Traditionally, name and address data are the most commonly cited examples of this challenge, but it applies to any type of weakly structured information.
Solutions to these types of problems – often referred to as "record linkage" [3] – go back many decades and aim to create the one "golden record" of cleaned and reconciled data about each entity. Traditionally, the process consists of multiple, sequential, partially overlapping cleaning and comparison steps. The choice of steps and the order is often determined manually, depending on the type of data, the categories of problems observed, and the skills and experience of the employee performing the process.
This process, now often referred to as "data wrangling" [4] , often falls to the data scientists, wasting up to 80 percent of their time. Their skills would be better spent analyzing data and gaining insights rather than cleaning dirty data. Data wrangling can certainly ease the pain of data scientists, but most of the time they just simplify and visually supplement the existing, manual cleaning steps, rather than addressing the underlying issues under the "dirty data" problem.
To solve this bangladesh telegram screening problem in a digital enterprise—a large number of individually dirty records that differ inconsistently across huge data sets from multiple, often conflicting sources—we need to think outside the box. The real requirement is not to clean dirty data, but to distill it into uniquely identifiable identities for unique real-world entities, even though individual records may still contain irreconcilable differences in their data. This shifts our focus from errors in the data values (naked data) to the context of data creation and use ( context-setting information , CSI), so that we can work around these errors.
By storing bare data and CSI separately and continuously aligning it, as described in "CortexDB Redefines the Database," an Information Context Management System (ICMS), such as CortexDB, enables the creation of an integrated and highly automated system for reconciling and cleansing data from multiple disjoint sources. A real-life scenario shows what this means.