{"id":16141,"date":"2021-11-17T10:25:33","date_gmt":"2021-11-17T10:25:33","guid":{"rendered":"https:\/\/www.orangemantra.com\/blog\/?p=16141"},"modified":"2026-03-02T06:16:00","modified_gmt":"2026-03-02T06:16:00","slug":"top-challenges-facing-big-data-analytics-in-healthcare","status":"publish","type":"post","link":"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare","title":{"rendered":"Top Challenges Facing Big Data Analytics in Healthcare\u202f"},"content":{"rendered":"<p><span data-contrast=\"none\">When it comes to big data analytics, the healthcare industry faces numerous challenges. These challenges may include security, visualization, and a wide array of data integrity concerns. Over the years,\u00a0<\/span><a href=\"https:\/\/www.orangemantra.com\/industries\/healthtech\/dashbording-and-analytics\/\">big data analytics in healthcare<\/a> <span data-contrast=\"none\">has emerged as one of the most challenging undertakings for the healthcare industry. For instance, healthcare professionals, who may not be well-versed with managing electronic health records, now need to gather actionable insights as well. Also, they are expected to apply those learnings to complex initiatives that enhance their overall reimbursement rates.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Healthcare organizations that successfully integrate data-driven insights into their clinical and operational workflow get significant results.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Some of the top benefits of turning data assets into data insights include healthier patients, lower care costs, transparent performance, and above all, patient satisfaction. However, it&#8217;s not easy to achieve all the goals. In this blog, we shall find out the opportunities and challenges associated with big data\u00a0<\/span>analytics in healthcare<span data-contrast=\"none\">\u00a0industry.\u202f\u202f<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_74 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Challenges_Associated_With_Big_Data_Analytics_in_Healthcare\" >Challenges Associated With Big Data Analytics in Healthcare\u00a0\u00a0<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Collecting_the_data\" >Collecting the data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Cleaning_the_data\" >Cleaning the data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Data_storage\" >Data storage<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Securing_patients_data\" >Securing patients\u2019 data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Querying_the_Data\" >Querying the Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Real-time_updates\" >Real-time updates<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#Summing_it_up\" >Summing it up<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#FAQs\" >FAQs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.orangemantra.com\/blog\/top-challenges-facing-big-data-analytics-in-healthcare\/#_1_What_do_you_mean_by_big_data_for_the_healthcare_industry\" >\u00a01. What do you mean by big data for the healthcare industry?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Challenges_Associated_With_Big_Data_Analytics_in_Healthcare\"><\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\"><span class=\"TextRun SCXW260803341 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW260803341 BCX0\">Challenges Associated With Big Data Analytics in Healthcare<\/span><\/span><span class=\"EOP SCXW260803341 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span>\u00a0<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Collecting_the_data\"><\/span>Collecting the data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Data insights are often retrieved from some source, but for the healthcare industry, they lack data governance habits. As a result, collecting data that is clean, accurate, and complete for use in multiple systems is a daunting task to accomplish.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">An incomplete understanding of why big data is crucial for providers is a contributing factor that disrupts data throughout the cycle. Providers can initiate with prioritizing valuable data types for their specific department, enlisting the data governance, and integrity expertise of every healthcare professional. They can also maintain a clinical documentation improvement program to ensure the data is useful for downstream analytics.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Cleaning_the_data\"><\/span>Cleaning the data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Many healthcare providers focus on keeping their clinics clean, but they lack keeping their data clean as well. Impure data can immediately derail a <a href=\"https:\/\/www.orangemantra.com\/services\/big-data-analytics\/\">big data<\/a> analytics report, especially when combining disparate data sources in slightly different formats. Data cleaning is referred to as cleansing or scrubbing the data to ensure the datasets are accurate, consistent, relevant, and not influenced by any third party.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Although most of the data cleaning processes are performed manually, some IT vendors offer automated scrubbing tools that implement logic rules to compare, contrast, and correct large datasets. These tools are highly sophisticated and precise as machine learning techniques are getting advanced. It saves time, money, and ensures high levels of accuracy and integrity in the healthcare sector.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Data_storage\"><\/span>Data storage<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Frontline doctors and health practitioners merely give a thought to where their data is being stored. But for the IT department, this information is crucial as it may raise security and performance risks. With growing healthcare data, many providers no longer keep track of their costs. As a result, it creates a substantial impact on the premise of data centers.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Although many providers feel it safe to store their data on-premises data centers. However, it can be expensive to scale, difficult to maintain, and prone to producing data silos across various departments. On the other hand, cloud storage offers nimble disaster recovery, lower up-front costs, and easy expansion.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Many healthcare providers end up using a hybrid approach for their data storage programs. However, while developing a hybrid infrastructure, providers must ensure that disparate systems can communicate and share data with other segments of the organization if required.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Securing_patients_data\"><\/span>Securing patients\u2019 data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Data security is the most crucial priority for healthcare organizations, especially with the risks of rapid-fire series of high-profile cyber-attacks and ransomware episodes. The HIPAA security rule includes a list of technical safeguards for healthcare organizations to protect health information, including transmission security, authentication protocols, and controls over access, integrity, and auditing.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">These safeguards translate into common security procedures like using updated anti-virus software, setting up firewalls, encrypting sensitive data, and implementing multi-factor authentication. However, even top-level security cannot ensure the fallibility of human staff members who tend to think about their convenience over hectic software updates and complicated constraints on data and software.