Test Data Management! The Anatomy & five tools to use.

Being part of the IT leadership in an organization has its advantages, but it also means you have to be familiar with technical “buzzwords”.

“Test Data Management” is one such term you might come across.

Do you know what it means and why TDM matters? And what about the available test data tools you can employ? If the answer to one or more of these questions is “no”, then this post is for you.

Let’s start by dissecting the expression into its various body parts. We’ll define each one and then reassemble the definitions. Once we’re done defining the term, we’ll get into the meat of the post by showing five existing test data tools that can help with test data management. Let’s get started!

Test Data Management: Breaking it Down

Let us break it down into its key components i.e. Test, Data & Management.

A definition of Testing.

Software testing is an investigation conducted to provide stakeholders with information about the quality of the software product or service under test. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not limited to, the process of executing a program or application with the intent of finding software bugs (errors or other defects).

A definition of Data.

Test data, unlike the sensitive data found in our production data, is any data that’s necessary for testing purposes. This includes test data for inputs, expected test data outputs, and test environment configuration details. Test data can come from a variety of sources, including production databases, synthetic data generators, and manual input.

A definition of Management

Management is the process of administering an organization, which can be a business, non-profit, or government body. This entails setting the organization’s goals and objectives and then coordinating the efforts of employees or volunteers to achieve these targets. The available resources that can be employed include financial, natural, technological, and human resources.

Bringing TDM Together

Now that we have the definitions for each word, it’s time to put all of them together to create a complete definition for “test data management.” Here it goes:

Test Data Management (TDM) is fundamentally test data preparation. It is the process of helping you prepare test data and maintain the test data in support of software testing. The goal of TDM is to provide a test environment that is as close to production as possible, and promotes data security while still being able to accurately test the software.

This may include, but is not be limited to underlying features like:

Test Data Profiling i.e. The Process of Discovery & Understanding your Data.
Test Data Preparation i.e. Generation of Realistic Test Data Using Automation to Fabricate Fake / Synthetic Data.
Test Data Security i.e. Using Production Data & Masking / Privacy Methods on the original production data. With the intent of ensuring “Personally Identifiable Information” (sensitive customer data) is removed and we prevent a data breach.
Test Data Provisioning i.e. Rapid Snapshotting, Cloning & Provisioning of Test Data/
Test Data Mining i.e. The ability to View and Access Valid Test Data.
Test Data Booking i.e. the ability to reserve Test Data for your engineering purpose

Here Are 5 Test Data Management Tools for Your Review

Here are five Data Generation Tools your organization can use to improve its approach to Test Data.

BMC (Compuware) File Aid

Compuware’s Test Data Management solution offers a standardized approach to managing test data from several data sources. Test Data Management with Compuware seeks to eliminate the need for extensive training by making it easy to create, find, extract, and compare data.

The solution can load subsets of related production data while maintaining database and application relationships. Test data management can help reduce the risk of errors, improve product quality, and shorten development timelines.

Broadcom (CA) Test Data Manager

Test Data Manager by Broadcom is a powerful test data management tool that enables organizations to manage their testing data more effectively and efficiently. Test Data Manager provides users with the ability to track, manage, and visualize their testing data in a centralized repository. Test Data Manager also offers features for managing test environments, managing test cases, and generating reports.

Enov8 Test Data Manager (DCS)

Enov8 Test Data Manager, originally known as DCS (Data Compliance Suite), is a Test Data Management platform that helps you identify where data security exposures reside, rapidly remediate these risks without error and centrally validate your compliance success. The solution also comes with IT delivery accelerators to support Data DevOps (DataOps), create test data, data mining, and test data bookings.

IBM InfoSphere Optim

IBM InfoSphere Optim is a tool that manages data at the business object level while preserving the relational integrity of the data and its business context. This allows you to easily create environments that precisely reflect end-to-end test cases by mirroring conditions found in a production environment.

InfoSphere Optim also offers other features such as data masking, ensuring data security, and subsetting, which can further help you reduce the risk of data breaches when testing in non-production environments.

