{"id":88070,"date":"2025-09-25T23:25:07","date_gmt":"2025-09-25T21:25:07","guid":{"rendered":"https:\/\/www.veeva.com\/eu\/?post_type=resources&#038;p=88070"},"modified":"2026-05-20T11:28:23","modified_gmt":"2026-05-20T09:28:23","slug":"clinical-data-industry-research","status":"publish","type":"resources","link":"https:\/\/www.veeva.com\/eu\/resources\/clinical-data-industry-research\/","title":{"rendered":"Clinical Data Industry Research"},"content":{"rendered":"<style>\n.hero-contained.hero-contained--gradient  {\ndisplay:none;\n}<\/p>\n<p>.veeva-2024 .veeva-footer-2024--counter-rounded-bottom-section {\nmargin-top: 0;\npadding: 104px 0 64px;\n}\n.veeva-2024 .orange {\ncolor:#f5670e;font-size:18px;\n}\n.veeva-2024 .content-block__content .h3, .veeva-2024 .content-block__content h3 {\n\tfont-size: 20px;\n\tline-height: 26px;\n\tmargin-top: 35px;\n\tmargin-bottom: 20px;\n\tfont-weight:500;\n}<\/p>\n<p>.veeva-2024 .content-block__content .img-responsive {\ndisplay: block;\nheight: auto;\nmargin-left: auto;\nmargin-right: auto;\nmax-width: 100%;\npadding: 0;\nmargin-bottom:50px!important;\nmargin-top:30px!important;\n}<\/p>\n<p>ol.color li, ul.color li {color:#f5670e;font-weight:700!important;}\nol.color li span, ul.color li span {color: #01040b;font-weight:400;}<\/p>\n<p>.veeva-2024 .content-block__content h1 {\n\tmargin-bottom:30px;\n\tfont-weight:500;\n\tfont-size:26px;\ncolor:#f5670e;\n}\n.veeva-2024 .content-block__content h2 {\n\tmargin-bottom:30px;\n\tfont-weight:600;\n\tfont-size:24px;\n}\n.veeva-2024 .content-block__content h4 {\n\tcolor:#f5670e;\n\tfont-size:18px;\n\tfont-weight:500;\n}<\/p>\n<p>@media (min-width: 767px) {\n.veeva-2024 .content-block__content h1 {\n\t\tmargin-bottom:35px;\n\t\tfont-weight:400;\n\t\tfont-size:36px;\n\t}\n.veeva-2024 .content-block__content h2 {\n\t\tmargin-bottom:30px;\n\t\tfont-weight:600;\n\t\tfont-size:30px;\n\t}\n\t.veeva-2024 .content-block__content h4 {\n\tcolor:#f5670e;\n\tfont-size:22px;\n\t}<\/p>\n<p>}<\/p>\n<p>.veeva-2024 .content-block__content blockquote span:first-of-type, .veeva-2024 .content-block__content blockquote span:only-child {\nfont-family: Poppins,sans-serif;\nfont-size: 14px;\nfont-weight: 400;\nletter-spacing: unset;\nline-height: 1.2;\nmargin-bottom: 0;\nmargin-top: 15px;\ntext-transform: unset;\n}\n.veeva-2024 .content-block__content blockquote {\nfont-size: 18px;\npadding: 2.2rem 3.2rem 2.2rem 2.4rem;\n}<\/p>\n<p>.veeva-2024 .content-block__content p>a:not(.resources-toc-target,.button-outline,.button-primary,.button-secondary,.button-tertiary) {\ncolor:#f5670e;\ntext-decoration: underline;\ntext-decoration: none;\ntransition: color .3s ease;\n}<\/p>\n<p>.veeva-2024 .button-secondary img {\nbackground-color: #f5670e;\n}\n.veeva-2024 .content-block__content .takeaway-box {\ndisplay: block;\nborder-left:3px solid #f5670e;\nborder-top-left-radius: 0px;\nborder-bottom-left-radius: 0px;\nfont-size:20px;\nwidth:100%;max-width:550px;\nmargin-top:40px;\n}\n.veeva-2024 .content-block__content .button-icon.button-icon--white {\nbackground-color:#f5670e;\nmargin-top: 5px;\n}<\/p>\n<\/style>\n<h2>1. Executive Summary<\/h2>\n<p>\nPharma and biotech teams face pressure to deliver faster trial timelines under increasingly<br \/>\ncomplex protocols and constrained budgets. Meeting these demands requires not just innovation,<br \/>\nbut a willingness to depart from &#8216;good enough&#8217; processes and technology for those carrying out<br \/>\nclinical data tasks.\n<\/p>\n<p>\nInefficiency is the norm for many clinical professionals. Data managers and clinical research<br \/>\nassociates (CRAs) \u2013 the people responsible for collecting, monitoring, and ensuring the data is fit for<br \/>\npurpose \u2013 are bogged down by manual processes and fragmented systems. Too much time is lost<br \/>\nto repetitive steps, re-entry of information, and workarounds outside core clinical platforms.