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Data Archiving: The differentiator of ERP services

Effective and efficient are the cornerstones of modern ERP (Enterprise Resource Planning) solutions. Today’s ERP vendors must address the compounding pressure of ever-expanding data. In other words, they need to cue in robust data archiving solutions. But, archiving ERP applications can get tricky at times. Many companies seek to retire those initial ERP applications – and, in some cases, the mainframe apps and proprietary platforms that preceded them – they’re not sure how to transition gracefully while maintaining access to all their data. This data still has value, not to mention liability potential, so archiving it takes careful thought. A skillful ERP solutions vendor can come to the rescue.

What is data archiving?

Data archiving refers to a process of identifying, extracting, and transferring data that is no longer in active use, to a secure and accessible location. In the initial phases of building an ERP application, the focus is more on making accessible as much data as possible – in one system, under one roof. But, on the flip side of this lies the complex parent-child relationships between data tables. It can get difficult to pull data out of relational databases without breaking something. Your ERP solutions vendor needs to have tools in place for efficient data archival.

1. Improves cost efficiency

In today’s data-overload environment, storage needs are at an all-time high. Without proper infrastructure, this can put unwarranted pressure on your servers, especially if you are storing everything on site. And, this further involves colossal investments for server upkeep. Many enterprise compliance standards demand that all corporate data (including ERP data) be stored for a substantial periods of time. So, when data deletion is not an option, it is always prudent to store data on remote/off-premise servers or in the cloud. And, this is what data archiving services are all about. In the absence of a well-maintained data repository, companies often have to outsource for intel discovery and incur huge costs in the process.

Data archiving services also take the load off your IT teams and make you more self-reliant when it comes to accessing the archived data. The archives are designed to be both affordable and accessible.

2. Enhances business productivity

Data archival solutions streamline ERP data end to end. It is record management of sorts. Business operation applications, such as ERP, often gather a lot of data in a very short period of time. It creates a lot of unnecessary pressure on your on-premise storage facilities. Cloud storage solutions are better suited for such requirements. All the data your software tools accumulate over a stipulated period of time can instead be archived for easy access and analysis. This not only saves on-premise service space but also ensures that all of your installations function quickly and efficiently.

By carefully structuring the collected data, the process of data archival also makes the case for easy data retrieval. All redundant data is removed and only the unique data is stored and saved. This further enhances business communication as all stakeholders access and collaborate on the same versions of the data files.

The benefits of leveraging data archiving services
Data archival brings multiple advantages to businesses

4. Streamlines enterprise storage

It’s always easier to gain insights from data when it’s stored in a single, shared location. If it is scattered across multiple devices and networks, it becomes difficult to leverage data as enterprise intel. To add to the challenge, data often comes in various shapes and sizes from varying sources – structured and unstructured, traditional and non-traditional. This, of course, means recurring efforts in server maintenance. A centralized repository of data (ERP or otherwise) with business-critical user access can make enterprise operations more efficient.

With data archiving solutions in place, storage can be streamlined and maintained in one go. You can plug in with select ERP service integrations for desirable outputs.

Why do many ERP vendors often miss out on data archiving?

If you’ve ever struggled with the process of purging, archiving, or extracting data for a mid-sized or large enterprise, you may have wondered aloud, “Why didn’t my ERP solutions vendor provide a better way to do this?”

In 1986, Oracle started developing a new generation of business applications that would transform the playing field. Up until that point, ERP systems had been proprietary. They were either written for IBM mainframes (as SAP’s accounting software suite was), or for one of the various mini-computer platforms.

Oracle’s new software would propel ERP one step closer to truly open systems. How? It was written for a new generation of hardware platforms—such as Sequent Computers and Pyramid Computers—that ran on a generic version of the UNIX operating system. Adding to the flexibility, the Oracle relational database could also run on proprietary platforms. Thus, companies using the new Oracle Financials suite with an Oracle database could run their software on virtually anything.

