How To Reap More Value from Your Data with Data Modernization

Data modernization is essential for an organization to make effective decisions and bridge the gap between the value their current structures capture, and the potential value offered by modern technologies.

By facilitating seamless data movement across different databases, companies can enhance data consolidation, eliminate silos, and prepare their data for analysis.

This guide will help you understand how you can create data solutions that are high-speed, real-time, agile, scalable, and future-ready.

What is Data Modernization?

Data modernization includes a broad variety of activities fundamental to ensure data security, compliance, reliability, and accuracy. The activities required under this process include the following:

• Developing systems and processes for handling large volumes of data
  
• Moving legacy systems to new data-driven systems like cloud databases
  
• Establishing a comprehensive data governance strategy
  
• Enriching modern data strategies to benefit from machine learning and artificial intelligence
  
• Investing in analytical devices to uncover valuable insights from user activity
  

Along with this, companies should establish security measures like access control and encryption processes to ensure data security throughout and after modernization.

Benefits of Data Modernization to Businesses

Studies suggest major companies have started their journey toward data modernization for various reasons. For instance, storing unstructured data such as customer voice audio, social media comments, images, or clinical notes in healthcare. Here are some benefits you get by unlocking the true potential of your data through modernization.

1. Enhanced flexibility, agility, and scalability: Modernizing data infrastructure can enable businesses to respond quickly to changing market conditions and rapidly scale their operations. Flexibility also refers you can access your data from any device, anywhere, and at any time.
   
   
2. Improved data quality: Data modernization efforts can help to standardize data formats, eliminate data redundancies, and ensure data accuracy, which can improve the overall quality of data. Through improved data quality, you can build scalable and adaptable processes that help increase margins, and profits for the company.
   
   
3. Increased productivity and efficiency: It helps to automate repetitive tasks, streamline workflows, and enable businesses to focus on high-value tasks that require human intervention. Modernizing data also reduces infrastructure costs by consolidating data silos, eliminating redundant systems, and optimizing data storage.
   
   
4. Better decision-making: Data modernization can provide businesses with more accurate and timely insights, allowing them to make informed decisions that drive growth and profitability.
   
   
5. Improved customer experience: The real-time and predictive insights offered by AI/ML help businesses get a comprehensive understanding of their customer’s needs and preferences, allowing them to deliver more personalized experiences. Technologies like AI/ML help to improve the capacity to create better, new, and more efficient business models, offering a competitive advantage to a business.
   

Best Practices for Effective Data Modernization

Today, data is the most valuable asset of any company and hence, this creates the need to have a data-focused strategy in place. Otherwise, data will be scattered in siloes and legacy software. Some of the key practices to unlock the advantage of your data include:

1. Determine the perfect level of data-first maturity: The level of data-first maturity is categorized as initial, basic, structured, managed, and optimized. Companies often do not linearly grow through these levels but have distinct levels in different sections of the business.
   
   The standard data-first maturity level depends upon several factors like organizational culture, data usage, data infrastructure, goals and objectives, and regulatory and industry landscape. Consistently striving for higher levels helps to remain competitive in a data-focused world.
   
   
2. Prioritize your investments: Depending upon the company’s strengths and weaknesses, you should prioritize your investments. Investment prioritization for data modernization includes data consumption and distribution, data strategy, data lifecycle management, data governance, data security, data ingestion and processing, data architecture, and data analysis.
   
   Investment in these domains can be prioritized depending on several factors such as risk and uncertainty, resource availability, strategic alignment, market demand, and potential impact.
   
   
3. Industrialize your data supply chains: A data supply chain is a complex system comprised of processes, technologies, and people who emerged in creating, processing, storing, and disseminating data within a company. Some of the steps to create an efficient data supply chain are:

• Establish management responsibilities
• Recognize and document the data and linked time, cost, and quality needs
• Describe the supply chain
• Define and build measurements
• Build process control and assess conformance to needs
• Investigate the supply chain to identify required improvements
• Make improvements and continually monitor
• “Quality” data sources

4. Unify data in disparate, multiple operating systems: A unified data model helps to create a single point of access for data centralization, integrated from several heterogeneous data sources such as ERPs, BI analytical tools, CRMs, and supply chain management models. Through a data warehouse, data scientists can analyze and define advanced machine learning algorithms to optimize every scenario.

Some of the common practices for the successful modernization of data are:

• Choosing an appropriate cloud migration platform
• Collecting and certifying significant business metrics and trusted reports
• Advancing data lakes environment such as access control and productivity
• Automated extraction of predictive analytics, workflow automation, and unstructured data by investing in AI and ML
• Designing a governance plan which includes comprehensive rules on how data will be applied, maintained, owned, and accessed
• Through cloud-native monitoring tools such as Informatics, Cloudyn, Amazon Cloud Watch, New Relic, and so on to monitor data and derive insights

Conclusion

Any business with the ability to adapt to reforming scenarios both functionally and technically can grow significantly. Just understand how to move siloed data from legacy systems to new modern cloud-derived systems. Overall, a modern data strategy unlocks the true potential of your data and resolves the most demanding business issues. So, begin your data modernization journey now and place your company on the right path to effective business outcomes.

WRITTEN BY

Team Eela

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