Mastering Service Management Data Migration: 3 Expert Tips and Strategies

There inevitably comes a time when you need to look at upgrading your Service Management system. Sometimes this can be relatively straightforward but other times more of a challenge. If you are staying on the same vendor platform and there is an automated path that just moves your data over to the new version then all is well and good. But what if you have customized the current version or the new version has changed in terms of data structures and configuration or you are moving to a completely different platform? There is no question that this can be a bit daunting but by leveraging the right approach and technology it can be significantly simplified. 

1.Don’t leave your data behind

In the past, when Service Management was less mature and data about customers, employees and incidents was not leveraged, an organization just left their data behind when they moved to a new system. But this is not true in modern Service Management organizations. Data is the power to go from good service management to great service management. Customer and employee experience is justifiably getting more focus now and is only really possible by knowing your customers and employees and this means knowing the history and previous communications with them. Self Service is a critical component of a modern service desk and this is powered by knowledge you certainly do not want to leave your existing knowledge behind. Then there is asset information, particularly the asset and lifecycle information which is not auto discoverable. You don’t want to have to recreate this. Last but certainly not least you have AI. Data is what enables generative AI and it can be incredibly powerful, such as for auto knowledge generation. If you want to leverage this power make sure it has access to the data.

2. Go out of the box

Resist the temptation to customize your new Service Management system. If you keep as close to the out of the box applications you will be more able to leverage the advantages that the new platform provides and as it is upgraded you will be able to move with it. With modern data migration automation tools, data can be refactored and mapped to fit with the new system without making changes which means you can have the best of both worlds: a new out of the box service management system with your data. Of course, there may be some changes that are highly valuable and worth making but if you follow the approach of minimizing these as much as possible you will be on the right track. 

3. Validate, validate, validate

As the title of this tip implies, it is difficult to over-emphasise the importance of validation for data migration. This applies both to rigorous testing of the migration process prior to the actual migration as well as immediately after the migration to the production system. The reality is that if you do this manually then at most you are only going to be able to validate a small subset of the data. There is nothing worse than finding out you are missing data you need after you have gone live on the new system and it can have drastic consequences. Complete validation of the migration is the answer. The advantage of modern data migration automation solutions is that they do the validation for every single piece of data migrated. What is more, they can provide logs and reports to show exactly what was migrated. 

Bonus Tip: Use a proven enterprise service management data migration tool

Most of the concerns about migrating Service Management data to new systems centre on how difficult, risky, and time-consuming it is. This may be true if you are trying to use outdated manual or script based approaches but with the right enterprise service management data migration you can have all your data where you want it reliably, quickly and cost effectively. Precision Bridge’s automated data migration tools greatly simplify the data migration task. For more information or to schedule a demo, contact Precision Bridge today.

Real life examples of how AI is impacting different industries

Artificial Intelligence has swiftly transformed from a futuristic concept to an integral part of our everyday lives. Its influence is being left across all sectors, from healthcare to finance, transportation to entertainment. In this blog post, we review some of the ways AI is shaping and revolutionizing our world.

Enhancing Healthcare

One of the most impactful areas where AI is making a difference is healthcare. AI algorithms analyze vast amounts of medical data to diagnose diseases, predict patient outcomes, and even personalize treatment plans. From early detection of illnesses to precision medicine, AI is augmenting healthcare professionals' capabilities, leading to more accurate diagnoses and better patient outcomes.
Use case example: Addenbrooke hospital in Cambridge, UK is using Microsoft’s InnerEye system to automatically process scans for patients with prostate cancer. The system takes a scan image (all scans are anonymized, so patient data is kept private), outlines the prostate on the image, marks up tumours and reports back. This is speeding up prostate cancer treatment, and the hospital is looking at using the same technology for brain tumour patients.

Revolutionizing Transportation

AI-powered technologies are reshaping transportation systems globally. Autonomous vehicles, guided by AI algorithms, promise safer, more efficient transportation. From self-driving cars to drones for delivery, AI is not only optimizing logistics but also transforming urban planning and reducing traffic congestion and carbon emissions.
Use case example: A major auto manufacturer piloting nuVizz’s RoboDispatch Solution in its inbound logistics operations. In this pilot program, RoboDispatch automates the dispatch process for the movement of full and empty trailers from parts supplier locations to its manufacturing plants.

Transforming Education

AI is redefining the landscape of education by personalizing learning experiences. Adaptive learning platforms use AI algorithms to tailor educational content to individual students' needs, pacing, and learning styles. Additionally, AI-driven tutoring systems provide instant feedback, enabling students to grasp complex concepts more effectively and fostering a deeper understanding of subjects.
Use case example: At the Georgia Institute of Technology, an AI-powered chatbot named Jill Watson, developed by IBM’s Watson, was employed as a teaching assistant for a course with 300 students.

With a 97% accuracy rate, Jill Watson was able to respond to around 10,000 student inquiries each semester with as effectively as a human.

