What is Data Modernization? A detailed Guide

Data is the most valuable asset for an organization. It is also the most expensive and obstinate to manage, with average companies accumulating approximately 25 years’ worth of data by the point at which their annual revenue reaches $1 billion. To remain competitive in the rapidly evolving business landscape, companies need to have prompt access to the relevant data whenever needed. However, this can prove to be challenging when the data is stored across various sources. This is where Data Modernization comes into play! Keep reading to discover how Data Modernization can assist you in streamlining your data, resulting in savings of time, money, and resources. 

Data Modernization in Depth  

Data modernization refers to the process of transforming traditional, legacy data systems and processes into more flexible, efficient, and scalable ones, using modern data technologies and strategies. This involves updating existing data systems and processes to keep pace with the ever-evolving data landscape, as well as enabling organizations to gain more value from their data. 

Once the data is modernized and transformed into a newer platform, consumers can explore it using attributes like advanced analytic functions, interactive visualization tools, mobile access, and machine learning capabilities. However, data modernization is critical to industries that are required to store unstructured data such as social media comments, images, audio notes, and even clinical notes.  

What are the Main Components of Data Modernization?  

1. Data Integration: This involves procuring data from various sources and formats to create a unified view of data across the organization. Data integration enables organizations to make better decisions and gain insights from their data by breaking down data silos and enabling cross-functional collaboration. 

2. Data Warehousing: A process of storing data in a central repository that can be accessed by different stakeholders across the organization. Data warehousing enables organizations to perform complex data analysis and reporting, as well as provide a single source of truth for their data. 

3. Data Analytics: This involves using advanced analytical techniques to extract insights from data. Data analytics includes techniques such as data mining, predictive modeling, and machine learning, which can help organizations to uncover patterns, identify trends, and make data-driven decisions. 

4. Cloud Computing: A cloud-based platform to store and process data. Cloud computing offers numerous benefits, including scalability, cost-effectiveness, and ease of deployment. 

5. Data Governance: This refers to the policies, procedures, and processes that ensure the effective and efficient use of data. Data governance involves establishing clear rules across data access, usage, storage, and sharing, as well as ensuring that data quality is maintained across the organization. 

Data Modernization Strategies for your Business 

There are several data modernization techniques such as data governance or open data management. The need of your organization or company defines the choice of strategy. Here are some strategies you can follow to optimize your data modernization efforts: 

• Integrate data from several sources into a single platform: An integrated database is an effective method to ensure data is up-to-date, accessible, and easier to find. By consolidating data from various sources into a single database, companies can uncover fresh insights and analyze all the diverse data sets they have accumulated over the years. 
• Mapping data assets: A detailed description of the types of data being stored by disparate sources, data format, age, and where data is situated around the network helps to understand the best strategy required for data modernization.  
• Investing in artificial intelligence and machine learning: AI and ML provide several advantages for data modernization. Workflow automation, predictive analytics, and automated extraction of unstructured data are a few significant functions of AI and ML. You need to modernize your machine learning process to get these benefits.  
• Data cleansing: It helps to remove invalid, inaccurate, redundant, and erroneous information from existing data such that only relevant and accurate data remains. It is a significant step before big data migration.   

Understanding the Significance of Data Stewards for Master Data Management  

Data stewardship refers to the management and responsible oversight of an organization’s data assets throughout its lifecycle, from creation to deletion. The goal of data stewardship towards master data management is to ensure the quality, security, accessibility, and appropriate use of data, as well as compliance with legal and regulatory requirements. Data stewardship involves a range of activities such as data collection, storage and management, analysis, sharing, retention, and deletion. Some of the main reasons why companies should invest in Data Stewardship are: 

• Enhancing data quality through streamlining procedures, aligning consumers to develop confidence and trust in data  
• Offers effective and efficient data analytics that improves quality and accessibility  
• Link consumers from around the globe to function altogether in understanding data, forming context, across data, raising questions, and enhancing roles and data owners 
• Enabling mapping of storage locations and data sources  

• Reduced costs in other data management sources 
• Effective risk management is linked with error, privacy, and legal problems in data management 

The Path to Data and Analytics Modernization 

Data and analytics modernization need modern data management principles. It allows your company clients to form smarter solutions through developed analytical functions. Cloud-derived, modern, and scalable planning and next-generation analytics features are required to migrate from legacy platforms and planning.  

Although this can be a significant investment, data, and analytic modernization help to enhance efficiency, performance, and decision-making.  

The Four Pillars of Data and Analytics Modernization 

1. Data Strategy: A well-defined data strategy is critical for organizations to achieve their business goals and objectives by leveraging the capabilities of data and analytics. It is important to identify the key data assets and the process of data collection and storage to meet the objectives and goals of a business. The best way is to develop a roadmap for the implementation of data governance policies and procedures, data architecture, and data science and analytics capabilities. 