\n<\/p>\n<p>\n<strong>This research identifies the scale and root causes of that lost productivity in typical Phase III trials,<br \/>\nand offers a path forward.<\/strong> We surveyed 88 data managers and CRAs and conducted in-depth interviews<br \/>\nwith select participants to gather first-hand accounts of the impact<br \/>\nof workflow inefficiencies.\n<\/p>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Executive-Summary.png\"><\/p>\n<h4 class=\"mt-40\">Main takeaways<\/h4>\n<ul class=\"color\">\n<li><span><strong>Inefficiencies are embedded in workflows<\/strong><br \/>\nMost data manager tasks take more time than they should, especially manual data reconciliation<br \/>\nand data review &#038; cleaning. CRAs say updating multiple systems, writing reports, and following up<br \/>\nwith sites all take longer than they should.<\/span><\/li>\n<li><span><strong>Manual work and disconnected systems are the root cause of inefficiencies<\/strong><br \/>\nCRAs are impacted the most by system fragmentation, with data managers citing too many manual<br \/>\nsteps and a lack of modern tool usage as the main reason for inefficiency. Most manual data<br \/>\nreconciliation work is happening outside of clinical systems, or using multiple different systems.<\/span><\/li>\n<li><span><strong>The cost of inaction is burnout and data quality<\/strong><br \/>\nBurnout or staff turnover and data quality issues were some of the greatest consequences for<br \/>\nCRAs and data managers if inefficiencies are left unaddressed. CRA burnout and turnover is a widely<br \/>\nreported issue, but could be an increasing trend for data managers.<\/span><\/li>\n<li><span><strong>Organizational resistance to change hinders progress<\/strong><br \/>\nDespite the challenges caused by inefficient processes and systems, almost half of data managers<br \/>\nand CRAs say that resistance to change is a key barrier to a more productive future state. A sense<br \/>\nof current workarounds being \u2018good enough\u2019 hides the damage caused by inefficiency in clinical trials.<br \/>\nNearly half of respondents think that SOPs fail to reflect real-world workflows or underutilize available<br \/>\ntools, demonstrating the organizational barriers to greater efficiency.<\/span><\/li>\n<li><span><strong>Future roles focus on pragmatic change<\/strong><br \/>\nAI support and risk-based approaches are anticipated to evolve data managers\u2019 work over the next two<br \/>\nyears, but data managers cited automation as #1 for their role evolution. The vast majority of CRAs<br \/>\nexpect risk-based monitoring to become the norm in their roles, but feel the stretch from greater study<br \/>\ncomplexity and budget\/resource constraints.<\/span><\/li>\n<li><span><strong>System modernization is a clear opportunity<\/strong><br \/>\nThree quarters of data managers and over half of CRAs say their teams are actively looking for ways to<br \/>\nmodernize trial execution. Nearly all respondents feel that better integration between clinical data and<br \/>\noperations systems would significantly improve execution.<\/span><\/li>\n<\/ul>\n<blockquote><p>\n&#8220;Identifying inconsistencies between two systems for the same kind of data should be easy in the<br \/>\nadvanced tech world we live in.&#8221;<\/p>\n<p> <span><strong>Data manager\u2019s response to the question<\/strong> \u201cWhy is [manual data reconciliation] so inefficient?