Empowering Businesses

In the business realm, AI is driving innovation and efficiency across various industries. From predictive analytics to customer service chatbots, AI-powered tools empower businesses to streamline operations, enhance productivity, and deliver personalized experiences to customers. Moreover, AI-driven insights gleaned from big data enable businesses to make data-driven decisions, gaining a competitive edge in the market.
Use case example: UPS uses an AI-powered GPS tool called ORION (On-road Integrated Optimization and Navigation) to create the most efficient routes for its fleet. Customers, drivers, and vehicles submit data to the machine, which then uses algorithms to create the most optimal routes,

Fostering Creativity

Contrary to popular belief, AI is not just about crunching numbers or automating tasks—it's also fostering creativity in unprecedented ways. AI-generated art, music, literature, and even film scripts are captivating audiences worldwide. Creative professionals are leveraging AI tools to explore new possibilities, experiment with novel ideas, and push the boundaries of artistic expression.
Use case example: Adobe Sensei, Adobe’s AI and machine learning platform, has transformed the creative process. Features like Auto Lip-Sync in Adobe Character Animator and Content-Aware Fill in Photoshop are powered by AI, making tasks that were previously labor-intensive and time-consuming much simpler.

Addressing Societal Challenges

AI is also being harnessed to tackle some of the world's most pressing societal challenges. From climate change to poverty alleviation, AI-powered solutions are providing insights and strategies to mitigate these complex issues. For instance, AI-driven models can optimize energy consumption, facilitate disaster response efforts, and even enhance agricultural productivity to ensure food security.
Use case example: Carnegie Mellon University in Pittsburgh recently launched FarmView, a project that combines AI with robotics to improve the agricultural yield of certain staple crops, in particular sorghum. In developing countries like India, Nigeria, and Ethiopia, this drought- and heat-tolerant plant is a valuable cereal crop that has huge genetic potential thanks to its more than 40,000 varieties.

Conclusion

As AI continues to advance and integrate into various facets of our lives, its transformative impact on the world will only intensify. From revolutionizing industries and empowering individuals to addressing global challenges, the possibilities presented by AI are vast and far-reaching. Embracing AI responsibly and ethically will be key to harnessing its full potential for the betterment of society. In this era of AI-driven innovation, the future is not just about what AI can do, but how we choose to wield its power for the collective good.

At Precision Bridge, our team is always exploring innovative methods to harness the power of AI. We're captivated by the transformative impact AI is having on the world and eagerly anticipate the possibilities it holds for the future. Data is the core of AI and our focus lies in utilizing AI to assist our customers ensure they have their data where it can be leveraged by AI through the automation of data migration, replication, and synchronization.

Get in touch with us today to discover how our leading data migration automation tool can revolutionize your business operations and enhance customer satisfaction.

Data Validation: A critical process that is often overlooked when migrating data

With the focus on digital transformation, customer/employee experience and AI, data is getting more attention than ever. This makes it even more surprising that one of the most overlooked processes when migrating data is validation! But, difficult to believe or not, it is the case and one of several reasons that, as Gartner research has shown, 83% of data migration projects either fail outright or fail to meet time and budget expectations.

Why is Data Validation getting overlooked?

What can be going wrong? Data validation is hardly rocket science. I mean all you need to do is to check that all the data you expected to be migrated from the source system to the target system has got there. So why is it getting missed? 

Well, as is often the case, the devil is in the detail. Many migrations involve a lot of data and on top of that, the data might have needed to be restructured and transformed to fit in the new system. This means it is not as simple as comparing the source and target. 

In fact, in most cases, it simply is not practicable to do any significant data validation manually. This is why data validation may just involve picking a few records on the source and seeing if they look right on the target. This is where the problem starts. 

So, what’s happened to my data?!?

We have been called into many data migration projects where significant chunks on data just seem to have disappeared! You can probably imagine the state of mind of those who rely of the data that has gone missing – not that happy. Especially when they have been told “It’s all done everything is there for you on the new system.” then to find it anything but. This is what can happen when you don’t do comprehensive data validation. 

What are the other problems that can be missed?

Some types of problems that can be missed without comprehensive data validation:

• Data-type mismatches, such as text data in numeric columns

• Incorrect column mapping, such as phone numbers in email address fields

• Missing objects, such as attachments and knowledge-base articles

• Incorrect foreign-key references, such as assignment- or approval-group membership

If Data validation is just some spot-checking here and there these are just some of the issues that can get missed.  This is why comprehensive validation is so important.

Validate, validate, validate!

Comprehensive data validation can really save the day – taking a potential failed migration to a great success.

A key element is the right data migration automation solution and, funnily enough, that is what we do! Precision Bridge’s automated data migration tools and make your migration faster, easier and more reliable with comprehensive data validation. So you can look forward to lots of thanks rather than irate users asking what you’ve done with their data!

To learn more about data migration automation and how Precision Bridge tools can help, contact us today.

Already leveraging AI for coding? Is there more you can be doing? Where do you draw the line?