Suddenly, enterprises truly had a choice of hardware and software. Although IBM had invented the relational database, Oracle was running with it.

However, that wasn’t the only pro-customer change. The user interface in these new applications was vastly improved. Previous generations of ERP had offered users a traditional mainframe green screen. You would fill the screen with data and press a button. Since there was no field-level validation, errors would only come back to you after you’d processed an entire screen.

Oracle’s new apps, on the other hand, offered much richer interaction between computer and user. And because they ran on a relational database, users could query data on demand, rather than using the cumbersome nightly reporting associated with mainframe computing. These innovations naturally enabled much greater productivity—not to mention, user satisfaction.

The dynamics between data management and data archiving in ERP

The uptake of ERP applications has been dramatic over time. And the urge to load these systems with huge amounts of corporate data has been unstoppable. So, it was only a matter of time for businesses to realize that the aging ERP applications come with a more serious problem: it’s really hard to get data out of them. And fairly, this set the stage for corporate data repositories, ergo, data archives to enter the picture.

Data protection strategy as the bedrock for application testing

Secure applications are at the core of a good data protection strategy. Businesses run rigorous web application security assessments to prevent even the smallest of data leaks. Today, data is the most precious resource needed to garner a sustainable ROI. Naturally, software security testing is integral to modern data protection and risk management.

Why do you need a data protection strategy?

The benefits of having a data protection strategy are manifold.

  1.  Protects the holistic integrity of enterprise data
  2.  Saves against financial loss and public relations hassles
  3.  Safeguards customer privacy; strengthens trust
  4.  Helps to maintain compliance with third-party regulations
  5.  Facilitates easier management of data and information

Data privacy has been one of the most pressing concerns since the last few decades. And, given the rapid growth of data, some of the breaches are proving to be devastatingly massive. For example, back in September 2018, Hotel Marriott International (Starwood) reported a sensitive data breach for half a million of its customer base. The ensuing investigation exposed unauthorized hotel network access for four-long years preceding the attack. It goes without saying that the PR aftershocks were massive. On top of this, the company was fined £18.4 million by the UK Information Commissioner’s Office in 2020 for failing to keep customers’ personal data safe.

Why do you need to adopt a ‘Data Protection Strategy’?
The benefits of having a data protection strategy in place

Should you include application testing in your data risk management plan?

Absolutely yes! A low-hanging fruit is to focus on the treatment of production database(s) while testing software applications. It’s not uncommon for large companies to maintain ten copies of production – full clones used for testing, training, and development purposes.

To make matters worse, the people who have access to these copies of production are often “outsiders” – third-party consultants who you may not have vetted as carefully as your actual employees. Giving them full access to sensitive corporate data creates a significant privacy risk.

Where to begin privatizing data? Many organizations struggle just to figure out where all their at-risk data lives in the corporate environment. The next step is to put in place a simple yet reliable mechanism for masking, or scrambling, that data so that it will still be useful for testing but won’t endanger the privacy of your customers and employees. In the face of a colossal threat to user data privacy, software testing security is a must-have function.

IBM InfoSphere Optim hosts excellent data privacy solutions

IBM InfoSphere Optim Data Privacy is a solution that minimizes risk without slowing down your testing. By masking personal information, IBM Optim protects confidential customer and employee data and ensures compliance with all levels of privacy regulations.

Of course, masking your data is only part of the game. It’s also a best practice to subset your production database, rather than use full copies of production, for testing and other non-production activities. IBM Optim Test Data Management facilitates that process. And when you use Optim Data Privacy and Optim Test Data Management in tandem, you can actually apply data privacy rules to production data while you’re subsetting it.

Top 5 data analytics service trends for 2022 and beyond

The 5 biggest data analytics service trends for 2022 and beyond

Big data stands at the focal point of most modern businesses. What it does for an enterprise’s growth, its analytics does for the business’s development. The boom of business intelligence platforms (BIPs) and BI software requirements bear testimony. Data analytics services today are incomplete without self-service business intelligence tools.

