Business processes are being made faster, smarter, and more efficient with modern technologies. Learn about data modernization, one of the core pillars of the digital enterprise. Image: James Thew/Adobe Stock Continue Reading

Business processes are being made faster, smarter, and more efficient with modern technologies. Learn about data modernization, one of the core pillars of the digital enterprise.

Image: James Thew/Adobe Stock

Data is a valuable commodity for companies because of its potential to power better decision-making. However, data management tools and processes must be equipped to handle the volume, velocity and variety of data that companies face today. The exploding volume of data and the many new applications that generate and consume it have created an urgent need for data management programs to help organizations stay on top of their data.

SEE: Data governance checklist for your organization (TechRepublic Premium)

Data management initiatives, including data integration and analytics, help organizations optimize their use of data as a strategic asset. Data modernization is a critical component of these initiatives. It addresses the challenges of legacy data management systems by moving away from rigid siloed systems and increasing the value of your data.

In this guide to data modernization, learn how it works and some of the best strategies for modernizing your own organizational data.

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Data modernization definition

Data modernization is the process of converting an organization’s data assets into more usable formats for the modern workplace. One way to do this is to move data from legacy systems to a new system that operates on a cloud-based platform.

Data modernization can also involve extracting data from legacy applications or databases, cleaning up the data, and importing it into a modern system. Once data has been modernized and moved to a newer platform, users can explore it with features like interactive visualization tools, advanced analytic functions, machine learning capabilities and mobile access.

Data modernization strategies

There are many data modernization strategies to choose from, including open data management or data governance. Your choice of strategy will depend on the needs of your company or organization. In most cases, however, you’ll want to take these steps to optimize your data modernization efforts.

Consolidate data from multiple sources into a single database

Data is frequently scattered across numerous systems and applications within a business. A consolidated database is a good solution to make sure data is easier to find, accessible and up-to-date. Creating a consolidated database that accesses disparate data sources can yield new insights, enabling companies to analyze all the different types of data sets they have collected over time.

SEE: Hiring kit: Database engineer (TechRepublic Premium)

Map your data assets

Knowing what data assets are available within your company is an essential step in any data modernization effort. A clear understanding of the types of data being stored by different departments, where data is located across the network, and data age and format will help you to identify the best course of action for modernizing that data.

Invest in machine learning and artificial intelligence

Machine learning and artificial intelligence technologies offer many benefits for data modernization. Some of the most important functions of AI/ML for data include automated extraction of unstructured data, predictive analytics and workflow automation.

Secure your data

Modernizing data often involves moving it around and exposing it to new applications and users. To keep your data secure, you should use encrypted connections when transmitting it over open networks like WiFi and cellular networks. Encryption scrambles the contents of a message or file, which makes it difficult to read data without authorization and keeps unauthorized parties away from sensitive company information.

Test for sensitive information

Sensitive information is anything that might violate regulations that govern privacy, intellectual property rights or business ethics. Examples of sensitive information include personally identifiable information (PII), health records, financial records and intellectual property. Identifying and testing sensitive information will ensure compliance with industry standards and regulatory requirements such as HIPAA and GDPR, regardless of how you modernize and move your data.

Establish a governance plan

In the data modernization process, it’s easy to overlook regulatory requirements while focusing on other data tasks. That’s why it’s important to have a governance plan in place. A governance plan determines who owns, maintains and accesses data and also includes detailed policies about how data should be used. With a strong governance plan and the right people and processes to enforce it, your data is more likely to stay secure and compliant while you focus on modernizing and transforming it.

Cleanse your data

One strategy for modernizing data is to cleanse it. This involves removing redundant, invalid, erroneous or inaccurate values from existing fields so that only accurate, relevant information remains. Going through the data cleansing process is also an important step to take before big data migrations.

Archive data

Data archives provide long-term digital information storage and often retain older versions of files. Once an archive has been created, copies of data can be distributed to other locations for backup purposes. The modernization process can lead to data loss for companies that aren’t careful, so it’s important to have data archives and backup solutions in place.

Benefits of data modernization

Data modernization makes it faster, easier and cheaper for organizations to access their data in an understandable and actionable format. It specifically makes data more useful by finding and replacing legacy terminology, identifying duplicate entries, standardizing inconsistent formats, adding metadata like tags and attributes to each piece of information, and eliminating obsolete information.

Data modernization services

Data modernization is an efficient and quantifiable way to improve business operations. Several companies offer data modernization services, including Infosys, Alation, Cognizant, Suneratech and Cloud4C.

These companies specialize in extracting data from disparate sources, such as on-premises databases or cloud storage systems, and moving that data to a centralized database. They also make it possible to generate analytics reports to assist in decision-making, providing a unified view of your company’s data. This is an essential service for businesses looking to improve customer experience.

Making data modernization work for your business

With the right data modernization services and strategies, as well as a strong data governance plan, businesses can focus on what matters most to their customers instead of wasting time and resources combing through various data stores for relevant information. The result is increased customer satisfaction and retention rates, along with substantial savings in time, resources and money spent across the enterprise.

Read next: Top data governance tools (TechRepublic)

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