LOGISTICS FOR DATA


database. Tis solution offers more effi- ciency, but also additional challenges. In a degraded network environment with inter- mittent or unstable connectivity, systems may not have access to the shared data resource. Novel data also may require an update to the structure of the fabric, just as with a database or data warehouse.


Despite all of these various data “build- ings”—the term “data architecture” can generalize to include the flow of data through systems and the inventory schema, expanding beyond the storage schema. Data architects can achieve data individ- uality, cataloging, discovery, accessibility, governance, analytics and retention peri- ods with a well-planned and optimized data storage capability. Data individuality or uniqueness is one of the more essen- tial characteristics; duplicate data brings on unwanted technical debt in the form of poor system performance, storage costs, data confidence concerns, data lineage problems and, eventually, archival issues. While digital forms make no distinction between original and copy, duplication is the process of replicating data and storing it separately, just as Alexandrian librarians did thousands of years ago.


MESS VS. MESH Te problem with any physical or digital warehouse is decreasing efficiency with more content. Like Jorge Luis Borges’ fictional “Library of Babel,” which contained not only every book but every possible book, the vastness of the collec- tion degrades access to either hardware or data. Amazon mitigated this problem in its warehouses through an inventory method called “random stow.” When goods arrive in the warehouse, Amazon employees shelve them wherever there is available space, with both the item tag and bin tag scanned and linked. Tis reduces time wasted adjusting allocated space to keep like items together. When items are picked


64 Army AL&T Magazine Winter 2024


for delivery, employees follow automated guidance to the closest instance of an item, thus reducing travel time and effort to retrieve the next listed object within the warehouse.


Libraries have adopted the same policy. Faced with the increasing volume of books and the cost of expanding publicly accessible storage space, larger libraries have made using the catalog, rather than browsing the shelves, the primary method of locating a book. Te book’s location no longer matters—including behind locked doors in the building or in cheaper offsite storage. Te University of Nevada, Las Vegas’ Lied Library competes for atten- tion with the fountains at the Bellagio with a glass-walled warehouse, where simi- lar-sized books are stored together in bins to minimize wasted space. A user clicks


a button to send a request for a book to the Lied Automated Storage and Retrieval system, which sends a robotic arm to fetch the associated bin and deliver it to a retrieval desk.


Similarly, the Army is implementing a data mesh. A mesh links producers and consumers of data via a central catalog that lists the available data products. By the library analogy, the catalog connects a reader to a published book. According to Data.world, a data product is defined as “a reusable data asset, built to deliver a trusted dataset, for a specific purpose,” and thus a book is a physical version of a data product. Other examples of data products could include an operational order, a firing target and its coordinates, or a research dataset. One data product could derive another, just as a nonfiction author may


OLD-SCHOOL DATA


Data management and storage often encounter similar problems to libraries—how do you best store vast amounts of data while making it easy for users to access? (Photo by Skitterphoto, Pexels)


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