Good data plays a crucial role in ensuring success in the modern business landscape. However, leaving data in complex and outdated systems makes it difficult for your team to generate useful analyses, insights, or strategies.

Staying ahead of the curve means implementing data modernization measures that keep your data organized, accessible, secure, and effective. Below, we take a closer look at data modernization and what it can do for your business.

What is Data Modernization?

Data modernization is a broad term referring to the many processes involved in upgrading an organization’s data systems, infrastructure, practices, and tools to accommodate modern business requirements. It aims to improve data accessibility, security, and effectiveness to provide greater business value.

One of the most crucial parts of data modernization is migrating data from data silos and legacy systems to modern computing platforms like cloud technologies. However, the term also encompasses other activities that make data easier to manage, process, and act on.

Here is a short list of common data modernization components. We’ll discuss them in further detail in the sections below.

• Data integration: Combining data from varying sources into a unified format.
• Data cleansing: Eliminating dirty data, such as duplicates, inaccuracies, and inconsistencies.
• Data warehousing: Creating a centralized repository of integrated data.
• Advanced analytics and business intelligence (BI): Using tools to generate actionable analysis and insights from collected data.
• Cloud computing and big data technologies: Leveraging cloud computing and big data technology to process, store, and analyze data for increased scalability.
• Data governance and security: Establishing data governance practices, policies, and standards for data privacy, security, and regulatory compliance.
• Data visualization: Using charts, graphs, maps, and other visual representations to allow an intuitive understanding of collected data.

Components of Data Modernization

Data modernization encompasses multiple activities, each streamlining data collection, data storage, data processing, or data analysis. Some components are techniques organizations can implement, while others are technologies and tools organizations can adopt.

Data integration

Due to developments in the digital landscape, the vast majority of businesses today have found themselves accumulating data from a wide variety of sources. This includes databases, cloud services, spreadsheets, apps, and APIs. You might draw useful conclusions by interconnecting results, but differences in format, location, and quality hinder you from placing the data under a uniform view.

The purpose of data integration is to consolidate data from multiple sources into a unified and usable format. By creating a consistent structure, data integration eliminates data silos and allows data to be stored in a single repository, such as a data warehouse, data lake, or data lakehouse.

Data cleansing

Another hindrance to effective data analysis is the accumulation of poor-quality data, such as duplicates, inaccuracies, inconsistencies, and other erroneous information. Poor-quality data, or dirty data, paints an incorrect big picture, resulting in inaccurate analysis and mistakes in strategic decision-making.

To ensure that data is relevant and usable, organizations need to perform data cleansing. The process inspects data for lingering issues, then eliminates all erroneous entries.

Data warehousing

If you collect data from different sources, you end up with data in different stores. The divisions make data difficult to collate and use for business intelligence purposes. With data warehousing, you create a centralized repository for all relevant business data, accommodating large volumes and supporting varying formats.

Data warehousing typically stored data in on-premises data centers. Evolutions in technology now let organizations store massive amounts of data in cloud-based platforms, reducing hardware costs.

When used in conjunction with data integration, data warehousing makes large amounts of data readily available for analysis, business intelligence, machine learning, and other business activities.

Advanced analytics and business intelligence (BI)

Data modernization isn’t just about streamlining data storage but also about improving data processing. One of the main components of data modernization is leveraging tools and techniques to generate actionable business insights.

Common data modernization tools include:

• Data Mining: Also known as knowledge discovery, data mining uses statistical analysis to scan large batches of data for recurring patterns and other information. Custom software will pull data from various sources, load them into a data warehouse, site, or cloud server, and then organize the data. Restaurants, for example, scrape point-of-sales (POS) systems for what products customers buy and at what time so they can prepare ingredients beforehand.
• Predictive Analytics: A technique that uses statistics and modeling to examine historical patterns for the purpose of forecasting future outcomes. For example, credit scores use customers’ spending patterns to predict their likelihood of repaying debt on time.
• Machine Learning: This subset of artificial intelligence uses algorithms to analyze large datasets. It trains to act based on information learned from data patterns and relationships. Product recommendations are a common example of machine learning at play; algorithms group customers of similar demographics and buying habits and suggest products based on other buyers’ purchases.

