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The Data Modernization Challenge
Data modernization and artificial intelligence are taking over the business world. These days, you can’t turn around without hearing phrases like “machine learning” or “digital modernization.”
Every business owner everywhere has at least a small stake in wanting to digitize their business. After all, it’s near impossible to remain relevant without modernizing legacy technology platforms. Data challenges are no stranger to every company on Earth since modernization is the driving factor behind those data challenges.
While many major corporations, big businesses, and modern start-ups have gotten a handle on modernizing their digital processes and embracing cloud computing, smaller but established companies are struggling to make the change. For the most part, these struggles relate to time and capacity.
Data Management in the Modern World
As data management continues to revolutionize, enterprises of all shapes and sizes are experiencing issues with data quality and integrating cloud-based technology platforms. While many businesses are right in the middle of an attempt at modernizing their current data, the way companies keep their data is evolving from an on-premise-centered approach to hybrid architecture.
Shaping Modern Data Architecture
As companies target legacy technology modernization across the globe, leaders in the tech industry have identified some significant players regarding how businesses choose to manage their data. Though the companies may be radically different, the data management elements remain the same.
Open-Source Frameworks
These templates for software development, typically designed by a social network of software developers, are extremely common among businesses shifting how they manage their data. Open-source frameworks are free to use, and they allow all companies to access the big data infrastructures necessary to implement modernization.
Cloud-Computing
Overall, cloud computing is relatively simple regarding user-friendliness and data storage. Many providers boast cloud storage and other cloud-related perks for relatively low prices. The availability of cloud-hosting companies is encouraging businesses to invest by integrating or moving their legacy systems to the cloud. Migration to the cloud is one of the leading players in data modernization, without question.
Analytics Tools
The evolution of analytics tools is playing its part in the desire that many companies have to modernize their data. Overall, analytics and end-user reporting are better (and more sophisticated) than they have ever been before.
The addition of the Citizen Analyst role is prevalent in many modernizing companies that focus heavily on analytics. A Citizen Analyst is a person who is knowledgeable in analytics and machine learning (ML) systems and algorithms. Your CA, should you choose to have one on staff, will assist the modernization process by identifying profitable business opportunities.
Data Challenges and Modernization Barriers
As the world races toward an even newer and more modern digital era, it’s clear that there are companies left behind. It was once possible to forego a presence on the internet as a business, but those days are long gone. To remain relevant and in line with, or above, your competitors, you have to focus on modernization and your customer journey.
Data challenges and modernization barriers are prevalent, but they don’t have to stop a business from being profitable digitally. However, it’s almost impossible to maintain profits while ignoring modernization.
Data Quality
We touched on this very briefly at the beginning of this article, but data quality is a massive hindrance regarding the mechanical aspects of modernization. Data issues, such as inconsistency and incompleteness, impact company migration to the cloud. Most of them stem from the inability to keep high-quality data both during and after the transition.
Data Sprawl
It can be incredibly challenging to integrate cloud data and on-premise data. The amount of various digital information created, collected, shared, stored, and analyzed by businesses make up their data sprawl. Depending on the size of the enterprise, the sheer size of this data may be overwhelming to move, primarily if you’re dealing with the data showing up as incomplete.
The Role of “Big Data”
Modernization through data strategy is a fantastic concept if properly embraced. Thousands of companies are not using a “big data” platform or data stored in a greater variety, with increasing volumes and more velocity.
This lack of use has nothing to do with the effectiveness of storing data on a “big data” platform. Instead, it suggests that companies have trouble finding the role that “big data” should play within their existing data. They know they have to modernize, but they don’t know where to start, and this state of overwhelm is one of the most significant data modernization challenges in existence.
Compliance Concerns
Data challenges are prevalent in the form of compliance concerns. With the consistent modernization and movement of primarily sensitive data, plenty of regulations and data protection mandates are rising to the surface.
Obviously, we need rules and regulations in place to protect sensitive data for businesses and consumers. However, many companies worry about the inability to meet ever-changing compliance regulations, potentially facing fines.
