8 Commandments for Successful Data Migration & Modernization – Part 2/2

Data migration and modernization projects are transforming companies worldwide from legacy on-premise database systems to modern, cloud-friendly databases. This shift enables companies to reap greater value from their data while making it easy to manage steadily increasing rates of unstructured data. However, such a transformation is highly complex and if anything goes wrong this could cost your organization significantly while causing lengthy delays. Therefore, a carefully planned approach is necessary for every stage to reach the golden state of successful data migration and modernization.

In part 1 of this blog series, we examined 4 commandments for this journey. Here are the remaining commandments for your data migration and modernization project.

1. Govern & Optimize

A lack of data governance often leads to organizations not deriving any clear benefits from their data, although they have invested in improving their organization’s data and management. A data governance framework entails the process of constructing a model in order to manage enterprise data. A well-defined framework for data governance enables an organization to define its own set of rules and guidelines for data management. This drives informed decision-making about how data assets are managed. It also assures efficient utilization of data that is trustworthy and well-governed across enterprise workflows. In addition, adopting a standard data governance framework and adapting it as required drastically reduces data management costs pertaining to data processing, storage and various operational costs.

Some of the other global frameworks for governance include:

• CMMI(Capability Maturity Assessment Model)
• ARMA(Information Governance Maturity Model)
• EDM Council- DCAM (Data Capability Assessment Model)
• DAMADMBOK (Data Management Body of Knowledge)
• Stanford Maturity Model

Such frameworks enable improvement by:

• Monitoring
• Tracking
• Measuring
• Iterating

Enterprises should evaluate the frameworks available and rank the ideal frameworks for their requirements based on criteria regarding data access, data privacy, scope, metadata management and so on. After picking a framework, the next step is to jumpstart execution of the framework components. Setting interim checkpoints during this execution is a good idea to ensure successful data governance.

2. Migrate Data & Processes

The migration of data and processes focuses on:

• Lift & shift of data
• Refactoring
• Batches/Real-time

At this stage, the actual transfer of data and processes from the legacy system to the modern cloud-friendly database takes place. Leveraging innovative technology can surmount the typical challenges this complex process involves. The DS Migrate toolkit harnesses the power of advanced automation to migrate schema, data and processes from legacy databases to modern, cloud-friendly databases. It has the unique advantage of automated schema re-design which expedites the process.

3. DataOps

The key to the DataOps stage is to ensure the business gains value and insights from the data it has collected in a timely manner. Data types and volumes keep growing and types of data users continue to evolve – these factors require DataOps for data management and delivery to avoid critical delays. Effective DataOps facilitates greater agility and speed for the entire pipeline of data. It goes beyond mere technology – it is an ongoing, transformational methodology entailing the synergy of data, processes, technology and people to deliver trustworthy, high-quality data to end users quickly. Leveraging a toolkit or platform that harnesses the power of automation accelerates the speed of outcomes and provides the ability to scale. It also helps overcome any inefficient processes related to data preparation, availability, integration and so on.

A data culture forms the foundation for effective DevOps that provides reliable support for teams with a high level of productivity thanks to automation that improves the quality and speed of data that is available to users.

The DS Integration toolkit is self-serviceable, business user friendly, metadata based, providing AI/ML driven data aggregation. It includes Data Curation for unstructured data and also consolidates and integrates data for domain specific data applications (PM, Supply Chain, Data Aggregators).

4. Consume & Analyze

The final stage is making data available for consumption by your business users. This stage involves providing self-serviceable models for data consumption. You can set up various models for the consumption of data. User could consume data in the manner of a “Data Buffet” that is self-serviceable using an AI bot and conversational natural language. This is similar to providing an Amazon store of data – all a user has to do is search and find what suits their requirement. Another possibility is to create a Data API studio where the relevant data is requested by API. Users can then use this data for their analytics and reap actionable insights to drive greater business value. The advantage of these modes of consumption is that any user can access data with no dependency on data engineers. It also provides visualization and dashboards.

This ensures that all business users have convenient and easy access to the data they need, quickly. Harnessing the right tools and technology is crucial to providing easily consumable data for business users. For instance, the DS Democratize toolkit is intuitive, no code, self-serviceable, conversational AI driven “Data as a Service” and is intended for various data and analytics consumption.

Why DataSwitch?

DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a Modern Database Platform. Our no code and low code solutions along with enhanced automation, cloud data expertise and unique, automated schema generation accelerates time to market.

DataSwitch’s DS Migrate provides Intuitive, Predictive and Self-Serviceable Schema redesign from traditional model to Modern Model with built-in best practices, as well as fully automated data migration & transformation based on redesigned schema and no-touch code conversion from legacy data scripts to a modern equivalent. DataSwitch’s DS Integrate provides self-serviceable, business-user-friendly, metadata based services, providing AI/ML driven data aggregation and integration of Poly Structure data including unstructured data. It consolidates and integrates data for domain specific data applications (PIM, Supply Chain Data Aggregation, etc.). DataSwitch’ s DS Democratize also provides intuitive, no code, self-serviceable, conversational AI Driven “Data as a Service” and is intended for various data and analytics consumption by leveraging next gen technologies like Micro Services, Containers and Kubernetes.

An automated data and application modernization platform minimizes the risks and challenges in your digital transformation. It is faster, highly cost-effective, eliminates error-prone manual effort and completes the project in half the typical time frame. Book a demo to know more.