Data management and data conversion are similar but not the same. And to make smart decisions regarding business operations and infrastructure, you need to know the difference.

Read on to learn what these processes encompass, when to use which, and how to make sure the approach you choose will fit your needs best.

Introduction to Data Management

The process of assembling, storing, and using data in a cost-effective, efficient, and secure manner is known as data management. Its primary objective is to help businesses optimize data use and facilitate the decision-making process while complying with corporate and legal regulations and making them beneficial for the companies themselves. As businesses increasingly rely on intangible assets to create value, an efficient data management strategy is more critical than ever.

Integrating data from a myriad of sources poses one of the main difficulties for engineers. Because so many storage tools and sources keep data in different structures and formats, it can be quite challenging to withdraw it from them and integrate it into a single file format. In addition, the integration process itself needs to adhere to a predefined consistent pattern.

Data Migration vs. Data Conversion Understanding the Differences and Choosing the Right Approach 03

Also, effective data management means complying with specific quality and security standards. For instance, companies need to follow the set timelines without compromising data consistency and correctness to guarantee customer satisfaction. Without paying attention to data quality, organizations may end up with a decline in customer satisfaction and wasted resources.

Another vital factor that companies need to keep in mind is data security. This especially concerns organizations that store clients’ private details, sensitive information, or intellectual property. Data breaches occur on a daily basis, and most of them are the result of human error and neglected security protocols. The results may be fatal: from jeopardized business reputation to financial instability and legal troubles.

Data migration and conversion are vital processes in our data-driven world. Let’s dig into details on each of them.

Understanding Data Migration

Basically, data migration is moving data from one system to another. There are numerous reasons why companies may need to undertake this, like system upgrades, data compliance, merging with other companies, etc. Regardless of the reason you need to migrate your data from one system to another, make sure that it remains intact, accessible, and usable.

The major benefits of data migration include:

  • Enhanced operational effectiveness. Moving data to another system allows businesses to build one centralized environment where information is always at hand. This fosters data management and administration and improves and facilitates employees’ performance. It also decreases the number of human-related mistakes, which can cost a lot of resources to fix. As an end result, businesses spend more time on crucial tasks without constant distractions for troubleshooting. For startups, in particular, it can provide some merit to their reputation on the market, specifically when they utilize top cloud storage, like AWS.
  • Agile processes. The fact that data migration increases a company’s agility is one of its main advantages. Since technology evolves faster than we can follow, what you can do is be prepared for an update anytime you require it. This includes the ability to move between systems without hassle and durable shutdowns. That’s why modern companies move to clouds: they allow for incredible flexibility and agility when it comes to data migration.
  • Better decision-making. Data migration provides businesses with the ability to make smarter decisions based on real-time facts and insights. The ability to base projects, services, and goods on educated consumer demand and trends gives this information tremendous power for decision-making and guiding the organization in the proper direction. For companies that operate on legacy systems, using analytics and, if cloud software is utilized, having access to data from any location and device is one of the primary advantages of moving data to newer software.

Though it does provide many advantages, we need to outline the drawbacks that data migration might entail. These include:

  • Data loss. If done without proper supervision and care, the movement of data might result in data loss. It can be caused by system shutdowns, fraudulent manipulations, or, most often, human errors.
  • Incompatibility issues. The first thing companies should take care of when moving data between systems is that the data format is readable by the receiving environment. In other cases, they should be prepared to spend a lot of time to make it compatible.
  • Downtime. Migrating data to another system takes time and may result in substantial downtimes, which in turn, entails potential revenue loss.

Understanding Data Conversion

The transformation of one data format into another is a data conversion. It is mostly a technical procedure carried out by software; very infrequently, however, hardware or human input is also used. Interoperability and maintaining all of the data while embedding as much information as feasible are the only goals of the data conversion.

Converting data into different formats might be quite challenging, though it does depend on the environment and data types involved. Still, it does provide many benefits, the most important of which are:

  • Better data quality. While converting data from one format to another, it undergoes a series of checks and validations. This process helps confirm that data is of good quality and contains no mistakes.
  • Improved accessibility. Data formats vary depending on the app or system. By converting data, companies boost its compatibility and interoperability across different systems. This, in turn, makes data more accessible.
  • Enhanced data analytics. Often, in the process of data conversion, previously undetected issues arise. For instance, the company may be storing and using data in an outdated format, which complicates its analyses. By converting data into new formats, businesses may unleash hidden opportunities and gain a competitive advantage.
  • Easy data migration. Data conversion oftentimes becomes a part of data migration. This is because moving data between systems more often than not entails converting data into a different format.

The drawbacks of data conversion include:

  • Increased risk of errors. Whenever data is transformed, there is a possibility of errors occurring. This risk is increased during data conversion. Any data errors can result in incorrect analysis, which could lead to poor decision-making.
  • Time and resource intensity. Data conversion can be a complex process that requires significant resources in terms of time, personnel, and financial investments.
  • Incompatibility issues. The transformed data may not be compatible with existing applications or systems, which can create integration challenges.

Organizations have to carefully weigh out all the merits and cons of data conversion to reach the best possible outcome from the process.

Differences Between Data Migration and Data Conversion

The major difference between data migration and data conversion lies in the process itself.

Although every database conversion project is unique, most conversions follow a few fundamental phases.

  • Evaluating both the target database and the original source data.
  • Creating a plan based on the project specifications and the end user’s needs.
  • Checking the findings for quality after at least three repetitions of the conversion test.
  • Converting the data into the format required by the destination database to carry out the plan.
  • Verifying the final results for quality.