Informatica Test Data Management

The test data management solution from Informatica, Test Data Management, is a tool that can identify ‘sensitive data,’ subset it, mask it, and create test data. It also allows developers and testers to save and share datasets to enhance overall efficiency.

Conclusion

As previously said, there are a lot of “buzzwords” in software engineering, and that trend isn’t going to change any time soon. Some of these words are simply fads. They seem like the “latest and greatest thing.” But just as quickly as the hip kids started using them, they fall out of favor.

However, Test data management isn’t one of those fads. It’s a process that your company must master and improve if it wants to stay competitive and promote values like Data Privacy. Test Data Management is essential in the understanding of data, it impacts our IT operations & project velocity & is key to our information security protocols.

In this article, we used a divide and conquer technique to define test data management. Test data management is the process of handling test data throughout the software development life cycle. Test data management tools help organizations manage this process by providing a way to store, track, and manipulate test data. There are many different test data management and data security solutions available on the market, each with its unique features and capabilities. So have a look & choose. Each is powerful and has its nuances. Look at the capabilities of each and decide which of the “Test Data Management” features are most important to you.

Author: Mark Dwight James

This post was written by Mark Dwight James. Mark is a Data Scientist specializing in Software Engineering. His passions are sharing ideas around software development and how companies can value stream through data best practices.

What Is Data Virtualization

Data has undergone a huge shift from being an unimportant asset to being the most valuable asset a company holds. However, just holding the data doesn’t bring many benefits to your organization. To reap the benefits of the data your company collects, data analysis helps you to find valuable insights for the data you hold.

Data lays at the core of many important business decisions. Many companies prefer a data-driven decision-making policy because it greatly reduces guessing and helps the company to shift toward a more accurate form of decision-making. This greatly benefits the company as you have more trust in the choices you make and you can reduce the number of “incorrect” decisions.

For example, say a product company wants to know if users like the new feature they’ve released. They want to decide if they need to make further improvements to the feature or not. To make a more informed decision, the product company collects user satisfaction scores about the new feature. The company can then use the average user satisfaction score to make this decision. Data virtualization helps you to quickly aggregate data from this survey, as well as other important data that influences the decision, in a single, centralized view. This allows your business to make more informed decisions quicker.

This article introduces you to the concept of data virtualization and how it can help your company to make better decisions. Before we start, what are the common problems companies experience with data?

Common Data Problems for Organizations

Here’s a list of data challenges companies commonly experience:

It’s hard to understand the data you’ve collected.
Different sources of data use different formats, which makes it harder to retrieve insights.
Your organization experiences data lag, which means that data isn’t directly available.
Your organization isn’t ready to handle and process data. This could be due to, for example, missing data infrastructure and tools.

Now that you’ve read the above data problems, make sure your organization is ready to handle and process data. So what is data virtualization?

What Is Data Virtualization?

Data virtualization is a form of data management that aggregates different data sources. For example, a data virtualization tool might pull data from multiple databases or applications. However, it’s important to understand that it doesn’t copy or move any of the data. You can have multiple data silos.

Data virtualization is capable of creating a single, virtual layer that spans all of those different data sources. This means your organization can access data much faster since there’s no need to move or copy data. Furthermore, this is a major benefit as you can access data in real time. Virtualization improves the agility of the system, and companies can run analytics faster, gaining insights quicker. For many companies, being able to retrieve insights faster is a great competitive advantage!

As mentioned, data virtualization doesn’t copy or move any data. It only stores particular meta information about the different locations of the data that you want to integrate into your data virtualization tool.

What Is the Importance of Data Virtualization?

First of all, data virtualization acts as the pinnacle of data integration. It allows an organization to integrate many different data sources into a single data model. This means companies can manage all of their data from a single, centralized interface.

Moreover, data virtualization is a great tool for collecting, searching, and analyzing data from different sources. Furthermore, as there’s no data copying involved, it’s also a more secure way of managing your data since you don’t have to transfer the data.

In other words, data virtualization helps companies to become more agile and use their data faster, creating a competitive advantage as you receive analytics and insights more quickly.

What Are the Capabilities of Data Virtualization?

This section describes the capabilities of data virtualization and why they matter for your business.

