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August, 2022

Modern Perspective to Modernization

Alex Thompson Digital Engineering, Modernization August 30, 2022

As the tech world continues to gravitate toward digital modernization, cloud-based platforms, and consistently updated applications, businesses of all shapes and sizes need to take a modern approach to sharing data. A modern approach to data sharing includes accounting for the ever-evolving landscape of data security and the technology that makes it possible.

Overall, it should be straightforward for people to share data in a way that boasts security and efficiency, and cloud applications are bringing that idea to life. Sharing data externally is proving to be highly beneficial from a financial standpoint.

The Modernization of Company Technology

Most companies understand that successfully competing in a digital age demands flexible and scalable IT systems. Experimenting with data sharing and the applications or cloud platforms that work best for your business is expected and allows for quick changes that won’t derail projects and workflows.

As a result of this experimentation, businesses on a global scale began to launch programs that focus on modernizing their technology and legacy systems, concentrating on rethinking the modernization process and how they should go about it. A vital component to digitally transforming a business is realizing the potential impact of sharing data in real-time. Real-time sharing of data and analytics is crucial to the modern approach to utilizing data insights.

Relying only on internal data is no longer sufficient. However, if companies can make the correct data information available to the right people, collaborations begin to form, and valuable insights and knowledgeable input can improve business decision-making.

Modernizing company technology can have an incredible impact on many industries where data in real-time is crucial to quality assurance, including food sourcing, customer experience, aviation safety, and efficient global supply chains. There isn’t an industry on Earth that won’t see improvements from sharing data in real-time because it enhances the ability to make educated decisions faster.

Improving How We Share Data

Several ways exist to improve how companies share their data with trusted sources. Blockchain technology, for example, is a distributed database that enforces secure and tamper-proof transactions. It’s a valuable tool for securely sharing data without bringing in a third party’s services.

Businesses could also consider improving their data-sharing capabilities through the use of the cloud, a prevalent way for most companies to modernize the way they share data without completely breaking down the current systems they have in place. Cloud storage creates an avenue for organizations to share and store large amounts of data and files without worrying about the amount of available storage space or limitations to the bandwidth.

With cloud storage, you can access business data from anywhere as long as you have approved login information. With the surge of remote workers and hybrid work environments across the globe, businesses have taken to operating on cloud platforms and updating their current systems to improve how their employees can log into work servers. It’s also easy to track logins, adding another layer of safety to the data-sharing strategy.

The data-sharing process has to begin somewhere, and instead of discussing the challenges that process can bring, we’ll focus on how to improve our understanding. To modernize the ways in which we share data, we must first grasp the importance of the API, fully comprehending the API journey.

Understanding the API Journey

Everyone has probably come across the acronym “API” at some point in their business journey, primarily if they have an online presence. API stands for application programming interfaces, and they’ve become a vital component of modernizing digitally for most businesses. APIs allow companies to link data and systems, playing a massive role in responsiveness and adaptability.

Still, it takes skill and expertise to implement APIs properly, and things can get messy when done incorrectly. When APIs are poorly executed, companies will find redundancies and limited transparency, adding confusion instead of clarity to how we share data. It’s crucial not to get caught up in wasting time rebuilding and replacing legacy systems by adding APIs without a plan, causing regression rather than progression.

Instead, understanding the API journey begins with defining the APIs our businesses need to build, fully comprehending which data needs real-time access and which employees and team members (and sometimes third parties) need access to that data. APIs are the bridge between the various systems that provide access to information.

APIs are relatively flexible, so choosing which ones to build can be overwhelming, with API tests taking up more time than you’d prefer. You must begin with enabling solutions that will provide your company with a solid technical foundation while improving your customers’ journey. Develop your APIs based on the goals you have for your business and how you intend to modernize those goals.

A great example is a traditional bank competing with a technologically advanced fintech by building an API that enhances its customer experience. Introduce efficiency to your consumer base through API architecture wherever possible.

Managing APIs

Building APIs and successfully managing them are two very different things. If a company has a large number of APIs, a solution to manage said APIs makes sense. API management is a way to centralize your APIs, simultaneously keeping your services secure while sharing necessary documentation and data.