\u201d<\/span>\n<\/p><\/blockquote>\n<h2 class=\"mt-40\">2. Key Findings<\/h2>\n<h4>2.1 Inefficiencies faced by data managers<\/h4>\n<p>\nData managers spend the greatest proportion of their<br \/>\ntime on data review and cleaning (18%) <strong>[Figure 1]<\/strong>.<br \/>\nThis task is also a top inefficiency, cited as the second<br \/>\nmost inefficient task (35%) after manual data reconciliation<br \/>\n(52%). Manual reconciliation occupies about 11% of data<br \/>\nmanager\u2019s time yet is their highest priority to improve,<br \/>\nindicating that inefficiency is a bigger driver of frustration<br \/>\nthan time spent on a task.\n<\/p>\n<blockquote><p>\n&#8220;We have home-grown tools for<br \/>\ndata cleaning but I don&#8217;t trust<br \/>\nthem because they cannot<br \/>\nhandle the variability in data<br \/>\nstructure between studies.<br \/>\nData sources have differences<br \/>\nin date formatting, extra spaces<br \/>\nin an output, column naming&#8230;<br \/>\nThey can differ by source and by<br \/>\nstudy. There is too much variation<br \/>\nso we stick with Excel.&#8221;<\/p>\n<p> <span><strong>Senior Clinical Data Specialist,<br \/>\n<\/strong>Global CRO<\/span>\n<\/p><\/blockquote>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Figure-1.png\"><\/p>\n<p>\n<strong>Tasks flagged as the most inefficient (manual data reconciliation<br \/>\nand data review and cleaning) are also among the tasks more<br \/>\noften performed outside clinical systems.<\/strong> For those citing data<br \/>\nreconciliation as a top inefficiency, 97% perform reconciliations<br \/>\neither outside of clinical systems (50%) or with a mix of systems<br \/>\n(47%). For data review and cleaning, 39% of respondents perform<br \/>\nthe task outside clinical systems, 52% with a mix of systems,<br \/>\nand only 9% within a single clinical system.\n<\/p>\n<blockquote><p>\n&#8220;It feels like for every study, we\u2019re<br \/>\nreinventing what data review is.<br \/>\nThere\u2019s a lack of good, reusable<br \/>\ndata cleaning programming,<br \/>\nwhich would help with both<br \/>\n[data cleaning and review and<br \/>\nmanual data reconciliation].&#8221;<\/p>\n<p> <span><strong>Data Analyst, <\/strong> Small Biopharma<\/span>\n<\/p><\/blockquote>\n<p>\nOne senior data manager from a global CRO estimates that of the three hours each data manager<br \/>\nspends on reviewing data, one hour is wasted reviewing unchanged data, and another hour is spent<br \/>\napplying Excel formulas. According to this account, data managers are only spending one third of<br \/>\ntheir data review time reviewing new data.\n<\/p>\n<p>\nThe senior data manager explains the challenges around a lack of insight into data changes during data<br \/>\nreview: &#8220;<em>I complete a round of reconciliation and the next month when I download the data there is no<br \/>\nway to tell what data has changed. We apply a lot of Excel formulas and effort to figure out what&#8217;s been<br \/>\nupdated and what queries got opened. Every month I pull the same reports and apply the same effort,<br \/>\neven though 70% of the data is the same.<\/em>&#8221;\n<\/p>\n<h4 style=\"margin-top:30px\">2.2 Inefficiencies faced by CRAs<\/h4>\n<p>\nWhile CRAs spend most of their time on monitoring visits (22%), perceived inefficiencies center on<br \/>\ndocumentation and manual tracking \u2013 tasks that collectively consume around 18% of CRAs&#8217; time yet<br \/>\ngenerate the highest frustration <strong>[Figure 2]<\/strong>. Documentation and manual tracking are identified as top<br \/>\npriorities for improvement by 44% of respondents.