Modernize Your Way of Data Management to be Competitive

In today‘s highly digital world, data has become one of the most valuable assets for businesses across all industries. Companies rely on data to gain actionable insights that can improve operations, identify new opportunities, understand customers better, and stay competitive. However, many organizations are still relying on legacy systems and outdated methods for managing their data. To fully capitalize on data in the modern business landscape, companies need to modernize their data architecture.

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The Growing Need for Data Modernization

Legacy data infrastructure like on-premise data warehouses and siloed department servers are no longer adequate for managing the volume, variety, and velocity of data most companies deal with today. As data sources and analytics needs evolve rapidly, old systems often struggle with:

Poor data quality: With data in disparate silos, there is lack of data governance leading to inaccuracies, inconsistencies, duplication, and gaps. According to IBM, poor data quality costs US businesses $3.1 trillion annually.
Limited scalability: As data volumes grow exponentially, old systems cannot easily scale up capacity. This leads to processing bottlenecks. It‘s estimated that every person online generates around 1.7MB of data per second.
Inadequate security: Outdated infrastructure lacks robust access controls, encryption, and security monitoring exposing data to cyber threats. Data breaches cost companies an average of $4.24 million each in 2021.
Lack of agility: Monolithic legacy systems make it hard to adopt new data sources, analytics tools, and delivery platforms in a timely manner. The need to constantly integrate new tools costs firms over $75 billion annually.
High costs: Maintaining complex legacy systems requires significant capital and IT resources with low ROI. Forbes reports that 92% of businesses say legacy systems hamper efficiency and growth.

Modernizing data architecture allows organizations to overcome these challenges and fully harness the potential of their data.

Data volume growth over years. Source: Forbes

Key Components of a Modern Data Architecture

A modern data stack typically includes:

Cloud storage: On-demand storage solutions like Amazon S3, Microsoft Azure Storage, and Snowflake enable practically unlimited capacity with high availability and reduced costs. Cloud now accounts for 92% of database revenues.
Data lakes: Central repositories that can store vast amounts of structured and unstructured data in native formats. This makes new data easy to ingest. Data lakes help cut costs by up to 50%.
Real-time analytics: Streaming analytics platforms like Apache Spark, Kafka, and Flink allow analysis of real-time data from IoT devices, web traffic, social media etc. Real-time analytics provides insights in milliseconds rather than hours.
Self-service access: Data democratization tools like data catalogs, governance policies, and BI dashboards empower users across departments to benefit from data. Forbes notes this can lead to business productivity gains of over $430 billion annually.
Security: Encryption, access controls, data lineage tracking, anonymization, and monitoring boost data security. Adopting zero trust model improves security posture by 60%.

By rebuilding aging data management systems using a modern tech stack, organizations gain increased agility, scalability, security, and ROI from their data.

Data Models Used in Modern Systems

There are a few common data modeling approaches used in modern platforms:

Relational model – Organizes data into related database tables with rows and columns. Well-suited for structured data.
Document model – Stores data in documents like JSON rather than rows/columns. Handles semi-structured data well.
Graph model – Represents data as nodes, edges, and properties in a graph network. Useful for interconnected data.
Columnar model – Stores data by column rather than row to optimize for analytics. Great for data warehouses.
Time-series model – Optimizes storage and retrieval of time-based data. Often used for IoT data.

Choosing the right model depends on the data types, relationships, and intended uses. Many modern systems combine multiple models to support different needs.

Steps for Successful Data Modernization

Migrating from legacy to modern data architecture involves careful planning and execution:

1. Assess existing infrastructure – Compile inventory of all data sources, infrastructure, analytics uses cases etc. Identify pain points and bottlenecks.

2. Define goals – Align to business objectives and plan capabilities needed like real-time analytics, self-service access etc.

3. Create roadmap – Outline phases, timelines, budgets involved based on assessment and goals. A typical roadmap spans 18-24 months.