Managing your APIs should include a gateway, developer portal, and analytics dashboard for consistent real-time monitoring. API management solutions can help you keep track of your website traffic, connections, security measures, and errors. An API management solution can be tricky to implement, and you must consider additional security layers for exposed systems, rigid access control regulations, lifecycle management, and maintenance.

Properly building APIs and employing them at their full potential is a long road full of challenges. The orchestration and security implementations alone can be time-consuming and frustrating, and onboarding and integrating new partners and systems doesn’t help alleviate the stress of enforcing a new API architecture.

Sharing data in real-time means growing your partner network, which comes with a load of technical work to function so that you can begin to recognize the value of the data presented. APIs are a fantastic data-sharing approach but don’t come without risk, effort, and extensive costs. It’s not uncommon for many businesses, primarily those short on time and funding, to struggle with constructing and working with API IT systems.

A Modern Twist on Traditional APIs

Most businesses today are driven by data, and many have begun to rethink the API approach to sharing said data. Overall, the API approach needs an upgrade to ensure data consistency, especially when various data sources are involved.

Earlier in this article, we mentioned cloud-based data sharing (a standard and typically fast replacement for legacy systems), but we also touched on the concept of blockchain technology. Blockchain technology is dripping with potential, but business leaders must recognize its limits when scaling storage, bandwidth, and power computing. However, connecting blockchain technology to cloud infrastructure could be the modern solution to a technology that would raise the bar for data sharing if it were serverless.

Serverless functions take away the complications within infrastructures that blockchain technologies typically present. Serverless functions provide the perfect solution for unlimited (but costly) computing resources, with no single infrastructure management necessary. With serverless functions, you can scale down resources when they’re not in use, making them on-demand and, therefore, more convenient than they’ve ever been before.

The combination of Blockchain and serverless technology presents a myriad of powerful capabilities. It’s essentially a way to scale data and provide business leaders with a solution entirely native to the cloud, employing desirable aspects like unlimited storage and never-ending networking capabilities.

Scalability and flexibility are must-haves concerning solutions that assist companies in data sharing. Flexible solutions allow storing data from different sources without creating competition for resources. It’s revolutionary.

The combination of serverless and cloud technology provides an innovative answer for companies that want to share data through various organizations. It presents the ability to focus on building applications driven by value and consumer preferences, demands, and expectations.

Business network participants could share data externally, with plenty of access control, in real-time. The number of doors this opportunity opens up is endless, and there is no need to dedicate precious resources to resolving and managing the consistent string of problems that comes with the (often incorrect) building of API architecture

Sharing Data from a Modern Perspective

The modern implementation of data-sharing solutions should be time-consuming. Modernizing technology was once an endeavor that took business leaders years to achieve, but collectively, we’re not there anymore. To modernize successfully, look to the tools that can help your business advance, share data externally and safely, and explore the full potential you have to create a partner ecosystem that works for you.

The idea of digital modernization is to get there faster but also in an efficient manner. Building the API architectures of yesterday is looking more and more like the modernization of the past with each passing day. However, combining serverless and Blockchain technology is the modern approach to data sharing that we’ve all been waiting for.

Using Accelerated Assurance to Build Future-Proof Applications

Alex Thompson Digital Engineering August 22, 2022

The consistent building and updating (or rebuilding) of applications is the beginning of the proof that accelerated assurance is necessary for companies to build future-proof applications. There’s no denying that enterprise applications are complex and sometimes challenging to manage.

Even the most straightforward task can involve various systems and applications. A simple process can use many technologies that require end-to-end testing, and this intense testing needs automation and an incredibly detailed and strategic approach.

Let’s talk a bit more about accelerated assurance and how you can begin to build accelerated assurance for your applications through test automation.

What is Accelerated Assurance?

Every enterprise has an end goal of achieving and maintaining the quality of its applications. It’s impossible to remain relevant, enhance the customer journey, or stay in line with (or ahead of) the competition without automating the quality assurance process.

Teams developed accelerated assurance to preserve application quality while leveraging test automation, tooling, technical testing, and performance engineering. As the tech industry turns toward transforming digitally with zero signs of looking back, business leaders must understand that the days of manually testing applications from front to back are over.

Accelerated assurance provides application quality through automating tasks, freeing your employees to focus more on other company aspects, and providing an accurate result that lacks human error. Tech companies on a global scale are pivoting toward automation, as it’s become the cornerstone for many operational components of a technology business today.