The need for regulated data safety isn’t going anywhere anytime soon, so companies must find a way to comply if they’re going to focus on digital modernization and the up-leveling of their business. Regardless of your feelings on the topic, there’s no question that it’s definitely a challenge for data modernization.
Successfully Modernizing Data
Harnessing the power of your current (and ever-growing) database is essential to achieving growth and excellence in your business operations. Successfully modernizing legacy systems means complying with mandates, enabling priceless analytics for your company, and providing a fantastic consumer experience.
Modernization barriers tend to come in the same form for every business, but this doesn’t mean you can’t succeed at launching a digital revolution. However, you’ll have to clear a few roadblocks (other than data quality) along the way.
Misaligned Employee Skills
More often than not, the current skill set of your employees does not align with your data management needs. Everyone struggles (to a certain degree) to find talent for their workforce. When it comes to data modernization, the amount of knowledge your employees have or don’t have can directly impact data management and the implementation of new solutions.
For example, you’ve hit a wall if you’re attempting to employ an advanced analytics platform that your employees do not have the skill set to use. Data Science professionals are essential to data modernization, so this is a problem for many companies.
Open-Source Hurdles
Even though open-source tools open the world of data modernization to almost everyone, many businesses are too wrapped up in security concerns to consider using them. When utilizing an open-source platform, the speed of change is significant, affecting the entire organization if everyone is operating on different pages.
Digital modernization requires company-wide support and effort. Maintenance is also a challenge for open-source, as is the implementation of the applications. As you can see, workplace talent is crucial to pulling off successful data modernization.
Early Stages of Basic Solutions
The most basic data storage solutions are in the (very) early development stages for many businesses, which is quite troublesome. Data lakes and data governance tools are foundational for data-driven companies. Still, because so many of these businesses are in the early stages of fundamental data storage, problems are sure to arise regarding the ability to move forward to a more modernized approach. They’re simply not ready.
Unsatisfied with Implementation
If there’s one thing that many companies have learned throughout attempting to modernize their data, purchasing or downloading the framework to upgrade the way you keep your digital information doesn’t automatically mean you have a complete solution.
Many organizations remain unhappy with the way their data tools are governed or implemented and their analytics platforms and data lakes. It’s not to say that this dissatisfaction comes from the tool itself, but instead that it lacks the ability to meet the needs of the business.
Our Recommendations
With so many companies stuck in the middle of a digital modernization mess, we understand that the bottom line for businesses is to have access to systems that show results immediately. However, technology cannot solve your problems on its own.
To get the best out of data modernization, we suggest:
Test emerging technology as it evolves at a rapid pace. The technology you implement today could become obsolete within the next five years, so select a provider that stays in tune with these changes.
Put the cloud at the center of your modernization strategy, as it’s designed to deal with operational workloads and analytics with high levels of security.
Choose to work with a provider that focuses on the priorities and initiatives of your business. Tangible results come from providers that understand outcomes.
Overcoming Data Modernization Challenges
It’s frustrating to sit in the middle of operating on old legacy systems and attempting to modernize your data with neither end of the spectrum working in your favor. Your best bet is to partner with a service provider that can focus on results while building hybrid strategies.
There are too many benefits to organizing and digitizing your data to work for your business, contributing to growth instead of simply existing for reference. Data modernization can’t be ignored, so ensure that you’re taking the right path.
How Do You Choose Your Microservices Strategy? Too Many or Too Few?
The technology industry is constantly evolving, and microservices are the perfect example of how we’ve made applications easier to manage. Of course, one of the biggest problems with microservices is that most people don’t know how to apply them using the best practices, or even worse, at all.
In the simplest terms, microservices act as applications within themselves. When you restructure your applications as a collection of microservices, you’ll achieve maintainability, easy and fast deployment, and scalability. These attributes make it much easier to manage and maintain your company applications.
What are Microservices?
Before you truly begin to think about how you’ll apply microservices to your current applications, you should know a bit about them. Best described as an evolved architectural pattern, microservices involve designing and developing an application as a collection of autonomous, loosely coupled services that can communicate with each other.
