Data Modernization Stages and Best Practices

Data is a priceless resource for any company. But, the pace of innovation using data differs from organization to organization. Organizations with sprawling legacy systems slow down innovation, while cloud-native ones rapidly benefit from modern analytics and AI through more straightforward implementation.

Companies with sizeable on-site data environments may plan to upgrade their data and use AI to overcome business obstacles. However, the transition from on-site to AI may seem intimidating to them.

This post is intended to help organizations clearly understand data modernization strategies to clarify the direction forward.

Data Modernization Stages 

If you aim to succeed in modernizing your data, it's crucial to ensure that the correct data is delivered to the appropriate application, along with gaining new business insights. However, relying on disparate data models and outdated data architectures can result in complex and expensive data quality problems.

The journey to updating an organization's data infrastructure can be divided into five stages. Each stage is built upon the previous one, forming the journey to real-time business insights, agility, superior customer experiences, and the ability to make the most of modern cloud-native applications:

Let's look at them in detail:

1. Data Discovery 

The first stage of data modernization is to acquire an understanding of high-level data issues and create a data backup for a safer recovery. Then you need to understand the existing data structure to eliminate possible issues or inefficiencies at the earliest. 

You can chart out a successful migration strategy that includes the following:

• Real-time tracking of source updation.
• Necessary databases or hardware.
• Impact of Migration on the size of the target database.
• Downtime is required for the migration.

You can classify standardized data and assess its ability to maintain by implementing the proof of concepts outlined in the strategy document.

2. Tasks Before Data Migration

To keep your business up and to run, you should implement data migration at specific intervals. And for this, you should be aware of a few pointers before commencing your data migration journey.

• The first step is to analyze the data's structure and categorization to guide the architecture for storage at the destination.
• Then one needs to select crucial data using to simplify cloud migration which saves time and cost to handle large data volumes.
• Standardizing business rules provides direction for data usage and establishing a data migration and translation strategy.
• After completing the above activities, one should determine the method for carrying out the migration, choose suitable data warehouse architecture, and develop a Sureshot execution plan.

3. Migration and Transformation 

Further, by utilizing data warehousing or mining techniques, one can enhance data by incorporating demographic and geographic information. The data integration merges information from different sources into one database, providing a consolidated view that leads to better decisions.

Most data integration platforms automate data validation to prevent corruption from context inconsistencies. The data can be transferred between databases or from outdated to modern systems using import/export tools and management methods.

4. Continuous Migration

After kicking off data migration, one needs to continuously monitor data quality, eliminate errors to ensure data accuracy and see that it is on track and all milestones are being met.

At the same time, it's crucial to keep stakeholders in the loop and update them about the entire migration process and possible issues.

5. Post-Migration Activities 

After migrating data, comparing the transformed data with the original legacy system data is to ascertain the completeness, accuracy, and security of all the data. For this, one can use manual checks, automated tools, or a combination of both.

Further, the legacy system should undergo an audit to ensure data quality and that no errors occur during migration. In case of any issues, such as missing or corrupted data, restoring the files from a backup taken at the beginning is crucial.

Best Practices for Data Modernization

Data serves as the foundation for digital strategy and transformation. But amalgamating a massive chunk of data scattered across public and private clouds and in on-premises environments requires a new and innovative management approach.

Here are a few data modernization practices that lend a helping hand; let's look at them:

1. Get Rid of Internal Data Silos

Data silos are like a norm for companies that are heavily dependent on legacy systems. Plus, they store their data in disparate repositories, so there are high chances of hacking and duplication. As a result, no one knows which information is correct. 

In such a scenario, businesses can use data modernization to make their data accessible to employees and stakeholders as and when needed. This modernization also helps them to break down the barriers of using old-fashioned legacy systems. Data modernization even cleans and validates data to ensure its accuracy and completeness.

2. Take an Inventory of Data Assets 

If one doesn't have much clarity about data assets, one may be unable to answer where they have limited access or are open for all. Therefore, the owners need to know the type of data that comes in, the data they create, and what they send out.

By using data modernization, one has to clarify the sources, break down crucial information, and simplify the problem statement to build an accurate data inventory. For this, enterprises can use a hybrid cloud strategy. 

3. Plan Your Investments 

After taking data inventory and analyzing the organization's position, one should prioritize investments for specific domains and subdomains. 

This can be done based on different factors like availability of resources, market demand, the impact of data, strategic alignment, and potential risks.

By prioritizing these activities in the best possible manner, making intelligent decisions by harnessing the power of real-time data insights becomes easier.

4. Blend Data Into Diverse Operating Models

Nowadays, enterprises utilize microservices in containers distributed across several servers. As a result, the infrastructure is typically spread out even if virtual machines are employed, with machine images transferring between host servers.

This complexity makes it challenging to manage and integrate different tools within your environment, primarily when each tool uses its data model.

A unified data model centralizes data from multiple sources, including CRMs, ERPs, and supply chain management models. This enables data scientists to optimize scenarios with advanced machine-learning algorithms. 

By contextualizing data across various services is possible by bridging different ecosystems.

Tying It All Together

With a clear understanding of data modernization stages and strategies and a robust data governance plan, businesses can focus on important things instead of wasting time and resources combing various data stores for relevant information. 

This shift in approach can result in improved customer satisfaction and retention rates and high cost and time savings across the entire organization.