4. Design new architecture – Select solutions for storage, processing, integration, security, and access layers.

5. Execute migration – Develop ETL processes to move data to new platforms in orderly fashion. This can take around 3-5 months.

6. Integrate systems – Connect new and old tools for unified analytics during transition period. APIs and ETL help integrate systems.

7. Test and validate – Perform QA thoroughly to ensure continuity in operations and analytics. Automated testing reduces risks.

8. Train users – Conduct workshops for users to adopt new platforms for self-service. Plan for 5-10 hours of training per user.

9. Optimize – Monitor performance and refine architecture as needed. Retire legacy systems. Optimization should be continuous.

Proper change management and communication strategies help smooth out the modernization process. Some temporary disruptions may occur so maintaining legacy systems in parallel until complete transition reduces risk.

Lift-and-Shift vs Refactoring Approaches

There are two primary methods for migrating data from legacy to modern systems:

Lift-and-shift: This involves moving data as-is from old systems to new platforms with minimal changes. Fast to implement but may just recreate legacy issues like poor quality or inefficient models in the new environment.

Refactoring: Restructures data for the optimal architecture. Involves cleansing, transforming, and optimizing data. Takes more effort but unlocks full benefits of modernization.

The right approach depends on timelines, cost constraints, and how much rework is needed to prepare legacy data for new use cases. A hybrid model is common, with temporary lift-and-shift while refactoring in phases.

Real-World Examples of Data Modernization Success

Here are some examples of the business benefits companies have realized by implementing a modern data architecture:

Walmart – Migrated to cloud data warehouse and lake. Achieved faster queries, reduced TCO by 20-30%, and rolled out advanced analytics.
Nissan – Consolidated siloed data into governed enterprise data lake. Enabled unified customer analytics to improve marketing.
Capital One – Built MCubed big data platform on Hadoop/Spark. Unlocked ability to use machine learning for fraud analysis and personalized offers.
Johnson & Johnson – Centralized HR data on a cloud data warehouse. Empowered self-service analytics and reduced reporting costs.
PepsiCo – Combined data from acquired companies into governed data hub. Gained unified insights into sales, inventory, and shipments.

Costs and Risk Mitigation Strategies

While modernization delivers huge upside, the process also involves notable costs and risks including:

Transition costs like cloud fees, professional services, and staff training.
Revenue loss from temporary disruption during cutover.
Cybersecurity and data privacy threats.
Integration challenges with existing systems.
Adoption issues as users get accustomed to new tools.

Some ways to mitigate these include:

Prioritizing use cases with fastest ROI.
Allocating sufficient budget and resources.
Incremental transition vs big bang rollout.
Extensive testing and rollback provisions.
Strong access controls and encryption.
User training and change management.

Key Data Management Trends

Looking ahead, some emerging technologies will further shape data architecture:

Artificial Intelligence – Machine learning and deep learning will be embedded in data pipelines. 61% of organizations already use AI-driven analytics.
Internet of Things(IoT) – New techniques will be needed to handle massive streams of IoT data. Gartner forecasts 25 billion IoT devices by 2025.
Blockchain – Shared ledgers can make data exchanges between organizations more secure and transparent. Blockchain for data is projected to reach $6B globally by 2024.
Augmented analytics – Automated insights powered by AI will reduce dependency on data scientists. This market is growing at 28% CAGR.
Cloud-native platforms – Containers and serverless computing enable highly scalable data processing. Over 90% of businesses are migrating analytics to the cloud.

Companies that continuously evaluate new technology and proactively evolve their data architecture will stay ahead of the curve.

Conclusion

Given data‘s importance for driving competitive advantage today, organizations simply cannot afford the limitations of legacy systems. A structured approach for modernizing data infrastructure unlocks capabilities that were previously out of reach. With the right people, processes, and technology in place, companies can make their data work smarter and harder – turning it into a true strategic asset. The time for data modernization is now.