Agility
A data virtualization tool allows you to represent data in different ways, format data, discover new relationships between data, or create advanced views that provide you with new insights. The options are endless. Agility is the most important capability of data virtualization as it decreases the time to a solution.
High performance
A data virtualization tool doesn’t copy or move any data. This contributes to its high-performance nature. Less data replication allows for faster data performance.
Caching
Caching frequently used data helps you to further improve the performance of your data virtualization tool. Whenever you query for data or a specific data view, part of the data is already cached for you. This puts fewer constraints on your network and improves the availability of your data.
Searchability
A data virtualization tool allows you to create data views that provide you with actionable insights. Furthermore, data virtualization provides you with a single, centralized interface to search your data.

Next, let’s explore the benefits of data virtualization for your organization.

What Are the Benefits of Data Virtualization?

Here are 10 important benefits of employing a data virtualization tool for your organization.

Helps with hiding the data complexity from the different underlying data sources, data formats, and data structures.
Avoids replication of data to improve performance.
Gives real-time data access and insights.
Provides higher data security as no data is replicated or transferred.
Reduces costs since no investments are needed in additional storage solutions.
Allows for faster business decisions based on data insights.
Reduces the need for development resources to integrate all different data sources.
Allows for data governance to be applied efficiently. For example, data rules can be applied with a single operation to all different data sources.
Improves data quality.
Increases productivity as you can quickly integrate new data sources with your current data virtualization tool.

Now that we have a better understanding of the benefits of data virtualization, it’s time to get serious. The next section explains how you can implement data virtualization in your organization.

How to Get Started With Data Virtualization

Do you want to get started with data virtualization for your organization? The most important tip is to start small. Assign a dedicated team who spends time on integrating one or a couple of data sources. Start with data sources that are most valuable for your organization. This way, you’ll see the benefits of data virtualization quickly.

Next, when your team has completed some simple data integrations, it’s time to scale up your operations and use the tool for most of your data sources. You can think about more complex data models, integrate complex data sources, or use data sources with mixed data types.

Furthermore, you can start to experiment with caching to see where it can be applied effectively to gain the most performance benefits. Remember to apply caching to frequently used data or data models.

As a general rule of thumb, prioritize high-value data sources to reap the most benefits.

Conclusion

One final note: data virtualization isn’t the same as data visualization. The two terms are often used interchangeably, but they have very different meanings. Data virtualization isn’t focused on visualizing data. The main goal of data virtualization is to reduce the effort of integrating multiple data sources and providing your organization with a single, centralized interface to view and analyze data.

In the end, the real business value of data virtualization lays in the agility and faster access to data insights. For many organizations active in the industry of big data or predictive analytics, it’s a real competitive advantage to access insights faster than your competitors. This allows you to make profitable decisions faster than the competition.

If you want to learn more, the following YouTube video by DataAcademy further explains the concept of data virtualization in easy-to-understand terms.

Why Test Data Management Is Critical to Software Delivery?

Imagine you are developing a system that will be used by millions of people. In a situation like this, a system has to be very well-tested for any type of error that can cause the system to break while in production. But what’s the best way to test a system for any possible system failure because of bugs? This is where test data management comes in.

In this post, I will explain why test data management is critical in software delivery. To develop high quality software products, you have to continuously test the system as it’s being developed. Let’s dive in straight to understanding how this problem can be solved by using test data management.

What Is Test Data Management?

Well, in simple terms, test data management is the creation of data sets that are similar to the actual data in the organization’s production environment. Software engineers and testers then utilize this data to test and validate the quality of systems under development.

Now, you might be wondering why you need to create new data. Why not just use the existing production data? Well, data is essential to your organization, so you should protect it at all costs. That means developers and testers shouldn’t have access to it. This has nothing to do with the issue of trust but security. Data should be highly regarded, or else there can be a data breach. And as you know, data breaches can cause loss to an organization.

How Can You Create Test Data?

So, now that we know why we need test data that is separate from our production data, how can we create it?

The first thing you must do is understand the type of the business you are dealing with. More specifically, you need to know how your software product will work and the type of end users that will use the software. By doing so, it will be easier to prepare test data. Keep in mind that test data has to be as realistic as the actual data in the production environment.

