by clicking on the page. A slider will appear, allowing you to adjust your zoom level. Return to the original size by clicking on the page again.
the page around when zoomed in by dragging it.
the zoom using the slider on the top right.
by clicking on the zoomed-in page.
by entering text in the search field and click on "In This Issue" or "All Issues" to search the current issue or the archive of back issues respectively.
by clicking on thumbnails to select pages, and then press the print button.
this publication and page.
displays a table of sections with thumbnails and descriptions.
displays thumbnails of every page in the issue. Click on a page to jump.
allows you to browse through every available issue.
GCN : January 2014
DURING PRESIDENT OBAMA'S 2012 re-election campaign, his technical team did something very bold: They broke down data silos by moving all the data into a single cloud repository. On top of that, the team built Narwhal, a set of services that act as an interface to a single shared data store for all of the campaign s applications, making it possible to quickly develop new applications and to integrate existing ones into the campaign s system. Those apps included sophis- ticated analytics programs like Dreamcatcher, a tool devel- oped to "microtarget voters based on sentiments within text." The applications enabled the campaign to synthesize data from across its informa- tion holdings and target its messaging at individual voters. In February of that year, Slate called this technology, "Obama s White Whale, and it is a fitting description for tech- nology that is almost mythical in federal agencies that have talked about "breaking down data silos" for years. Fortunately, migrating data to the cloud o ers agency IT managers another oppor- tunity to break down those silos, integrate their data and develop a unified data layer for all applications. In doing so, it s important to know how to design metadata to enable the description, discovery and re- use of data assets in the cloud. Here are the basic methods of metadata description -- what I like to call the "Mag- nificent Seven" of metadata! -- and how to apply them to data in the cloud: Identification represents the ability to distinguish one data asset from another. Examples are attributes like name, <entity>ID, loca- tion and signatures. Most cloud-based NoSQL stores use key-value pairs where the key is a unique identifier. A best practice is to create unique identifiers; however, in rela- tion to linked data, the best practice would be to make the identifier dereferenceable (like a URL). Static measurement is used to measure a constant or very slow-changing characteristics of a target data asset. Examples include fixed attributes like format, size, creation date, creator, security classification and other content-specific measurements. In the cloud, lineage becomes critical to cen- tralization and enabling trust. Dynamic measurement details variable or changing aspects of a data asset. A typi- cal example requiring dynamic measurement is capturing state information on a data as- set. Other examples are usage counts, ratings, sales, plays, location tracking, ranking, etc. Many of the cloud benefits in- volve dynamic characteristics like metered billing, uptime, server utilization and storage utilization. Degree scales measure both an artifact s progress along a continuum and the meaningful inflection points along that path. Of course, in a numeric scale, the inflection points are a given. Examples of this are time scales, perfor- mance scales and opinion scales. In the cloud, usage scales and thresholds are key for automated scalability. Also, degree scales can be used to measure subjective charac- teristics like user satisfaction. Some interesting cloud ex- amples are types of infrastruc- ture-as-a-service instances (tiny, small, medium, large, etc.) and the degree of appli- cation migration complexity in a migration scorecard. Categorization enables the division of a population of data assets into manageable groups based on common- alities of all the members within a group. A hierarchical arrangement of the groups facilitates discovery and roll-up. Examples of taxono- mies are genre/subgenres in music, product taxonomies on Amazon.com, and even NIST s Cloud Taxonomy. Cat- egorization is very important to improve the discovery of data assets and even cloud applications. Relationships create predi- cates (also known as relation- ships) between the metadata record and its target data asset or between the metadata record and other metadata artifacts. Examples of relation- ships are Facebook "friends" (the social graph), Amazon recommendations and linked data. In the cloud, relation- ships are key to modeling how cloud items should interact with one another. Commentary provides free-form textual descrip- tion for human readers of the metadata record. This is the most common form of meta- data and should be indexed for discovery. Once security and privacy concerns are alleviated by cloud providers who certify their compliance with security and privacy controls (like FedRAMP), the push will be on to integrate and centralize data stores to provide agen- cies a 360-degree view of key customers or end-users. • --- Michael C. Daconta is vice president of advanced technol- ogy at InCadence Strategic Solutions and the former metadata program manager for the Homeland Security Department. BIG METADATA: 7 WAYS TO LEVERAGE YOUR DATA IN THE CLOUD REALITY CHECK BY MICHAEL DACONTA [Obama's] technical team did something very bold: They broke down data silos by moving all the data into a single cloud repository. GCN JANUARY 2014 • GCN.COM 13