Food for Thought Before Commencing a Data Modernization Project

Careful planning will help save a lot of time and money when you are facing a legacy data and/or application modernization project. Data modernization enables a holistic approach that leverages the latest, next-gen technology for data and application modernization. Part of the reason for this shift is the need for cloud-friendly databases that support business agility and more efficient operations. By 2022, 75% of databases and 90% of new applications will be in the cloud indicating that many organizations will require data modernization. Data migration, data quality, data governance and archiving are just some of the aspects of data modernization.

Data modernization supports businesses with scalability, automation and better availability of data and applications.

To understand the value of data modernization, you can read our blog on Why Data Modernization is the Key to Boosting Scale & Availability of Existing Applications.

Here are some points to consider before beginning your project, in order to ensure a successful modernization project that supports your business in working efficiently.

1. Phased Migration or One-Shot Migration

You must decide whether you will do a one-shot migration or phased migration. This decision will dictate the project’s timeframe and cost, as well as other aspects like when staff will be required and the amount of risk that needs to be addressed. The choice of implementation typically depends on an organization’s strengths and style, the budget that is available, the priority of timelines and the level of complexity of the various legacy systems and workloads in your organization. So there is no one-size-fits-all approach. You will have to evaluate your organization and make a decision accordingly.

2. Pay Attention to Data Strategy and Data Quality

A well-managed team for data governance is vital to document processes and define data standards and strategy that will support these processes. Such a team will minimize data chaos and help manage the migration from legacy systems to cloud-friendly, modern systems.

It is also essential for data modernization to have high data quality. Poor data costs companies millions of dollars a year, according to a Gartner survey. If you do not have quality control measures in place that monitor data quality, such data is going to be full of errors and can wreak havoc with your modernized system.

Companies require trusted and accurate data for optimal operations. In the case of external, third-party data strong ongoing quality measures are necessary. External data is especially useful for identifying more opportunities for your business. To learn more, take a look at the importance of third-party data for modern business analytics and get some insights into how to manage third-party data and ensure data quality.

3. Resource Requirements for the Project

Resource requirements will fluctuate at various stages of the data or application modernization project. In case you are working with a modernization partner, internal staff will still be required to some extent. In most cases, the maximum number of internal resources are required at the start and the finish line of the project. Your team will often need to perform data and source code collection in addition to detailing functional and infrastructure requirements. Meetings to plan the implementation approach and clearly plan resource requirements with details of team member’s roles and responsibilities will ensure smooth implementation. Remember that during migration, team members with strong decision-making and execution capabilities are important to keep progress on track. It is imperative to allot IT and business resources in a dedicated manner during the final stages of testing and go live. They will ensure that all user and functional expectations have been met.

4. Understand the Various Data Relationships across the Business

For an organization to use data effectively, there must be an in-depth understanding of various data relationships that span the business. The fact is while you may focus on one area of an organization for data modernization, this data is often applicable to several departments. To gain enterprise-wide value from data, build a team with a member from each department for the modernization project. This holistic approach will overcome many of the typical obstacles and ensure that every both organizational and each department’s key business objectives are considered in the implementation of the project. This supports data integration across all levels and departments of an organization.

This is important because business operations need to happen quickly and this hinges on availability of data, services and systems. Modernizing data and applications provides this agility and speed to further business value. Data modernization will ensure that all data is of adequate quality to be leveraged across the enterprise in a reliable fashion, with no delays.

5. Leverage A Data Platform that is Flexible

The crucial component to successful data modernization is to pick a platform that is adequately flexible for the job. This makes information governance much easier, sometimes through a templated approach that allows processes and work product to be saved and reutilized across any other project on data migration, data quality or data management. Ideally, the platform should also provide the ability to manage the people aspect of the project ranging from tasks and teams to managing project plans. Configurable, real-time dashboards and reports as well as convenient accessibility through mobiles and tablets empowers executives to visualize and track progress.

As you can see, picking the right solution is crucial for data migration, modernization and improving the quality of third-party data. 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. It is faster, highly cost-effective, eliminates error-prone manual effort and completes the project in half the typical time frame.

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.). 

An automated data and application modernization platform and toolkit minimize the risks and challenges in your digital transformation. Book a demo to know more.