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Tredence Launches AI Data Cleanser using Machine Learning to Cleanse Large Volumes of Data for Enterprise Organizations

New Analytics Platform Cleanses and De-Dups Large Data Sets for Businesses for More Reliable Information That Drives Better Insights

Tredence, an analytics services company focused on the last mile of analytics adoption, announced today it has launched its AI Data Cleanser, a data cleansing and enrichment solution that uses machine learning and deep learning technology to give businesses more reliable data.

According to a recent study from IDC, total worldwide data will swell to 163ZB by 2025. The advancements in big data and IOT technologies have caused data volumes to explode exponentially, and there is a clear need to cleanse the data coming in from multiple online and offline sources to establish a single source of truth.

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Traditional data cleansing/de-dup solutions have largely relied on a rule-based solution which is not scalable. As the volume of data grows exponentially, these traditional data cleansing solutions will not be able to keep pace. Tredence has built AI Data Cleanser, a data cleansing and enrichment solution using machine learning/deep learning techniques to cleanse and enrich any master data.

How it Works

Tredence’s new analytics platform:

  • Reconciles semantically consistent data and deploys data solutions to the end-user organization;
  • Uses cloud and on-premise delivery models to deploy solutions to clients;
  • Provides customizable data solutions to give enterprises the flexibility to generate specialized MDM solutions across various industry verticals like marketing, healthcare, and industrials;
  • Offers flexible and dynamic metadata management abilities to provide data governance solutions and offer consumable data to businesses;
  • Supports operational and analytical MDM requirements by allowing the utilization of the master data for both operational and analytical purposes, providing an end-to-end data solution.
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AI Data Cleanser uses features like Smart Cleanse and Dynamic Enrichment to not only profile and integrate diverse data sets but also extract intelligent features from the data to apply machine learning/deep learning algorithms and derive insights. Cleansed data as a service feature allows customers to integrate their 3rd party applications with AI Data Cleanser to generate cleansed/enriched data after passing source data as input.

When a networking giant in the U.S. (Bay Area) tackled customer data that was in volumes of tens of millions, spread across 11 varied channels, they needed to create a centralized data source to achieve optimal utilization. The AI Data Cleanser was used to cleanse, enrich and unify customer data and deliver high-value data that was accessible on a single platform.

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“Our mission is to help large enterprises solve business problems using insights informed by reliable data,” said Shub Bhowmick, CEO and co-founder of Tredence. “As the volume of data grows and continues to stream in from multiple sources, companies are overwhelmed with how to clean and de-dup the data quickly and know the data they are using to make important business decisions is accurate. Our AI Data Cleanser platform will allow large enterprises across a number of verticals to know they are using clean data to inform their decisions, and our solution has been designed as a scalable one that will grow as the volumes of data continue to rise.”

Headquartered in San Jose, Calif., Tredence partners with clients across verticals, with deep experience in the retail, CPG, industrial, travel and hospitality, food and beverage, telecom, pharmaceutical and financial services spaces.

There are four broad steps to enabling last-mile analytics adoption within enterprises: data analysis, generating insights, making recommendations, and translating those recommendations into action for the front lines leading to sustainable business impact. While other analytics services firms focus only on the first three steps, Tredence covers all four. The last step is crucial toward operationalizing analytics.

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