As 2021 begins to end, we must start monitoring data intelligence service trends that are going to dominate 2022 and beyond. Let’s look at the innovations that will soon be turning data into insights.

‘Veracity in versatility’ is the feature you should root for when looking for a BI SaaS tool in 2022. Smart analytics solutions help in driving insights and closing business decisions with adroitness. An efficient BI reporting tool also engages teams across your organization. Digital transformation becomes more of a culture change as a result.

According to Finances Online, 60% of companies feel that big data analytics improves process and cost efficiency.

In the digital COVID-19 world, 57% of C-suite executives have been exploring various data intelligence tools to stir business growth.

The years ahead are sure going to disrupt further roads to innovation. Here are the top 5 trends in data analytics services for 2022 and beyond.

1. Self-service analytics will strengthen man-machine partnerships

Modern data analytics service is the perfect blend of technical and human intelligence. And, self-service business intelligence tools are its manifestation. These tools equip you to glean actionable insights using a sound business intelligence platform. By generating real-time reports, they guide where to look and which areas to address first. The automation of data and analytics in recent times has made self-service BI tools even more pivotal in reducing costs of operation.

This process of fact-based decision-making is a smart business move. It’s going to stay around for some time now. It facilitates easy interpretation of data for your technical and non-technical teams alike. The rise of decision intelligence (hybrid of rule-based approaches and AI & ML-based modern analytics) is a classic case in point. So for the right analytical capabilities, make sure to opt for a self-service BI tool.

Big data will continue to be a big deal for businesses. Read how big data has been changing your industry.

2. Predictive analytics will disrupt BI SaaS tools

The modern business milieu will continue to hustle – not only to know what data says today but also to know what data might be saying tomorrow. Goes without saying, predictive analytics is the game-changer that will continue to influence decision-making in the times to come. In 2020 itself, 52% of companies across the world used predictive analytics to optimize operations as a part of business intelligence platform solution.

Analytic process automation (APA) has accelerated both predictive and prescriptive analytics. X Analytics (a term coined by Gartner) will be clubbed with AI and other techniques in the recent future for predicting business crises and opportunities. Such business intelligence platforms would work by leveraging varied structured and unstructured data.

A sound big data enterprise analytics provider will continue to offer the convenience of operational reporting. Fast data analytics auto-generates comprehensive reports so that your teams can focus on core routine activities. Self-service BI tools assemble data from multiple sources for dynamic dashboarding.

The 5 biggest data analytics service trends for 2022 and beyond
The 5 biggest data analytics service trends for 2022 and beyond

3. Cloud migration will be a key BI software requirement

The future belongs to cloud-native analytics. A sound BI solution provider must offer seamless data migration services from premise to your desirable SaaS cloud. The migration of legacy data should be equally coherent.

In the face of the pandemic, cloud operations will continue to be necessary for streamlining operations. According to Gartner, cloud-based AI will increase file-fold momentously by 2023. AI would be one of the top workload categories in the cloud. Cloud innovations from there on must complement your on-premise products platform/s.

Businesses will also go for in-memory computing for scaling data in real time and addressing storage limitations.

4. Advanced analytics will further the big data game

Present-day BI software requirements place prime importance on the inclusion of advanced analytics. Deliverables get optimized when your business intelligence platform supports advanced data management methodologies.

There are myriad advanced analytics modules that are going to frame the big picture in 2022. NLP & conversational analytics (that captures chatbot and audio data cues) and graph analytics (that leverages graphs) are some examples of this. Your BI software requirements must be in line with the latest analytical deployments.

5. Data will be more mobile and visible in Augmented Reality

On-the-go access to data analytics will be a key component of futuristic data analytics services. With bookmarks, widgets, and Face ID enhanced security, mobile data analytics will help close business decisions faster than ever before.

Additionally, the fusion of Augmented Reality would help in viewing datasets and dashboards in interactive real-world simulations. This would make working on smaller screens both convenient and intuitive.

