The ultimate guide to Legacy System Migration.

Every minute you keep your legacy data stored in outmoded systems, you’re creating unnecessary costs and compromising your security, compliance, and efficiency. When you use a modern, AI and Machine Learning-enabled platform for legacy system migration, you’re able to cut costs, reduce risk, and extract new value from historic data.

As data management systems go out of date, cybersecurity threats advance, and the energy expenditure of maintaining legacy systems skyrockets, it’s more urgent than ever to migrate business-critical data to accessible cloud-based platforms. Legacy system migration is the obvious solution, helping you exit obsolete platforms and data centres, reduce costs, and keep historic data secure.

But how can you make sure it’s done right – that is, in a way that saves you money, maximises operational efficiencies, and unlocks the valuable insights stored away in your historic data vaults? And how does an effective legacy system migration strategy bring mission-critical historical data into your entire data picture, so you can start operationalising AI in the enterprise and using it for decision-making?

We spoke to Aiimi’s Head of Solutions Engineering Matt Eustace about how legacy decommissioning and system migration can be enhanced with AI and Machine Learning. Matt has over 20 years of experience working with unstructured data and is an expert in cybersecurity and data protection.

Why is it so important to decommission legacy systems and migrate their data to cloud-based platforms?

Maintaining legacy applications can consume over 75% of your organisation's IT budget. These costs can creep up for a number of reasons.

• For one, there’s the cost of renewing your legacy system’s software licensing every year. If you’re running older applications that aren't in the cloud, you’ll be paying to run expensive infrastructure that you might otherwise be able to get rid of. Besides the sheer energy cost of storing and maintaining historic data on in-house servers, there’s also the carbon emissions generated and the associated environmental impact.

• Due to its age, your legacy system likely contains security holes that put your data, your customers’ data, and your business at risk of costly cybersecurity breaches, in addition to fines for non-compliance. Moreover, you cannot obtain certain information security certifications, such as ISO 27001 compliance, without demonstrating a high level of information security across your organisation, including your legacy systems.

• Plugging security holes within an outdated application and restoring compliance is no easy fix, not to mention expensive. For legacy systems in particular, the skills required to fix the gaps may be scarce and the few people equipped to do so may charge you a premium.

• On that point, maintaining legacy systems can generate unnecessary staffing costs, particularly if the only people in your company with the skills to operate the product are at the end of their careers and expensive. Factor in the prospect that these experts will retire at some point, and you’re looking not only at steep costs, but at an imminent deadline to migrate your historic data to a more efficient and accessible system.

To avoid spending unnecessarily, you’ll want to move historic data aside and keep it secure and compliant. However, there will still be times when you want to access it, to understand how it relates to your current operations as part of your complete data picture. From that position, you can unlock unrealised value from historic data, rather just storing it away in a cold archive. Unlike traditional legacy system migration, an AI-powered solution enables you to extract insights from your legacy data and use them to make better, smarter business decisions. In turn, cleaning up your legacy data, classifying it, indexing it, and making it searchable helps set you up to operationalise AI across your enterprise.

What do people get wrong about legacy system migration?

Typically, people don’t think enough about data loss and data quality when planning to exit a data centre or migrate to a new platform. Moving data from the source to the destination system can reveal data gaps – sorting through and fixing these disparities can be a labour-intensive and tedious process. That's where the cost of moving your data from old to new really hits and the migration often turns out to be a bigger undertaking than people think.

However, a legacy decommissioning strategy can eliminate that issue if it uses a flexible data model that can deal with gaps and quality problems. It could even highlight those problems and fix them across your organisation.

This is when AI and Machine Learning come in handy because they can help us plasticise data and make it more malleable. As tools, they can be leveraged to fix those gaps, generate metadata, apply however many labels you need, and drive operational efficiency.

So, what options do businesses have when it comes to decommissioning legacy systems?

You could build your own decommissioning capabilities from scratch; take the Microsoft Azure environment, build an Azure Data Factory pipeline, plug in Azure Cognitive Services and the other Microsoft products. You could do all that plumbing for yourself, but it’s a lot of work.

Another option is to choose an off-the-shelf legacy system migration product that will simply move your data onto a newer platform and make it searchable. It’s not a rich data experience but it will archive your data and give you the option to view it in an evergreen file format such as PDF. You know the data is there and you can access it if you choose to.