You can use automated tools to generate test data. Another way of creating test data is by copying or masking production data that your actual end users will use. Here you have to be creative as well and create different types of test data sets. You can’t rely only on the masked data from production data for testing.

Benefits of Test Data Management in Software Delivery

Test data management has many benefits in software delivery. Here are some of the benefits of test data management in software delivery in any software development environment.

High Quality Software Delivery

When you apply test data management to software delivery, it will give software developers and testers to test the systems and make solid validations of the software. This enhances the security of the system and can prevent possible failures of the system in the production environment. Testing systems with test data gives assurance that the system will perform as expected in the production environment without defects or bugs.

Faster Production and Delivery of Software Products to the Market

Imagine that, after some months of hard work of developing a software application, you’ve just released a software application on the market, only for it to fail at the market level. That’s not only a loss of resources, but it’s also a pain.

A system that’s well-tested using test data will have a shorter production time and excel at the production level. That’s because it’s much more likely to perform the way it was intended to. If the system fails to perform in production because it was not tested well, then the system has to be redone. This wastes time and resources for the organization.

Money Needs Speed

Test data management is critical when it comes to software delivery speed. Having data that’s of good quality and is similar to production data makes development easier and faster. System efficiency is cardinal for any organization, and test data management assures that a system will be efficient when released in production. Therefore, you start generating revenue as soon as you deploy the system.

Imagine having to redo a system after release because users discover some bugs. That can waste a lot of time and resources, and you may also lose the market for that product.

Testing With the Correct Test Data

Testing with good quality test data will help in making sure that the tests you run in the development phase will not change the behavior of the application in the production phase. For example, you might test that the system is accepting supported data by entering a username and password in the text box with all types of data that a user can possibly input into the system.

No matter how many times you test the software, if the test data is not correct, you should expect the software to fail in the production phase. This is why it is always important to ensure that test data is of great quality and resembles your actual production data.

Bug and Logical Fixes

How can you know that the text box is accepting invalid input such as unsupported characters or blank fields from users? Well, you find out by validating the system through testing.

The whole point of having test data in software delivery is to make sure that the software performs as expected. Additionally, you need to make sure that the same tests will pass in production and have no loopholes that could damage the organization’s reputation. Therefore, test data becomes a critical part of software delivery life cycle, as it helps to identify errors and logical problems in the system. Thanks to this, you can make fixes before releasing the software.

For example, imagine a loaning system that makes incorrect calculations by increasing the interest rate by a certain percentage. That can be unfair to the borrowers and can backfire for the lending company.

Earning Trust

Trust is earned, and if you want to earn it from the end users or management, you have to deliver a software product that’s bug-free and works as expected. In fact, every software development and testing team should utilize test data management. Test data management enables teams to deliver software products that stand out and earn trust from management. After all, you can’t ship an error-prone system to the market and expect happy users.

Why Test Data Management Matters

Test data management is essential for ensuring that software applications will function as expected in a production environment. By testing with realistic data, organizations can gain assurance that their software will not fail in production, strengthening their relationship with clients and reducing the chances of fixing bugs in production and rollbacks. Test data management also speeds up the software development life cycle, reducing costs and improving the speed of software delivery. This helps organizations stay competitive in a rapidly changing market by detecting errors at an early stage and fixing them before release.

Additionally, test data management helps reduce compliance and security risks, provides Product Owners and their Steering Committees with assurance that the software they are releasing is of high quality, reduces the risk of data breaches by ensuring only valid and secure data is used in testing, and helps them make informed decisions about product features by evaluating the impact of changes on performance, scalability, and usability.

Summary

In simple terms, test data is simply the data used to test a software application that’s under the software testing life cycle. Test data management, on the other hand, is the actual process of administering data that’s necessary for use in the software development test life cycle.

You can’t deny that test data management is an essential part of testing and developing software. It plays a crucial role in helping you produce high quality software that’s bug-free and works as expected.

You should take test data management seriously and apply it when delivering software. If you do so, your organization will gain more revenue because you’ll deliver higher quality software products. Higher quality products make the customers happy instead of giving them a reason to complain about some bug.