\n<\/p>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Figure-2.png\"><\/p>\n<p>\nManaging documents in multiple systems and carrying out<br \/>\nrepetitive steps are major sources of inefficiency. One CRA from<br \/>\na top 20 biopharma expresses surprise that there aren\u2019t more<br \/>\nautomated solutions available: &#8220;We still have to do so much manual<br \/>\ntracking, there\u2019s no connection between the systems.\u201d\n<\/p>\n<p>\n³Ô¹Ï±¬ÁÏ benchmark analysis from over 30 biopharma companies<br \/>\nspanning 10 years shows that a total of 1,286 days per 1,000<br \/>\nmonitoring visit reports (MVPs) was wasted. This equates to<br \/>\n$1,028,888, based on an 8 hour work day at a $100\/hour rate.<br \/>\nWorking in trackers such as Excel, OneNote, and Word accounted<br \/>\nfor the most time wasted, costing $326,112 per 1,000 MVPs. If your<br \/>\nCRAs complete 20,000 MVPs, for example, the cost would reach<br \/>\n$6,522,240.\n<\/p>\n<blockquote><p>\n&#8220;I use OneNote for documenting<br \/>\nmonitoring visits before<br \/>\ntranscribing into the CTMS.<br \/>\nIt\u2019s not that I like it \u2014 I don\u2019t have<br \/>\nany other tool to keep everything<br \/>\nin one place. I have to capture<br \/>\n[data] in a non-validated system.&#8221;<\/p>\n<p> <span><strong>CRA Subject Matter Expert,<br \/>\n <\/strong> Top 20 Biopharma Company<\/span>\n<\/p><\/blockquote>\n<h4>2.3 Cost of queries<\/h4>\n<p>\nBoth data managers and CRAs reported that non-EDC queries take<br \/>\nfar longer (71%) than EDC queries to resolve. Non-EDC queries<br \/>\nare those generated from third-party data, such as imaging, eCOA,<br \/>\nand central labs. On average, an EDC query takes 9.6 days to resolve,<br \/>\nfrom when it\u2019s created to closure, whereas non-EDC queries take<br \/>\n16.4 days. In terms of personal effort on query management,<br \/>\nrespondents use 34% more active time to resolve a query on thirdparty<br \/>\ndata than a query on EDC data. This difference in resolution<br \/>\ntime is due in part to the delay in non-EDC data reaching the data<br \/>\nmanager or CRA, and the additional effort required to investigate<br \/>\nwith the vendor.\n<\/p>\n<p>\nIt is estimated that a <a target=\"_blank\" href=\"https:\/\/www.jscdm.org\/article\/id\/20\/#:~:text=In%20our%20sample%20of%2020,than%201.7%25%20of%20the%20data.\" rel=\"noopener\">query costs between $28-$225 to resolve<\/a><br \/>\nand that, on average, a Phase III study generates 96,980 queries.<br \/>\nNon-EDC queries require 34% more active time, suggesting that their<br \/>\ncost ranges from $32.50-$300 per query. This suggests that the<br \/>\ncost per query is growing to $32.50-$300, and that inefficiencies in<br \/>\nquery management \u2013 particularly non-EDC queries \u2013 reach millions<br \/>\nof dollars in Phase III trials.\n<\/p>\n<blockquote><p>\n&#8220;Non-EDC queries take longer<br \/>\nto resolve in part because data<br \/>\nmanagers aren\u2019t receiving the<br \/>\ndata frequently. When you are<br \/>\nquerying the data it is months old.<br \/>\nThe site has to check different<br \/>\nsystems to find the actual reason<br \/>\nor value. We may also keep the<br \/>\nquery open until the next data<br \/>\ntransfer, to confirm whether the<br \/>\nquery was answered correctly<br \/>\nor if there is a change on the<br \/>\nvendor side.&#8221;<\/p>\n<p> <span><strong>Senior Clinical Data Manager,<br \/>\n <\/strong> Global CRO<\/span>\n<\/p><\/blockquote>\n<h2>3. Root Causes of Unproductive Effort<\/h2>\n<h4>3.1 Top drivers of inefficiency for data managers and CRAs<\/h4>\n<p>\nDealing with disconnected systems is the primary cause of inefficiency for CRAs (71%). Too many<br \/>\nmanual steps or re-entry (74%) is the main reason why tasks require extra effort and time in data<br \/>\nmanagers\u2019 daily work <strong>[Figure 3]<\/strong>.