The need to understand both the source and the new format causes the complexity of database conversion. Without this information, data integrity may be ruined or compromised. Other frequent problems include data duplicates that may need to be combined, outdated information that must be removed or updated before conversion, and inaccurate data that may require human correction.

As for data migration, the primary purpose of this process is to maintain data consistency and accessibility. Reviewing the database as it is, data mapping to identify any potential differences, moving the data to the new database, and lastly, testing to ensure that all the data was migrated correctly are all steps in the data migration process.

However, while the process of database migration may appear straightforward at first, it is actually fairly complicated. If the existing data is not compatible with the new system, it may be lost during the transfer process. As part of database conversion, the procedure necessary to guarantee that data sets are correctly mapped during migration is typically carried out beforehand. Prior to completing a database migration, careful planning is essential to minimizing the possibility of problems.

So, the three major differences between these processes lie in:

  1. The primary purpose. Data migration might be necessary in case an organization upgrades its systems or merges with another one. Data conversion aims to make data usable across different systems.
  2. The scope of the process. Since data migration entails transferring data from one system to another, and data conversion transfers it into different formats, the two processes might take various amounts of time and human resources involved.
  3. The outcome. Data migration needs to ensure a smooth transfer between systems, meaning without the loss of data or its quality. Data conversion end results should allow for easy data use across systems.

Choosing the Right Approach

To ensure effective data management, companies have to pick the right approach that suits their specific needs. There are four key indicators organizations need to pay close attention to in order to secure themselves a robust operational environment. They include:

  1. Data volume: while smaller organizations can manage their data simply using a spreadsheet, enterprises with large datasets need complex management systems to operate efficiently.
  2. Data complexity: depending on the number of variables involved, the relationships and interactivity between data points and data sets, some data might be more complex than others. Sometimes, companies even employ specialized software to handle their complex data sets.
  3. Data quality: the accuracy, reliability, and relevance of data are essential for effective decision-making. So, implementing data validation and verification and regularly auditing data sets are a must for both small and large businesses.
  4. System compatibility: last but not least, it is crucial to ensure system compatibility with modern IT infrastructure and software systems. This will come in handy in future operational processes, including data migration and data conversion.

Data Migration and Data Conversion Best Practices

Both data migration and data conversion processes require substantial resources in terms of planning and implementation. We’ve got several tips for you to ease the process.

The process of data migration differs from business to business depending on the goals and tools utilized in the process. But there are some general rules applicable to any situation, following which companies might reduce costs and time and avoid common pitfalls.

  • Utilize data migration as a chance to spot and fix data errors and address issues previously undetected (those often arise in the process). Take it as a chance to enhance the quality of your data overall.
  • Employ experts in data migration, and establish a team within your company to handle every step in the process.
  • Try to minimize the volume of data that you intend to move. This is especially important for large enterprises that move to the cloud, which can be costly.
  • Prior to the creation of mapping scripts, data profiling is necessary. This includes a thorough analysis of data sources, structure, and interrelationships.
  • Be prepared for shutdowns in case primary efforts for data migration fail. In this case, you will have to roll back changes without losing data.

To avoid pitfalls and guarantee that the data conversion procedure won’t degrade the quality of your data, pay attention to the following:

  • Before you begin, consider all aspects of the project, including its duration, frequency, budget, guidelines, etc. Your chances of effectively implementing the planned increase as you become more comprehensive.
  • Your data may contain some fields that are more important than others for the success of your data conversion process. This data may be more prone to mistakes and require close scrutiny. You can avoid cross-referencing difficulties by anticipating these important elements.
  • You can make sure that the quality is constant throughout the conversion process by using clearly specified data quality criteria. To identify mistakes before conversion, you must continuously test the procedure at each step.
  • If you want your data conversion to be accurate, data profiling and cleansing must be a part of the process. Setting up appropriate data cleansing techniques will make it simpler to adhere to the criteria you’ve set for data quality.

Data Migration and Conversion Case Studies

This may surprise you, but the large-scale systems migration period might count in years. As an example, for Sabre, a world-renown GDS, the transfer of software and mainframe system data to a virtual server took over a decade. In fact, it is in progress but is planned to be completed somewhere in 2023.

Aiming to restructure and refine a network of over 2,000 grocery stores, the supermarket wholesaler and retailer Supervalu hired a professional company to move the legacy systems to the cloud and convert data into a more accessible format. As a result, the retailer managed to replace fixed-cost internal hardware equipment with flexible cloud-based services that it may scale and modify as necessary. The business switched its workloads over to a safe, highly scalable cloud platform from those legacy platforms.

The best real-world example of outsourcing data conversion services is in the healthcare sector. Several healthcare organizations guarantee medical billing, which depends on precise and current patient medical information. It may be part of this process to change paper-based records to digital ones so they may be shared with insurance providers and accessed easily. Yet, running this procedure internally can be time- and money-consuming.

Mercy Health, a healthcare provider, used a third party to handle its data conversion needs. They were able to lower expenses, increase productivity, and guarantee the accuracy of their medical records by doing this. Using these services, Mercy Health reportedly saved up to $250,000 annually, according to an article in Healthcare IT News. A work that would have taken Mercy Health several years to perform internally, the outsourcing firm was also able to convert 1.5 million pages of paper records to digital format in just six months.

Final Word

Transferring information from one place to another is as easy as copying and pasting. When it comes to transferring millions of data units into a new system, everything becomes a lot more difficult. Data is essential to any modern business, however, many of them view data migration as a simple job. But according to recent surveys, 73% of data transfer operations take a year or longer and entail bigger spending.

So, before making any decisions, creating a plan or consulting a company that can help you make one is better. Forbytes helps companies across the globe create innovative solutions and improve their software. Get in touch in case you need any help.

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