Accelerated assurance teams extend far beyond writing a few scripts to run necessary tests. Instead, they’ll rely on strategy to successfully automate thorough testing throughout the company or enterprise application stack.

If you’re lost, stick with us. We’ll take a moment now to show you how to automate under accelerated assurance.

Automation and Accelerated Assurance

Companies have been automating their quality assurance departments for the better part of a decade, even longer for some. Automating your assurance department does not mean ridding your team of humans, and it’s long been proven that we need human beings to contact customers just as much as we need machines to pick up the slack.

Instead, automating accelerated assurance means ensuring that your team doesn’t waste precious time on daily, tedious tasks. Also, automating our application testing, data and analytics can provide us with more details regarding customer experience, the quality of their interactions with our applications, and what they expect from future exchanges. Test automation helps us gather as much information as possible to keep applications running steadily and the consumer journey seamless.

Understanding Test Automation

Test automation for enterprises is driven toward the quality of company-run applications and what they mean for the business. Test automation takes a top-to-bottom approach regarding setting up pipelines and processes that establish compliance with organizational standards.

Most organizations embrace test automation simply because manual testing is impossible regarding scale and time, and testing based on samples doesn’t provide enough information. Here are a few types of testing that we emphasize when we discuss test automation.

 

Building Accelerated Assurance Strategy

Now that you understand why automating accelerated assurance is necessary for overall application quality and best business practices, and we’ve touched on the different types of tests that applications and systems need, let’s talk about how to build your automated and accelerated assurance strategy.

Client, API, and Web Test Automation

Building strong and resilient web applications that work across all browsers, devices, operating systems, and screen sizes, online and offline, is incredibly challenging. This development process requires a strategic approach to quality assurance for the entire duration of the methodology.

If you want the perfect application, you’ve got to keep an eye on assurance throughout its entire lifecycle. There are no days off when it comes to accelerated assurance or quality assurance, which is why automation is so important. You must cover the following to keep your application steady, working, and reliable.

 

Additional Strategic Components

As we’ve mentioned, every accelerated assurance strategy will look different from company to company. Every enterprise requires something different, but in addition to the most critical strategic components (API, client, and web test automation), business leaders should also consider:

Safeguard Your Applications with Accelerated Assurance

Employing automated testing for the lifecycle of your applications and systems is necessary for applications that will stand the test of time. You can literally future-proof your applications (avoiding restructures and rebuilds) simply by automating the processes that will ensure you have access to their performance and making improvements when required.

DevSecOps and Data Science

Alex Thompson Data and AI August 19, 2022

Before understanding DevSecOps and how it pertains to data science, it’s crucial to grasp the concept of DevSecOps and how it differs from DevOps. DevSecOps is a revolutionary approach to automation, culture, and platform design while integrating security throughout the entire IT lifecycle.

 

Data has become a significant part of all business operations, and it’s become nearly impossible to operate a successful business without analyzing and using that data to make critical business decisions. Today, the combination of information technology and software development is the future of DevSecOps.

DevOps vs. DevSecOps

DevOps doesn’t focus solely on development and operations departments. DevOps is well-known for agility and responsiveness, but if you want to take full advantage of the DevOps approach, you must integrate IT security.

In the past, security remained isolated to a specific team, present only in the final stages of development. Development cycles used to last months (sometimes years) but now that efficient DevOps practices ensure frequent and rapid development cycles, security throughout the process has become imperative.

If your security practices are outdated, your DevOps will not move along as smoothly as you’d like. When collaborating with DevOps and security, you can create a strategy that encourages shared responsibility integrated throughout the entire IT lifecycle. Security in every step is a crucial mindset, and DevSecOps emphasizes the need to build security into the foundation of all aspects of your business processes and initiatives.

DevSecOps means employing security in your application infrastructure from the beginning. Automating certain security gates will keep the DevOps workflow from slowing or stopping creating agile practices and IT operations. By selecting the right tools to integrate security consistently, your company can build on the cultural changes that DevOps brings, integrating security as soon as possible.

DevSecOps and Automated Built-In Security

Regardless of what you call it, DevSecOps (or DevOps) has always been an integral part of the entire life cycle of an application. DevSecOps focuses on built-in security, not security that functions around data and applications. If you save your security features for the end of the development pipeline, you’ll find your business stuck in the long development cycle you were trying to avoid in the first place. It takes a substantial amount of time to go back and apply security once development is complete.

