{"id":14926,"date":"2024-08-02T13:38:17","date_gmt":"2024-08-02T13:38:17","guid":{"rendered":"https:\/\/trustpair.com\/blog\/smart-data-when-quality-and-reliability-take-precedence-over-volume\/"},"modified":"2025-07-15T13:53:25","modified_gmt":"2025-07-15T13:53:25","slug":"smart-data-when-quality-and-reliability-take-precedence-over-volume","status":"publish","type":"post","link":"https:\/\/trustpair.com\/gb\/blog\/smart-data-when-quality-and-reliability-take-precedence-over-volume\/","title":{"rendered":"Smart Data: when quality and reliability take precedence over volume"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Unlike Big Data, which is based on a large volume of data, the <strong>Smart Data<\/strong> approach is based on the quality of the available data sets. From this point of view, Financial departments seek to identify which data they need to process and for what purpose.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">At the same time, they pay particular attention to the internal and external processes and technologies at their disposal to ensure the highest possible level of reliability.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Redefining the use of data for greater reliability<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">The limits of the Big Data model<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Big Data is based on three main principles:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Volume<\/b><span style=\"font-weight: 400;\">: traditionally, enterprise CIOs managed data volumes measured in terabytes. They now have to deal with petabytes (<\/span><span style=\"font-weight: 400;\">1015 <\/span><span style=\"font-weight: 400;\">bytes) or even exabytes (<\/span><span style=\"font-weight: 400;\">1018 <\/span><span style=\"font-weight: 400;\">bytes) because of the constantly increasing volume of data<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Variety<\/b><span style=\"font-weight: 400;\">: the usual structured data are now supplemented by numerous semi-structured and unstructured data from internal sources (ERP, CRM, etc.) but also external sources (open data, social networks, etc.).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Speed<\/b>: the frequency with which data is generated and updated is continually increasing. Companies need to take the right steps, both in terms of IT infrastructure and software solutions, to take advantage of this speed.<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The benefits of Big Data are multiple, at least on the surface: <\/span><b>operational efficiency, cost reduction, fraud detection and prevention, risk analysis, regulatory compliance, etc.\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">What are the reasons for the limitations of Big Data projects?<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Fragmentation and siloed data<\/b><span style=\"font-weight: 400;\">: like many organisations, the data collected is still too often siloed, making it impossible to achieve overall coherence.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/trustpair.com\/gb\/blog\/data-management-and-governance-a-strategic-issue-for-financial-departments\/\" target=\"_blank\" rel=\"noopener\"><b>Data quality<\/b><\/a><span style=\"font-weight: 400;\">: the same data can sometimes be duplicated in several places in the information system, with very different levels of quality.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Weak data culture<\/b><span style=\"font-weight: 400;\">: many Big Data projects fail because of the weakness of the data culture within companies, which has repercussions on internal and external processes but also on the capacity of the entire organisation to accommodate such projects (resistance to change, lack of skills, etc.). <\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Necessary investments<\/b>: a Big Data project requires significant investments in terms of IT infrastructure, software and also skills. Data scientists are indeed a scarce resource, and therefore complex to recruit and retain.<\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Datalakes as evidence of the model&#8217;s exhaustion\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Closely related to Big Data, data lakes are intended to provide a storage space for all the information available to a company, regardless of its origin and format.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But, like Big Data projects, there are many aspects to the deployment of data lakes:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lack of data structuring and prioritisation<\/b><span style=\"font-weight: 400;\">: these shortcomings can hamper decision- making within the business units and senior management of an organisation.