8 Commandments for Successful Data Migration & Modernization – Part 1/2

Digital transformation is one of the latest trends sweeping companies globally, particularly with regard to how enterprise data is managed. There is a shift underway due to the increasing volume of unstructured data. Studies indicated that by 2022, 99% of all data in digital universe will be unstructured and the volume of this data is increasing at a rate of 62% YoY. These gigantic volumes of unstructured data are driving data modernization initiatives from legacy databases to the cloud. Such a shift provides better availability of data and applications with scalability, automation and a high degree of cost-effectiveness.

Research indicates that by 2022, 75% of databases and 90% of new applications will be in the cloud. However, this journey is complex and rife with challenges. Failure can be expensive with thousands of wasted dollars and months of wasted effort. So careful planning is essential for your data migration and modernization journey.

Here’s an in-depth look at the 8 commandments for attaining the golden state of successful data migration and modernization. A well-qualified data migration and modernization partner can complete commandments 1-5 in about 6 to 8 weeks of effort, while the latter stages typically require 8 to 12 months of effort.

1. Establish a Data Culture

Although data culture is a fairly recent concept, it is gaining traction as organizations lean toward more innovative digital transformation strategies that reap greater value from the organization’s data. The crux of such a culture is treating data as an organizational asset instead of an asset that belongs to specific departments in silos. It is not merely about using data for decision-making, it empowers employees at all levels including frontline employees by providing accessible, quality data. There must be a focus on capturing, cleaning and curating meaningful and high quality data that is relevant to your business.

In our modern digital economy, speed is essential and using up-to-date data can drive quickly testing ideas and decisions regarding design to roll out and optimal product or service that entices customers. The advantage of readily available data that is of high quality is that it enables business agility and shifts to a data-driven, decentralized decision-making process rather than gut instinct decisions. Both of these factors are invaluable for sustainable success. Achieving such a culture necessitates a high standard of data literacy so everyone can perform better with a strong foundation of data for business differentiation.

This stage entails investment in:

• Talent
• Training
• Support

2. Define Data Objectives

Aside from establishing a data-driven culture where everyone is on-board with the concept of data as a foundation for greater business value, collaboration and agility, you must frame some clear goals for this data culture. A nebulous picture of what a data-driven culture looks like can lead you off-track when you commence your digital transformation. Discuss expected value of data with business users at all levels and document your expected goals and outcomes that must arise from a successful data migration and modernization. These objectives will act as guiding stars for the direction of your data modernization journey so that it is successful

3. Assess As-Is Data Ecosystem

This stage involves thoroughly charting the existing data ecosystem and underlying technologies. You must meticulously approach recording the tools and systems that are in use as well as the current metrics that are being used when it comes to the data ecosystem. A comprehensive picture of the current landscape will provide a solid foundation for the next stage of identifying promising use cases. In layman’s terms, you have to know where you stand before you can plan your journey to the golden future of a modernized database system that is cloud-friendly, cost-effective, and easier to manage and utilize.

4. Define Use Cases

To reach your end goal of the golden state, you must define a data strategy and figure out which use case to prioritize based on a framework for use case discovery and prioritization. This stage focuses on a thorough evaluation of data sources, desired usage and end users. One approach is ideation which involves brainstorming new ideas. However, such theoretical designs often stumble when it comes to actual implementation.

Another approach is a data-driven approach which leverages the data at hand, possibly adding additional data. Pattern recognition can be utilized to discern what lies in the data and potential use cases. This method is often more practical and is sometimes combined with the ideation approach to identify feasible use cases.

Identifying the most promising use cases at an early stage is a good practice to reduce costs and minimize the risk of failure.

In part 2 of this blog series, we will explore the remaining commandments for a successful data migration and modernization journey.

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.