{"id":492750,"date":"2022-03-30T16:37:00","date_gmt":"2022-03-30T16:37:00","guid":{"rendered":"https:\/\/www.capgemini.com\/pt-en\/?p=492750"},"modified":"2025-03-26T09:33:35","modified_gmt":"2025-03-26T09:33:35","slug":"the-missing-links-in-your-data-platform","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/pt-en\/insights\/expert-perspectives\/the-missing-links-in-your-data-platform\/","title":{"rendered":"The missing links in your data platform"},"content":{"rendered":"\n<header class=\"wp-block-cg-blocks-hero-blogs header-hero-blogs\"><div class=\"container\"><div class=\"hero-blogs\"><div class=\"hero-blogs-content-wrapper\"><div class=\"row\"><div class=\"col-12\"><div class=\"header-title\"><h1>The missing links in your data platform<\/h1><\/div><\/div><\/div><\/div><div class=\"hero-blogs-bottom\"><div class=\"header-author\"><div class=\"author-img\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2021\/05\/Robert-Engels.jpg?w=720?w=200&amp;quality=10\" alt=\"\" loading=\"lazy\"\/><\/div><div class=\"author-name-date\"><h5 class=\"author-name\">Robert Engels<\/h5><h5 class=\"blog-date\">14 Sep 2022<\/h5><\/div><\/div><div class=\"brand-image\"> <\/div><\/div><\/div><\/div><\/header>\n\n\n\n<section class=\"wp-block-cg-blocks-group undefined section section--article-content\"><div class=\"article-main-content\"><div class=\"container\"><div class=\"row\"><div class=\"col-12 col-md-1\"><nav class=\"article-social\"><ul class=\"social-nav\"><li class=\"ip-order-fb\"><a href=\"https:\/\/www.facebook.com\/sharer\/sharer.php?u=https:\/\/www.capgemini.com\/pt-en\/insights\/expert-perspectives\/the-missing-links-in-your-data-platform\/\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-fb\"><\/i><span class=\"sr-only\">Facebook<\/span><\/a><\/li><li class=\"ip-order-tw\"><a href=\"https:\/\/twitter.com\/intent\/tweet?url=https:\/\/www.capgemini.com\/pt-en\/insights\/expert-perspectives\/the-missing-links-in-your-data-platform\/&amp;text=The%20missing%20links%20in%20your%20data%20platform\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-tw\"><\/i><span class=\"sr-only\">Twitter<\/span><\/a><\/li><li class=\"ip-order-li\"><a href=\"https:\/\/www.linkedin.com\/sharing\/share-offsite\/?url=https:\/\/www.capgemini.com\/pt-en\/insights\/expert-perspectives\/the-missing-links-in-your-data-platform\/&amp;text=The%20missing%20links%20in%20your%20data%20platform\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-li\"><\/i><span class=\"sr-only\">Linkedin<\/span><\/a><\/li><\/ul><\/nav><\/div><div class=\"col-12 col-md-11 col-lg-10\"><div class=\"article-text article-quote-text\">\n<h3 class=\"wp-block-heading\">Enterprises can potentially reap immediate benefits through improvement in data discoverability, enabling data teams with a better understanding of data and its lineage, and establish a platform for exploratory data analysis through visual tools. The catch, is to find the missing links in the data lake.<\/h3>\n\n\n\n<p>Data platform (data lake\/data warehouse) initiatives have been around for quite a long time now. However, they have not always lived up to the promise of transforming an enterprise into a data driven organization. The premise of data platforms has been around building a data repository of an organization\u2019s digital footprint and gathering insights by asking questions of the data that were earlier impossible to answer. The problem enterprises face with this approach is the inability to understand the connectedness of data, it\u00b4s quality, history and the business rules associated with different digital footprints.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" height=\"601\" width=\"1024\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/26\/2022\/11\/missing-link-picture.jpg?w=960\" alt=\"\" class=\"wp-image-512705\" srcset=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/missing-link-picture.jpg 1320w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/missing-link-picture.jpg?resize=300,176 300w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/missing-link-picture.jpg?resize=768,451 768w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/missing-link-picture.jpg?resize=1024,601 1024w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>The data lake\/data warehouse approach has recently been looked afresh with an alternative approach founded on domain driven design principles, termed the data mesh (<a href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\" target=\"_blank\" rel=\"noreferrer noopener\">Dehghani, 2019<\/a>). The approach identifies a core problem in the data engineering and data consumption pipeline viz the inability of technology focused teams to understand data domains and the need to treat data as a product. The knowledge that domain experts have is both explicit and implicit. The general non-availability of this knowledge to the organization hampers the goal of data democratization that is a fundamental pillar of a data driven enterprise. Of course, the data mesh thinking additionally brings to forth both organizational aspects and data product thinking to data platform architectures and way of working.