Also known as microservice architecture, microservices are a piece of modern software development that enables fast and accurate delivery of large, complex applications. Microservices allow your company to broaden and improve its technology stack. They’re becoming a much-desired component of digital modernization.
Building a Successful Microservices Strategy
Building a microservices strategy can be stressful, to say the least. It can be challenging to know how many (or how few) microservices to employ right out of the gate. As usual, evolving digitally takes a lot of careful planning, and microservices are not an exception to that rule.
There are practices you can put into place that will help you build your microservice architecture that will help new microservice adopters transition from monolith architecture. Before you begin your next project, consider these microservice tips, as many of them will work well for those new to these technology concepts.
These tips will work well for those unfamiliar with microservices to help create a more descriptive and easier transition.
Microservices Planning and Organization
Before you move forward with attempting to create a microservices architecture, you’ll want to ensure that it’s the right move for your business. Don’t commit to changes just because larger organizations are doing it. Instead, break up your requirements and notice where microservices will add real value.
Also, you’ll have to ask yourself if you can successfully divide your applications into microservices. After all, you’ll want your applications to maintain their core functionality and features, and microservices should enhance these features.
The Transition
The transition from a monolithic architecture to microservices is incredibly long and involved. While most of these changes will fall on the shoulders of your development team, you’ll have to consider your stakeholders.
Think about the amount of time and expertise needed to implement these infrastructure changes and the amount of work your engineering team will have to take on. To convert to a microservices architecture successfully, everyone has to be on board.
Building Teams
There’s no question that the conversion to microservices requires teamwork. One of the first steps in the process (other than getting your team members excited about the transition) is to begin building independent microservices teams. Assign different groups to handle different microservices independently.
Designing Your Microservice
You want to avoid building microservices that are too large or too small or have too many or too few. Microservice architecture is all about finding the perfect balance, which can be challenging for many companies.
If your microservices are too large, you’ll be unlikely to see any benefits from utilizing the architecture, which is disappointing once you’ve put in months of work. If your microservice architecture is too small, you’ll unintentionally drive up operational costs.
Microservices Perform One Function Well
Microservices are meant to perform one function very well (also known as high cohesion), so they’ve got to be developed not to depend on other services to perform that function. You want to achieve a Domain-Driven Design regarding your microservices, ensuring your service covers a single-bounded context.
Security
In the light of any new technological development, you must consider security. Adopting a Development Security Operations (DevSecOps) model helps ensure that your microservice framework is secure. Microservices exist on a distributed structure, which means they are more likely than other services to attack vectors.
Security for microservices requires an entirely different technique than when we’re dealing with a monolithic architecture. DevSecOps will work wonderfully here.
API Communication
Microservices should communicate through an API gateway that handles account and user authentication, requests, and responses. When you have that API gateway set in place, you can redirect traffic away from the gateway and to the most recent version of your application whenever an update takes place.
Microservice Development
If you’ve made it to the development stages of your microservices strategy, you’ve likely realized the number of microservices you should put into place to benefit your company. Remember, development for microservices is a considerable undertaking, and it’s best when done with balance.
Separate Control Strategies
Keep your control strategies for your microservice development separate. This way, you can implement changes that will not affect other services while keeping control logs tidy.
Backward Compatibility & Development Environment
Backward compatibility will assist your company in building production-ready applications quickly. It enhances the service components of the application without breaking callers.
The development environment of your microservices should be consistent across virtual machines, establishing the framework and allowing developers to get started quickly.
Storing Data and Managing Microservices
Each microservice needs a different storage database. You don’t have to use the same data storage for each of your microservices, as long as the two are well-matched. You’ll have to customize storage infrastructure to match your microservice, keeping in mind that storage is one of the key ways to build a solid microservice architecture.
The bottom line here is that you must manage each service independently. At the same time, they must work with other services seamlessly.
Launching and Hosting Your Microservices
Deploying your microservices is crucial to success and helps to save time while you’re coordinating regular upgrades. You don’t want one service to use up an unfair amount of resources because it negatively affects your other microservices.