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Healthcare organizations must always remind their staff members of the highly volatile nature of data security protocols and constantly review who has access to high-value data assets to avoid any third-party causing damage or threats.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Querying_the_Data\"><\/span>Querying the Data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Robust metadata and effective stewardship protocols make it easy for healthcare organizations to query their data and get the answers on time. The ability to ask questions is the foundation of report and analytics, but the providers\u00a0have to\u00a0face numerous challenges before engaging in meaningful analysis of their big data assets.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Many organizations use Structured Query Language (SQL) to fetch large datasets and relational databases, but it is also an effective solution for users who trust the accuracy, completeness, and standardization of the data available.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Real-time_updates\"><\/span>Real-time updates<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Healthcare data are not always the same. Most of the elements require frequent updates to stay relevant. Understanding the sensitivity of big data, or how often and to what extent it should change, is a challenge for healthcare providers who don\u2019t consistently monitor their data assets.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Many providers keep track of which datasets need manual updating and which can get automated. Also, they ensure to complete a process without the downtime and update the dataset without damaging its quality and integrity.\u202f\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Providers ensure that they don\u2019t create duplicate records while attempting to update an individual element. Any duplicity will make it challenging for healthcare providers to access crucial information for patients\u2019 health-related decision-making process.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Summing_it_up\"><\/span>Summing it up<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span data-contrast=\"none\">The benefits of big data for healthcare\u00a0is\u00a0enormous. Smart machines and algorithms can help process large data that a human brain\u00a0can not\u00a0possibly comprehend; as a result, big data helps with early diagnostics, treatments, and accurate forecasts.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Most importantly, the large amounts of data fed into the algorithm not only can it help make better decisions, but also enhance speed and performance.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">At <a href=\"https:\/\/www.orangemantra.com\/\">Orange Mantra<\/a>, we ensure our team caters to the needs of our valued clients when it comes to offering the best big data analytics and services. Get in touch with our well-renowned team to get further insights on big data for the healthcare industry.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h3><span class=\"ez-toc-section\" id=\"_1_What_do_you_mean_by_big_data_for_the_healthcare_industry\"><\/span>\u00a01. What do you mean by big data for the healthcare industry?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span data-contrast=\"none\">Big data in healthcare is defined concerning the three &#8220;Vs&#8221;:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"none\"> \u202f \u202f \u202f<\/span><b><span data-contrast=\"none\">Volume:<\/span><\/b><span data-contrast=\"none\">Maintaining a large amount of data<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\"> \u202f \u202f \u202f<\/span><b><span data-contrast=\"none\">Velocity:<\/span><\/b><span data-contrast=\"none\">Ensure the data arrives at high speed<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<li><span data-contrast=\"none\"> \u202f \u202f \u202f<\/span><b><span data-contrast=\"none\">Variety:<\/span><\/b><span data-contrast=\"none\">Monitor the different types of data<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">In simple words, big data in healthcare refers to largely routinely or automatically collected datasets that are collected electronically and stored for future references. It is reusable and connects the existing database to improve patients&#8217; health and health system performance.\u202f<\/span><\/p>\n<p><b><span data-contrast=\"none\">\u00a02. Who can use Big Data for healthcare?<\/span><\/b><\/p>\n<p><span data-contrast=\"none\">Big Data can be used by healthcare providers, academia, policymakers, and the industry. A few examples of the use of Big Data in healthcare are:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li><span data-contrast=\"none\"> To achieve better health outcomes<\/span><\/li>\n<li><span data-contrast=\"none\"> To improve the effectiveness of treatments and increase patient safety<\/span><\/li>\n<li><span data-contrast=\"none\"> To monitor healthcare services<\/span><\/li>\n<li><span data-contrast=\"none\"> To detect population-level effects<\/span><\/li>\n<li><span data-contrast=\"none\"> To get more efficient recruitment and patients\u2019 selection for clinical trials<\/span><\/li>\n<\/ul>\n<p><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0 <strong>3. <\/strong><\/span><b><span data-contrast=\"none\">What are the benefits of using Big Data for patients?<\/span><\/b><\/p>\n<p><span data-contrast=\"none\">Big Data is attributed to having \u201ctransformative potential\u201d in healthcare systems, with benefits across the entire pathway of care delivery for the stakeholders.\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Linkage of previously separated data sets and their analysis using Big Data analytics offer new ways to accelerate research and identify the right treatment for individual patients (personalized medicine).\u202f<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Access to large data sets gives a more comprehensive picture of patient&#8217;s health condition, allows patient-related outcomes to be measured perfectly, and supports decision-makers in taking the right decision for the entire healthcare system.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When it comes to big data analytics, the healthcare industry faces numerous challenges. These challenges may include security, visualization, and a wide array of data integrity concerns. Over the years,\u00a0big data analytics in healthcare has emerged as one of the most challenging undertakings for the healthcare industry. For instance, healthcare professionals, who may not be [&hellip;]<\/p>\n","protected":false},"author":18,"featured_media":16142,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[958],"tags":[],"class_list":["post-16141","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-healthtech"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.6 (Yoast SEO v22.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top Challenges Facing Big Data Analytics in Healthcare\u202f<\/title>\n<meta name=\"description\" content=\"At Orange Mantra, we ensure our team caters to the needs of our valued clients when it comes to offering the best big data analytics and services.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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