Using advanced analytics and BI techniques helps you maximize data-driven insights. It streamlines the processes of analyses and interpretation to create meaningful conclusions, sparing you from the load of analyzing data manually.

Cloud computing and big data technologies

You can streamline data storage and processing by leveraging cloud and big data technologies. These tools increase scalability and improve your ability to manage large batches of data.

Data is traditionally stored in on-premises data centers, which restrict operations to a single physical location and cost resources to maintain or upgrade. Cloud migration transfers data to cloud-based platforms, enabling increased cost-savings, scalability, and accessibility.

Cloud-based platforms allow data to be accessed from anywhere with internet access. This enables real-time updates to shared data, empowering organizations to work from anywhere.

Meanwhile, big data technologies like SQL, Apache Spark, and Hadoop enable streamlined data collection and processing for large data workloads. These technologies use programming models across networks of computers to transform petabytes of data into usable information.

Data governance and security

Security measures and compliance with industry data protection regulations are necessary for keeping sensitive organizational information safe. That’s why good data governance plays a crucial role in data modernization. Effective data governance requires organizations to set clear definitions for data ownership, data access controls, and usage monitoring.

Data visualization

You can streamline data interpretation further by providing visual representations of datasets. Data visualization uses graphic representations, such as dashboards, charts, graphs, maps, and infographics to make data easy to interpret at a glance.

Without data visualization, your team will be stuck studying entries on logs or tables without seeing the big picture. Data visualization helps communicate data trends efficiently, especially to stakeholders and team members unfamiliar with

Some data visualization tools can be synced to datasets to create graphic reports in real-time, sparing you the effort of updating charts manually.

Benefits of Data Modernization

Data modernization empowers your organization to keep up with the demands of the modern business landscape.

Operational Efficiency

Data modernization practices, such as eliminating data silos, creating centralized data storage locations, and streamlining data processing through technology, aim to reduce the manual load of data management. Making these processes easier will speed up workflows and increase day-to-day productivity.

Decision-making

One of the main aims of data modernization is to make your data easier to retrieve and read. Data integration and data warehousing make data more accessible, while data visualization makes data more readable. Quick access to clean and actionable data empowers you to make smarter business decisions faster.

Customer Experience

With less time wasted on manual data management, you can focus on building your customer relationships. Plus, processes like data integration and data warehousing centralize disparate data, which allows you to string together a more cohesive picture of customer profiles, needs, and preferences.

Using modern technology to streamline data processing also enables you to make decisions that maximize your impact on your customers. Machine learning, for example, can study groups of customers to provide product recommendations that are relevant to their interests.

Compliance and Security

Effective data governance ensures that you comply with industry regulations for data protection. It implements policies, measures, and standards that keep your data secure. These practices help you avoid the legal consequences of non-compliance and the cost of losing sensitive data to malicious parties.

Competitiveness

The above benefits significantly increase your market competitiveness. By streamlining operations, you can reduce downtime and focus on value-adding activities, helping you stay ahead of the curve. Meanwhile, increased clarity of information helps you formulate strategies that give you an edge over competitors.

Finally, improving customer experience increases their satisfaction and loyalty. Maintaining a good relationship with your customers improves your reputation, potentially attracting more customers in the future.

Modernize Data Analytics with Bronson.AI

Bronson.AI’s modern data analytics services help you implement data-driven strategies, adopt advanced analytics techniques, and leverage data visualization. We provide and improve your data visualization dashboards to support data-driven decision-making.

Additionally, we implement advanced analytics tools for faster data processing. For more information, read the Bronson.AI page on modern data analytics services.

Frequently Asked Questions

What is the difference between data modernization and data optimization?

The main goal of data modernization is to upgrade the systems, infrastructures, and technologies used to support business data. Meanwhile, data optimization aims to improve the useability and quality of the data itself. While they are different practices, they sometimes overlap. Data cleansing is a practice present in both data modernization and data optimization.

What is the difference between data migration and data modernization?

Data migration refers to transferring data from one type of computing system to another. Data modernization is a broader term that refers to all activities involved in upgrading data systems for increased accessibility, security, and business value. Data migration is typically part of data modernization.

What are the benefits of data modernization?

Key benefits of data modernization include enhancements in operational efficiency, strategic business decision-making, customer satisfaction, compliance and security, and competitiveness.