<\/p>\n\n\n\n<p><em>So, how can we try to solve this problem? Enter the semantic data mesh.<\/em><\/p>\n\n\n\n<p>The <strong><em>semantic data mesh<\/em><\/strong> augments the capabilities and promise of a data platform with an envelope of context\/meaning. The semantic data mesh approach provides the missing links in the data platform and extends the data mesh pattern to cover both data continuum and data quantisation. The knowledge of how different data elements (objects, domains or ensembles) relate to each other. As a solution, the semantic data mesh presents an architecture that augments the data platform (built on-premises or on the cloud) using knowledge graphs through construction of ontologies. But let\u2019s take a step back and understand all these new terms.<\/p>\n\n\n\n<p>In the world of business intelligence and data warehousing, we have been quite accustomed to established principles of dimensional modelling, cubes, or the data vault. Terms such as knowledge graphs, ontologies may be quite unfamiliar to a unit dealing with conventional approaches to analytics.<\/p>\n\n\n\n<p>The term <em>knowledge graph<\/em> was introduced by Google, in a blog post in 2012 (<a href=\"https:\/\/blog.google\/products\/search\/introducing-knowledge-graph-things-not\/\" target=\"_blank\" rel=\"noreferrer noopener\">Singhal, 2012<\/a>), with a corresponding <a href=\"https:\/\/patents.google.com\/patent\/US20120158633A1\/en\" target=\"_blank\" rel=\"noreferrer noopener\">patent<\/a> filed earlier in 2005. The knowledge graph for Google was a way to enable searching, not for strings or words, but for things, words with meaning. As exemplified on the blog, a string \u2018Taj Mahal\u2019 may mean a landmark, one of the seven wonders of the world, or a local restaurant where one might be looking to spend the evening.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" height=\"683\" width=\"1024\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/26\/2022\/11\/shutterstock_151905218.jpg?w=960\" alt=\"\" class=\"wp-image-512707\" srcset=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/shutterstock_151905218.jpg 5472w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/shutterstock_151905218.jpg?resize=300,200 300w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/shutterstock_151905218.jpg?resize=768,512 768w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/shutterstock_151905218.jpg?resize=1024,683 1024w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/shutterstock_151905218.jpg?resize=1536,1024 1536w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/shutterstock_151905218.jpg?resize=2048,1365 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>There is no formal definition for a knowledge graph. Only a common understanding of what it represents i.e. a way for machines to read and represent information about the world (<a href=\"https:\/\/mitpress.mit.edu\/books\/knowledge-graphs\" target=\"_blank\" rel=\"noreferrer noopener\">Kejriwal et al., 2021<\/a>). However, a good starting definition to understand knowledge graphs may be the one defined by Stanford University which states:<\/p>\n\n\n\n<p><em>\u201cA knowledge graph is a directed labeled graph in which the labels have well-defined meanings. A directed labeled graph consists of nodes, edges, and labels. Anything can act as a node, for example, people, company, computer, etc. An edge connects a pair of nodes and captures the relationship of interest between them, for example, friendship relationship between two people, customer relationship between a company and person, or a network connection between two computers. The labels capture the meaning of the relationship, for example, the friendship relationship between two people.\u201d<\/em><\/p>\n\n\n\n<p>The second term we use in this blog is <strong><em>semantic<\/em><\/strong> which refers to meaning in language or logic. This term has been quite widely used when describing the semantic web (<a href=\"https:\/\/www.scientificamerican.com\/article\/the-semantic-web\/\" target=\"_blank\" rel=\"noreferrer noopener\">Berners-Lee et al., 2001<\/a>), which uses distributed graphs with a formal semantics for knowledge representation and reasoning on the internet. The purpose of the semantic web is like the one stated for knowledge graphs \u2013 providing a capability to software agents to retrieve content from the web and understand it\u00b4s meaning (as opposed to finding documents).