2. Data Architecture: This pillar involves designing and implementing a data architecture that supports the collection, storage, processing, and analysis of data. It includes choosing appropriate data storage technologies, defining data structures and models, and creating data pipelines to move data from various sources to the analytical systems. A modern data architecture includes:  

• Less regulated and less latent paths to data: It enables data to move in less governed routes like a data lake or persistent staging layer instead of the data warehouse.  
• Modernized data warehouse and data lake: Modernized data warehouse helps in streaming data and to conduct real-time analytics which is the main element of modern data architecture. Under this, raw data often goes through a data lake wherein you can store ample volumes without defined use and being governed. 

3. Data Management: It refers to the process of gathering, keeping, and utilizing data cost-effectively, efficiently, and securely. The goal of data management is to assist individuals and companies; to optimize the application of data under rules and guidelines to form smarter decisions and take actions that increase the overall organizational benefit. Some of the common principles to consider are: 

• Risk management 
• One place for all your data 
• Stability, security, and scalability 
• Agility 

4. Analytics tools: This pillar involves developing analytical capabilities that enable organizations to extract insights from data. Techniques of AI and ML help to build analytical models and algorithms which help to create reports or dashboards to visualize and provide insights. Effective data science and analytics enable organizations to make data-driven decisions and gain a competitive advantage. 

Benefits of Data Modernization for Your Business Environment

Some of the common benefits of data modernization are discussed below:

1. Enhances efficiency in data processing: Data modernization helps companies to use their data efficiently. It can happen through data visualization, warehousing, and mining. 

2. Enhances accessibility: Data modernization helps to convert data to a more user-friendly format. It helps consumers to access and change data easily. Another technique is to enhance data management processes such that data is easy to find and organized.  

3. Increases business growth: It helps companies to enhance their operational efficiency by conducting automation of tasks. It helps businesses to comprehend their users and markets effectively. Also, provides brilliant services and enhances customer satisfaction.  

4. Allows the use of reliable databases: Data modernization allows the utilization of a data warehouse and reliable database which makes it easier to store data in a format that is easily accessible and can be analyzed quickly. Enhancing the quality of data can decrease the rate of errors. Hence, improving the reliability of databases. 

Roadmap to Start Your Analytics Modernization   

Remote working and user interaction have become the norm leading to higher data production volumes. By modernizing companies’ legacy infrastructures, they have come to the realization that data stored in separate and isolated systems can be utilized and applied to accomplish their objectives. To make data modernization your winning strategy, consider following these steps: 

1. Get clarity from CXOs: The roadmap to data modernization begins with senior leaders as they are the decision-makers. The first step is to engage with individuals who deal with large amounts of data. 

2. Calculate your success rate: Find out about several touchpoints of performing analytics modernization like increased scalability, decreased downtime, enhanced security, data democratization, and others. Engaging with senior authorities and finalizing the multiple modernization use cases related to their company’s objective is significant.  

3. Foster a culture of data accessibility and data literacy: Data literacy is a common problem that companies face when implementing analytical frameworks and tools. Organizations must prioritize the need for a culture wherein all the people at different levels have access to data sets that enhance the quality of their decisions and deliverables. To unleash new business opportunities and insights, the cross-functional department should understand how data is shared and how it can be explored in several ways.  

4. Understand the level of data maturity you wish to gain: When any company invests in technology, it has objectives. Understand your company goals, and the data maturity level they want to achieve. This will help you to decide your long-term goals and how you can link them with the analytics modernization framework.  

Why is Data Modernization Important for Digital Transformation?  

Covid-19 has led to the emergence of digital transformation in several areas. Handling data day in and day out offers little value to a company. Although, data modernization is doing wonders for any organization. This section will help you understand why data modernization is critical for digital transformation.  

• Accomplishing data-focused business opportunities: In today’s world, data has become a high asset, much like oil. Any organization must manage data effectively and extract insights from it, to provide practical solutions and enhance end-user experiences. The IT industry is currently undergoing a natural shift that not only sustains businesses but also drives them towards adopting cutting-edge IT solutions. Upgrading solutions without disrupting the entire system is now possible, enabling businesses to thrive in a highly competitive market. Therefore, to succeed in this environment, it is essential to modernize assets and leverage data-driven business opportunities. 

• Modernizing data silos: Data modernization predicts and reduces those data silos which were available in various databases. In today’s time, the cloud plays a significant role for companies who wish to get rid of old-fashioned software, solutions, and technologies.  
  
• Digital transformation with cloud computing: Cloud computing has completely reformed the picture for digital transformation through sustainability. Cloud migration supports the beginning of remodeling any business strategy to experience the cloud computing benefits. It supports the transfer of your assets to the digital world and assists in decreasing the total costs of assets. It also offers improved customer experience, flexibility in including other data security, data privacy, software, and effective business decision-making capability through data analytical tools.  

Conclusion  

Overall, data modernization is a critical process for organizations looking to stay competitive in today’s data-driven economy. By leveraging modern data technologies and strategies, organizations can unlock the full potential of their data and make smarter decisions. 

WRITTEN BY

Anjali Goyal

Anjali Goyal is a content writer at TechEela. She helps businesses increase their online presence with optimized and engaging content. Her service includes blog writing, technical writing, and digital marketing.

Follow

0