Dedicated infrastructures are essential to hosting each microservice individually, isolating them from affecting each other directly. Your microservices strategy could crash and burn if you do not intend to keep them separate. Isolation will help avoid a complete and total shutdown in the event of the outage of one service.
Container Storage
Container storage is a fantastic way to keep your microservices organized and operating unassisted. When you containerize your microservices, you can launch services independently without messing with systems and services that exist on different containers.
Containers tend to match the goals of microservice technology because they offer platform independence. This blends perfectly with the purpose of microservices and how we can best maintain them.
Separate Builds and Automate Deployment
There’s no doubt that automation is the future. Automating your microservices through a DevOps workflow is the best way to handle and improve efficiency. Individual builds for your microservices are also essential for facilitating CI and CD.
Maintaining Microservice Operations
A centralized monitoring and logging system will save discrete logs for every microservice you establish. The correct maintenance and operations for microservices include a centralized logging system, aiding in handling errors much faster than possible on monolithic architecture.
Making the Transition
Deciding if your company is ready for microservices is challenging. The transition can be tricky, often requiring the entire team to lend a hand as changes occur. There’s no question that microservices help manage applications more efficiently, but it’s not right for every business.
In many cases, the delay in transitioning to microservices isn’t due to the architecture being a poor fit for a business, and more that the transition is so complex it keeps companies from trying. How you’ll go about implementing your microservices is very different from how other businesses will choose to do it.
However, the practices mentioned here are universal, fundamental ways to keep yourself on track and your company headed in the right direction regarding developing your microservices.
The Goal of Microservices
The goal of microservices is to improve the development of positive application attributes, including maintainability, testability, and deployability. In short, microservices allow any organization to develop better software at a faster rate.
You’ll be looking to achieve a framework of services that are loosely coupled, distributed, and independent of other application services. Microservices essentially remove the need for applications to be dependent on one another.
A DevOps model is a massive component of helping your microservices endeavor run smoothly. You’ll want to establish the perfect balance of microservices for your company while enabling automation and efficiency.
If your company needs the perfect architecture for continuous delivery, microservices could be perfect for you. The ability to edit services within their containers, taking away their ability to affect other services during maintenance, is revolutionary.
Remember, the microservices have to make sense for an application to work correctly. You may have some instances where microservices will work for you and others where they will not. Any organization that can find a way to implement microservices (with a good balance, not too few, and not too many) should begin planning out the shift today.
The Future of Design Operations
In recent years, the demand for a successful online presence has brought the need for a flawless customer journey to the top of the list for many companies regarding goals. Brand touchpoints placed throughout the user experience can help establish your brand, remaining at the forefront of customers’ minds when they need your products or services.
The rise of eCommerce and the current requirement to stand out among the competition has increased the workload for design teams and freelancers. With that increase came the revolution of Design Operations, often referred to in the industry as DesignOps.
What is Design Operations?
Design Operations can come in many forms. The position can exist as a single person, a remote freelance group, or an in-house team dedicated to planning, defining, and managing the design process within a company.
It’s important to note that while the responsibility of Design Operations is to manage the design department, they may not be designers. The job calls for more management and planning experience than it does actual design.
The role of DesignOps is to streamline the department processes and provide the design team with the tools needed to succeed. The specifics will vary from company to company. Still, for the most part, DesignOps will remove inefficiencies from the design approach and create operational workflows.
The Design Operations Process
A typical DesignOps team focuses on many workplace responsibilities, from managing projects to budgeting and hiring. They rarely have a background in design, as it’s not necessary to do well in the position.
The areas of focus for Design Operations do not have anything to do with the design itself. Most departments within any company have three overall focal points; people, business, and workflow. DesignOps is no exception to this rule.
People
A massive part of a successful DesignOps process is ensuring that the people, the designers, have what they need to work toward personal and company growth. A great Design Operations manager will help designers define their career path and identify any gaps in skills that need addressing.
The goal here is to build a world-class design team capable of bringing a brand to the next level.
Business
A mindset for business is necessary for DesignOps management. DesignOps can provide designers with the software and problem-solving tools required to do their jobs efficiently by determining and securing the proper budget for the design team. Working in Design Operations isn’t only about streamlining the process. It also means acting as the voice of your designers.