<\/p>\n\n\n\n<p>Knowledge graphs are powered by a different kind of data model founded on graph data models (<a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/1322432.1322433\" target=\"_blank\" rel=\"noreferrer noopener\">Angles &amp; Guiterrez, 2008<\/a>), as opposed to entity-relationship models &nbsp;that are founded on relational theory (a relation, mathematically is a table). Database technologies that support graph data models are commonly missing in the technology stack that are behind most data platform implementations which usually comprise of:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Distributed file stores e.g. HDFS, AWS S3, Azure Storage Account, Google Cloud Storage<\/li>\n\n\n\n<li>Distributed computing frameworks e.g. Databricks, Apache Spark<\/li>\n\n\n\n<li>Relational data stores for information marts<\/li>\n\n\n\n<li>Data catalogs<\/li>\n\n\n\n<li>Data pipeline solutions<\/li>\n<\/ul>\n\n\n\n<p>As you can see, a key element on capturing the meaning of data elements within the data lake is missing. This <strong><em>missing link<\/em><\/strong> is the <strong><em>enterprise knowledge graph<\/em><\/strong> that powers the semantic data mesh. Enterprise knowledge graphs build on the principles of open world knowledge graphs but leverage an organization\u2019s internal data assets that originate in applications that maintain different business processes (<a href=\"https:\/\/arxiv.org\/abs\/2003.02320\" target=\"_blank\" rel=\"noreferrer noopener\">Hogan et al., 2003<\/a>).<\/p>\n\n\n\n<p>An enterprise knowledge graph (EKG) provides a robust method to capture knowledge on a range of information representations including structural metadata, business metadata, business logic, data classifications and information security. An EKG comprises the following conceptual layers (<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7439340\" target=\"_blank\" rel=\"noreferrer noopener\">Galkin et al., 2016<\/a>):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A\u00a0<strong><em>schema <\/em><\/strong>layer that provides a machine-readable description of knowledge<\/li>\n\n\n\n<li>An\u00a0<strong><em>instance <\/em><\/strong>layer that provides data elements for the elements of the schema<\/li>\n\n\n\n<li>A\u00a0<strong><em>metadata <\/em><\/strong>layer that provides information about information sources<\/li>\n\n\n\n<li>A\u00a0<strong><em>coherence <\/em><\/strong>layer that provides links to other knowledge sources<\/li>\n<\/ul>\n\n\n\n<p>For those more familiar with the relational database logical models, the schema in a graph may be thought of as a table and column definitions while data instances may be thought of as rows within that table. Understanding the nomenclatural differences between the relational and graph worlds is crucial towards a coherent communication between teams coming from both these disciplines.<\/p>\n\n\n\n<p>Of course, the EKG goes beyond the scope of a semantic data mesh and analytical use cases. However, since enterprises across industry verticals have embarked on constructing data lakes either on premise or on cloud, it becomes easier to initiate construction of an EKG on top of the data lake rather than taking a logical federated approach. We see the semantic data mesh as a good starting point for enterprises to start an EKG project and use this experience to continue towards a more comprehensive EKG initiative.<\/p>\n\n\n\n<p>The same approaches that have been used to link open data on the Web are seen to be equally applicable to linking enterprise data. The <strong><em>linked data<\/em><\/strong> concept refers to a set of practices for structured data connectedness on the Web (<a href=\"https:\/\/www.igi-global.com\/gateway\/article\/37496\" target=\"_blank\" rel=\"noreferrer noopener\">Bizer et al., 2009<\/a>). In the linked open data domain, the same semantic web principles that were used for the web of documents were applied to data. Recent research has shown that its possible to extend the same approach within the enterprise leading to the development of ideas around <strong><em>linked enterprise data<\/em><\/strong> (<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8713719\" target=\"_blank\" rel=\"noreferrer noopener\">Novak &amp; Tjoa, 2019<\/a>). The business value linked enterprise data generates is an efficient data management infrastructure that allows different stakeholders to find information faster.