Enterprise Data Modernization: How To

Modernizing your applications on the cloud can offer significant efficiency advantages, saving you costs while improving security and end user experience. Read about the different paths to data modernization and how they can help you meet your goals.

As enterprise businesses grapple with mounting technical debt, vendor lock-in and siloed data, it’s clear that data modernization efforts must begin in order to keep pace with disruptive cloud native startups. This is particularly apparent in traditional industries like financial services, healthcare, life sciences and retail. 

These businesses often grow through acquisition and in many cases, such companies struggle to have visibility into all of the applications and databases in the environment. The prospect of how to begin modernization efforts can be overwhelming, even when there is buy-in at all levels of the organization. 

While the day-to-day frustrations may be top of mind as business leaders try to mine databases for reports and insights, what often goes unrecognized is the fact that decades of historical data is sitting there waiting to propel them past the competition. 

What is data modernization?

Data modernization involves updating and transforming an organization's data infrastructure, systems, and practices to leverage modern technologies and methodologies. It involves migrating legacy data systems to more flexible, scalable, and efficient platforms, often cloud-based. The purpose of modernizing data is to enhance an organization's ability to store, process, analyze, and derive insights from its data assets more effectively.

Enterprises typically modernize their data by adopting cloud-native services, implementing big data technologies, leveraging artificial intelligence and machine learning capabilities, and employing advanced analytics tools. This process often includes transitioning from on-premises systems to cloud-based solutions, adopting data lake architectures, implementing real-time data processing, and embracing DataOps practices for continuous improvement and delivery of data products.

Why is data modernization important now?

Data modernization has become crucial in today's rapidly evolving business landscape. Data-driven enterprises typically excel more than their competitors because they have a clear view into past and current trends in their data, enabling them to make informed decisions quickly and adapt to market changes. As the volume, variety, and velocity of data continue to increase, traditional data systems struggle to keep up, making modernization essential for businesses to remain competitive and agile.

Efficient data processing

Modern data architectures enable faster and more efficient data processing. Cloud-based solutions and distributed computing technologies allow organizations to handle large volumes of data in real-time or near real-time. This improved processing capability enables businesses to respond quickly to market changes, customer needs, and operational challenges.

Reduced costs

Data modernization often leads to significant cost savings. Cloud-based solutions eliminate the need for expensive on-premises hardware and reduce maintenance costs. Pay-as-you-go models allow businesses to scale resources up or down based on actual needs, optimizing spending. Additionally, automation of data processes reduces manual labor costs and minimizes errors.

Improved data quality

Modern data systems incorporate advanced data quality management tools and processes. These include automated data cleansing, standardization, and validation techniques that ensure data accuracy and consistency across the organization. Improved data quality leads to more reliable insights and decision-making.

Improved decision making

Data modernization enables more sophisticated analytics and reporting capabilities. Advanced visualization tools, predictive analytics, and machine learning models can uncover deeper insights from data, supporting more informed and timely decision-making. This can lead to improved strategic planning, better resource allocation, and increased competitive advantage.

How to modernize enterprise data

As businesses pivot to become a data-driven organization, there are generally four main ways to approach modernization. 

Data management

This is a traditional approach to data that is applicable for the cloud. Most enterprises have a large collection of relational database systems and likely at least one data warehouse. Large ETL jobs are used to extract data from operational data stores, denormalize the data into dimensions and store it in a data warehouse. Reports are then generated out of the data warehouse on a periodic basis and used by executives to make business decisions. 

In an on-premise environment, maintaining these systems requires a large IT team. This team needs to constantly update and patch operating systems, database systems and all of the associated hardware and network infrastructure. 

With today’s modern cloud environments, equivalent systems can be easily built without the heavy burden of maintenance. Using PaaS tools such as Amazon RDS, Amazon Glue and Amazon Redshift, configuring an equivalent, low-maintenance cloud environment is achievable with significantly lower IT and maintenance costs. 