In the ever-evolving landscape of technology, the quest for productivity improvements and software innovation continues to drive advancements. One such transformative force is the rise of generative AI, with ChatGPT leading the charge. In this blog post, we will explore the path to achieving increased levels of productivity and software innovation through the integration of ChatGPT and other generative AI technologies.

AI for Software Development

Generative AI, powered by sophisticated models like ChatGPT, is revolutionizing the way we approach problem-solving and creativity in the software development process. By leveraging vast datasets and complex algorithms, AI models can generate human-like text and code, offering a unique and powerful tool for developers and innovators.

Where can AI help?

GPT technology, exemplified by ChatGPT, can increase productivity in pivotal domains like software development and delivery. One notable application is its assistance to DevOps and platform engineering teams in crafting code snippets sourced from software libraries. Moreover, it expedites issue resolution in custom code by incorporating root-cause context into a GPT, enhancing problem tickets or alerts with this contextual information, and leveraging it as the foundation for automatically generated remediation. These instances showcase substantial enhancements compared to existing, time-consuming manual processes. These processes include the laborious task of composing routine and easily replicable code or navigating through numerous Stack Overflow pages before stumbling upon a viable solution.

Beyond this, GPTs contribute to swiftly onboarding team members onto new development platforms and toolsets. This technology enables individuals to learn about solutions by posing questions in a search bar, such as inquiries like, ‘How do I import and export test cases between my environments?’ or ‘What’s the best way to integrate this solution with my toolchain?’

Accelerated Prototyping

With generative AI, the prototyping phase becomes more efficient and dynamic. Developers can articulate their vision through natural language, and AI models like ChatGPT can generate corresponding code snippets or even entire functions. This accelerates the prototyping process, enabling rapid iterations and experimentation to find the optimal solution.

Code Optimization

Generative AI is a valuable ally in the pursuit of optimized and efficient code. By analyzing codebases, identifying redundancies, and suggesting improvements, AI models contribute to cleaner, more maintainable software. This not only enhances productivity by reducing debugging time but also paves the way for software that is scalable and adaptable to future requirements.

Enhancing AI models for continuous learning:

ChatGPT and other generative AI models are designed to learn from diverse inputs, making them adept at staying current with the latest industry trends and best practices. Additional contextual data can be provided to improve results. Developers can benefit from the continuous learning capabilities of AI, staying ahead of the curve in terms of innovation and incorporating cutting-edge techniques into their projects.

Where to draw the line - what are the limitations?

Organizations must understand the limitations as they leverage LLM-based generative AI, such as the technology behind ChatGPT and similar platforms. This form of AI is susceptible to errors and manipulation, relying heavily on the accuracy and quality of the information it draws from publicly available sources, which may inherently be untrustworthy or biased. Within the realm of software development and delivery, potential sources include code libraries that could be legally protected or harbour syntax errors. Additionally, there’s the risk of these libraries containing vulnerabilities deliberately inserted by cybercriminals, aiming to perpetuate flaws and create exploitable opportunities. Consequently, engineering teams must adopt a proactive approach, thoroughly scrutinizing the code generated by GPTs to ensure it does not pose risks to software reliability, performance, compliance, or security. This ongoing verification process becomes indispensable in safeguarding the integrity of the software development lifecycle. The reality is that generative AI needs human validation is the vast majority of cases. This is a clear line to be drawn for the current iterations of AI.

Conclusion

The path to achieving increased productivity and software innovation can be facilitated by the integration of generative AI, such as ChatGPT. As developers embrace the capabilities of these advanced models, they open the door to a future where collaboration is enhanced, prototyping is accelerated, code is optimized, learning is continuous, and creativity is empowered. The synergy between human intelligence and generative AI is reshaping the software development landscape, propelling us towards a new more productive future.

Why Migrating Doesn't Have To Mean Leaving Your Data Behind Anymore

There was a time when migrating to a new system meant leaving your data behind. The simple truth was that it was difficult to move the data to a new system and hence the costs we high and the reliability was low. Often, integrators were reluctant to recommend moving the data and customers were unwilling to pay the price.

The Old Way

Of course, there were times when the data needed to be moved no matter what, and when that was the case there was a great deal of manual work coupled with Excel spreadsheets and some bespoke scripting or coding that was cobbled together to try and get the data out of the old system, restructured and translated for the new system and then loaded in. As you can probably imagine, the possibilities for errors were high hence the rework and overruns on the project. This is why the chance of a migration project exceeding cost and time projections was so high. Studies by Gartner have shown that 83% of all data migration projects either fail or exceed their budgets or schedules. The reality is that if these approaches to data migration are used then the chance of a data migration project meeting its budget and timescales are low.

The Challenges

There are significant challenges in moving data from one system to another. Often the data structures are different and even when they are not, data will need to be translated so that it fits within the processes of the new system. Coupled with this there are the relationships between data that must be maintained which can be challenging to ensure on the new system. Another area is how to migrate complex data such as attachments, images, links, formatting, and more. Finally, validating that the data has been correctly migrated can be an arduous task if attempted manually.