DevSecOps emphasizes the need to bring in security teams and set a plan for security automation. It highlights that developers should write code with security in mind, sharing visibility, feedback, and insights into known threats like malware.

A great DevSecOps strategy determines a business’s risk tolerance to fully comprehend which security controls are necessary within a given application. Automating repeated tasks is essential to a successful DevSecOps plan because running manual security checks can be incredibly time-consuming.

Data Science and DevSecOps

Overall, the concept of DevSecOps is not new for a data scientist. Many data scientists adopt DevOps into their daily work lives, such as testing algorithms for validity, and the presence of DevOps practices provides more reliable results. Data scientists can save time by honing a consistent process that continuously increases accuracy.

It’s undeniable that DevSecOps is forever increasing in need and popularity. Many companies offer foundational knowledge programs to assist other businesses in developing a solid sense of DevSecOps throughout the IT lifecycle, encouraging them to begin utilizing these “security throughout” ideas in their careers.

Security can exist alongside a DevOps culture. Still, it takes a bit of work and company-wide communication to get everyone on the same page. For example, suppose a data scientist is already familiar with the concept and processes of DevOps. In that case, it’s not challenging to employ the idea of DevSecOps as it applies to data science, but business leaders must clearly communicate the ideas behind the concept.

Data Science and Automation

It should go without saying that data science is a particular field. Though most modern data scientists feel comfortable using automation, that wasn’t always the case. For a while, the fear that automated processes would cause inaccuracies in data was prevalent, but as artificial intelligence and machine learning continues to improve, their use is growing substantially.

Today, automation is a significant component of DevSecOps, and data scientists that choose to use DevSecOps must be comfortable with automated processes, as it’s the best practice for methodology. Data scientists often run automated scripts when attempting to understand what a large influx of data contains and when dealing with quality assurance.

Not all data scientists deal with the same type of data. For example, data scientists that work with terrorism and fraud require automation to avoid falling behind in studying an influx of crucial data. Generally, data scientists always place plenty of focus on security, regardless of the type of data they’re responsible for, even when not an official member of the company DevSecOps team.

Due to a high level of security concerns and knowledge, data scientists tend to fall easily into DevSecOps roles. Many employees and team members will need constant reminders when implementing a DevSecOps business model, but data scientists rarely forget to include the security component.

DevSecOps and the Inevitable Emphasis on Data

Business operations today, regardless of industry, emphasize data. Data has become an integral part of how businesses run, from providing essential consumer demographics to pointing toward potential security breaches or weaknesses.

Global internet users understand that they cannot use a website or social channels without sharing information. It’s become entirely acceptable, as long as the companies that receive that information store it and share it responsibly.

However, data breaches are not uncommon, and when associated with massive social sites like Facebook and major retailers such as Target, people tend to become wary. The application of DevSecOps principles can assist data scientists in helping to promote privacy and security for companies (like social media giants) to keep up with the constant evolution of technology while keeping data safe.

Data Science and the Benefits of DevSecOps

Knowing the benefits of DevSecOps is crucial to understanding how it ties into data science practices. While data scientists often embrace DevSecOps practices without being a part of the internal “team,” there are still many advantages to learning and applying DevSecOps to the daily workflow, including:

Data scientists can benefit greatly from integrating a DevSecOps mindset. Not only does it place security at the forefront, but it keeps it present at all times, regardless if the task is automated or manual.

An Awareness of DevSecOps

All data scientists should be aware of the concept that is DevSecOps. There’s an undeniable influx of data consistently coming into every company worldwide, ranging from consumer statistics to potential data risks. Data scientists need to understand the notion and gain full awareness of what it means to apply it.

Most data scientists already work under strict security measures, but regardless of how they work with data, the principles of DevSecOps can apply and enhance their current techniques.

Getting Started with Feature Transformation for Machine Learning

Alex Thompson Data and AI August 16, 2022

Machine learning is a modern yet essential piece of the digital transformation and data analytics processes. On the other hand, feature transformation is the process of modifying data but keeping the information that data provides. Data modifications like these will make understanding machine learning (ML) algorithms easier, delivering better results.

