Data quality key to effectiveness of Artificial Intelligence and Machine Learning
AllSight's new data quality app takes aim at identifying overall data health to improve synthesis and analytics
AllSight, a leading provider of customer intelligence and insight software located in Toronto, Ontario, now adds a data quality application to its Intelligent 360 solution which includes Customer Perspective, a 360 dashboard; Visual Analytics, a segment-level visualization tool; Customer Link, a data stewardship application;
Customer-centric initiatives such as marketing transformation, omni-channel customer experience, and analytics via data lake require high data quality to be effective. It is estimated that poor customer data costs US firms $611B1 each year. The cost to verify the quality of a record is $1, cleaning a record costs $10, and working with a record that has never been cleaned costs $100. That cost stems from incorrect addresses leading to incorrect mailings, or inaccurate analytics leading to incorrect segmentation and personalization, for example.
With Artificial Intelligence (AI) and Machine Learning algorithms becoming more pronounced across data management solutions, data quality is more of a business-led issue. Incomplete or inaccurate data used to train algorithms can create bias and affect results, resulting in misleading synthesis (matching), predictions, and actions. According to a Forrester report by Brandon Purcell, Principal Analyst Customer Insights at Forrester, "When the training data used to teach a machine learning algorithm to perform its function does not accurately reflect the population the model will treat, the result is algorithmic bias."2
AllSight aims to identify and analyze data quality issues so that organizations can resolve and continuously improve, resulting in better automation and improved business decisions and actions across sales, marketing and service. The new data quality application includes five (5) out of the box charts to identify source (MDM, CRM etc.) issues, anonymous values, specific field level values and track issues over time to assess overall health. Users can filter and drill down into specific areas of the charts by severity and dynamically view record level attributes.
Also included in this release are enhancements to AllSight's powerful AI and Machine Learning-driven Reasoning capabilities. As with other releases, AllSight continues to improve its Genetics Algorithm feature to include a new workflow that trains and analyzes the impact of new configurations on existing matches. Each of these features strengthens AllSight's ability to automatically synthesize and enrich the customer profile with inferred intelligence.
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AllSight is a solution-based software company located in Toronto, Canada that creates actionable customer intelligence. AllSight is the industry's first Customer Intelligence Platform designed to create and manage an enterprise Customer 360 for any business that aims to be customer-centric. AllSight's Intelligent 360 helps organizations across insurance, banking, retail, hospitality and more to personalize interactions, offer faster service, increase cross-sell revenue, comply with customer information initiatives such as GDPR, and reduce overall IT system costs as compared to traditional customer data management solutions.
1 The Real Cost of Bad Data: Six Simple Steps to Address Data Quality Issues, TDWI, January 1, 2018
2 The Ethics Of AI: How To Avoid Harmful Bias And Discrimination, Forrester Research, Inc., February 27, 2018
Jennifer McGinn, Director of Marketing, AllSight