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Lab data management is crucial to GxP requirements. Pharmaceutical lab data has come under a variety of cyber-based attacks in recent years. As such, on-premises and cloud data storage solutions to data storage and temperature monitoring systems require robust security systems.


There are two main approaches to lab data management today. In this section we will explore the pros and cons of each.


When it comes to professional lab data management, there is no single method that will suit every type of biotech business or pharmaceutical enterprise.


The strategic problem that many decision-makers in these sectors face is determining which lab data management model best fits the current and future operational needs of the organization.


Cloud-based solutions offer some distinct advantages over local data storage; However, on-premises data storage solutions should not be ruled out as outdated or of secondary significance simply because they don't provide all of the flexible benefits of the cloud.

It is of crucial importance to understand why on-premises data storage is still a viable solution for many organizations.


The Benefits of On-Premises Lab Data Management


On-premises lab data management solutions will often work with legacy systems which may not function well if they are to be networked with cloud-based servers.


By making use of local data servers and other storage media, businesses in the life sciences will be able to use current infrastructure without necessarily having to add new hardware or software systems.


Security is another key consideration when it comes to on-premises lab data management. Simply put, organizations can feel much more secure with a closed system whereby the data are stored locally and not easily accessible via third-party servers.


This approach is especially worth considering when stored lab data are of high commercial value (private details of individuals involved in field trials, for example), making it an attractive target for hackers to steal.


On-premises lab data management systems are not necessarily more secure than cloud-based. However, in-house with data management may provide an organization greater control over their systems and their data.

Attempted hacks and data breaches can still occur against servers behind a firewall.

Another key benefit of the on-premises model is that it means that complying with regulatory controls, such as the FDA's 21 CFR Part 11 or the EU's General Data Protection Regulations (GDPR) becomes simpler.


When data is not stored on third-party servers in the cloud, it is, of course, possible to breach such regulations but, with greater control, such issues are less likely to occur.



Cloud vs on Premise
Cloud vs On-Premises: Cost Comparison for Laboratory Monitoring Systems


Modern laboratories are often equipped with valuable equipment and irreplaceable specimens. It pays to do extensive research when deciding on a cloud vs. on-premises data storage solution.



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What Advantages Do Cloud-Based Lab Data Management Systems Afford?


Modern lab monitoring systems often create huge amounts of data that need to be stored for long periods of time.


Third-party server systems provide scalability for laboratory monitoring systems. The easy ability to add storage space is a notable economy of scale.


Building up an on-site hardware infrastructure needed to store large data sets can be extremely expensive compared to the lower-cost alternative of using an off-premises solution. In addition to hardware costs, other operating expenses such as physical space, energy costs and additional personnel can quickly add to the bottom line.

Another noteworthy advantage of using cloud data management systems is that, in most cases, they are backed up more securely than on-premises network alternatives.

Highly secure cloud services may be accessible over the internet but they are more difficult to physically access. Popular cloud server solutions are in highly secure locations with 24/7 security measures in place.


Since cloud services are often backed up in multiple secure locations, a server going down or under attacks may not cause a long outage data storage is redundant.


Whether lab technicians need access to certain pieces of information from their place of work or another location, the networked status of cloud-based data management systems means they can get what they need when they want it.


So long as the correct authentication procedures are followed, there is typically no compromise between this degree of convenience in lab data management when it is set against security and regulatory prudence.


In addition to the decreased costs of managing data and the greater convenience the cloud affords life sciences companies, there is another positive aspect to this approach. The cloud-based lab data management model also allows for software updates to be managed from anywhere.


This could be as simple as setting and rescinding data access rights to rolling out entirely new data modelling software systems. Remember, too, that good data management providers will offer all of the information audit trails and data integrity reporting functionality one could wish for.



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Comparing Private and Hybrid Cloud Solutions With Public Ones


It should be mentioned that in many regards, choosing between a cloud-based lab data management system and an on-premises model is something of a false dichotomy. This is because there is more than one way to implement a cloud-based data monitoring and management system these days.


So far, we have looked at the sort of public cloud servers which are available to all sorts of commercial organizations, the sort of thing your personal email account uses, for instance. Although secure, such data servers are not as secure as the other two main options, private and hybrid clouds.


To be clear, private clouds still afford all of the advantages biotech and life science organizations can expect of public cloud services. Although, in fairness, they tend to cost more. By adopting this model, one can also gain many of the advantages offered from on-premises solutions as well.


This is because a private cloud model means obtaining an individual server that is dedicated solely to the needs of one organization, offering greater isolation despite the flexibility of a virtually networked solution.


Hybrid clouds are still less expensive, but these blend private servers (which are utilized to handle the most sensitive data sets) with public clouds for everything else.






Any organization generating and storing large data sets needs to think strategically about its approach to meet best industry practices. This is certainly the case with the often sensitive nature of laboratory-generated data.


Although on-premises solutions afford certain advantages, cloud-based alternatives-especially where more privacy is maintained via at least partial use of private servers-can offer the best of both worlds.

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