Winning Data Platform Migration strategies for smooth transition

Data is everywhere. As businesses continue to transform digitally, companies must turn big datasets into real-time business intelligence. Traditional data analytics often provide retrospective insights because they rely on batch-processed information stored in databases, primarily used for report generation. 

However, with projections indicating that over 75% of business analytics software will incorporate AI capabilities by 2027, coupled with ongoing advancements in analytics and technology, there's an urgent need for a modern data platform. This platform should be capable of handling diverse data types from multiple sources. In various formats, all in real-time, breaking down the data silos that have historically limited comprehensive analysis. 

From sourcing to structuring and big dataset analysis, data platforms promise enterprise-wide intelligence much faster while eliminating technical debt. The result is analytics-driven architecture, improved scalability, and better data efficiency. Modern data platforms represent a quantum leap from traditional data management systems, offering a sophisticated blend of cutting-edge technologies and methodologies. 

Our previous blog, “Is data platform modernization essential to insight-driven transformation?” under “Data Platform Modernization series,” explored how an enterprise data platform empowers data engineering teams to manage and activate data for specific domain use cases by seamlessly integrating tools from multiple vendors. Additionally, we learned modernizing legacy data platforms approach plays as a pivotal role in improving operational performance, customer experiences, API-led connectivity to future-proof your IT infrastructure, and responding effectively to the dynamic global environment.

The evolution of data platforms has been remarkable, and the core pillars of modern data architecture typically contemplate a set of fundamental and interrelated components that define its architecture. Interestingly, these platforms are designed to meet the complex data challenges of the digital age, where organizations must handle unprecedented volumes of diverse data, derive real-time insights, and maintain stringent security and compliance standards.

The backbone of modern data platforms: Key components

The key elements of modern data platforms work in concert to create a robust, flexible, and intelligent data ecosystem. From cloud-native architectures that provide scalability and cost-efficiency to advanced analytics and AI capabilities that unlock deep insights, these elements form the backbone of data-driven organizations.

Let's explore the critical components that define modern data platforms, including:

Cloud-native architecture:

Cloud-native architectures utilize extensive and elastic cloud services appropriate for the structure. They employ ‘containerization,’ ‘Microservices,’ and ‘Serverless’ systems, which permit the setting up of a swift and huge scale. This approach is cheaper through its pay-as-you-go options and is universally available. Cloud-native platforms enable the distribution of processing and storage, hence the optimization of the solutions.

Data integration:

Unlike earlier humble platforms, today’s ones are endowed with strong data integration from different sources. They support the concept of ingestion of real-time data, ETL/ELT operation, and API connections. These platforms capture structured, semi-structured, and unstructured data while offering a single-use across the business. Sophisticated connectors and data virtualization methods allow for getting the needed data while avoiding its repeated acquisition.

Data governance:

Holistic approaches to data management drive efficient data management, security, and adherence to industry standards. Some of them are data catalogs, data lineage, and metadata. The current platforms introduce features such as machine-assisted data categorization, rights-based data access, and data tracking. They also approve data privacy regulations through concepts such as data masking and encryption. Strong governance solutions allow organizations to sustain an acceptable level of trust in their information resources.

Advanced analytics:

Present data platforms have evolved and contain advanced analytic functions. It supports various forms of business intelligence, such as descriptive, diagnostic, predictive, and prescriptive analytics. They provide cost efficient processing re-quantization for the large scale statistical modeling and machine deep learning. They may also have libraries and tools for data analysis, visualization and reporting integrated to assist users glean value out of large data sets.

Artificial Intelligence integration:

AI and Big data solutions are inseparable concepts nowadays, and AI functions as an essential enabler in most current data platforms. These facilitate the creation, training, as well as deployment of machine learning models at a mass level. Most of the platforms provide functionalities for model selection and tuning of hyperparameters. They allow for the preprocessing of data to enhance the features of the data and detect anomalies using Artificial Intelligence. Other components, such as natural language processing and computer vision, are usually incorporated to deal with different types of data.

Real-time processing:

Present-day platforms are also good at handling real-time data and information as well as the analytics of the same. They employ stream processing engines to process flows of high-velocity data. These systems facilitate CEP and allow for system-driven analysis on a real-time data feed. Real-time makes decision-making reactive and is useful in situations where quick decisions have to be made such as in fraud detection or recommending products for a customer.

Proven strategies for successful data platform migration

Businesses are gradually realizing the importance of upgrading their data platforms to stay competitive and derive maximum value from their data assets. Data platform migration certainly is a path forward to modernize legacy systems to capitalize on new and advanced technologies and make the data processes more agile.