However, data migration automation has changed this. Now it is possible to migrate data across even the most complex systems reliably and cost effectively. Which means you can have your data where you want it.

Why is moving Data Important

The modern approach to Service Management means keeping your data and below are some of the reasons why:

• Customer Experience (CX) and Employee Experience (EX) is an important part of good Service Management and the key to this is knowing your customers and employees. To know them is to know your history with them.

• AI relies on data and leveraging the latest generation of AI solutions means having data. To maximize your ability to leverage AI on your latest platform ensure you have the data there.

• Migrating knowledge base articles is a significant time saver. You won’t have to copy and paste or rewrite articles. Migrating is also an excellent opportunity to review your knowledge base, update articles that are out of date, and eliminate those that are no longer relevant.

• Asset data can be an essential input to your company’s finances, enabling proper calculation of depreciation and the company balance sheet. If you can migrate your asset data, you won’t need to refer to two different Service Management systems for this information, thereby reducing complexity, and you can make a cleaner break from your old Service Management system. Plus, you have the flexibility to opt-out of migrating data for assets that have already been retired.

• Migrating Historical ticket data ensures continuity in your trend data and key performance metrics. Switching platforms doesn’t mean starting over from scratch.

The Solution

Data Migration Automation Solutions simplify the process of migrating data which means faster, lower cost data migration projects.

Not only do they have the tools to enable migration of any type of data between different systems but the best ones have pre-built templates for many of the most popular systems which save more time in setting up the migration project. In addition to this, they include features to increase migration speed such as multi-threading, comprehensive reporting for validation and the ability to handle network interruptions.

For more information about how you can leverage data migration automation or to schedule a demo, contact Precision Bridge today.

When is a Service Management “Fresh Start” not so Fresh?

There are many reasons why organizations change their Service Management system. Sometimes it’s as simple as scale/performance issues or a lack of available support for the current system. However, the majority of the time it is driven by the need for more value by getting a better return on investment.

Whatever the reason, one phrase you might hear when discussing a Service Management migration project is “Fresh Start”. Most often when someone says this what they mean is starting your new Service Management system with little or no data.

There was a time when you could forgive a migration project that did not move data to the new Service Management platform since it had to be done manually and was highly prone to error. This meant you would either have inaccurate data or pay for a lot of manual work, but not anymore!

In this article, we discuss why it’s best practice to take your data with you to your new Service Management system and how to do it faster and save money at the same time.

Why Migrate Data to a New System?

Let’s start with the reasons some people may try to use to justify not moving your data:

●       “It’s not important”: In the past, historical data was not seen as important and certainly in no way nearly as important as data associated with live processes that are underway. However, as we know now, historical data is a powerful asset, especially with AI and data analytics.

●       “We don’t need trend reporting”: However, data analytics and trending are how modern organizations report now and how they can see into the future to address potential issues before they become major problems.

●       “We can rebuild our knowledge base and workflows from scratch”: Much of modern service management practices are predicated on the knowledge acquired by an organization as the basis for service and efficiency improvements. With modern migration techniques, you can carry all your knowledge over including attachments, formatting, embedded links and images.

●       “All that old data will hurt system performance”: This could be an argument for leaving some data behind–perhaps if you have a lot of very old data that may be less useful, but not all of it. Bring enough historical data to maintain continuity for the IT staff and their customers. And if you are really not going to take it over to the new system then make sure you archive it somewhere where it can be accessed if needed.

Irrespective of the above, there are three important reasons to make sure you take your data with you:

●       Knowing your customer: All those historical tickets and knowledge-base articles represent a wealth of information specific to your customers, whether they are internal or external.

●       Leveraging modern tools: Modern ITSM systems can leverage modern technologies such as machine learning to suggest solutions and relevant knowledge base articles based on historical data. These tools won’t be helpful to you for years if your historical data is inaccessible.

●       Regulation and Internal Policies: Applications such as CRM and HR require information to be stored and immediately available and as such must be migrated.

ITSM Data Migration Made Simple

In the past, many organizations with significant and often complex data used to fear a Service Management data migration, which would have been a highly manual, error-prone, and costly process. Even when they tried to automate some of the processes with scripting or coding things would go wrong. Sometimes very wrong!

And based on manual migration approaches they would not have been wrong. Gartner reported that 83% of all data migration projects either fail or exceed their budgets and schedules, and a Bloor Group study found that more than 90% of data migration projects run over time, over budget, or both. With Precision Bridge, you can overcome these obstacles and provide a smooth transition for your IT staff and customers

But there is a better way. Solutions like the Precision Bridge Data Migration Automation tool can deliver a fast and efficient migration from one Service Management system to another. By providing templates, automatic data mappings/transformations and data validation, Precision Bridge makes the errors, frustration, and high costs of data migrations a thing of the past.

For more information about the Precision Bridge suite of ITSM data migration tools or to schedule a demo, contact Precision Bridge today.