Managing Workflow
Any operations department is accountable for the workflow that exists within the department. As Design Operations continue to evolve, workflow responsibilities will begin to expand, and it’s imperative to set up sufficient design management software.
DesignOps should manage the workflow by concentrating on design research and scalable processes to contribute to company growth. The goal is a creative production flow that works regardless of company or team size.
The Importance of Design Operations
Staffing a Design Operations team wasn’t always necessary for running a successful business. Today, DesignOps are essential.
The days when success didn’t require a distinct online presence are long gone. Consumers expect to find prospects online for most industries, and eye-catching, precise design is the only way to grab their attention.
Design Operations act as a crucial piece of the design puzzle, ensuring that businesses deliver targeted content to meet market demands. Well-known companies are utilizing DesignOps on a global scale.
Still, smaller start-ups often can’t yet embrace the financial commitment that comes with staffing someone to handle the job of company-wide DesignOps. If this is the case, you can set up a design process that will keep your designers on task, eliminating their distractions and scaling their workflow.
Developing a DesignOps Mindset
To greatly assist your design team without hiring a specialized DesignOps manager, you’ll have to form your decisions and actions around a DesignOps mindset. Prepare to provide your team with full support, which can look different for every business but include the same basic principles.
The Objective
The objective behind your DesignOps team, or your DesignOps mindset, is to support your design team members fully. At the same time, they concentrate on consumer and business goals.
You can provide that support by giving them what they need to get their jobs done without asking them to complete side tasks or take meetings that interrupt the creative flow. It’s all about using the correct input to keep your design team consistent.
Provide Purpose & Obtain Talent
Please make sure the design teams know their purpose. When they understand why they exist, they can better ingrain themselves in company goals, aligning with success.
Create a workplace culture that makes your designers want to stay. Even if you don’t have the budget for a DesignOps team, take over that role until you do. Good employee experiences are the key to attracting top talent in the future.
Streamline a Process & Provide Tools
Even if you have the best talent, turning them into a team that functions seamlessly for day to day operations is a process. It allows for open communications and teamwork, allowing everyone to participate in team successes.
Successful communication requires tools for collaboration. The right tools extend beyond just design software. They can remove any potential friction and roadblocks within the team design process. Think of anything that increases team communications and makes the job of your designers creatively easier as an effective DesignOps tool.
Focus on Structure
With proper structure within a design team comes the ability to act accordingly. The structure is crucial to the future of DesignOps, as it establishes communication methods and allows everyone to know how decisions are made and who will make them. It’s a massive part of leading any team successfully, primarily when the goal is an efficient workflow.
DesignOps Teams for the Future
It’s become increasingly clear that DesignOps is the future of design departments. It’s an essential piece of the puzzle, freeing up design teams and allowing them to do what you hired them to do; bring your brand design to the next level.
DevOps: A Discipline, A Way of Life, A Culture Change
It’s become apparent, primarily in recent times, that company culture is imperative to the way that company performs. The culture in our company gives plenty of information regarding the values, morals, and norms within a company or organization.
Neglecting to manage company culture, whether in-person or virtual, can lead to purposeful or accidental wrong practices. Culture encompasses the way those within a company will approach one another and whether or not they’ll succeed as a team.
Development Operations (DevOps) are part of the company culture. DevOps is a way of life in many organizations, often taught by management or external sources as a workplace discipline. The demand for constantly improving digital experiences is at an all-time high.
Most brands adopt DevOps to streamline the development, deployment, management, and maintenance of software at scale. There are certain principles of DevOps that need to fall into place before you can consider it a way of life within your organization.
DevOps: A Way of Workplace Life
DevOps is so much more than just a buzzword or a thing. Instead, it’s a literal set of principles that lay the foundation for an agile DevOps work culture. The primary objectives of the DevOps process include:
Speeding up the deployment time for a product or service
Apply improvements in an ever-changing environment
Streamline a development process
The fastest way to achieve an agile transformation to a DevOps environment in your organization is to combine your operations and development teams, encouraging them to communicate and collaborate more. This partnership should allow for the design, implementation, and management of continuous integration and continuous delivery (CI/CD) frameworks.
