{"id":11053,"date":"2021-05-11T09:43:09","date_gmt":"2021-05-11T07:43:09","guid":{"rendered":"https:\/\/www.digdash.com\/?p=11053"},"modified":"2021-05-11T09:47:21","modified_gmt":"2021-05-11T07:47:21","slug":"data-management-6-tips-for-managing-large-volumes-of-data","status":"publish","type":"post","link":"https:\/\/www.digdash.com\/en\/news-articles\/tips-and-tricks\/data-management-6-tips-for-managing-large-volumes-of-data\/","title":{"rendered":"Data management: 6 tips for managing large volumes of data"},"content":{"rendered":"<p>Big Data is the new El Dorado for companies in the 21st century, and data management is becoming a key concern for many organizations involved in large-scale projects.<br \/>\nBut how do you process and analyze several tons of data? At a glance, how can you make even the most complex data speak for itself? How can you make it intelligible?<br \/>\nSo that you can understand all of this, here are 6 data management tips that will help you effectively harness very large volumes of data.<\/p>\n<h2>Adopt a well-recognized strategy<\/h2>\n<p>Companies receive and process immense data flows every single day \u2014 a genuine maze where you can easily get lost. Before even analyzing the data, companies must therefore know<strong> what kind of information<\/strong> they are looking to extract from it.<br \/>\nIn other words, companies must<strong> implement a genuine strategy<\/strong> and set out various objectives. Innovation, cost optimization, product repositioning, etc. The possibilities are endless. In any case, these objectives will serve as a roadmap, making it possible to know what to look for and where to find it. You will therefore be able to conduct data analysis to find an exact solution to your problems.<\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Customer Testimonial:<\/b><\/p>\n<p><strong><a href=\"https:\/\/www.digdash.com\/en\/customers\/airbus-data-volume-analysis\/\" target=\"_blank\" rel=\"noopener\">Airbus analyzes very large volumes of data for its BIO project<\/a><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<blockquote class=\"wp-embedded-content\" data-secret=\"K2bi1YsE1s\"><p><a href=\"https:\/\/www.digdash.com\/en\/customers\/airbus-data-volume-analysis\/\">Airbus | Analysis of a large data volume for its BIO project<\/a><\/p><\/blockquote>\n<p><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;Airbus | Analysis of a large data volume for its BIO project&#8221; &#8212; DigDash\" src=\"https:\/\/www.digdash.com\/en\/customers\/airbus-data-volume-analysis\/embed\/#?secret=rr03d69rsd#?secret=K2bi1YsE1s\" data-secret=\"K2bi1YsE1s\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<h2>Organize and classify data<\/h2>\n<p>To effectively manage very large volumes of data, meticulous organization is essential. First of all, companies must know where their data is stored. A distinction can be made between:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><strong>Inactive data<\/strong>, which are stored in files, on workstations, etc.<\/li>\n<li><strong>Data in transit<\/strong>, which are found in e-mails or transferred files, for example.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><strong>The category<\/strong> to which each piece of data belongs, as well as its owner, must therefore be identified. Customer files, banking information, financial reports, health data, etc. Depending on their nature, data will need to be processed differently, particularly in terms of security and confidentiality.<\/p>\n<p>It is also vital that companies understand <strong>how the data is used<\/strong>. What links are there between the data and the company&#8217;s various business activities? Are they used regularly or rarely? For what purpose?<br \/>\nThe level of priority of the data must also be assessed, as well as its sensitivity (in terms of security).<\/p>\n<h2>Do not overlook unstructured data<\/h2>\n<p>As we have seen, organization is a key component of data management. However, unstructured data account for a significant proportion of the information collected by companies. In fact, the majority of the data owned by companies is most often <strong>unstructured.<\/strong><br \/>\nIt is therefore imperative to compile a list of <strong>all the data available<\/strong> at the organization, whether dormant or actively used. However, these data are difficult to analyze, especially because they have been derived from many actors and sources: employees, customers, social networks, small desktop servers, laptops, etc.<br \/>\nDespite this, these data often prove essential to decision-making processes, so they must be taken into account. Gathered in a data lake, these unstructured data can be easily analyzed and retrieved using a dedicated data visualization tool.<\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Customer Testimonial:<\/b><\/p>\n<p><a href=\"https:\/\/www.digdash.com\/en\/customers\/caceis-conciliate-analytics-and-digital-tranformation-with-dataviz\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"><strong>How Caceis manages over 26 tons of data with DigDash<\/strong><\/span><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<blockquote class=\"wp-embedded-content\" data-secret=\"obp3f8QZ69\"><p><a href=\"https:\/\/www.digdash.com\/en\/customers\/caceis-conciliate-analytics-and-digital-tranformation-with-dataviz\/\">CACEIS Bank | Conciliate Analytics and digital transformation thanks to dataviz<\/a><\/p><\/blockquote>\n<p><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;CACEIS Bank&lt;br\/&gt; | Conciliate Analytics and digital transformation thanks to dataviz&#8221; &#8212; DigDash\" src=\"https:\/\/www.digdash.com\/en\/customers\/caceis-conciliate-analytics-and-digital-tranformation-with-dataviz\/embed\/#?secret=7SNp78H4ac#?secret=obp3f8QZ69\" data-secret=\"obp3f8QZ69\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<h2>Capitalizing on data visualization<\/h2>\n<p>Many companies are equipped with a data processing platform. However, although this kind of software is perfectly adapted to store billions of lines of data, it does not allow users to optimally harness such data. To conduct an in-depth analysis of these data, they must be fed through a <a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/business-prospects\/benefits-of-data-visualization-for-your-company\/\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #069394;\"><u>data visualization tool<\/u><\/span><\/strong><\/a>, in order to generate key performance indicators (KPIs) and perform all the necessary aggregates and calculations.