\n<\/p>\n<p>\nOne data manager&#8217;s description of why manual reconciliations are so inefficient highlights the impact<br \/>\nof too many manual steps on workflows: &#8220;The worst part of data reconciliation is tracking issues from<br \/>\nthe prior reconciliation to the new data load. Most of us use Excel formulas and manually copy\/paste,<br \/>\nuse Vlookup, carry forward the comment, and then add the latest status. <strong>It is painful.<\/strong>&#8221;\n<\/p>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Figure-3.png\"><\/p>\n<h4>3.2 Tools and processes as sources of inefficiency<\/h4>\n<p>\nWhen asked whether technology issues or process issues mostly contribute to workflow inefficiencies,<br \/>\nor both equally, 48% of respondents said both technology and process issues equally. CRAs were slightly<br \/>\nmore likely than data managers to point towards tech-specific challenges (30% vs. 25%), as were those<br \/>\nworking for larger companies (>10,000 employees).\n<\/p>\n<p>\nSponsors are twice as likely to cite technology issues as the primary source of inefficiency than CROs<br \/>\n(37% vs. 16%), while CROs more often point to process challenges (29%) or a combination of the two<br \/>\n(49%) <strong>[Figure 4]<\/strong>.\n<\/p>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Figure-4.png\"><\/p>\n<h2>4. Implications of Lost Productivity<\/h2>\n<h4>4.1 Risks and consequences of inaction for data managers and CRAs<\/h4>\n<p>\nInefficiencies in Phase III clinical trials have wide reaching impacts, on the trial itself and on those<br \/>\nworking on the studies. Only 6% of respondents believe that there will be little impact if inefficiencies<br \/>\nremain as they are <strong>[Figure 5]<\/strong>. For CRAs, the greatest concern is burnout (91%), highlighting an urgent<br \/>\nneed to act. Clinical data managers also cite burnout as a key risk, but are most concerned with data<br \/>\nquality issues (71%).\n<\/p>\n<p>\nAs risk-based data management approaches become more mainstream, this potentially exacerbates the<br \/>\nthreat to data quality as teams move away from reviewing 100% of the data. The significant concern over<br \/>\ndata quality could impede any move towards risk-based reviews, as data managers may be reticent to<br \/>\nreview anything less than 100% of the data. However, with data volumes growing and burnout risk rising,<br \/>\nit is untenable to continue reviewing 100% of the data. Inefficiencies must be addressed for better data<br \/>\nquality and data manager upskilling.\n<\/p>\n<p>\nAs expected, higher trial costs and longer timelines are commonly predicted consequences of inaction.<br \/>\nRespondents working at midsize companies are more concerned about higher trial costs (74%) than<br \/>\nthose at larger companies (52%). These respondents are also more likely to cite regulatory risk as a key<br \/>\nconcern (42%) compared to larger companies (26%).\n<\/p>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Figure-5.png\"><\/p>\n<h4>4.2 Barriers to a more productive future<\/h4>\n<p>\nThe biggest challenges to realizing a more efficient future state are<br \/>\nprotocol complexity (58%), budget\/resource constraints (57%), and<br \/>\nresistance to change (48%). Data managers were especially likely<br \/>\nto cite training gaps (57%), highlighting the need for support as<br \/>\nprocesses and technologies evolve. CRAs were concerned about<br \/>\nstudy complexity and budget\/resource constraints equally (65%),<br \/>\nwith only 22% feeling that training or skills were a barrier.