Category: Data

Transition of Legacy Data using Cloud-based AI/ ML Tools

With this transition from legacy to cloud platforms, some tech execs are struggling with the integration of legacy data with newer cloud-based application data.  This is where cloud-based AI and machine learning tools can help. 

Businesses still rely on legacy systems that contain valuable data, and don’t necessarily want to incur the cost of migrating all this data, which presents a challenge to integrate this data with modern cloud application data. There are best practices that can help effectively integrate legacy data using cloud-based AI and ML tools for maximum efficiency and accuracy. Those steps include:

  1. Understand the data – Before integrating your legacy data using cloud-based AI and ML tools, it is crucial to have a thorough understanding of the data.

  2. Choose the right integration approach – Depends on the volume, complexity, and sensitivity of the data. Choose batch, real-time or hybrid integration approaches.

  3. Ensure data governance – Establish proper for data ownership, access controls, and data security protocols.

  4. Leverage Automation – Use automation to streamline data migration, transformation, and synchronization processes.

  5. Monitor Performance – Ensure tracking data quality, accuracy, and timeliness.

Tools are enablers, and data is critical to the success of your AI/ ML frameworks.  A well-thought-out plan on how your data will be ingested will add to the success of your initiative.

How to Leverage Decades of Legacy Data on the Mainframe with Cloud Technology and AI

Companies that have relied on mainframe technology for decades are often hesitant to upgrade to modern technology platforms. This is understandable, especially considering the amount of legacy data that these companies hold. However, with the rise of cloud technology and AI, companies can seamlessly move their mainframe data onto the cloud and continue to leverage it, without having to invest in new infrastructure. Let’s explore how to leverage decades of legacy data on the mainframe with cloud technology and AI.

  1. Assessing your data – The first step in leveraging your mainframe data is to assess its size and complexity, alongside how it can be best used in the cloud. You need to determine if your data needs to be transformed, optimized, or just migrated to a new platform for easier analysis. By doing this, you’ll be able to determine its value and how to extract it. Additionally, you also need to consider the security requirements for your data. Ensure that your data privacy and compliance policies are up-to-date to meet modern standards.

  2. Migration Strategy – After assessing your data, you need to choose a migration strategy. You can decide whether to move data all at once or in chunks gradually so as to maintain consistency and avoid data loss. You can use migration services that eliminate the need for human intervention, prevent migration errors, and automate the migration process, thus saving time and money. Such services can move data from mainframes to different cloud providers such as AWS, Azure, or GCP.

  3. Cloud Storage – Once your data is on the cloud, you can use various storage solutions depending on the nature of your data, the frequency of data storage, and whether your data is temporary or permanent. Cloud storage providers offer options such as Amazon S3, Google Cloud Storage, and Azure Blob Storage among others. Each storage option has various advantages, and you need to be mindful of factors such as security, accessibility, and cost.

  4. Artificial Intelligence – Once you have migrated your data to the cloud, you can use AI to gain valuable insights. AI can identify hidden patterns, predict trends, and mine your data for valuable insights that can help you make decisions that improve your bottom line. With AI-powered analytics tools, you can continue to learn from historical data and more easily identify trends as they emerge in real-time.

  5. Managing your Mainframe Data – Although it’s now on the cloud, your decades-old mainframe data is a critical asset to your business. And while moving everything to the cloud might seem like the logical step, it’s not always necessary or feasible. Rather, managing mainframe data with integrated solutions that maintain data integrity and security, as well as compatibility with modern tools, can be a much more efficient option. Tools such as mainframe virtual tape libraries and third-party storage management programs can help manage mainframe data at a lower cost.

Companies that have tons of legacy data on a mainframe don’t have to continue relying on outdated technology. Migration to cloud technology provides an opportunity to modernize operations by improving data accessibility, security and analytics. In addition, the implementation of AI can help exploit critical business insights from historical data. By leveraging mainframe data, companies can gain a competitive advantage and position themselves for future growth.

Legacy Data – unlocking the value

Tech execs are driven to modernize legacy systems for increased agility to match the fast pace of business. A valuable asset locked up in legacy systems is data. By planning a migration to the cloud organizations can leverage their legacy data and transform it into actionable insights.

This transformation can unlock new business opportunities, improve decision-making processes, and increase competitive advantage. With the cloud’s scalability and flexibility, tech execs can easily access and analyze vast amounts of data in real-time.

Businesses hold decades of untapped legacy data, ready to be utilized for harnessing its advantages.

True, a good portion of it could be aged, corrupted, or duplicate. Cleaning this data is crucial for unlocking its true value and making smarter, focused business decisions. Thankfully, with the advancement of technology and data management tools, this process has become much more efficient and effective.

Migrating from legacy to the cloud enables organizations to utilize cloud service providers’ (CSP) capabilities for big data processing, real-time analytics, and machine learning. These capabilities allow for the extraction of insights from the data. But before this can happen, the data needs to go through a thorough cleaning process.

The first step in this process is identifying and removing any duplicate or redundant legacy data.

This saves storage space and ensures accurate data analysis, avoiding result discrepancies.

Next, the data must be organized and standardized.

This involves formatting text strings, converting data types, and resolving missing values. Without proper organization, it becomes difficult to make meaningful connections or draw insights from the data.

When migrating legacy data to the cloud, consider the target data structure based on the use cases.

Data currently in structured format may be okay to dump into an unstructured data lake for analytics, as an example.

The data format in your legacy environment may differ from that in the cloud. It’s going to depend on the business need. So, there will be a lot of thinking required for how you are going to use this information. Are you going to query it? Are you going to run analytics on it? All these considerations will impact how you structure and store your data.

A major plus of cloud data storage is the flexibility to scale as required.

Traditional on-premises solutions require organizations to predict storage needs and invest in costly infrastructure that might end up underutilized. In the cloud, resources can be added or removed as needed, providing cost savings and increased flexibility.

Another important consideration when moving data to the cloud is security.

Although cloud providers offer strong security measures, organizations must enforce their own protocols and monitor data access regularly. This becomes even more critical if sensitive or confidential data is being stored in the cloud.

Besides scalability and security, leveraging the cloud for data storage can enhance collaboration and productivity in an organization. Teams can effortlessly collaborate on documents or projects from anywhere with internet access, eliminating delays due to location or time differences.

Furthermore, utilizing the cloud for data storage can also lead to cost savings for organizations. By ditching physical servers and gear, while cutting maintenance costs, businesses can greatly reduce their IT expenses. This allows them to reallocate resources towards other areas of their operations.

Lastly, the cloud offers reliable backup and disaster recovery options.

During system failure or natural disasters, cloud storage keeps data safe and accessible. This eliminates the risk of losing important information and allows for a quick recovery in case of any unexpected events.

To sum up, Cloud Service providers offer various services and partner tools to aid in migrating your data, including files, databases, machine images, block volumes, and tape backups. Successful data migration hinges on knowing what to move, where to move it, how to utilize it, and having knowledgeable individuals who can apply the data for making business decisions.

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