{"id":536,"date":"2017-11-03T02:33:12","date_gmt":"2017-11-02T23:33:12","guid":{"rendered":"http:\/\/kusuaks7\/?p=141"},"modified":"2025-03-25T11:05:38","modified_gmt":"2025-03-25T11:05:38","slug":"the-smart-thing-to-do-practical-applications-of-monetizing-big-data-in-finance","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-smart-thing-to-do-practical-applications-of-monetizing-big-data-in-finance\/","title":{"rendered":"The smart thing to do: practical applications of monetizing big data in finance"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to learn Big Data?\u00a0Browse courses<\/a>\u00a0like\u00a0Big Data Training & Certification<\/a> \u00a0developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn financial services, the dangers associated with monetizing big data are nearly as great as the rewards. The promises of machine learning, data science and\u00a0Hadoop<\/a>\u00a0are tempered by the realities of regulatory penalties, operational efficiency and profit margins that must quickly justify any such expenditure.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAdditionally, there is often another layer of complexity best summarized by Carl Reed, who spent more than 20 years overseeing data-driven applications at\u00a0Goldman Sachs<\/a>\u00a0and\u00a0Credit Suisse<\/a>. According to Reed, who currently serves as an adviser to the\u00a0Enterprise Data Management Council<\/a>,\u00a0PwC<\/a>\u00a0and\u00a0Cambridge Semantics<\/a>, the sheer size of the customer base and infrastructure of larger organizations compounds the issues. He says, \u201cCredit Suisse [had] 45,000 databases; Goldman Sachs [had] 90,000 databases. If you let the financial industry continue to implement technology vertically versus horizontally\u2014because you\u2019ve got no C-suite saying data isn\u2019t a by-product of processes, it\u2019s an asset that needs to be invested in and governed accordingly\u2014you\u2019ll end up with today 10s, tomorrow 100s, and in time 1000s of instances of Hadoop all over your organization.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe most acute lesson learned from Reed\u2019s tenure within financial services is to avoid such issues with a singular investment in data architecture that pays for itself over a number of applications across the enterprise. In doing so, organizations can justify data expenditure with a staggering number of use cases predicated on variations of the same investment.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Link analysis<\/em><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe crux of Reed\u2019s approach was to focus on semantic graph technology that linked the numerous silos existent across the organizations for which he worked. \u201cOne of the first problems we addressed at Credit Suisse was to harmonize its different infrastructure,\u201d Reed says. \u201cWe had\u00a0Cloudera<\/a>, we had\u00a0Hortonworks<\/a>, we had open source, we had open source Hadoop.\u201d Such infrastructure is readily connected on a semantic graph with standardized models and taxonomies. The graph-based approach pinpoints the relationships between the nodes, empowering the enterprise with several use cases of what is essentially the same dataset. The most eminent is arguably an improved means of determining data lineage for regulatory compliance, which could be the biggest challenge financial entities face after last decade\u2019s fiscal crisis.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\u201cThe new world of big data and the evolving world of regulatory and operational reducing margins is about having the data associated with our business at the main sector being first-class citizens,\u201d Reed said. \u201cNothing\u2019s going to change that. But the relationships between them are now first-class citizens too.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nThe graph approach to managing relationships proved equally valuable for improving operations and creating business opportunities. By determining how even seemingly unrelated nodes can contribute to a certain business problem, organizations can transcend regulatory compliance and further enterprise objectives. \u201cFor the type of causal reasoning you need to do for this style of link analysis\u2014whether you\u2019re understanding client social circles, how a market is behaving, how a potential change in your environment is going to have positive or negative ramifications, how to triage something that\u2019s gone wrong\u2014it\u2019s all about the linkage between objects,\u201d Reed observes.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Employee entity relationship graphs<\/em><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSuch linkage offers additional utility for monitoring the enterprise and its employees for multiple use cases. For instance, the underlying semantic graph approach is ideal for insider trading surveillance, a task predicated on illustrating the relationships between people who may have knowledge of a trade or business development. \u201cIf I\u2019ve got a person who\u2019s an insider and a person who\u2019s a trader, how do I link those two people together to understand whether there has been a chance that the wrong information\u2019s traveled from one to another? I have to start thinking about people relationships,\u201d Reed explains. The ensuing conceptual modeling can contain appropriate organizational structure, electronic communication, employee information (including geographic location and scheduling) and other aspects of the people modeled.