This article will discuss the importance of feature transformation, a crucial step in the preprocessing stage. Feature transformation allows for the maximum benefit of the dataset’s features and the long-term success of the application or model.

Applying various mathematical techniques to existing application features can result in new features or feature reduction. Modifying existing data and increasing the available information and background experience can increase the model’s success by keeping the information constant.

The Need for Feature Transformation

You might find yourself asking why feature transformation is necessary in the first place. The need becomes more apparent when you understand that if you have too few features, your model will not have much to learn from, while too many features can feed a plethora of unnecessary information. The goal is to be somewhere in the middle.

Data scientists often work with datasets that contain various columns and different units within each column. For example, one column might be centimeters while the other is kilograms. So you can see the range, we’ll use another example of income, with columns ranging from $20,000 to $100,000 or more. Age is another factor with many variables, ranging from 0 to upward of 100.

So, how can we be sure that we’re treating these variables equally when dealing with machine learning models? When feeding features to a model, there is a chance that the income will affect the result because it has a more significant value. However, it doesn’t mean that it’s a more important predictor. To give importance to all variables, feature transformation is necessary.

How to Identify Variable Types

We’ve touched on how feature transformation can affect variables’ effect on an outcome, but how can we determine variable types? We can typically characterize numerical variables into four different types.

When you begin a project based on machine learning, it’s essential to determine the type of data in each feature because it could severely impact how your machine learning models perform. Here are four variable types in feature transformation for machine learning.

Data Preparation

Feature transformation is a mathematical transformation, and the goal is to apply a mathematical equation and then transform the values for our further analysis. Before we do this, however, it’s crucial to prepare the data you’ll be changing.

Analyzing data without preparation is impossible, and you can’t apply genuine feature transformation without examining. So, here are the steps you should take to prepare your data for feature transformations.

The Goal of Feature Transformation

The goal of feature transformation is to create a dataset where each format helps improve your AIML models’ performance. Developing new features and transforming existing features will significantly impact the success of your ML models, and it’s important to think logically about how to treat your prepared, collected data and the current list of variables you have.

When you enter the model-building phase, you should go back and alter your data by utilizing various methods to boost model accuracy. Collecting and taking the time to ensure your data is ready for transformation will reduce the time you spend returning to the transformation stages.

Feature transformation will always be beneficial for further analysis of collected data and changing how our machine learning models operate. Still, knowing how to prep your data and categorize it is crucial, so your transformations provide accurate, helpful, eye-opening results.

Security Management Architectures: Balancing Security and Complexity

Alex Thompson Digital Engineering August 11, 2022

We’ve advanced well into the age of modern technology, primarily regarding business operations and the digital modernization of daily workflows and processes. The importance of securing a company against cybersecurity-related threats and hazards is impossible to ignore.

Security breaches are astoundingly costly, adding up to millions in damages for many companies annually. When caught unprepared, security problems can wreak havoc on your organization.

Security architecture boosts encryption and reduces the risk of cyber-attacks, and protects your company and consumer assets and data from harm. We’ll start with an overview of security architecture and what it means for your business to establish a strong foundation of security management.

Understanding Security Architecture

Security architecture has many different meanings and definitions for various companies. Ultimately, the security architecture is a set of established security principles and methods that align with your company objectives and keeps your sensitive information safe from potential cyber threats and attacks.

Security architects examine the current status of your business regarding security, and then they’ll produce a blueprint, or a plan, to help you achieve your desired security outcome. A security architect will guide your teams, helping you establish security management and rules and regulations to keep your data safe from every angle.

The Purpose of Security Architecture

The purpose behind security architecture is to protect your organization from outside threats. To meet this goal, security architects will often insert themselves within your daily business practices to learn as much as possible about you, your company, and the people who work for you.

They’ll have conversations with your employees, team leaders, and management to seek an understanding of your business goals, what your systems require from a security perspective, the needs of your customer base, and other critical factors.

Once they’ve gathered all the necessary information, they can construct a plan and offer guidance that suits your business objectives and cyber risk. An expert security architect will help you find the perfect balance between security and complexity, securing your systems and data without enforcing over-the-top security measures that might interfere with your current processes.

