{"id":26422,"date":"2021-06-11T06:00:00","date_gmt":"2021-06-11T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26422"},"modified":"2021-06-12T23:06:01","modified_gmt":"2021-06-13T06:06:01","slug":"surpassing-decentralized-data-management-woes-with-data-virtualization","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/06\/11\/surpassing-decentralized-data-management-woes-with-data-virtualization\/","title":{"rendered":"Surpassing Decentralized Data Management Woes with Data Virtualization"},"content":{"rendered":"\n

Perhaps the lone certainty in today\u2019s tenuous business climate is that, whatever else may come tomorrow, the burgeoning data landscape will continue its march towards more and more decentralization.<\/p>\n\n\n\n

The uncharted growth of the cloud, edge computing, the Internet of Things, and the remote work paradigm easily reveal as much.<\/p>\n\n\n\n

Although these developments are terrific for distributed collaborations and a consummate view of customers while reducing the latency of information for intelligent decision-making, they have very real ramifications for the fundamentals of data management\u2014not all of which are as rosy.<\/p>\n\n\n\n

For many pragmatic use cases (like simply querying data, performing data discovery, and engineering data for application or analytics consumption), the growing distribution of the data landscape simply reinforces the need for centralization. Most organizations respond by replicating data between locations which, although providing some short term viability, isn\u2019t truly sustainable.<\/p>\n\n\n\n

\u201cWe can\u2019t just keep moving and copying data in order to manage it,\u201d warned Stardog<\/a> CEO Kendall Clark. \u201cThere\u2019s an endpoint where that just doesn\u2019t work anymore and generally, we\u2019re closer to that point than people realize.\u201d<\/p>\n\n\n\n

Data virtualization has arisen as a dependable alternative to endlessly replicating data and incurring the woes this method produces. When properly implemented with modern data models, it creates two benefits that solve the riddle of \u2018centralizing\u2019 decentralized data by enabling users to \u201cleave data within existing data sources and perform all these complex queries where the data lives, on-prem or in the cloud,\u201d Clark affirmed.<\/p>\n\n\n\n

Thus, without data ever moving, organizations can access them in a centralized fashion for a number of gains in data quality, data integration, and other pillars of data management.<\/p>\n\n\n\n

Data Quality<\/strong><\/p>\n\n\n\n

One of the paramount reasons constantly copying data is untenable is this practice\u2019s noxious impact on data quality<\/a>. Replicating data between locations can reinforce data silos and raise questions about which versions are correct\u2014which inevitably happens after manipulating data downstream in different places. In this instance, the particular use case determines the data quality \u2018standards\u2019, which aren\u2019t always trustable when using those data for more than one application or more than one time.<\/p>\n\n\n\n

\u201cThe data copying or data movement that I\u2019m concerned about is when we make copies and everyone can see all the different copies,\u201d Clark commented. \u201cNot everyone, but when they\u2019re visible to the organization. That brings on issues like which one is current, which one are we updating? It causes confusion.\u201d This situation is readily ameliorated by the abstraction layer data virtualization provides, in which data remain where they are but are accessed via a centralized platform. With the other approach, companies risk \u201cup to date data, data currency, data staleness, and data freshness issues,\u201d Clark indicated.<\/p>\n\n\n\n

Schema<\/strong><\/p>\n\n\n\n

Another pivotal distinction of virtualization technologies<\/a> is that data\u2014as described in data models\u2014are effectively liberated from their storage layer. This supports data management benefits like reusable schema for modeling data for a sundry of use cases, as opposed to tying down data models to specific applications.<\/p>\n\n\n\n

Such reusability is advantageous for expediting aspects of data preparation and decreasing the time to action for data-driven processes. \u201cIn this regard, what powers the virtualization capability from the user\u2019s point of view is that same business level meaning and context data modeling,\u201d Clark observed. This capability is substantially improved by relying on universal data model standards characteristic of semantic graphs.<\/p>\n\n\n\n

Data Integration<\/strong><\/p>\n\n\n\n

The fundamental benefit of this aspect of virtualization is data integration, which is more important than ever with the surplus of heterogeneous data sources outside the enterprise, many of which involve structured and unstructured data. \u201cIf the integration and connection [of data] exist only at the physical layer, then changes at the physical layer break the integrations\u2014or they can,\u201d Clark commented. \u201cAll we\u2019re trying to do is uplevel the game and make there be another place where you can do the integration and the connecting that\u2019s abstracted from the storage.\u201d<\/p>\n\n\n\n

Therefore, organizations can move data (if they want to) wherever it makes the most sense, such as next to where the compute occurs for time-sensitive use cases in the cloud<\/a>, perhaps.  \u201cThis is a good thing now, because now the storage level can evolve independently,\u201d Clark remarked. \u201cThat\u2019s a good thing for the bottom line.\u201d Most of all, when organizations do want to move data, they can do so \u201cwithout breaking things,\u201d Clark mentioned\u2014or spending lengthy periods of time recalibrating data models, working on integrations, and delaying time to value.<\/p>\n\n\n\n

Unstructured and Semi-Structured Data<\/strong><\/p>\n\n\n\n

The utilitarian nature of standards-based data models complements the universal accessibility organizations achieve with data virtualization. Semantic graph models are ideal for conforming even unwieldy semi-structured and unstructured data to the same schema used for structured data. By leveraging this model to buttress data virtualization capabilities, \u201cThe benefit to adding graph to the virtualization story is the ability to virtualize or connect over a bigger percentage of the enterprise data landscape that matters,\u201d Clark revealed. \u201cWe\u2019re just not in a world anymore where it\u2019s just about relational data.\u201d<\/p>\n\n\n\n

The virtualization of semi-structured data alongside structured data makes both equally accessible to enterprise users. Moreover, the data virtualization approach eliminates the need to even conceive of data in these terms, particularly with the standards-based approach to data modeling true knowledge graphs<\/a> utilize. \u201cThe key benefit to bringing graph and virtualization together from the customer point of view is you can just get at more of the data,\u201d Clark summarized.<\/p>\n\n\n\n

The Chief Value Proposition<\/strong><\/p>\n\n\n\n

The increasingly distributed nature of the data landscape signifies many things. It\u2019s a reflection of the remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data. However, it also emphasizes issues pertaining to data quality, schema, and data integrations that are foundational to data management.<\/p>\n\n\n\n

Data virtualization enables organizations to surmount the latter obstacles to focus on the former benefits. Supplementing it with mutable graph data models boosts its applicability to data of all types so companies can confidently \u201cquery data where it lives, without moving or copying it,\u201d Clark explained. \u201cIf you had to summarize the value proposition in a little bit of an abstract way\u2026the primary one is querying data to drive some business outcome without having to move or copy the data that\u2019s relevant to that business question.\u201d <\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance and analytics.<\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, editorial consultant Jelani Harper discusses how data virtualization enables organizations to surmount obstacles (i.e. data quality, schema, and data integrations that are foundational to data management) and to focus on benefits (i.e. remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data). Supplementing it with mutable graph data models boosts its applicability to data of all types. <\/p>\n","protected":false},"author":10513,"featured_media":24770,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[115,87,180,56,97,1],"tags":[117,594,176,593,96],"acf":[],"yoast_head":"\nSurpassing Decentralized Data Management Woes with Data Virtualization - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2021\/06\/11\/surpassing-decentralized-data-management-woes-with-data-virtualization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Surpassing Decentralized Data Management Woes with Data Virtualization - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, editorial consultant Jelani Harper discusses how data virtualization enables organizations to surmount obstacles (i.e. data quality, schema, and data integrations that are foundational to data management) and to focus on benefits (i.e. remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data). 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