\n<\/p>\n<p>\nMost respondents (81%) agree that better system connectivity<br \/>\nwould improve trial execution. Only 57% of CRAs say that their team<br \/>\nis actively seeking ways to modernize, compared with 75% of data<br \/>\nmanagers. However, 41% feel that SOPs fail to reflect real-world<br \/>\nworkflows or underutilize available tools, highlighting a cultural and<br \/>\nprocedural gap that may slow progress.\n<\/p>\n<blockquote><p>\n&#8220;Some trials are too complex. If I<br \/>\ndon&#8217;t understand something as<br \/>\na user acceptance tester, I have<br \/>\nto ask someone else which takes<br \/>\nmore time than training would<br \/>\nin the long run. Lack of training<br \/>\non therapeutic area databases<br \/>\ncan also result in inadequate<br \/>\ntesting and incomplete scenario<br \/>\ncoverage. This can lead to issues<br \/>\nthat require post-production<br \/>\nchanges, potentially affecting<br \/>\nsubject data.&#8221;<\/p>\n<p> <span><strong>Senior Clinical Data Manager, <\/strong>  Global CRO<\/span>\n<\/p><\/blockquote>\n<p><img decoding=\"async\" class=\"img-responsive m-auto\" alt=\"\" src=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Figure-6.png\"><\/p>\n<h2>5. The Future Role of Clinical Data Professionals<\/h2>\n<h4>5.1 Evolution of the clinical data manager role<\/h4>\n<p>\nData managers have a pragmatic view of how their role will evolve over the next two years. Most expect<br \/>\nto use more automation for data cleaning (71%), whereas fewer think AI assistance will support them in<br \/>\ndecision-making, completing actions, or surfacing data insights (59%). This shows that data managers<br \/>\nexpect \u2013 and prioritize \u2013 more automation in their operations before pursuing AI assistance.\n<\/p>\n<p>\nInterestingly, fewer expect their roles to shift in a more strategic direction (37%), despite the industrywide<br \/>\nfocus on clinical data science, and only 22% think they will be involved earlier in trial design. This<br \/>\nsuggests that data manager role evolution will be constrained if inefficiencies are not addressed first.\n<\/p>\n<h4 style=\"margin-top:30px\">5.2 Evolution of the CRA role<\/h4>\n<p>\nThe vast majority (83%) of CRAs expect risk-based monitoring to become the norm over the next two<br \/>\nyears. One CRA explains their vision for a future-state:\n<\/p>\n<blockquote><p>\n&#8220;I would love to have a risk-based tool where the monitor can assess whether a site needs more SDV,<br \/>\nbased on factors like number of PDs, timeliness of data activities, and number of queries. We need good<br \/>\nconnectivity between systems, because we capture PDs in one system and EDC data in another. A good<br \/>\nRBQM tool needs to read across all systems to understand the state of a site.&#8221;<\/p>\n<\/blockquote>\n<p>\nCompared with data managers (9%), 22% of CRAs think there will be minimal change to their role over<br \/>\nthe next two years. AI and automation are both seen as likely to impact future roles, as is a greater use of<br \/>\nremote monitoring and centralized review (57% each). These shifts point to a future CRA role that\u2019s more<br \/>\ntech-enabled and focused on critical risk signals, not just site visits.\n<\/p>\n<h2>6. Conclusion and Recommendations<\/h2>\n<p>\n<strong>This research highlights a clear message from clinical data managers and CRAs: an unacceptable<br \/>\namount of time is spent on manual tasks and disjointed workflows. These inefficiencies are<br \/>\nrisking data quality, burnout, and avoidable costs. It marks an executive call-to-action for change,<br \/>\nnot complacency with past ways of working.