Read what to look for in your mobile business intelligence tool.

Impact of data analytics services - some statistics to know for 2022 and beyond
Impact of data analytics services – some statistics to know for 2022 and beyond

Incorta is a modern and lightning-fast business intelligence solution

Incorta’s self-service business intelligence tool is the perfect mix of data visualization, reporting, and analytics. A simple Incorta integration helps to derive real-time business insights from complex datasets. With Incorta, you will be well-prepared for whatever the industry demands in 2022 and beyond – fast data loading, mapping, ETL processing, and more.

Watch Incorta’s expertise in big data analytics solutions.

Estuate is a premier Incorta implementation partner

Our Incorta engagement is driven by BI experts equipped with best-in-class partner support. We address 360-degree BI software requirements (from management to reporting). In the process, we support business journeys from being data-driven to being driven by insights. Be it for large digital transformation projects or smaller sandbox initiatives, our data analytics services are customizable for all.

  • 300+ data platforms, analytics, and ETL projects
  • 300+ archiving project rollouts
  • 30+ in-house Incorta architects and platform engineers
  • 20+ Incorta accelerators in visualization and analytics
  • 15+ Incorta implementations

Here is a crisp compilation of our capabilities in the business intelligence arena.

if you are looking for experts to help you with your business intelligence needs, we’re right here to help.

Which trend do you think will be the most significant for data analytics services in 2022?

Data Masking in the Wake of Big Data Management

Data Masking in the Wake of Big Data Management

Modern businesses feed on data for breakfast, lunch, and dinner. Today, so significant is good clean data for business growth that even the minutest of data compromise is capable of wreaking havoc on brand positions. Daily headlines on data leaks and thefts bear testimony to this. Goes without saying, this has ushered privacy tactics like data masking straight into today’s council of big data management solutions. If you have only been opting for data archiving solutions to uphold data integrity till date, right now would be the right time to go ahead and opt for data privacy as a service as well.

Remember Quora’s 2018 mega data breach or the more recent MyRepublic data leak? The shock waves these events triggered were massive at both the business and customer levels.

Opting for data privacy as a service and masking data can save your business from such undesirable scenarios.

What is Data Masking?

Data masking is a smart way of creating ‘realistic fake’ data. This fictionalized data is similar to its real counterpart albeit encrypted, shuffled, substituted, or tweaked in some other way. The ingenuity of this big data management solution is that it ably caters to all your functional needs (training, testing, auditing, etc.) while simultaneously protecting the sensitive information from unintended users. As and when needed, the data can always be engineered back to its original state.

For instance, when testing a net banking application for quality, testers need to log in as a user and process transactions – putting confidential customer information at the risk of exposure/misuse. However if the data is masked in the non-production testing environment, the risk can be easily averted.

Textbook examples of business data that require masking are corporate intelligence assets and personally identifying information (PII) like full names, email addresses, and national identifiers of personnel, customers, or business partners.

Such careful data governance along with other data management hacks like data archiving can make a world of difference for your business.

Non-Production Data Masking: Part of Big Data Management Solution
Non-Production Data Masking: Part of Big Data Management Solution

Understanding Data Masking Needs – Why Should You Put a Mask on Data?

Data is a multi-dimensional and multi-utility business resource. It is present everywhere from your CRM software to the third-party interfaces you are affiliated with, making it one of the most vulnerable company assets. Toward this, robust big data management solutions recognize masking and data archiving as quintessential for protecting overall data integrity.

You can unlock the following capabilities at the functional level with data masking solutions in place:

Shield sensitive data (structured/unstructured)

All thanks to the big data boom, research suggests that by 2025, the global data bank will exceed a staggering volume of 180 zettabytes! For businesses, this means a rapidly expanding inventory of information stored across structured databases and unstructured image files, documents, forms, etc. You must have the flexibility of protecting your sensitive data irrespective of its nature and also divulging it to desired shareholders as needed.