Big data

Enterprises already have vast amounts of unstructured data just waiting to provide key insights into their business. These can come in the form of emails, social media posts, documents, images and spreadsheets. Because all of this information doesn’t present itself in a traditional relational structure, it requires a different form of storage and processing. 

A data lake, built on top of Amazon S3, is a perfect place to store and organize this type of information. Once a data lake is built, you can use data ingestion services, such as Amazon Managed Streaming for Kafka or Amazon Kinesis, to bring files in and out of the data lake at incredible volume and speeds. 

Having all of this data available, Amazon EMR (Elastic MapReduce) allows for efficient processing of this vast amount of data directly into an insight engine. It’s also possible to use Amazon Athena inside of your data lake using traditional techniques such as SQL queries.

Artificial intelligence/machine Learning

Machine Learning (ML) is fundamentally a way of recognizing formerly unknown patterns in data or of quickly identifying known patterns in data that have not been seen before. The mathematics behind ML have been around since the turn of the century, but only recently have we had the computational power and storage capacity to implement these models in the real world. Although AI has been quite the buzzword in the past few years, general artificial intelligence has not yet been demonstrated. 

Enterprises tend to believe that harnessing the power of ML requires a large team of data scientists to build custom learning models. However, recent advances in tools like Amazon SageMaker AI have significantly lowered the barrier to entry for typical business use cases.

You can easily turn data that is structured in a spreadsheet or relational database table into a predictive model using Amazon Sagemaker Autopilot. In addition, while there are still complex use cases that require a team of data scientists, many common applications of this technology (such as text recognition, image recognition, voice generation, etc.) have been modeled by Amazon and packaged behind easy to consume web APIs such as Amazon Rekognition, Amazon Textract and Amazon Polly. There are many paths to achieving business value from machine learning without requiring a team of data scientists. 

Data visualization

Once organizations have used one of the preceding methods to achieve actionable insights, tools like Amazon Quicksight allow you to visualize your data in a more consumable format than tables and reports. These tools also give you the ability to slice and dice your data output to derive even deeper insights into the business and drive strategic decision making.  

Our Data Modernization on AWS eBook outlines tips to achieve these paths, how to overcome common challenges and the competitive advantages of data-driven insights.

Develop a data modernization strategy

A data modernization strategy is a comprehensive plan that outlines how an organization will transform its data infrastructure, processes, and culture to meet current and future business needs. The purpose of this strategy is to ensure that the modernization efforts align with business objectives, are executed efficiently, and deliver tangible value. A well-developed strategy helps organizations prioritize initiatives, allocate resources effectively, and manage risks associated with the transformation. Let's explore the steps and items to include in your data modernization strategy:

Business goals

The first step in developing a data modernization strategy is to clearly define the business goals that the modernization efforts will support. These goals should be specific, measurable, and aligned with the overall business strategy. For example, a retail company might set goals such as improving customer personalization, optimizing supply chain efficiency, or increasing online sales conversion rates. By starting with clear business objectives, organizations can ensure that their modernization efforts deliver tangible value and gain buy-in from stakeholders across the organization.

Assess and plan

This phase involves conducting a comprehensive assessment of the current data landscape, including existing systems, data sources, data quality, and skill sets within the organization. Based on this assessment, develop a detailed plan that outlines the steps needed to achieve the desired future state. This plan should include a prioritized list of initiatives, resource requirements, timelines, and key milestones. For instance, an assessment might reveal that a company's customer data is scattered across multiple siloed systems, leading to a plan that prioritizes data integration and the implementation of a centralized customer data platform.

Data integrations

Identify and plan for the integration of various data sources within the organization. This may involve implementing ETL (Extract, Transform, Load) processes, adopting API-based integrations, or setting up data streaming pipelines. The goal is to create a cohesive data ecosystem that allows for seamless data flow across the organization. For example, a manufacturing company might plan to integrate data from IoT devices on the factory floor with their ERP system and customer relationship management (CRM) platform to gain a holistic view of their operations and customer interactions.

