Data cleansing, matching, integration and MDM are essential actions that give context and completeness to your data. However, ensuring data quality is a complex and time-consuming process that takes 80% of your time.
Automate your data management processes using proven, pre-built solutions
Build custom workflow that includes automation, data integrations, and human curations to solve your unique problems
Build accurate datasets at scale for your ML models with data quality management tools
Get rich insights through accurately matched diverse data sets
Manage data with Master Data Management solutions
Gather data from millions
of sources for further processing, migration, storage,
Augment data from reliable third-party sources and enhance data quality to get in-depth insights
Target a specific feature and parts of a data point to equip your ML algorithms with granular details
Classify, categorize, and label data based on formats/samples to strengthen predictive analytics of the training datasets
Normalize and standardize data values to make it homogeneous and compliant to universal formats
Get data delivered in any format - CSV, JSON or integrate directly to ERP, BI systems with powerful APIs
Detect outliers, spot errors, and eliminate bias in your datasets to ensure data is clean, comprehensive, and complete
Remove personal information from the database if your analytics or ML models do not require them
Our proprietary data quality tools include configurable bots, Worxtream, and Mojo that ensure end-to-end data quality.
Data Validation Bot More than 350 validation and quality checking rules that are configurable
Record Matching Bots Available across different data types and contexts, for matching, clustering, and deduplication using fuzzy logic and AI
Reference Bots Look-up internal data sources or web/public data sources for confidence scoring
Data Conversion Bots Conversion from electronic format to another using rules and algorithms
Human curation integration Mojo platform for curation, validation, and advanced rules configurations and flexible integrations at the end or in between process steps
Human curation solution as downstream of automation workflow
Feedback from the human-in-loop workflow for ML model enhancement
Get a broader picture of our data quality and annotation platform and the features
A minor glitch or mishap in the quality of information fed into your ML algorithms can cause a major dysfunction. For ML models to function efficiently, they must be fed with large datasets that are clean, well-labeled, accurate, complete, and consistent.
ML models go through multiple training iterations. Achieve the desired level of cognitive capability with Mojo that helps in laundering the database, normalizing, standardizing, and labeling data with a human-in-loop approach to ensure data quality down to a granular level.
Data scientists spend a lot of time in preparing the data before building the training sets. The manual approach is prone to severe errors. Automate the process with Worxtream - a cognitive process automation platform, to identify data types, mismatches, potential data quality issues, and integrate data into your systems through APIs.
Building an ML model is an on-going process. You may add additional datasets or review, modify, and edit the existing ones. We can handle millions of data seamlessly and enrich the training modules to help you achieve the desired level of cognitive capability.
Our data quality solutions will help you integrate structured, clean, and error-free data from disparate systems to your analytics pipeline.
Bring together data lying in silos with IT departments, ERP systems, or other databases within a single organization. Data mining and preprocessing helps you transform siloed data into an understandable format before bringing them into a data warehouse or big data lake or repository.
Eliminate data anomalies by normalizing and standardizing various formats. Feed the right data into warehouses for accurate insights.
As data scientists and data engineers who deal with huge datasets for machine learning and analytical projects, you need a single source of truth to store and manage millions of data.
X-tract.io facilitates in building MDM by streamlining the process of data management through data annotation, data cleansing, enrichment, and transformation.
Acquire and centralize data to a single repository (on-premises, in the cloud, or from third parties)
Enrichment of master data records to make it complete, comprehensive, and error-free
Create a trusted and holistic view to access data for both analytical and operational purposes
PIMworks, our home-grown Product Information Management platform facilitates eCommerce product data management. Our solution experts can help in building custom solutions to build your MDM.
Your CRM is the home for millions of data accumulated over the years. To implement successful campaigns, it is necessary that your database is accurate, fresh, and up-to-date. We can help you enrich your CRM with quality data and seamlessly integrate with your CRM with APIs.
Refresh and enrich your CRM
Run campaigns with confidence
Deliver insights based on analytics
Integrate data into CRM with APIs
Exclusive Whitepaper for Sales and Marketing Leaders.
Data ingested into BI tools like Tableau, Qlik, Power BI, and many others are in the form of complex and semi-structured files.
X-tract.io helps you transform this data, no matter how large or complex your files are through an extensive process of normalization, standardization, profiling, labeling, cleansing, and enrichment.
You don’t have to compromise on the quality of insights you derive from these BI tools owing to poor data quality . We can automate the entire process with an AI-powered data orchestration platform, Worxtream, that helps you achieve consistency in data structure before they can be fed into visualization tools.
A market research company conducted a study about an adult protein drink and presented their findings based on insights through data analysis and visualization. But, the foundation of this analysis was to own a clean database. Their database had information such as area wise population, population density, purchasing power parity, population growth, people with vitamin deficiency, sex ratio, etc. in an unstructured format with errors, missing values, obsolete data, and more.
We helped them with structuring and blending of data followed by data cleansing and enrichment in order to make it fit and functioning for further analysis.