De-identify data in non-production environments

Non-production databases (development, testing, training) are highly vulnerable in nature. Methods that protect production environment live data (multi-factor authentication schemes, biometrics, etc.) cannot simply be applied here as the privacy pre-requisites of non-production data are often unique.

Data masking is necessary to be adopted under such scenarios.

Monitor real-time access to data

Certain databases are so confidential that they require 24*7 monitoring to control who is accessing them. Data privacy as a service can help here by masking data or terminating connections by analyzing access patterns.

At the organizational level, data privacy as a service is equally empowering. It helps you to:

Arrest the risk of data breach

This is perhaps the most crucial advantage of having a data masking solution in place. By shielding confidential information, you can keep the risks of data loss, insider threats, and privacy violations at bay. A sound data masking solution de-identifies the data in such a robust way that even if the data is lost or stolen, the perpetrator will not be able to derive any benefit from it.

Here are some other proven ways of avoiding data breach at your enterprise.

Strengthen the customer’s trust in you

Skyrocketing cases of data leak and identity theft have been emphasizing the need for data privacy as a service. Data breach does not only affect your brand equity and revenue, but it also upsets your entire business growth by disturbing the value you provide to your customers. A study of retail banking customers found that the latter prefer engaging only with brands that can safeguard their privacy.

Improve data compliance and governance

It is not enough to only secure data internally, businesses also need to protect sensitive data that may be exposed during third-party audits. With data masking solutions in place, it would always be easy to level up on these fronts. Pre-defined actionable data privacy classifications and rules help to increase compliance preparedness.

Benefits of Masking Data for Increasing Data Privacy
Benefits of Masking Data for Increasing Data Privacy

Welcome to the World of IBM – InfoSphere Optim Data Privacy as a Service

One thing that often bemuses most businesses is the universality of data. They are often not fully aware of all the pockets where confidential data resides or how exactly to protect the same. Identifying this need, IBM had added the InfoSphere Optim Data Privacy Solution in its ambit. It pertains to an end-to-end data privacy and governance solution across on-premise or cloud applications, reports, and databases – irrespective of the level of complexity of the associated IT environment. Like its test data management and data archiving solutions, data privacy as a service is another stellar offering from the IBM family.

Shared below is a quick summary of the capabilities it offers.

A Good Data Masking Solution? Here’s What to Expect –

  • Composite data masking techniques – e.g., substrings, arithmetic expressions, random or sequential number generation, date aging, concatenation
  • Coherence with the application for which it is masking data – it must adhere to permissible structures, values, and patterns; masked data must make functional sense to the recipients
  • Pre-defined capabilities for masking standard customer data like national identifiers, email addresses, etc.
  • Data coherence and integrity – the masking procedure must be scalable across all related databases and applications to avoid erroneous test results
  • Flexibility – there should be provisions to mask the data before loading into non-production environments

Estuate has best-in-class expertise in IBM Data Privacy Implementation Solutions

In the wake of big data management, we understand the importance of maintaining data sanctity. With our expert IBM Optim data privacy capabilities, safeguard your sensitive data in non-production environments.

  • We are IBM’s go-to partner for IBM Optim solutions across many platforms and use cases.
  • We have a successful track record with over 350 Optim implementations.
  • We have in-house domain experts to provide business-specific consultation across various industry verticals.

Watch this webinar conducted by Estuate’s IBM specialists on data privacy concerns in the gaming industry.

If you are looking for robust data privacy as a service, we would be more than happy to help. We’re just this click away.

What are your thoughts on data privacy as a service? Do you think that masking data can help your business?

Test Automation in Agile Product Development

The ongoing demand for user-friendly, cutting-edge technology business applications has compelled software companies to deliver stable, cost-effective products in shorter time frames. Aiming for extraordinary product speed without compromising product quality is often a challenge for most companies. As a consequence, when it comes to product development, businesses understand the value of test automation in achieving both product quality and a faster time to market. With an inevitable arbitrate between quality and time-to-market, test automation often outperforms conventional testing methods and helps businesses stay ahead of the competition.

