In the final data integration series article, we'll show you how to develop a data integration master test plan, the cornerstone of data verification efforts.
In part one of this 3-part series, we covered why assessing risks early and often is key, the best practices for addressing risks, and best practices for common risk mitigation. Part two covered examples of quality risks for integration projects and best practices for tackling them. In the final article of this series, we'll show you how to develop a strong data integration master test plan.
Although Agile testing tends to deprioritize test planning, teams working on data integration projects would be remiss to overlook the long-standing motives and rationale for a project-wide, data integration master test plan (MTP).
A “Data Integration**Master Test Plan”_ (_MTP**) represents a plan of action and processes designed to accomplish quality assurance from the beginning to the end of a data integration development lifecycle. The test plan should describe all planned quality assurance for each SDLC phase and how QA will be managed across all levels of testing (ex., unit, component, integration, system testing, etc.). The MTP provides a project-wide, high-level view of the quality assurance policies (often based on IEEE Standard 829).
Such a plan may be developed using the data project documentation
It’s essential to purge the data integration target data of the most severe and disruptive bugs. The sooner data quality/testing objectives are defined, the better your chances of exposing issues early when they’re easier, faster, and less costly to fix.
Give your developers a standard test plan document that lays out a logical sequence of actions to take when performing integration tests. Doing so keeps testing consistent across the project and allows project managers to allocate the right resources to begin the integration testing process.
A data integration MTP should describe the testing strategy/approach for the entire data integration and project lifecycle. The MTP will help the project team plan and carry out all test activities, evaluate the quality of test activities, and manage those test activities to successful completion.
Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue.
Building your models and analysis on solid foundations.Garbage in, garbage out. So goes the familiar phrase, born in the early days of Computer Science, pressing the importance of validating your inputs.
Only data-driven companies can compete in the era of digitization. In the increasingly complex world of data, enterprises need reliable pillars. Reliable data is a critical factor.
Data Quality Testing Skills Needed For Data Integration Projects. Data integration projects fail for many reasons. Risks can be mitigated when well-trained testers deliver support. Here are some recommended testing skills.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.