Single Source Data Management for Cost and Error Reduction, and Improved Decision Making
The Need for a Better Data Management Approach
SaaS and AI are both growing sectors across industries and enterprises, putting pressure on companies large or small. To be able to leverage what these new technologies offer, business leaders must get a grip on their organization’s data quality before taking on future opportunities.
While data migration and system conversions are common technology projects, how companies have approached these projects have largely remained the same. The data and technical challenges for data migration efforts have only become more nuanced and complex, and organizations need to invest into these projects with cutting-edge technologies and automation.
At EZOPS, we are seeing clients renew their interest in facing these challenges by:
- rationalizing older technologies
- digitizing towards the cloud and centralized data management
- and exploring ways to automate the monumental tasks ahead.
However, upgrading fit-for-purpose monolithic systems to business solutions platforms is not as simple as copying over data from one database to another. And neither is migrating data to the cloud. Depending on what the data is stored for and how it will be accessed in the future, modern-day technologies and databases can connect to multiple systems, from inside and outside of the organization, and be constantly evolving. Worst of all, the data is likely to be littered with errors with each step and every iteration, resulting in a cascade or exponential effect.
The EZOPS Approach: Technology Builds Efficient and Productive Data Operations
At EZOPS, we focus on providing clients with a flexible, robust, and scalable solution to qualify, manage, and report on the data quality of their data migration efforts.
Below are a few top challenges we have solved for when we see data from complex technologies and databases that require migration or upgrading:
1) Clients often use high-level reporting (such as P/L, market value reporting to validate trade execution system migrations and upgrades) to identify differences between the old and the new, but any material differences identified at the reporting level are difficult to link to the data level that is causing the issue.
2) Datasets have relationships - trade to position, accounting to corporate actions, risk modeling with transactions. And rounding or decimal differences can have large ramifications at scale. This means fixing data issues in one place will have an impact on another. Organizations should have controls in place to know if significant impacts should be escalated.
3) Most vendors and system owners don’t build QA and testing tools that automatically check for issues. The onus is forced onto the client IT teams to write scripts, rules, and stored procedures to check for issues. These custom QA tools often can’t scale, are difficult to manage, and lack controls and reporting to track progress. They often can start a project but can’t complete one to satisfaction.
And it’s not one size fits all. Depending on the type of data and technology in question, the unique requirements that must be met to provide a confident data quality picture can disqualify many data and automation solutions out in the marketplace.
We would like to open a dialogue with you around how EZOPS can help you meet or exceed your goals. Please contact Roc Peng Du at firstname.lastname@example.org and let’s talk!