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Poor quality<\/b><span style=\"font-weight: 400;\">: of the main areas of data governance, quality is certainly the most important. Too often, data lakes have weaknesses in data consistency, accuracy and reliability, which affects the overall quality of the data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lack of involvement of business units<\/b><span style=\"font-weight: 400;\">: too often, the business value of a data lake project is eroded over time, in favour of technical considerations or cost issues.\u00a0<\/span><\/li>\n<\/ul>\n<blockquote><p><i><span style=\"font-weight: 400;\">\u201cAlthough data lake implementation projects have exploded in recent years, many have failed because the use of the data was not sufficiently well defined and the expected quality was not always achieved<\/span><\/i><span style=\"font-weight: 400;\">\u201d &#8211; <\/span><b>Fr\u00e9d\u00e9ric Malagoli<\/b><span style=\"font-weight: 400;\">, PwC\u00a0<\/span><\/p><\/blockquote>\n<p><!--HubSpot Call-to-Action Code --><span id=\"hs-cta-wrapper-2d38aaff-e954-4f5c-a6b6-3c7f0863b353\" class=\"hs-cta-wrapper\"><span id=\"hs-cta-2d38aaff-e954-4f5c-a6b6-3c7f0863b353\" class=\"hs-cta-node hs-cta-2d38aaff-e954-4f5c-a6b6-3c7f0863b353\"><!-- [if lte IE 8]>\n\n\n<div id=\"hs-cta-ie-element\"><\/div>\n\n\n<![endif]--><a href=\"https:\/\/cta-redirect.hubspot.com\/cta\/redirect\/5278241\/2d38aaff-e954-4f5c-a6b6-3c7f0863b353\" target=\"_blank\" rel=\"noopener\"><img fetchpriority=\"high\" decoding=\"async\" id=\"hs-cta-img-2d38aaff-e954-4f5c-a6b6-3c7f0863b353\" class=\"hs-cta-img\" style=\"border-width: 0px;\" src=\"https:\/\/no-cache.hubspot.com\/cta\/default\/5278241\/2d38aaff-e954-4f5c-a6b6-3c7f0863b353.png\" alt=\"Data Management - White Paper - Trustpair x Altares\" width=\"1040\" height=\"544\" \/><\/a><\/span><script charset=\"utf-8\" src=\"https:\/\/js.hscta.net\/cta\/current.js\"><\/script><script type=\"text\/javascript\"> hbspt.cta.load(5278241, '2d38aaff-e954-4f5c-a6b6-3c7f0863b353', {\"region\":\"na1\"}); <\/script><\/span><!-- end HubSpot Call-to-Action Code --><\/p>\n<h2><span style=\"font-weight: 400;\">Towards Smart Data for a successful strategy\u00a0<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Faced with the failure of Big Data and its associated data lakes, many companies are turning to a different approach: <\/span><b>Smart Data<\/b><span style=\"font-weight: 400;\">. Deciding to implement a Smart Data strategy means giving priority \u2013 through the notion of &#8220;intelligence&#8221; \u2013 to processing less data but in a more refined way.\u00a0<\/span><\/p>\n<blockquote><p><i><span style=\"font-weight: 400;\">&#8220;When we talk about Smart Data, we combine the notions of linking and history. The value of data is revealed when it is linked. Historical data allows us to determine a trend and a forecast.\u201d <\/span><\/i><span style=\"font-weight: 400;\"><strong>Fr\u00e9d\u00e9ric Paresy<\/strong>, Altares<\/span><\/p><\/blockquote>\n<p><span style=\"font-weight: 400;\">Linking is perfectly in line with the notion of reference data. When data is linked, it should also important be linked to references and history in order to understand the history of the data, and once trust in the information has been created and sustained, it becomes possible to perform analysis and prediction using data science.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Technology for an effective Data Management strategy<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Technology at the service of data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Today, the technological solutions managed by company\u2019s<\/span><span style=\"font-weight: 400;\"> ISDs<\/span><span style=\"font-weight: 400;\"> enable to simplify and secure all processes. This translates into:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Improved reporting<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">More detailed monitoring of <\/span><span style=\"font-weight: 400;\">actions in real time<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">More effective and relevant decision support<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Better <\/span><span style=\"font-weight: 400;\">management of skills.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The growth of companies is better supported and sustained as a result.\u00a0<\/span><\/p>\n<p><b>Integration solutions are strategic in this respect<\/b><span style=\"font-weight: 400;\">. They respond to the need to simplify the complexity of companies, by securing transactions and facilitating the work of employees. Thanks to them, companies gain in performance, carry out updates <\/span><span style=\"font-weight: 400;\">and continuous monitoring, thus saving repetitive and tedious tasks.