Consequently, centralization efforts involving \u201cdata mesh and data fabric are something we\u2019re seeing quite a lot of out there,\u201d reflected Alation<\/a> Director of Data and Analytics Julie Smith. \u201cBecause of data mesh, data fabric, and that whole method of work that they involve, it\u2019s going to make your data modeling practices become incredibly pragmatic and have to evolve.\u201d<\/p><\/blockquote>\n\n\n\n A plethora of methods including data fabrics, revamped cloud native Master Data Management capabilities, and governance frameworks employing cognitive computing to point-and-click at sources for detailed cataloging of their data are viable means of implementing data models across the heterogeneity of the modern enterprise\u2019s data.<\/p>\n\n\n\n Triumphing means more than fulfilling the foregoing business imperatives, but ultimately propels organizations ever closer to systemic interoperability to meet every use case with the most appropriate data\u2014regardless of location or source.<\/p>\n\n\n\n Schema Differentiation<\/strong><\/p>\n\n\n\n The path towards data interoperability for business cases spanning the breadth of organizations\u2019 data assets typically involves some form of centralization such as data fabrics<\/a> or data meshes. \u201cData meshes and data fabrics are very similar,\u201d Smith specified. \u201cBoth are approaches where you\u2019re getting the data moving through different places rather than trying to have it in one place and bring it together.\u201d Nonetheless, resolving schema differences is still a time-honored hardship of integrating or aggregating variegated data sources for any central application. However, data fabrics implemented with data virtualization, query federation, and what Stardog<\/a> CEO Kendall Clark termed a \u201cgraph query model\u201d obsolete this impediment in several ways to deliver the subsequent boons:<\/p>\n\n\n\n Data Cataloging<\/strong><\/p>\n\n\n\n The data model evolution implicit to multi-tenant schema and its innumerable combinations is considerably aided by data cataloging<\/a>\u2014which in turn informs the data discovery process for devising interoperable data models with the most meaningful information. Today\u2019s catalogs rely on machine learning to point at sources and illustrate what Smith characterized as \u201cthe current reality: these are the fields, entities, relationships, and this is how they\u2019re being used.\u201d This basic understanding is critical for initiating schema, revising it, and understanding the requisite conditions for combining them for singular use cases. According to Profisee<\/a> VP of Product Marketing Martin Boyd, data modeling best practices involve \u201clooking at all the different places schema for a specific domain exists, then pulling it to create the schema.\u201d<\/p>\n\n\n\n Data cataloging enhances this step in several ways, foremost of which is its means of centralizing information about data in distributed sources. In addition to valued metadata, statistical information stemming from data profiling, and subject matter expert input, catalogs also house collective \u201cknowledge any number of users gained around a system or dataset,\u201d Smith mentioned. They also provide lineage and other annotations about how datasets\u2014and specific schema\u2014were used. Collectively, this documentation allows users \u201cto look at data models and where we\u2019re going to take something,\u201d Smith observed. \u201cSo, information from this data catalog can feed into the evolution of that data model.\u201d<\/p>\n\n\n\n Entity Modeling<\/strong><\/p>\n\n\n\n Entity modeling and creating master data models for individual domains furthers the advancement towards interoperability advantages of reusable schema, comprehensive insight across sources for analytics, and increased adaptability. Multiple domain Master Data Management plays an invaluable role in modeling entities by using fuzzy logic, cognitive computing, and other approaches to automate matching records of entities and merging them as needed. Such an automaton is beneficial for completing these data modeling facets at scale because this aspect of data management \u201cis a process,\u201d Boyd noted. \u201cOnce you\u2019ve established that process and the rules, the system keeps on enforcing them.\u201d<\/p>\n\n\n\n As previously mentioned, standardizing data\u2019s representation in sources is a precursor to coalescing them for horizontal use cases, especially when the results of those entity models are pushed back to sources. Once users \u201cstandardize all of that information from a format perspective, that makes it more interoperable,\u201d Boyd maintained. \u201cSo now different systems holding data in different formats can speak to each other, contribute to the master data model, and share that information back to them.\u201d It subsequently becomes much easier to use such an array of distributed sources for data science attempts<\/a> to build predictive models or to create applications across departments, sources, and domains for things like customer loyalty programs or security analytics. <\/p>\n\n\n\n Data Quality<\/strong><\/p>\n\n\n\n The data quality rules for the sources informing different aspects of data modeling (such as entity modeling, logical modeling, and conceptual modeling) are critical for providing the standardization at the base of any interoperability attempts. Once organizations discern which sources have attributes or data impacting these modeling dimensions, they must homogenize how they appear in the sources so that \u201ceach field has data quality rules that mandate how they should [appear],\u201d Boyd donoted. <\/p>\n\n\n\n Oftentimes, formulating those rules for standardizing data is a cooperative process involving subject matter experts, data governance personnel<\/a>, and other stakeholders. The result is data become standardized across sources according to \u201cdata quality rules, consistency rules, there\u2019s referential integrity, and all the things you expect in a normal database design,\u201d Boyd explained. The ultimate benefit of imputing data quality to the fundamentals of data modeling is quality assurance and \u201chow much you can trust that information and the different sources it comes from,\u201d Smith added. <\/p>\n\n\n\n Interoperability Possibilities<\/strong><\/p>\n\n\n\n The possibilities for making data more interoperable by combining data models or crafting unified models across use cases, departments, and domains is significant for a host of reasons. Firstly, it enables the enterprise to incorporate more of these lucrative data assets into everyday business applications to inflate the ROI on their substantial data management expenditures by using all<\/em> the resources\u2014or the best of them\u2014for individual deployments. It\u2019s also a reliable way to tame what\u2019s otherwise the escalating disorder accompanying the greater decentralization of where data are accessed, stored, and required, which is why the data fabric tenet has persisted.<\/p>\n\n\n\n This methodology \u201cleads to interoperability and interoperability at the data layer,\u201d Clark commented. \u201cAt the data layer there\u2019s one big pool, fabric, or graph of data. That doesn\u2019t just mean you dump the data in one location, but that it\u2019s connected so applications become simpler and easier to build, you can reuse the business logic, and you can reuse these connections that become like different views of a comprehensive fabric of data.\u201d<\/p>\n\n\n\n Modeling Tomorrow Today<\/strong><\/p>\n\n\n\n Regardless of which approach to centralization is deployed, organizations must adopt some method for countering the silos that otherwise occur with the dispersal of data across different clouds, on-premise settings, and geographic regions. Preparing for systemic interoperability at the granular level of data modeling by overcoming schema differentiation with effective data cataloging, entity modeling, and data quality mechanisms is nothing short of providential for what challenges lie ahead.<\/p>\n\n\n\n \u201cThere\u2019s two ways to do future-proofing: the shrewd and the dumb way,\u201d Clark propounded. \u201cThe first is to future-proof by doing data modeling that describes parts of your larger business that\u2019s enduring overtime. Schema reuse across different parts of the business gets you future-proof benefits with high quality assurance and ROI. The dumb way is for smart people to do data modeling for data modeling\u2019s sake, which leads to nowhere.\u201d<\/p><\/blockquote>\n\n\n\n About the Author<\/p>\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 Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n![\"\"](\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/10\/Jelani-Harper.jpg\")
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