<\/p>\n<p>Organizations also tend to rely on data scientists, that is, statisticians and mathematicians, to extract information from <a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/business-intelligence-bi\/difference-between-big-data-and-business-intelligence\/\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #069394;\"><u>Big Data<\/u><\/span><\/strong><\/a>. Nevertheless, data science cannot present data intelligibly to provide <strong>exact solutions to business problems<\/strong>. Decision makers must therefore draw on data visualization to make strategic choices based on large volumes of data.<\/p>\n<h2>Choosing the right graphical representations<\/h2>\n<p>Organizing and managing data on a large scale involves very dense and rich information. However, the more complex the data are, the more difficult it is <strong>to visually represent them<\/strong>. The information must be prioritized and displayed in a way that the recipient fully understands.<\/p>\n<p>This is where data visualization takes on its full meaning once again, since it allows you to easily switch from one <a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/business-intelligence-bi\/dataviz-charts-kpi\/\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #069394;\"><u>graphical representation<\/u><\/span><\/strong><\/a> to another, according not only to the information communicated, but also to the audience. Curves, histograms, tables, maps, etc. Each format has its own specificities and is more or less adapted to different types of data.<\/p>\n<table>\n<tbody>\n<tr>\n<td><b>To learn more, see:<\/b><\/p>\n<p><a href=\"https:\/\/www.digdash.com\/fr\/news-articles\/lactualite-de-digdash\/embedded-analytics-digdash\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"><strong>DigDash embedded analytics: a real asset for sites and software<\/strong><\/span><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<blockquote class=\"wp-embedded-content\" data-secret=\"qeS3MIjwwj\"><p><a href=\"https:\/\/www.digdash.com\/fr\/news-articles\/lactualite-de-digdash\/embedded-analytics-digdash\/\">L\u2019embedded analytics DigDash : un v\u00e9ritable atout pour les sites et logiciels<\/a><\/p><\/blockquote>\n<p><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;L\u2019embedded analytics DigDash : un v\u00e9ritable atout pour les sites et logiciels&#8221; &#8212; DigDash\" src=\"https:\/\/www.digdash.com\/fr\/news-articles\/lactualite-de-digdash\/embedded-analytics-digdash\/embed\/#?secret=getAeuhlZX#?secret=qeS3MIjwwj\" data-secret=\"qeS3MIjwwj\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<h2>Harnessing the cloud&#8217;s potential<\/h2>\n<p>Nowadays, cloud computing is everywhere in businesses. It reduces capital expenditure on software and associated services, on the one hand, while its flexibility and its potential to bring about economies of scale make it particularly attractive, on the other hand.<br \/>\nBut cloud computing can also become a valuable ally, in terms of <strong>managing large volumes of data.<\/strong> In fact, industry players now allow organizations to switch between their data center and the cloud, in order to better distribute their workload and data.<br \/>\nTo ensure fully transparent data management, it is even possible to physically access company data in the cloud provider&#8217;s data center. You will know exactly <strong>where the data is stored and how it is managed<\/strong>, even when there are billions of lines.<br \/>\nIf you want to go even further in terms of data security, confidentiality and accessibility, you may wish to consider <a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/digdash-news\/the-digdash-cloud-bi-is-now-hds-certified\/\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #069394;\"><u>HDS-certified cloud hosting.<\/u><\/span><\/strong><\/a><\/p>\n<p>In conclusion, while data visualization is an essential tool for managing very large volumes of data, its use is not sufficient on its own. It must be part of a very precise strategy and requires meticulous work to be performed to identify and classify the organization&#8217;s data, whether structured or unstructured. This process is essential if you wish to avoid the pitfalls of data analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Big Data is the new El Dorado for companies in the 21st century, and data management is becoming a key concern for many organizations involved in large-scale projects. But how do you process and analyze several tons of data? At a glance, how can you make even the most complex data speak for itself? How &#8230; <a title=\"Data management: 6 tips for managing large volumes of data\" class=\"read-more\" href=\"https:\/\/www.digdash.com\/en\/news-articles\/tips-and-tricks\/data-management-6-tips-for-managing-large-volumes-of-data\/\" aria-label=\"Read more about Data management: 6 tips for managing large volumes of data\">Lire plus<\/a><\/p>\n","protected":false},"author":21,"featured_media":11041,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[27],"tags":[],"class_list":["post-11053","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tips-and-tricks"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>6 tips for managing large volumes of data<\/title>\n<meta name=\"description\" content=\"Discover how to set up an effective data management 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