Top Questions to Review with Your Migration Team

Before implementing any IT project it is important that it has been carefully planned and this is just as true for a Service Management Migration Project. Whether you are moving to a new Greenfield instance to get back to the baseline out of the box apps, consolidating multiple instances into one, or splitting one instance into multiple targets, you and your migration team should work out some important details together beforehand to ensure the project goes off without a hitch.

In this article, we discuss a few key questions to review with your migration team before starting work on a Service Management migration project.

Customization and Data Questions

The first set of questions relative to your existing Service Management environment: what you will keep, what will be discarded and what will be added for the new system.

How much customization was applied to the source environment and how much will you keep?

Custom functionality can often be difficult to migrate from one Service Management environment to another, even when both are instances of the same platform. Factors to consider in relation to this question include:

• What, if any, of the custom functionality is available “out of the box” (OOTB) in the target environment?

• Whether the business processes supported by the customizations are still needed. Can you transition to industry best practices instead?

For any customizations that you do migrate, how will you migrate the code and data models for those customizations?

If it is not practicable to run your business without a specific customization, use the migration as an opportunity to review and check whether there is a more efficient way of implementing it on the new system. Remember, however, that when migrating the data related to these customizations, it must be transformed to fit any new data model.

Data Questions

Is there any data that should be left behind?

A better way to ask it might be: If my users no longer had access to the old data, what would happen? The reality is that in most cases, it would make their lives much more difficult because they would lose accumulated knowledge and would have to start all over with trend reporting, among other issues. For a seamless transition, migrating transactional data, such as incident tickets, change requests, and problem tickets, is often the best course of action together with asset and knowledge data. The best practice is if in doubt make sure the data is available on the new system.

What about performance issues?

If there were existing performance issues on the previous system then it is important to establish whether these were due to the volume of data and also whether this will be naturally improved by the new system or if the volume of data may need to be reduced. If there is any possibility of not taking data over to the new system then this must be considered very carefully.

Can we migrate and still meet our compliance obligations?

Businesses subject to regulations or standards such as HIPAA and PCI-DSS must be mindful of the security of that data before, during, and after the migration. Also, if any data will not be migrated then will it be archived and how can it be accessed in the future?

Migration Approach Questions

These might be the most important questions of all because the answer can make the difference between project success and failure. Gartner’s research has shown that 86% of migration projects fail or overrun in terms of time and costs.

Who will perform the migration?

It is important to check the level of experience of those involved in the migration. Not only their experience of migration projects but also of the source and target systems. Application level experience can also be highly valuable in determining the correct migration approach.

What tools can we leverage to streamline the migration?

Far too often data migration is attempted using manual, time-consuming and error prone processes which is why there is such a high failure rate to deliver on time and to budget. This explains why integrators have said in the past  “Have a fresh start. Leave your data behind”. It has just been too difficult to get it right. Leaving data behind is no longer a choice given the focus on service excellence, knowing your customers/employees and leveraging Artificial Intelligence.

There are tools that automate migration making it quicker and easier than ever to get your data where you want it. There is no need to leave data behind because it is too difficult or expensive to move it anymore and there is no need to use spreadsheets or scripts to get it done. Precision Bridge automates data migration for you whether it is knowledge, assets, tickets, workflows, attachments, images, journal entries or audit information.

For more information on Precision Bridge ServiceNow Instance Migration tools or to schedule a demo, contact Precision Bridge today.

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The Importance of Careful Planning before Implementing a Migration

Easy to say but sometimes difficult to follow: “Create a solid plan before performing an important project” and this is equally true for migrations! In this article, we discuss the criticality of a robust plan when performing data migrations, alongside some of the key considerations to ensure seamless execution…

Why You Need a Solid Migration Plan

What are some of the reasons it is so important to put appropriate effort into pre-planning a migration project?

• Customizations: If your source system has extensive customizations, you must decide which ones will journey be migrated to the new system. Some customizations may be supported out-of-the-box (OOTB) in the target system so you will not need to change the target system. However, others may require either changing the system or mapping data related to customizations to out-of-the-box fields for historical record purposes. Careful planning is required to figure out how to fit customized data into the new system.

• Dependencies: Depending on the complexity of your ITSM environment, specific data objects may need to be migrated in a certain order so as to maintain the database’s referential integrity. Migrating tables without careful planning can often compromise the data relationships, causing unexpected problems you did not account for.

• Business processes: An ITSM migration is a good opportunity to remove nonstandard business processes and more fully embrace industry best practices, which most ITSM applications support OOTB. Doing so will affect what features you implement in the new system, which in turn informs what data needs to be migrated from the old system.

Moreover, creating a plan help you to break the project down into logical steps that can be executed in sequence rather than trying to do everything all at once.

Pro Tip: Work with an Integration Partner

If you have never performed an ITSM system migration, or if you have not performed one in a while, putting together a robust, cohesive migration plan can be daunting, and important considerations can often be overlooked.

Unlike most IT organizations, which might do an ITSM migration once in five to 10 years, many integration consultants do it all the time and have the experience to develop a comprehensive migration strategy.

