and errors are all captured and stored in separate, isolated systems.

Synthesized can help you unlock the full potential of your data projects and AI. We automate all stages of data preparation and provisioning with cutting-edge AI. The platform synthesizes data without exposing personal information or causing compliance issues. Software to prepare and provide accurate synthetic data for building better models at scale. Synthesized solves the problem of data sharing for businesses. 40% of companies that invest in AI can't report business benefits. Our easy-to-use platform allows data scientists, product, and marketing teams to focus on revealing critical insight. Without representative data, testing data-driven applications can be difficult. This can lead to problems when services go live.

Identify code issues and optimize code in real-time. Prevent data incidents before deployment. Manage code changes that impact data from the operational database all the way to the dashboard. Data lineage is automated, allowing for analysis of every dependency, from the operational database to the reporting layer. Foundational automates the enforcement of data contracts by analyzing each repository, from upstream to downstream, directly from the source code. Use Foundational to identify and prevent code and data issues. Create controls and guardrails. Foundational can be configured in minutes without requiring any code changes.

The Convertr platform gives marketers visibility and control over data processes and lead quality to create higher performing demand programs. When you take control of your lead processes in the beginning, you build more scalable operations and strategic teams that can stay focused on revenue driving activities. Improve Productivity: Weeks to months of manual lead data processing can be reallocated to revenue driving activities Focus on Performance: Teams work off trusted data to make better decisions and optimize programs Drive Data Alignment: Data moves between teams and platforms in usable, analyzable formats

With exceptional functionality for data integration, quality and cleansing, maximize the value of all structured and unstructured data in your organization. SAP Data Services software increases the quality of enterprise data. It is part of SAP's Information Management Layer. It delivers timely, relevant, and trusted information to help drive better business outcomes. Transform your data into a reliable, always-available resource for business insights and use it to streamline operations and maximize efficiency. Get contextual insight and unlock the true potential of your data with a complete view of all your information. Access to any size data and any source. Standardizing and matching data can improve decision-making and operational efficiency. This will reduce duplicates, identify relationships and address quality issues proactively. Use intuitive tools to unify critical data whether it is on-premise, in the cloud or within Big Data.

You can unify your data catalog, reduce communication overhead, and enable quality control for any employee of your company without having to deploy or install anything. Rudol is a data platform that helps companies understand all data sources, regardless of where they are from. It reduces communication in reporting processes and urgencies and allows data quality diagnosis and issue prevention for all company members. Each organization can add data sources from rudol's growing list of providers and BI tools that have a standardized structure. This includes MySQL, PostgreSQL. Redshift. Snowflake. Kafka. S3*. BigQuery*. MongoDB*. Tableau*. PowerBI*. Looker* (*in development). No matter where the data comes from, anyone can easily understand where it is stored, read its documentation, and contact data owners via our integrations.