Suppose you’re wondering how to cultivate such an engineering culture and an agile mindset among teams. In that case, there are certain principles you can apply to ensure a smooth transition into such a culture change.
A Collaborative Environment
For brands to achieve a unilateral team that focuses on delivering common objectives, they must encourage operations and development to communicate. By breaking down silos and bringing these teams together, your company can align its people, tools, and processes toward a customer-focused culture.
Fostering collaboration prepares employees for the cultural shift that must take place as DevOps gains a foothold within your organization. These changes have to start from the top, lining up executive sponsorship before anything. There’s nothing wrong with a well-executed initiative that comes from the top, as long as it’s completely transparent and well-understood by company teams.
Management should focus on putting the right people on their teams, those that will lead a culture change with confidence.
Responsibility On All Ends
Operations and development have entirely different roles in the traditional software development models. However, DevOps asks these teams to work together on every project and company application from beginning to end. Responsibility from start to finish is one of the core principles of DevOps.
Gone are the days when development wrote the code and operations set that code into action. There are far too many opportunities for error in the separation model, leading to many inefficiencies that you can fix by employing a DevOps mindset. DevOps helps avoid differences in production, performance problems, and unpredictable environments. Overall, it’s a collaborative push.
Encourage Improvement
The DevOps culture comes to those who encourage continuous improvement. Responsibility from end-to-end means constantly adapting to circumstances that change, including customer needs, the emergence of new technology, and changes in legislation.
The DevOps way of life places a focus on optimizing performance through constant improvement and the cost and speed of delivery. A valid application of DevOps means uniting teams to support your CI/CD pipelines, enabling the efficiency of the deployment of applications. Automated application release allows for minimal downtime.
Automation is Key
When it comes to a thriving DevOps environment, automation is the key. Striving for continuous improvement is a significant part of building a DevOps discipline that lasts, and high cycle rates and the ability to respond quickly to customer feedback are essential. Your brand has to begin automating repetitive processes to successfully pull this off if you haven’t already.
Automation is the best way to release new software to your consumer base rapidly. Automation within DevOps looks like this:
Automation of infrastructure provisioning
Software deployment and testing
The building of new systems
Verifying functionality and security
It’s only natural that DevOps teams will begin to automate their own processes as well, from building and running software to safely making changes to their running services. By automating services within DevOps teams, your organization can get the software to the masses in a more reliable fashion than ever
Customer-Based Focus
The DevOps mindset is very focused on the needs of the customer. Regardless of size, it requires every brand to act like a start-up, continuously innovating and pivoting when established strategies are no longer working. DevOps means investing in software features that will deliver a fantastic customer experience across the board.
The demands of consumers are constantly changing, and if you’ve done the work to establish a DevOps culture, your team is listening, continually reviewing automated data, and working with it instead of against it. However, DevOps also means that your team focuses on the correct automated data.
Don’t obsess with your metrics, primarily if you’re new to the DevOps way of life. Instead, focus on the metrics that matter to your company and the people that utilize your services. It can be easy to get lost in the noise of analytics, but a DevOps mindset can work through that noise to the information that matters.
Learning from Failure
With any intentional culture change, no matter how positive, comes failure. DevOps requires that organizations embrace a whole new way of working, including cloud computing. Accepting potential and inevitable failures on every company level fosters a learning climate where employees feel safe making mistakes.
Failures are nothing but opportunities to learn, and this is an attitude that you should foster early in the switch to a DevOps state of being. The willingness to learn from mistakes can create a massive shift within company culture as well.
Uniting Teams and Expertise
DevOps is very much about unison and sharing expertise. It’s an incredibly collaborative business culture that encourages learning, automating repetitive processes, and streamlining a company through conveyed experience. DevOps teams have to be very involved at every stage of software development.
From planning and building to deployment and feedback, DevOps is there, acting as a cross-functional team with a very well-rounded and balanced set of skills. There is a place for DevOps within every company, seamlessly streamlining operations in various companies and multiple components of the technology world.