Furthermore, a good integration partner will have access to migration tools, such as Precision Bridge ServiceNow Instance Migration. This tool is purpose-built for migrations between Service Management platforms. Leveraging Precision Bridge Data Migration Automation tools eliminates manual migration tasks and scripting shortening the project timeline and reducing errors and frustration.

For more information on how Precision Bridge tools can help streamline your Service Management migration project, contact Precision Bridge today.

How to Avoid the Custom Scripting/Development Trap for Data Migrations

When first faced with an enterprise data migration project some of the complexities can be well hidden, making the project appear quite straightforward - when this may be far from the truth.

For example, one would think that migrating from one instance of an IT service management (ITSM) application, such as ServiceNow, to a more up-to-date instance of the same application would be simple enough. But if the data structures have changed significantly between the old version and the new, or the old system has extensive customizations, a project team that hasn’t been made aware of the challenges might find itself stuck between the proverbial “rock and a hard place.”

The first reaction is often to look at building some in house scripts/code but as we explore below, this can lead to some unexpected challenges.

Potential Problems with Custom Migration Software

If your project team has access to development resources with the necessary skills to build migration scripts or custom programs, you are ahead of those that do not have that access…but not by much. Here are some of the issues that organizations often face when trying to develop custom software to support a system migration:

• A significant and often underestimated amount of development effort is required to develop the custom software and migration scripts to deal with all the different data transformations.

• Migrating attachments, embedded images and links, text formatting, and other more complex data types can be significantly more complex than anticipated, resulting in missing or incomplete data on the destination system.

• This type of project requires development skills in general and specific domain knowledge in both the source and target systems. Not having this complete skill set leads to significant delays in migration projects while the developers get up to speed with the respective systems.

• Migration projects are fraught with numerous traps to fall into, such as incorrect removal of leading zeros, maintaining correct record relationships, and many more.

The Bottom line is that custom development for migration projects can be an expensive and high-risk proposition.

Avoiding the Custom Software Trap

Imagine the convenience of having a ready-made tool that effortlessly simplifies the migration between any two ITSM instances, sparing your team from the need for custom coding—wouldn't seizing this opportunity be an irresistible choice, given the significant time, effort, and frustration it could save?

Such a tool does exist: the Precision Bridge Data Migration Automation tool. In the hands of a migration partner, this tool can greatly simplify the migration process because the mapping and transformation between ServiceNow instances have already been worked out.

Precision Bridge data migration automation can also help you reduce or eliminate the customizations in your source environment by mapping your custom data objects to appropriate out-of-the-box data structures in the target environment. It can handle data objects that are difficult to migrate by code, such as attachments, knowledge base articles, and workflows.

To find out more about how to avoid the data migration custom scripting/coding trap or to arrange a demonstration to explore the full suite of ITSM migration tools offered by Precision Bridge don't hesitate to get in touch.

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How to Avoid The Common Do It Yourself Data Migration Mistakes

When planning an application data migration, especially a migration from one instance to another on the same vendor platform, many organizations first impulse is to take the “do-it-yourself” (DIY) approach. This is understandable, after all, how difficult can it be to move the data?

Most such organizations learn the hard way that a DIY migration can be much more difficult than expected. In this article, we discuss some of the reasons why DIY migrations from one instance to another can often lead to failed projects or large time and cost overruns.

The Pitfalls

Many DIY migrations follow this unfortunate sequence of steps:

1. Export data from the source system to spreadsheet or xml files.

2. Massage the data to make it compatible with the target system.

3. Copy and paste the data into new files ready for importing

4. Import the data into the target system using native tools such as import sets and transform maps (this may require some development).

5. Encounter numerous data errors.

6. Remove loaded data and start over from step 1.

7. Eventually, run out of money, time, or executive patience.

8. Declare the project a failure.

Here are some of the reasons that DIY migrations end up being so much trouble:

• Many iterations are required to get the data in the right format for the target system leading to far more time and money being needed to complete the project.

• All the manual transformations, copying, and pasting is error-prone and introduces a great deal of rework.

• The data structures, types and values of the target system may not match that of the source system(s). Data from a single table in the source may be split into different tables in the target, requiring additional work to manage the changes in data and ensure the data relationships are maintained correctly.

• Reference field Sys-Id values such as assigned users and groups may not be the same on source and target so additional work is required to update all references to point to the correct records in each case.

• Some objects, such as complex data types, attachments, and workflows, can’t be migrated using simple text but need to be formatted and re-connected to different target records to ensure the users of the new application do not lose any data integrity.

• Spreadsheet applications are notorious for making unanticipated transformations to your data, such as trimming leading zeros, so “01234” becomes “1234” which is not an accurate reflection of the source data.

• If you have custom tables or fields in your source system that will not be in the new system then additional transformations to combine fields or push to different locations will be required.

• Native exporting and importing often has limits on the volume of records that can be processed.

• Migrated records may be updated on the source platform later so the whole process needs to be repeated.