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" height=\"683\" width=\"1024\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/26\/2022\/11\/Picture2-2.jpg?w=960\" alt=\"\" class=\"wp-image-512708\" srcset=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/Picture2-2.jpg 1326w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/Picture2-2.jpg?resize=300,200 300w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/Picture2-2.jpg?resize=768,513 768w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/Picture2-2.jpg?resize=1024,683 1024w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Within the scope of a semantic data mesh, enterprises can potentially reap immediate benefits through improvement in data discoverability, lowering the dependency on domain experts through enabling data teams with a better understanding of data and its lineage and establish a platform for exploratory data analysis for business analysts through visual tools for graph mining (<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2212827120310349\" target=\"_blank\" rel=\"noreferrer noopener\">Ziegler et al., 2020<\/a>). Increasingly the knowledge graph and its underlying graph databases are seen to be fundamental components of the modern data platform that provide a foundation for AI solutions (<a href=\"https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/1487\/1\/012016\" target=\"_blank\" rel=\"noreferrer noopener\">Zou, 2020<\/a>).<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-authors\"><strong>Authors:<\/strong><\/h4>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/26\/2022\/10\/Picture4-3.jpg\" alt=\"\" class=\"wp-image-509378\" style=\"width:225px;height:225px\" srcset=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/10\/Picture4-3.jpg 300w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/10\/Picture4-3.jpg?resize=150,150 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong><br><a href=\"https:\/\/www.linkedin.com\/in\/aniruddhakhadkikar\/?originalSubdomain=se\" target=\"_blank\" rel=\"noreferrer noopener\">Aniruddha Khadkikar PhD<\/a><br><\/strong><em>Managing Delivery Architect,<br>Insights &amp; Data, Capgemini Sverige<\/em><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"165\" height=\"165\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/26\/2022\/10\/Picture4-2.jpg\" alt=\"\" class=\"wp-image-509379\" srcset=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/10\/Picture4-2.jpg 165w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/10\/Picture4-2.jpg?resize=150,150 150w\" sizes=\"auto, (max-width: 165px) 100vw, 165px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong><br><a href=\"https:\/\/www.linkedin.com\/in\/robertengels\/\">Robert Engels PhD<\/a><br><\/strong><em>CTO, Insights &amp; Data,<br>Capgemini Norge<\/em><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-related-webinars\"><strong>Related Webinars:<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"300\" src=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/26\/2022\/11\/Data-mesh-part1-flashcard.jpg\" alt=\"\" class=\"wp-image-512709 size-full\" srcset=\"https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/Data-mesh-part1-flashcard.jpg 600w, https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/42\/2022\/11\/Data-mesh-part1-flashcard.jpg?resize=300,150 300w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<h4 class=\"wp-block-heading\"><strong>Data meshes and Knowledge graphs<\/strong><\/h4>\n\n\n\n<p>How you can add the human way of understanding data better.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.capgemini.com\/pt-en\/news\/events\/data-meshes-and-knowledge-graphs\/\">Read more<\/a><\/div>\n<\/div>\n<\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":336,"featured_media":510011,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"cg_dt_proposed_to":[],"cg_seo_hreflang_relations":"[]","cg_seo_canonical_relation":"","cg_seo_hreflang_x_default_relation":"","cg_dt_approved_content":true,"cg_dt_mandatory_content":false,"cg_dt_notes":"","cg_dg_source_changed":false,"cg_dt_link_disabled":false,"_yoast_wpseo_primary_brand":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","featured_focal_points":""},"categories":[1],"tags":[],"brand":[],"service":[],"industry":[],"partners":[],"blog-topic":[83],"content-group":[],"class_list":["post-492750","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","blog-topic-data-and-ai"],"yoast_head":"<!-- This site is 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