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Essential tools for prediction and decision-making<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Companies are very mature when it comes to creating a &#8220;rear view&#8221;, i.e. when they have to analyse the past. But their goal is to move towards prediction, in order to detect trends and insights that help them in their decision-making. This requires a certain amount of data that is reliable, of high quality and clean across all functions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The job of any data team within a company is first and foremost to define the use of the data in close collaboration with the business departments. The more history you have on a piece of data, the better you can predict the future.\u00a0<\/span><\/p>\n<blockquote><p><i><span style=\"font-weight: 400;\">\u201cOne of our jobs is to predict the failure of companies. But while history is important, it needs to be re-evaluated! It&#8217;s letting data sleep that&#8217;s dangerous.\u201d &#8211; <\/span><\/i><b><i>Michael Lisch, <\/i><\/b><i><span style=\"font-weight: 400;\">Altares<\/span><\/i><\/p><\/blockquote>\n<h2><span style=\"font-weight: 400;\">A transformation of the processes necessary for reliable and secure data<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Challenge internal processes for better data protection<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Ensuring data security and protection throughout the company is a long-term task. In addition to the legal measures that need to be put in place (review of contracts with important or at-risk subcontractors and deployment of internal charters aimed at raising awareness of cybersecurity risks among employees), organisational measures must be taken.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These measures aim to develop strict internal processes:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mapping of internal data flows<\/b><span style=\"font-weight: 400;\"> in<\/span> <span style=\"font-weight: 400;\">order to make all data exchanges fluid and secure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Implementation of <\/span><b>an Identity <\/b><span style=\"font-weight: 400;\">and<\/span><b> Access Management <\/b><span style=\"font-weight: 400;\">(IAM) <\/span><b>strategy <\/b><span style=\"font-weight: 400;\">defining access rights and specific authorisations for each employee<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Implementation of <\/span><b>alert thresholds according to <\/b><span style=\"font-weight: 400;\">the amount or sensitivity of a transaction.<\/span><\/li>\n<\/ul>\n<blockquote><p><i><span style=\"font-weight: 400;\">\u201cTo achieve good Data Management and data governance, it is essential to combine: a technological solution, good internal organisation, defined processes and team awareness.\u201d <\/span><\/i><b><i>Betty Sfez, <\/i><\/b><i><span style=\"font-weight: 400;\">Cabinet Solegal<\/span><\/i><\/p><\/blockquote>\n<h3>Want to know more about Data Management and Smart Data?<\/h3>\n<p>Get the last white paper &#8220;<a href=\"https:\/\/trustpair.com\/gb\/resources\/data-management-and-b2b-payment-fraud\/\" target=\"_blank\" rel=\"noopener\">Data Management: the cure for wire transfer fraud<\/a> &#8221; paper co-branded by Trustpair and Altares!<\/p>\n<p><!--HubSpot Call-to-Action Code --><span id=\"hs-cta-wrapper-21a257d2-f976-4775-b119-a69debc849d3\" class=\"hs-cta-wrapper\"><span id=\"hs-cta-21a257d2-f976-4775-b119-a69debc849d3\" class=\"hs-cta-node hs-cta-21a257d2-f976-4775-b119-a69debc849d3\"><!-- [if lte IE 8]>\n\n\n<div id=\"hs-cta-ie-element\"><\/div>\n\n\n<![endif]--><a href=\"https:\/\/cta-redirect.hubspot.com\/cta\/redirect\/5278241\/21a257d2-f976-4775-b119-a69debc849d3\"><img decoding=\"async\" id=\"hs-cta-img-21a257d2-f976-4775-b119-a69debc849d3\" class=\"hs-cta-img\" style=\"border-width: 0px;\" src=\"https:\/\/no-cache.hubspot.com\/cta\/default\/5278241\/21a257d2-f976-4775-b119-a69debc849d3.png\" alt=\"Data Management - White Paper - Trustpair x Altares\" width=\"1040\" height=\"544\" \/><\/a><\/span><script charset=\"utf-8\" src=\"https:\/\/js.hscta.net\/cta\/current.js\"><\/script><script type=\"text\/javascript\"> hbspt.cta.load(5278241, '21a257d2-f976-4775-b119-a69debc849d3', {\"region\":\"na1\"}); <\/script><\/span><!-- end HubSpot Call-to-Action Code --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Unlike Big Data, which is based on a large volume of data, the Smart Data approach is based on the quality of the available data sets. From this point of view, Financial departments seek to identify which data they need to process and for what purpose. At the same time, they pay particular attention to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":14927,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[115],"tags":[],"class_list":["post-14926","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-management-gb"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Smart Data: when quality and reliability take over volume - Trustpair<\/title>\n<meta name=\"description\" content=\"Smart Data, the revenge on Big Data? Read our white paper dedicated to Data Management, in collaboration with Altares. 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