However, the journey to modernization can be challenging, and organizations must consider several effective strategies to navigate this major transition. These strategies, when implemented effectively, can help businesses overcome common hurdles, minimize risks, and maximize the benefits of their modernization efforts. Let’s delve deeper into proven data platform migration strategies:

Clear business objectives:

This involves a set of precise objectives, such as enhancing customer perception, increasing business flow, or providing IT solutions for decision-making purposes. For instance, a retail firm may set a target of harnessing real-time big data analytics for customized client suggestions for purchase to boost revenues and consumer satisfaction levels.

Phased approach:

Instead of transforming the whole system during the first phase, you might cover one or several data pipelines or departments at a time at most. For instance, you may commence by enhancing information systems for sales data, followed by the marketing line, and so on. This makes it possible to learn from experience and adapt as the process is being implemented.

Cloud adoption:

This could be the transition from on premise Data Centers to Cloud services from vendors such as AWS, Google Cloud or Microsoft Azure. Cloud services include data storage such as data lakes or data warehouses and analysis aids that are highly expandable or shrinkable. For instance, you would employ Amazon S3 for closely storing simple data, Snowflake for data combining, and Databricks for big data analytics.

Data governance:

This involves such areas as the policy on data quality, and data policy and security policy. It might cover data lineage by using a tool to track data, data access control based on the role of the users, and the process of data cleaning and data validation. The forms of data governance can be implemented with the help of tools – Collibra or Alation.

Cost analysis for Data platform migration

The journey of data platform migration involves upgrading or migrating an organization’s data infrastructure to more advanced and scalable systems. This process can be complex and involves various costs, which can be broken down into several key areas:

1. Initial assessment and planning

The step begins with assessing the existing data platform to understand its capabilities, limitations, and areas that need modernization. Additionally, developers also identify the goals and requirements of the new data platform, such as scalability, performance, security, and compliance. The company needs data engineers to create a modernization roadmap including architecture design, technology selection, and migration planning.

2. Technology and infrastructure costs

The cost primarily involves software licenses for new software, databases, data management tools, or analytics platforms that are part of the modernization effort. Also, expenses associated with cloud-based platforms and on-premises infrastructure may involve ongoing subscription costs, while on-premises might require significant upfront investment in hardware.

If maintaining an on-premises data platform, costs might include purchasing new servers, storage devices, and networking equipment. Tools and services are also required to migrate data from the old platform to the new one, ensuring minimal downtime and data integrity.

3. Implementation costs

The costs associated with developing, configuring, and customizing the new platform to meet specific business needs. The data platform modernization journey also involves integrating the new data platform with existing systems, applications, and workflows. This could include API development, middleware, and testing.

What’s more, the process of transferring data from legacy systems to the new platform may involve data cleaning, transformation, and validation. This can be resource-intensive and require specialized skills. During the migration journey, comprehensive testing, including load testing, security testing, and user acceptance testing, is also required to ensure the new platform functions correctly and meets performance requirements.

4. Operational Costs

Ongoing costs for maintaining and supporting the new platform, including software updates, bug fixes, and technical support, will be involved, along with implementing new security protocols, encryption, and compliance measures to protect data on the new platform.

Risk mitigation and contingency costs are other important factor driving the data platform modernization estimates. This is related to potential downtime during migration and the impact on business operations. Contingency planning, on the other hand, may involve setting up temporary systems or backups.

The total cost can vary widely based on the organization’s size, complexity, and specific needs. Still, this framework provides a starting point for understanding the cost components involved in data platform modernization.

Role of organizational culture in data platform modernization

Culture plays a critical role in determining how an organization embraces data modernization – the advancement in technologies and practices that enable greater use of data in decision-making processes

The need for change is paramount. Organizations implementing change management should embrace change and view data as a strategic asset. Such a culture fosters flexibility, innovation, and a move toward evidence-based practices, which are imperative for managing the challenges of modernizing data architectures. 

At the same time, adherence to a conservative culture heavily slows down the work because the employees are afraid to change something in the established workflow and do not believe that the change can only bring benefits. 
 
Another cultural factor that affects data transition is cross-functional cooperation. Stakeholders are encouraged to embrace change by increasing organizational communication and participation. However, isolated departments where each works independently create problems with the fragmentation of modernization and consequent inconsistency. 

Employing the services of experts and staff—Involving the employees in the process of modernization through training, offering, and encouraging them will help ease the implementation of the change and ensure that the employees are more committed to the change.

What’s more? Businesses with a culture that embraces innovation and uses advanced tools will likely develop and deploy innovative data technologies. On the other hand, a risk-averse organizational culture might hinder such strategies, as employees may be reluctant to embrace change and experimentation due to possible negative outcomes. The workflow within the organization is also very important, particularly trust and openness. 

A culture of trust in data ensures that employees accept new platforms and are willing to use them to their total capacity. The openness in handling data ensures that public accountability is maintained, thereby promoting a good culture towards data modernization.