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBy contextualizing the information with temporal data that indicates points in time people could have exchanged information, that form of link analysis can demonstrate the likelihood of insider trading. Moreover, it\u2019s based on the same graph framework used to demonstrate regulatory compliance\u2014and can involve some of the same data. After creating an exhaustive model for insider trading surveillance centered on those working for a company, \u201call of a sudden, you\u2019ve got an employee entity relationship graph,\u201d Reed says. \u201cNow you can say these are the traders that are exhibiting potential insider trader activity. These are the people who\u2019ve got the information that, if I can show a path to those traders, something needs to be investigated.\u201d According to Reed, the same graph-based link analysis is used by the intelligence community to determine the movement of money among terrorist organizations.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe true merit of link analysis graphs is that they are reusable for additional organizational functions. Employee entity relationship graphs are useful for more than just monitoring insider trading. \u201cThe graph I talked about for insider trading, we built that at Goldman,\u201d Reed adds. \u201cWe actually had the security division come to us because they knew we had people relationships.\u201d Internal enterprise knowledge graphs are extremely similar to the employee entity relationship graph described by Reed, but also include knowledge, skills and experiences alongside relationships between parties. The graphs can strengthen security, monitor insider trading and determine which employees are most appropriate for new tasks or client interactions. Their visual representation of which workers have relationships, knowledge and experience relevant to strategic objectives is influential in selecting the best candidate for a project.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tReed says, \u201cWhenever I had a new client or strategy that I wanted to present to an existing client and I went to my salespeople, a bunch of hands went up and they all said \u2018me\u2019 because they wanted the revenue recognition. I can use the [employee relationship] graph to understand, before I go into that conference room, who\u2019s had the most contact with the client and who\u2019s the strongest candidate based on records of calls, conversations and interactions with the client to disambiguate the hands in the room.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Operational change management<\/em><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThere are also several impact analysis use cases predicated on an initial investment in semantic graph technology. Impact analysis is critical to optimizing operational efficiency\u2014particularly when change management is involved. By modeling all the different functions, infrastructure, applications and departments involved in a datacenter in a relationship graph, organizations can understand the effect of each of those objects to streamline operations. Such a graph becomes vital to the proper implementation of change management, which should be done as painlessly and quickly as possible to improve workflows. Otherwise, companies face reactionary situations in which there is no definite knowledge of a proposed change\u2019s impact, which simply leads to delays in which, \u201cno one is sure enough to say \u2018no\u2019 or \u2018yes\u2019,\u201d Reed explains. \u201cSo you thought the decision was no and you build up more and more technical debt as things fall more and more behind in terms of patch levels and homogeneity in your dataset, to the point where you were forced to do something. When you did that something, it was macro and you needed a small army of people to negate the risk.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tConversely, by graphing all the aspects of the datacenter down to individual switches, companies not only see how something\u2019s affected by a change but also the best time to make it to minimize downtime. The same datacenter relationship graph can be deployed for triage analysis, which further demonstrates the compounding benefits of the approach. In the latter example, organizations can elucidate how to mitigate any issues involving the datacenter and the various objects contained in its graph. \u201cIf something breaks, you can now use the same graph to say this is the collateral damage and these are the people I need to get involved to get the business back up and running,\u201d Reed says. \u201cThen I can start doing my root cause analysis to prevent it from happening again. And by the way, I\u2019m going to use the same graph to do that too.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Market intelligence categories<\/em><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAccording to Reed, business intelligence for finance involves four categories: client intelligence, market intelligence, operational intelligence, and risk and reputational intelligence. \u201cThey all share common things and common relationships between things; they just use those in a different way,\u201d he says. The insider trading graph addresses risk and reputational intelligence and can be repurposed as an employee entity relationship graph to provide client intelligence. The datacenter graph delivers operational intelligence, while a similar graph yields understanding of the market forces influencing that vertical.