Discovery & Mapping
Every data sharing and integration initiative first requires an understanding of what type of data is present and where relationships exist between the data sources. All downstream implementation efforts rely on this "mapping" process to be completed as quickly, accurately, and efficiently as possible to prevent the need for rework.

Challenge
The mapping process is a highly manual process that relies heavily on the availability of subject matter experts (SME). As a result, it has the following short comings:

• It is error prone;
• Human cognitive limitations allow only for field-by-field mapping, which requires an iterative process that is very costly and time consuming;
• Implicit relationships are often missed and undiscoved without deep analysis.

Solution
Sypherlink Harvester’s automated discovery and heuristic mapping capabilities enable organizations to quickly understand what information is available and where field-level relationships exist between multiple data sources. Harvester significantly reduces the time and effort it takes to complete mapping exercises by combining:

• A patented, heuristic-matching and probability-driven technology that employs a form of artificial intelligence to analyze, evaluate and provide confidence scoring on the similarities and compatibilities between field names, field attributes, referential data and actual values;
• A feature-rich user interface that enables facilitates exceptions-based mapping;
• Reports which can generated to show the relationship information and data quality metrics captured by Harvester.

Benefits

• Reduces mapping time, effort and cost by 50% or more;
• Improves the quality of the mapping exercise by validating explicit relationships and uncovering implicit relationships between data sources.