Wavicle Data Solutions Introduces Augment™, Its New Machine Learning-Powered Augmented Data Management Platform
Augment™ Solution Offers Completely Unified Platform to Fill Market Gaps for Faster Time to Value for Data Integration, Data Quality, and Data Privacy Compliance
Chicago-based Wavicle Data Solutions, a leading data analytics firm offering cloud migration services and data management consulting, announced the launch of Augment™, the company’s new data management platform for achieving clean, compliant data as quickly as possible. Augment was specifically developed to address the ongoing challenges Wavicle teams experienced with their client’s data analytics and data management projects and the gaps that lead to expensive developments and delayed data delivery.
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- Eliminates source and target database limitations during integrations and migrations. One of the most common challenges faced by enterprises during a data integration or cloud migration is the ability to connect a large number of data sources through target databases easily and quickly without driving up project costs. Augment takes a unique approach to how it builds out it’s connections. Augment leverages the most popular databases so users can connect to virtually any source available. During initial setup, Wavicle’s Augment services can also build out API connections, doing in days or weeks what can take months to accomplish with other products available.
- Changes the trust paradigm surrounding data quality. According to Gartner, 60% of organizations say the need for data quality across data sources and environments is their biggest data management challenge. Augment automates data quality assessments and provides a dashboard view of more than 20 of the most commonly used data checks to give you a snapshot of the health of your data, and make sure data is as clean as possible and ready for analytics. With a user-friendly interface and point-and-click functionality, it is easy for technical and non-technical users to operate with no additional coding required. Additional data checks can be added rapidly with almost no downtime and minimal cost and effort.
- Automates compliance and data privacy regulations. CIODive reports that 67% of companies that met the GDPR compliance deadline worry about maintaining compliance. Frequently Wavicle customers are faced with daunting compliance and data privacy challenges that can result in monetary penalties and damage if not managed properly. Augment uses machine learning and NLP capabilities to pre-emptively secure sensitive data before/after ingestion, ensuring that it doesn’t end up compromised.
- Speeds time to value, freeing up data engineers, citizen developers, data scientists, and budget for other strategic, complex work. Augment has baked in most features enterprises would need for their data management and governance activities to make integrations and migrations simple and quick, but it also complements the full gamut of ETL tools and data quality and PII tools. For example, Augment has successfully accelerated development of data pipelines by 90% for one client, reduced the time spent on data quality processes by 70%, and decreased the time spent managing data privacy rules for another client by 90%.
“As a data analytics firm, we’ve seen hundreds of cloud migration and data management projects and over and over again, we see the same challenges emerge with most of our clients – particularly around data quality and compliance,” stated Naveen Venkatapathi, president of Wavicle Data Solutions. “By automating some of the most common and critical integration processes that directly impact data quality and privacy, we have been able to help our clients achieve analytics-ready data at a fraction of the time and cost.”
In addition, Wavicle has taken extra steps to ensure that Augment is truly an augmented and unified platform by implementing sophisticated machine learning techniques. And where many tools in the space claim to be unified platforms, Wavicle has seen where the challenges lie and created a platform that can stand alone as a single unified data management platform, or it can be implemented with other tools to achieve a more simple and unified data governance platform.