That’s simple legacy migration – storing historic data in a cold archive with a simple label. The problem is, when it comes to retrieving data down the line, that label can’t tell you much about the data. If you think about physical archives versus digital archives in the old days, you’d have a box that you’d put on a shelf with an index card or a tag that tells you what’s in it. But if the information you need isn't on that tag, you'll never know which box to look in or, beyond that, which record has got the information that you want.

That’s where AI and Machine Learning come in. Part of the Aiimi Insight Engine, our legacy decommissioning capability is a pre-packaged solution containing everything you need to migrate your historic data and start extracting new value from it. It can not only tell you what historic data you have, but it can also thematically understand the meaning of it all: who created it, how it's linked to everything else in your data estate, and which valuable insights it contains that you might not otherwise be able to see. In short, it can help you manage, enrich, and navigate your entire data picture, both old and new.

How can AI and Machine Learning enhance the outcomes of legacy system migration?

By interrogating the data you’ve got stored on a legacy system, the Aiimi Insight Engine can tell you which pieces of information are valuable to keep and how easily you need to be able to access them:

• Some pieces of information need to be stored on a lightning-fast system that you can access quickly and easily.

• Other records that you don’t need regular immediate access to can be stored on slower, lower cost storage.

• Some information can be identified as ROT (redundant, obsolete, or trivial). This data does not need to be migrated or stored and can be discarded.

This is all part of getting your data house in order. Whereas data was previously siloed into buckets of old stuff and new stuff, AI-powered legacy decommissioning enables you to bring together your business-critical data into one universal information layer. So, if you wanted to tap into your legacy data to find out more about your history of incident management or asset performance, you could use a tool such as the Aiimi Insight Engine’s Map Lens feature to plot all the information you have over a custom timeframe or area. By using legacy data in this way, you can access insights with much greater fidelity, to find out what’s causing problems or where there's a concentration of issues, and how you have solved these issues in the past. As such, instead of just having your legacy data sit stored away in a digital archive, using AI in business enables you to tap into its value.

How can using AI to migrate legacy systems set you up to start operationalising AI across your enterprise?

Migrating historic data onto new systems can enhance your use of AI, as it can serve as a rich source of training information. This is particularly valuable for fine tuning models to make more accurate calculations about your business and the information you store. It can tell you what you did historically, what was associated with that previous action, and how that relates to what you’re doing now. For example, by using OData architecture as an API, you can plug in Power BI and start experimenting with how you visualise data, to unlock insights and use them to make smarter decisions.

Similarly, that corpus of historical information can be used to train AI chatbots and personalise the interactive experiences they generate for you. Say you ask a chatbot to show you all the data your business has about a specific customer – but you only want information from active contracts from the past six months, not from the past 10 years. Your legacy data-trained AI chatbot can interpret the context of your question and choose whether or not it should include information from the historic archive in its response. With that, you’ll have richer, more intuitive interactions with your chatbots and more easily be able to connect to the answers you need.

What success have Aiimi’s customers had with our legacy system migration solution?

Enhanced with AI and Machine Learning, legacy decommissioning becomes more than just a filing exercise. By retiring outdated systems and migrating information to platforms that better suited their needs, we’ve enabled customers to cut costs dramatically, flesh out their corporate knowledge, and use historic data to their advantage.

For example, we helped RES, the world’s largest renewable energy company, migrate 5.7 terabytes of mission-critical data to an Azure archive. As a direct result, RES was able to:

• Replace 27 servers across three locations with the single cloud-based platform.

• Significantly reduce its carbon emissions over five years.

• Label and enrich its historic data through the Aiimi Insight Engine, our enterprise AI platform.

By decommissioning its legacy systems with Aiimi, RES has been able to better store, manage, and search its legacy data, while scaling down its overall data requirements and driving operational efficiency.

Maintaining legacy systems will eat away at your bottom line, make you vulnerable to cybercrime and non-compliance, and impair your view of your data landscape. With Aiimi’s AI-powered legacy system migration solution, you can retire outdated systems and extract valuable insights from your historic data, while cutting costs and driving operational efficiency. Talk to us about migrating your legacy systems to protect your business and uncover valuable new insights.