So, how can you avoid the pitfalls that can sabotage your project?

It’s quite simple: Don’t do DIY migrations. They are simply too risky for anything other than the smallest basic systems.

A Better Approach: Tool-Based Migration

A faster, more reliable, and more comprehensive approach to ServiceNow instance migration is to engage the services of a competent migration partner with access to robust migration tools such as the Precision Bridge Data Migration tool.

Precision Bridge data migration performs all the data transformations for you, sparing you the error-prone and time-consuming process of doing it manually. Moreover, the tool can migrate your historical ticket data, knowledge articles, assets, audit information, attachments, workflows, and complex data types to ensure complete information continuity on day 1 in the new environment.

Each Precision Bridge migration is supported by over 100 migration templates specifically designed to accelerate and streamline the migration of a wide range of applications to ensure optimal delivery and data integrity.

So, if you have a data migration project coming up, avoid the inevitable stress of a DIY migration by leveraging automation.

Contact Precision Bridge today for more information on how Precision Bridge can simplify your ITSM application migration project.

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Planning a ServiceNow Migration? Don’t Forget These Technical Considerations

By any measure, ServiceNow is among the most popular IT service management (ITSM) tools available today. Organizations worldwide have relied on ServiceNow to manage every aspect of their ITSM, from day-to-day helpdesk operations to problem management, change control, knowledge management, and more. Many organizations are long-time ServiceNow customers – some stretching back 10 years or more.

This allegiance can be a double-edged sword. A consistent and well-understood platform and interface makes life easier for the users, but older ServiceNow implementations are often heavily customized, making it difficult to move forward to newer ServiceNow versions. Often there comes a point where it makes sense to migrate to a new instance of ServiceNow on the newest release with the latest out of the box applications.

If your organization is contemplating such a migration, you have no shortage of things to consider. IT system migrations are never easy, even when migrating from one version to another on the same platform. You may fall short of your goals if you don’t look at the project from all angles (including the technical, business process, and user- and customer-experience aspects).

In this article we discuss, at a high level, the top technical considerations you must account for in a ServiceNow migration.

Technical Considerations

These top technical considerations can bite you if you aren’t careful:

• The manual migration trap: A migration from one ServiceNow instance to another can make it tempting to perform the data migration manually–that is, by exporting data to spreadsheets, massaging the data to fit the import templates for the new instance, uploading the data, and getting a significant number of data errors. Correct the errors and try again. Lather, rinse, repeat. It’s a most inefficient way to migrate data and can stretch your project timeline and costs to the breaking point.

• The low-level development trap: As an alternative to manual migration, some organizations choose to develop custom scripts or applications to handle the migration. This approach can be just as problematic as manual migration. There are many considerations that are needed including validation, logging, handling errors, handling complex data types and transformations. Not only does the effort tend to be significantly underestimated but there is also a high risk that some data will be missed or migrated incorrectly which could be disastrous for the project. Also, there will need to be extensive testing and debugging using up valuable resources often for far more time than was planned.

• The incomplete migration strategy trap: Your migration strategy should be planned with extreme care so that your shiny new ServiceNow instance has all the data and functionality you expect, with no gaps, bugs, or unforeseen issues. The issue here is that it can be tempting to leave data behind especially if trying to migrate without a specialist migration automation tool. However, this will significantly impact the business’s ability to capitalise on existing data including knowledge, asset, historical data, trend reporting and customer/employee service history.

A particularly important aspect of your migration strategy involves customizations. Catalog your existing ServiceNow customizations and determine which ones are supported out-of-the-box by the new instance, which ones no longer relate to your current business processes, and which ones must be replicated in the new instance.

Address These Considerations with Tool-Based Approaches

The good news for ServiceNow migrations is that tool-based solutions are available that eliminate the need for manual migration and low-level development and can even inform some of your migration planning. The Precision Bridge ServiceNow Data Migration Automation tool can analyze your existing environment and map it to the new instance, no manual migration or scripting is needed.

This approach streamlines not only the routine data migration tasks but also handles all types of data including audit history/journal entries,  links, text formatting, embedded images and attachments and much more. The result is a more robust migration that meets your goals and timelines.

For more information on how migration tools from Precision Bridge can simplify your ServiceNow migration project, contact Precision Bridge today.

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Staying Ahead of ServiceNow Migration Challenges

By their nature, most IT projects are difficult in many ways: Difficult to plan, difficult to fund, and difficult to execute. For some projects, it’s even hard to define what problem it is that you’re solving or the expected benefits of the project.

Those difficulties, among others, help explain why so many IT projects fall short of expectations or fail altogether. IT service management (ITSM) system migrations are no different, and many suffer the same maladies that other IT projects experience.

But just because an IT project is complex doesn’t mean it must be faced with dread. With careful planning, a solid team, and robust tools, an ITSM system migration can go according to plan and be delivered on time and within budget.

Typical ITSM Migration Challenges

Here are some common challenges related to ITSM system migrations:

●       Customizations: If the source system has extensive customizations, figuring out which of them to migrate to and how can be a thorny task.