Need for Automation Testing

Product creation is a time-consuming process. When it comes to software development, we absolutely cannot ignore testing because it is such an essential part of the process. Though the agile method of product development is a quicker, less expensive, and better approach, it is vital to automate the testing processes, which saves time and offers insight into errors that manual testing methods might not be able to detect. Automation has more tremendous advantages than conventional testing methods, enabling businesses to develop high-quality products faster and with fewer defects. Companies that use agile development methods employ automation testing to handle continuous development and deployment.

Automation Testing Benefits in Agile Development

Automation testing serves as the basis of Agile software development methodology because of the benefits that it offers. Automation enhances the revenue, brand recognition and retention of customers.It aids companies in meeting industry standards, establishing market authority, and ensuring the timely delivery of high-quality software or applications.

Here are some substantial benefits of implementing automation in agile development.

Quicker Go To Market

Automation enhances the overall efficiency of the product development. Since testing is performed at each stage of the agile process, any issues or bugs detected are corrected early. This saves significant time and reduces the software’s time to market. Automated tests are quick to complete and can be repeated any number of times. Automation testing allows for more frequent updates, faster app improvements and enhancements, a shorter product development cycle, and faster time-to-market delivery.

Accelerated Speed & Accuracy

Test automation reduces human errors dramatically, resulting in more accurate test outcomes. Automated test cases are reusable, meaning they can be run several times in the same or different ways. Automation allows test practitioners to focus on more complex, case-specific assessments while automated software performs routine, often redundant, and time-consuming tests. Automation thus reduces time and effort by delivering results more quickly.

Higher Test Coverage & Performance

Automated software tests vastly increase software quality by increasing the scope and depth of testing. It allows for more detailed analysis and evaluation of various software components, which is rarely possible through a manual testing approach. With automated testing, you can quickly build a large number of test cases, even complicated and lengthy ones. It enables you to execute hundreds of automated test cases at once, allowing you to quickly test the app across various platforms and devices. This is something you can’t do if you decide to test the application manually. Furthermore, automatic tests can be performed with little human intervention, resulting in greater resource efficiency.

Cost Reduction

In the long run, automated testing is less expensive because once you’ve developed the test scripts, you can reuse them at any time without incurring extra costs. Although automated agile testing is often seen as an expensive endeavor, the initial investment in automation will pay off quickly. The return on investment is measured by the number of automated tests; the higher the count, the higher the ROI. Furthermore, defect documentation in each sprint and routine repository maintenance contribute to early defect identification, which decreases post-production failures and, as a result, project costs.

Test Automation Challenges

Despite the numerous advantages of an automation framework, implementing test automation is not easy. While there are multiple advantages to product engineering automation, there are numerous challenges when it comes to implementing automation testing in an agile environment. The most significant barrier is the upfront expense, which includes training, tooling, configuring, automating, and so on. Also, legacy applications may not be compatible with newer DevOps and Test Automation tools. Since Open Source tools are widely used for automation, a number of security concerns are raised. As a result, a well-organized test automation process may help mitigate these issues.

Agile practices lead companies to better and more advanced software development. The approach to automation testing in an agile environment is determined by the project’s requirements, as different projects need different automation tools. To get the most out of agile automation testing, we suggest that businesses looking to automate should find a strategic partner with prior experience in product engineering automation to increase their chances of success. One can find many IT service providers who excel in automation testing services, and Estuate is one among them. Consult our experts to learn how to increase efficiency and productivity with a well-thought-out automation strategy.

Top 3 reasons why a good Test Data Management strategy is critical

The world is considering the growing importance of data to develop and leverage new and emerging technologies for a variety of purposes. As the technology landscape evolves, software development companies need to adapt and grow rapidly to stay afloat. For that, they need to embrace new technologies, approaches, strategies, processes and indulge in principles such as DevOps, agile, continuous deployment and test automation. When testing software applications, virtually everyone has a data crisis, and test data management is critical to achieving a reliable test automation strategy. The Test completeness and coverage depend primarily on the quality of the available test data. Software teams often lack access to the necessary test data, which fuels the need for better test data management. Quality data availability can ensure successful software testing.

