Having effective test data management strategies in place can help businesses strengthen their operations, minimize risks due to compliance issues or technology limitations and improve quality assurance processes.
A comprehensive TDM approach should include tools for masking data to support compliance, generating data to boost variety and provide scalability, and enabling database virtualisation to reduce storage costs.
Defining the Requirements
Test data management can be difficult because of a variety of factors such as the volume, veracity and consistency required for modern digital application testing. A TDM process can be simplified and accelerated by automating it, such as with scripting, data generation and data masking, as well as by cloning, provisioning and archiving.
A modern TDM approach should offer scalable, flexible and useful data coverage analysis and reporting to support testing efforts. This allows the team to quickly identify gaps and refocus their effort on areas that require it.
A TDM solution should also help to reduce the amount of unmonitored test data copies and slash costs by sharing standard data blocks across similar copies (saving on storage). It should also facilitate the ability for testers to furnish their own data through self-service, without needing to submit an IT ticket for the request. This will increase the efficiency and agility of the TDM process. It will enable greater test data accuracy and compliance with regulations like GDPR, while ensuring that sensitive data is not exposed in the testing environment.
Collecting Data
The data collection process is a critical first step in the overall strategy of collecting effective information for decision-making. It’s important to use an approved method to collect the data you need and do it properly to protect its integrity and credibility.
Data collection methods include a wide variety of sources, from qualitative to quantitative, said Liam Hanham, data science manager at Workday. The data can be captured through highly technical, deductive processes or highly involved, inductive methods, he added.
For example, if you are testing the behavior of a system with multiple users, the insights gained from just one user may not be sufficient for your purpose. This is why randomly generated test data (or property-based testing) is useful, such as using Hypothesis or a similar library for your language of choice (like QuickCheck). These libraries generate random values to check the inputs against your function(s) and manage foreign key references automatically. It’s also a lot less time-consuming than trying to find arbitrary numbers yourself!
Organizing Data
It is essential for businesses to organize their data in a logical manner. This process helps the company staff to easily locate specific data files when required. It also allows them to keep confidential information and other sensitive material safe from non-authorized users.
Several methods of organizing data are available to meet different needs. For example, a user that requires getting the median of a given data collection may sort it by arranging the repeated values in decreasing or increasing order. Similarly, another user who wants to know the mode of a particular data collection may arrange it by gathering all of the occurrences of that value.
When a company organizes its data, it helps the team members to quickly understand the context of each file and its meaning. This makes the whole process more efficient and reduces errors. It is advisable to set up and document a logical filing scheme so that the team can follow it without reorganizing the data again in the future.
Analyzing Data
Identify the most important questions that your analysis hopes to answer. These should be measurable and closely related to a business problem.
Collect the raw data sets you need to help answer these questions. This may involve a combination of internal and external sources. It may also involve cleaning your data, which typically means purging duplicate or anomalous information, reconciling inconsistencies and standardizing the structure and format of your information with the help of the test data management tools.
When you have the data you need, analyzing it involves examining it in ways that reveal relationships, patterns or trends. This may include comparing your data to that from other groups (like a control group or statewide statistics), or subjecting it to statistical operations like trend spotting or correlation analysis. It may also mean creating graphical representations of your data to better understand it.
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