●       Historical data: Do you migrate all of it, some of it, none of it? What do you lose if you start life in the new system without the continuity afforded by bringing at least some historical data?

●       Different data models: Even when migrating between two instances of the same application, such as ServiceNow, the data models might differ, so you can’t just point the new version to the old database. Extensive analysis, mapping, and data transformation may be required.

Meeting These Challenges with Tool-Based Migration

All of these challenges, and more, can be overcome by applying the proper tools. Gone are the days when migrating between instances of ServiceNow required painstaking, endless iterations of manual data exports, transformation in spreadsheets, imports, and error checking. Gone is the need to develop custom software to help with these manual migration tasks.

Competent integration partners can access robust tools like Precision Bridge ServiceNow Instance Migration. This tool provides advanced capabilities to simplify your ServiceNow migration by reducing or eliminating manual data manipulation or low-level development work.

Precision Bridge ServiceNow Instance Migration streamlines both the planning and execution of your migration between any two ServiceNow instances of any version by automatically mapping the data and transferring key data tables, including both master data and transactions (such as incident tickets). Attachments, workflows, and knowledge base articles are migrated with ease.

Precision Bridge ServiceNow Instance Migration can even help you migrate any customizations you can’t live without.

Contact Precision Bridge today to learn more about the ServiceNow Instance Migration tool or schedule a demo

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Data Replication vs. Data Back Up: What's the Difference?

Precision Bridge is a trusted partner in IT service management (ITSM), and in our years of operation, we’ve refined our solutions for data migration, archiving, replication and synchronization. One question we sometimes receive is “What about backups?”. This isn’t surprising, as backups are a bit easier to understand than data replication for those outside the IT world. 

In this article, we'll explore both the similarities and the differences between data replication and backup and how both can benefit your business.

Comparing Data Back Ups, Replication, and Archiving

 “Whilst data replication and data back up sound different, they’re actually similar”

Whilst data replication and data backup sound different, they’re actually similar in application. In fact, data backup is just one particular use case within the larger data replication umbrella. Let’s review each to make sure we’re all on the same page:

• Data backup: A process that creates a secondary copy of data for the purpose of recovery in case of data loss, corruption, or system failures.

• Data replication: Replication involves creating and maintaining multiple copies of data across different locations or systems, enabling quick access to data in case of system failures or disasters.

• Data archiving: The purpose of data archiving is to preserve data for long-term retention, compliance, or historical reference. Archived data is typically less frequently accessed and may be retained for regulatory or legal requirements, business analysis, or other specific purposes.

With both data replication and backups, you copy data from one location to another without deleting the source data. This is a key distinction from archiving, which involves moving source data to storage systems designed for long-term preservation.

Both backups and replication can be run in real-time or on a scheduled basis, and both can involve replicating data between different servers, storage devices, or even geographic locations.

Data Recovery Options: Which Should I Use?

Although data backups are one of the most prevalent use cases for data replication, the motivations for choosing each these data management activities are quite different. In other words, why would a company choose to perform replication over a standard backup?

Data backups are specifically focused on creating a copy of data for disaster recovery and data loss situations. These could be the result of a natural disaster, human error, or a cybersecurity incident such as a ransomware attack. Should an event occur, the data backup reaches out to an external database to restore the data to its original system. Sometimes, the backup will be at the system level (e.g. a backup of everything including the hardware, software, application, and data) to create a full standby system.

Test your backups before disaster strikes – by then it’s too late!

Let’s compare this with data replication and its benefits, the first of which is performance.

System performance can be significantly affected by high levels of data access that may be required when companies run large or frequent reports. By replicating data to storage where it can be accessed independently, users reduce the workload on the application and improve performance overall.

Another benefit has to do with reporting and analytics. Reporting can be improved across business locations by replicating the data into a centralized store, such as a data warehouse or data lake. This allows all reporting to be run from a single location and can greatly improve efficiency compared with conventional reporting processes.

Finally, though no less important, is how data replication cuts costs. Many systems charge per user, and strategic replication helps companies disperse data to relevant staff and reduce the number of licenses required.

Final Considerations for Data Management

Keep in mind that there are other important factors to consider when deploying a disaster recovery strategy. These include selecting the frequency of backups or replications, ensuring data integrity, handling complex data objects, and managing incremental updates.

The good news is that all of these can be handled with the right solution. At Precision Bridge, we’ve spent years designing a proprietary solution that takes care of all aspects of this process, allowing for simple configuration, automation, and validation to ensure it is straightforward and fast to set up data replication and archiving. Visit our site to learn more and prepare for your next migration project.

About the Author

Mark Herring is a co-founder of Precision Bridge and has been working in the ITSM space for nearly 30 years. Mark co-founded Precision Bridge in 2015 to address the market need for ITSM cross-platform migrations and has been involved in over 100 migration, replication, archiving, and data synchronization projects across a diverse range of ITSM platforms.

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