\u00a0The Global Information Classification Standard<\/a>\u00a0(GICS) is one of the most commonly used tools to analyze those forces. Reed explains that modeling its data on a semantic graph is influential for ascertaining \u201chow you can traverse it. A lot of companies spend a lot of time trying to figure out, for example, how do I look at supply and demand to understand credit risk across GICS categories. Again, that\u2019s all about relationships.\u201d Once organizations have invested in semantic graph architecture, they can also facilitate cluster analysis to illustrate the density of relevant financial concepts. For instance, cluster analysis can depict collateral concentrations of specific customers or financial organizations according to factors such as entities and region.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nCentralized governance<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nDue to the numerous regulatory entities and punitive measures for non-compliance, financial companies all but require the uniform consistency of centralized data governance. By harmonizing their data (regardless of source or location) in an RDF graph, they can readily implement such governance with a top-down approach. Governance dictated by business unit, application or enterprise function is simply a way to increase the number of silo Hadoop implementations. When attempting to demonstrate provenance for multiple regulatory entities and purposes, local governance aggravates what\u2019s already a difficult task. In contrast, the holistic governance of Reed\u2019s method heightens regulatory compliance capabilities while demonstrating data lineage across the enterprise. Thus, organizations save money on potential non-compliance penalties and perform better with the plethora of use cases graphs facilitate. \u201cThe beauty of [centralized] governance in place is that you can start applying this new technology to any existing silo problems and improve it incrementally over time while you deal with these new data fronts with the right underlying architectural approach,\u201d Reed says.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Data as an enterprise asset<\/em><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tEvery organization looking to profit from data-driven practices must invest accordingly. The nature of that investment, however, can determine how successfully and efficiently a company monetizes its big data. In finance, organizations have a number of ways in which they can justify what is essentially the same investment for an interminable number of use cases across Reed\u2019s four dimensions of BI. The use cases explained in this article are all variations of the enterprise knowledge graph concept, which is partly so named because of the infinite number of applications it supports. Still, the way to avoid non-compliance fines, inefficient operations and unjustifiable returns on data expenses is to spend wisely.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tReed says, \u201cFor me, there\u2019s an intuitive sense once you get into the space where you start realizing data is an enterprise asset, a concrete asset. And, I think a lot of people are there. When you start thinking about assets, it\u2019s far more intuitive to say why wouldn\u2019t I invest in that asset once and leverage it as many times as I could, versus having my organization create it for themselves by process on demand. That makes no sense whatsoever.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nBy modeling all the different functions, infrastructure, applications and departments involved in a datacenter in a relationship graph, organizations can understand the effect of each of those objects to streamline operations.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nOriginally published at KMWorld<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

In financial services, the dangers associated with monetizing big data are nearly as great as the rewards. The promises of machine learning, data science and Hadoop<\/a> are tempered by the realities of regulatory penalties, operational efficiency and profit margins that must quickly justify any such expenditure.<\/p>\n","protected":false},"author":68,"featured_media":3002,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[122],"ppma_author":[1626],"class_list":["post-536","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data"],"authors":[{"term_id":1626,"user_id":68,"is_guest":0,"slug":"jelani-harper","display_name":"Jelani Harper","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Harper","first_name":"Jelani","job_title":"","description":"Jelani Harper, an editorial consultant at Knowlege Management World, services the information technology and healthcare markets. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/536","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/68"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=536"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/536\/revisions"}],"predecessor-version":[{"id":37450,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/536\/revisions\/37450"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3002"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=536"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=536"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=536"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=536"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}