Significance of Test Data Management (TDM)

Test data management is one of the major challenges we face in the software industry. A good test data management strategy becomes necessary for any success of the project. Efficient test data management ensures optimum return on investment, and complements the testing efforts for maximum performance and coverage levels, reducing delays in the testing and development process. TDM is gaining popularity as it implements the methodology of structured engineering to evaluate data specifications for all potential business scenarios and allows easy availability of structured and well-segmented data. Data masking, data refresh, sub-setting, Extract Transform Load (ETL) and synthetic development standardized by Test Data Management (TDM) are some of the primary TDM operations. Test Data Management ensures that test data-related issues and vulnerabilities are detected and addressed before production, enabling the implementation of quality applications at the right time.

What is Test Data Management (TDM)?

TDM is a method of obtaining and managing the data needs of a test automation process with minimal user intervention. It merely means generating non-production data sets that accurately imitate the actual data of the organization, so that system and application developers can perform accurate and legitimate system tests. The TDM process involves phases such as planning, analysis, design, build, and maintenance of test data. TDM guarantees the accuracy of the test data, the quantity, the right format and the timely fulfilment of the test data requirements.

The Need for TDM

Failure to have a good test data management strategy can have devastating implications for businesses. If the test data is not managed and treated sensibly, it can lead to delays in the process of testing and production causing a detrimental effect on the company, as the go-to-market pace of the application will be affected, resulting in business losses. Through setting up a dedicated test data management team and a systemic TDM process, both the company and the customer would benefit greatly. Here we’ve therefore listed three main reasons why it should be imperative to give priority to your organization’s solid data management strategy.

TDM reduces testing time accelerating application time to market

The main objective of TDM is not only the quality of the data but also its availability. TDM has a dedicated service-level agreement (SLAs) data provisioning team that guarantees the timely availability of data. TDM tools promote quick identification of scenarios and the development of the corresponding data sets. With flexible sets of test data accessible over time, testers can focus on real testing rather than worrying about data creation, reducing the overall test run time. Adequate test data, compact test design and execution cycles all lead to smoother and more accurate testing, which in turn allows faster time to market for applications.

TDM guarantees data security and compliance

Organizations that adopt emerging technologies and the digital world store and transmit a significant amount of sensitive data electronically. Although cloud services and data virtualization offers enormous business benefit, it also poses a threat to confidential data. Tech firms are bound to comply with specific legislation and regulatory requirements by the authorities to protect sensitive customer test data else it could result in severe financial or legal losses, damaging the credibility of the company. Since data masking is one of the critical element of a TDM process, you can quickly and easily mask sensitive information through the use of TDM tools and can also produce reports and perform compliance analysis. TDM strategy guarantees the privacy of confidential data, giving priority to data security and compliance.

TDM prevents duplicate test data copies

In a project, teams can replicate various copies of the same production data for their use resulting in duplicate copies that lead to misuse of storage space. With several redundant copies of production data, both maintenance and operational costs get impacted due to the increase in data volumes. Data can be managed efficiently with a good test data management plan, minimizing high storage costs associated with hundreds of duplicate copies of production data. TDM’s data subsetting function allows selective test data extraction enabling all teams to use the same database, resulting in partial duplication and diligently making use of storage space. TDM adds significant value to different teams with approved access points to its central data repository that can easily give each test team an own (masked) test data set that is refreshable on demand. Quality data and improved test data coverage are the main benefits of a TDM process. Adopting an enterprise test data solution will dramatically reduce risk, increase quality and reduce operating costs. Together with IBM InfoSphere Optim, Estuate has deployed robust test data management solutions for automated data management, unified data governance, data identification and analysis. We have the most efficient test data management solutions that can solve the complexities of your test data and eventually accomplish business goals more proficiently.