Secret Architecture: The Other Side of Security Architecture

If you’ve got a good and effective security architecture in place, the chances are that everyone in your company is a part of that architecture. From routinely creating new log-in passwords to sending secure emails, your employees know the deal regarding security and the responsibility they have surrounding it.

However, even by a skilled professional, building a security architecture doesn’t always involve secret architecture, which has become notoriously challenging to manage. Managing secrets, or sensitive entities such as passwords and API keys, continues to prove troublesome for security teams.

Many developers continue to store plaintext passwords inside a collaborative platform like GitLab or keep sensitive credentials inside Docker images that anyone can access. It’s crucial to find a somewhat simple solution to preventing outside leaks, especially when talking about the ability of software to deliver while remaining reliable.

Of course, access to secrets like passwords is typically restricted, but companies must consider which route to take in various situations concerning secret architecture. For example, what happens when the employee leaves? Many angles require examination from external parties with access to security breaches when discussing secret architecture.

Performing a monthly audit to determine who accessed company secrets, establishing different levels of access groups, and understanding how different cloud platforms access different applications are essential components of understanding the path your business should take in security and secret architecture. Of course, secret architecture will not be the same from company to company, but asking yourself the right questions creates a fantastic starting point.

Building Your Secret Architecture

There are various technologies that businesses can combine to find a solution that makes sense through the balance of security and complexity levels. Many tools labeled “too complex” are often misunderstood.

Still, it’s crucial that companies and business leaders understand that specific tools should be used only when it’s beneficial to the project in question. The stronger a security solution, the more complexities it could add to your development process.

Define Your Project Goals

Developing a secret architecture for your business and DevOps processes is nothing other than a modern tech journey. Before establishing security or secret architecture, you must maintain open, honest communication with your team members.

Talk to them privately or in a group setting (whatever works best for your business and team dynamic) about the following:

Your team members should state the current status, any concerns, priorities, and expected timelines regarding each of the talking points mentioned above. Access to this essential inside information will help you define the fundamental (if not more complex) features of the secret architecture you want to implement.

It’s not advised to introduce complicated secret management solutions immediately. Because security measures must be a company-wide effort, especially in DevSecOps, you must ensure everyone is on board.

Begin by allowing your teams to become comfortable with new technology, keeping the lines of communication wide open. As time passes, you can evolve the difficulty level of new software and technologies, increasing the sophistication of your secret architecture process.

Gradually Adding Complexity

It helps to think of your secret architecture as three steps concerning the addition of complex measures your team will eventually have to take. When you map out the levels of complexity for them, you can spark discussion among the team and assist them in visualizing how secret management architecture will look and evolve.

Secret Architecture Technology

Now that we’ve mapped out your potential plan for putting your secret architecture into place, you’ll want to understand a few technologies you can utilize to get the job done. They needn’t be used all at once, and you may find that some will serve you better than others.

Git-Crypt

Git-Crypt encrypts files in a git repository. It’s an open-source project that relies on git hooks and requires the knowledge of a professional to execute. Any file listed with .gitattributes will automatically encrypt before it goes to the repository for storage. Decrypting these files is not easy, and as a whole, Git-Crypt comes with inevitable conflicts that your secret architect can help you address.

AWS Key Management Service

Hosted by AWS, encryption key management can encrypt an encryption key. Once you’ve encrypted the original key, it is ineffective in decrypting any of your secrets, which means it’s safe to store your encrypted version along with your other secrets.

You can choose to discard or store the original encryption key as long as you have access to a safe and reliable backup. AWS has it handled and can decrypt your encrypted encryption key.

ChefVault

ChefVault is a somewhat complex provisioning tool that allows users to describe everything from registered users to installed applications. ChefVault stores your descriptions in their “recipe” system, which you can contain in their “cookbooks.”

Secrets tend to be necessary to complete various tasks during a provisioning process, such as needing password access for a new database installation or onboarding aspects like creating new users. ChefVault makes it possible to obtain these secrets without jumping through (too many) hoops.

 

The Perfect Balance Between Security and Complexity

The secret to finding the perfect balance between security and complexity lies in knowing how many layers of protection your company needs and which technology can help you execute those layers. Open communication and a slow secret architecture implementation will keep your team from becoming overwhelmed, allowing you to establish the security you need without confusion. When you keep the process slow and steady, you prevent it from becoming too complicated.

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