<\/strong>\n<\/p>\n<p>\nA notable new trend is the burnout risk among data managers \u2013 a longstanding challenge for CRAs but<br \/>\na historically less reported concern for data managers. As leaders look towards increasingly innovative<br \/>\ndata management approaches, this research reminds us that the people doing the work are not enabled<br \/>\nby modern technology or efficient processes. We recommend a practical approach:\n<\/p>\n<ol class=\"color\">\n<li><span><strong>Prioritize system integration<\/strong><br \/>\nInvest in solutions that connect clinical data and operations systems.<br \/>\nThis will reduce manual data entry, and provide a more unified view of<br \/>\ntrial progress.<\/span><\/li>\n<li><span><strong>Embrace automation<\/strong><br \/>\nCentralize data review and reconciliation efforts in an automated tool that<br \/>\ndirectly addresses the most time-consuming and frustrating manual tasks,<br \/>\nwithin a system connected with clinical operations. See how others are <a href=\"\/eu\/resources\/eliminating-manual-reconciliation-of-clinical-data-sources\/\">optimizing these processes<\/a> in this video.<\/span><\/li>\n<li><span><strong>Streamline workflows<\/strong><br \/>\nReview and update SOPs to ensure they reflect real-world workflows and<br \/>\nfully leverage available technologies. This may involve revisiting processes<br \/>\nthat are overly complex or rely on outdated manual steps.<\/span><\/li>\n<li><span><strong>Invest in ongoing training and change management<\/strong><br \/>\nProvide adequate training and ongoing support to data managers and CRAs for<br \/>\nlong-term adoption of new tools and processes. This will help overcome resistance<br \/>\nto change and ensure that professionals can effectively use new systems.<\/span><\/li>\n<li><span><strong>Foster a culture of efficiency<\/strong><br \/>\nEncourage a mindset that values efficiency and continuous improvement.<br \/>\nThis means actively seeking feedback from clinical professionals and empowering<br \/>\nthem with the tools and processes they need to succeed.<\/span><\/li>\n<\/ol>\n<p><a class=\"\" style=\"margin-bottom:80px;color:black\" href=\"https:\/\/www.veeva.com\/wp-content\/uploads\/2025\/09\/Clinical-Data-Market-Research-Report.pdf?\" target=\"_blank\" rel=\"noopener\"><\/p>\n<div class=\"takeaway-box\">\n<div style=\"float:right\" class=\"button-icon button-icon--white\" target=\"_self\"> <\/div>\n<div>Get the <span style=\"color:#f5670e\">full report<\/span> to see the study methodology and more data\n   <\/div>\n<\/div>\n<p><\/a>\t<\/p>\n","protected":false},"excerpt":{"rendered":"<p> Industry research shows that the majority of clinical data managers and CRAs are concerned over data quality as a result of Phase III trial inefficiencies.<\/p>\n","protected":false},"featured_media":88071,"parent":0,"template":"","class_list":["post-88070","resources","type-resources","status-publish","has-post-thumbnail","hentry","resource-area-20-clinical-data-management","resource-area-20-clinical-operations","resource-product-20-dqs","resource-product-20-edc","resource-type-white-papers"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/resources\/88070"}],"collection":[{"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/resources"}],"about":[{"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/types\/resources"}],"version-history":[{"count":1,"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/resources\/88070\/revisions"}],"predecessor-version":[{"id":96022,"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/resources\/88070\/revisions\/96022"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/media\/88071"}],"wp:attachment":[{"href":"https:\/\/www.veeva.com\/eu\/wp-json\/wp\/v2\/media?parent=88070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}