Data migrations

Outline the approach for migrating data from legacy systems to modern, often cloud-based platforms. This should include strategies for data cleansing, validation, and reconciliation to ensure data integrity during the migration process. Consider phased migrations to minimize disruption to business operations. For instance, a financial services company might plan a phased migration of its transaction data from an on-premises data warehouse to a cloud-based solution, starting with historical data before moving to real-time transaction processing.

Data governance and security

Develop a robust data governance framework that defines policies, procedures, and standards for data management across the organization. This should include data quality standards, metadata management, and data lineage tracking. Additionally, outline security measures to protect sensitive data, ensure compliance with relevant regulations (such as GDPR or CCPA), and implement access controls. For example, a healthcare provider might implement role-based access controls, encryption for sensitive patient data, and audit trails to ensure HIPAA compliance in their modernized data environment.

Training

Create a comprehensive training plan to upskill employees on new technologies, tools, and processes introduced as part of the modernization efforts. This might include training on cloud platforms, data analytics tools, or new data governance practices. Consider a mix of formal training sessions, hands-on workshops, and ongoing support to ensure successful adoption. For instance, a company implementing a new data visualization tool might offer a series of training sessions, followed by regular "office hours" where employees can get help with specific questions or projects.

Change management

Develop a change management strategy to address the cultural and organizational impacts of data modernization. This should include plans for communication, stakeholder engagement, and addressing resistance to change. Consider appointing "data champions" within different departments to help drive adoption and showcase the benefits of the new data capabilities. For example, a retail company undergoing data modernization might create a cross-functional team of data champions who meet regularly to share success stories, address challenges, and promote data-driven decision-making across the organization.

Continuous improvement

Establish processes for ongoing evaluation and improvement of the modernized data environment. This might include regular audits of data quality, performance monitoring of data pipelines, and feedback loops to capture user experiences and suggestions. Set up key performance indicators (KPIs) to measure the success of the modernization efforts and inform future improvements. For instance, a company might track metrics such as data processing times, query response times, and user adoption rates of new analytics tools, using this information to guide ongoing optimizations and investments in their data infrastructure.

The Caylent approach to data modernization

The idea that "data is the new oil" doesn't go far enough. With a modern data ecosystem, enterprises can reuse, repurpose, combine, enrich, and otherwise mine their data to create sustainable competitive advantages. Now is a great time to start taking advantage of untapped or underutilized potential in legacy data assets.

Whether you're just beginning to explore cloud-native data services or have already migrated to AWS and need help optimizing your data infrastructure and pipelines, our experts work closely with your team to create a tailored roadmap that aligns with your business goals and unique requirements.

Our data modernization and analytics capabilities on AWS include:

• Data Modernization Strategy: Collaborate with customers to develop a roadmap encompassing data infrastructure, pipelines, productization, monetization, and management while balancing cost and value
• Database Migration & Optimization: Architect and migrate legacy databases to purpose-built, fully managed services like Amazon Aurora, Amazon DynamoDB, or Amazon Relational Database Service (Amazon RDS)
• Data Lakes & Big Data Pipelines: Build robust, scalable data solutions optimized for performance, security, and cost-efficiency using AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Kinesis, AWS Glue, and Amazon EMR
• Operational Analytics & Business Intelligence: Enable self-service, real-time analytics, and data-driven decision making through solutions built on AWS like Amazon QuickSight, Amazon Redshift, Amazon EMR, and Amazon Athena
• DataOps: Enable continuous delivery of data products to business units through DataOps, making customers more agile and data-driven
• Data Governance: Ensure customers’ data is accurate, consistent, secure, and compliant by implementing data quality standards, cataloging, and security best practices on Caylent’s Governed Data Platform™

Next Action

Are you interested in learning how we can help you with your data goals? Get in touch with our team!