It’s not easy to find a well-rounded IT professional, though they exist. Instead of trying to find the perfect members for your DevOps team, create them. Encourage shared expertise at every turn, and stress how important it is to live a DevOps lifestyle when present in the workplace. Above all, lead by example.
DevOps doesn’t have to apply only to newer companies that are more susceptible to change. It works everywhere, and it does not mean the erosion of functional responsibilities. Instead, team members with different expertise areas will share responsibility for running code in the production process.
DevOps is the new definition of teamwork.
Building Your Modern Data Platform with Data Lakehouse
Modern data platforms require a separate storage and processing layer to work efficiently. A data lakehouse is a solution that combines a data warehouse structure (typical in most original legacy tech systems) with the more advanced and convenient features of the data lake.
Data lakehouses enable the same schema and structure as those in your data warehouse, and they apply that structure to unstructured data, like what you’d find in a data lake. Data lakehouses allow users to find and access information more quickly, so your team can begin putting that stored data to work.
Building a Data Platform
Once done out of convenience, building a data platform within your business is now a necessity. Improving your customer experience based on data-given actionable insights will increase revenue and define your brand. However, it can be difficult for companies to pinpoint the right ways to define their data platform.
The technology industry hasn’t exactly developed a blueprint for IT teams to follow, and data layers will look different for every company, typically based on the industry and type of company in question. In this article, we’ll talk about how you can lay the foundation for a modern data platform and utilize that data lakehouse.
Understanding a Data Platform
Think of your data platform as the central nervous system of your company data. Your platform should handle the collection, cleansing, transformation, and application of all data in storage and use it to generate insights. Many companies are data-first and have embraced housing data as an incredibly effective way to scale data.
Gone are the days when companies treated data as a means to an end, final product, or outcome. Instead, data has become more like a type of software. Most companies dedicate entire teams and plenty of time to maintaining and optimizing their data and, in doing so, can achieve accurate data-driven results.
ETL/ELT data pipelines should be layered, which can bring in a certain level of confusion for teams that might be unfamiliar with the data lakehouse or a modern data platform.
How to Build a Modern Data Platform
You cannot build your data platform without a foundation, and each of the platform layers mentioned will assist you in establishing your data lakehouse from the hypothetical ground up. It can be challenging to know where to start, but every business has the same core layers regarding a modern data platform, and they are as follows.
Storage and Processing
You cannot physically have data if you don’t have a place to store and process that data. Not many companies transform and analyze their data when it becomes available, so storage is an absolute necessity. As your company grows, you’ll likely begin to deal with large amounts of data that will become overwhelming if it doesn’t have anywhere to reside in the meantime.
Businesses of all sizes are moving their data to the cloud. The emergence of data storage native to the cloud is everywhere. From data lakes to lakehouses, it’s challenging to come by a company that doesn’t store at least a partial amount of their company data in the cloud.
The cloud offers affordable and accessible storage options for on-premise solutions. The type of storage you’ll choose is entirely related to your business needs, but we’re laying the basis for an effective data lakehouse. Regardless of your direction, you cannot build modern data without the cloud.
Data Delivery
Every modern data platform needs an efficient way to deliver data from one system to another, known as data ingestion. As the amount of data builds, infrastructures tend to become incredibly complex, and many teams are left dealing with mass amounts of structured and unstructured data from various sources.
There are plenty of tools available today to assist internal tech teams in ingesting data. However, there’s no shortage of data teams that build custom tools with code to deliver data from internal and external sources. Artificial intelligence workflow automation is an essential component of the data delivery layer.
Data Transformation
Original data must be cleaned up and readied for analysis and reporting. This cleaning process is called data transformation, and you have to do it to build a modern data platform such as a data lakehouse.
Once you’ve transformed your data, you can move to the modeling stage, which creates a visual representation of your data within the lakehouse. Changing the data makes it understandable, while modeling makes it comprehensive visually. When the graphic layer is complete, you can ready your data for the ever-important analytics phase.
Analytics
There is no point in collecting data if your business can’t effectively use it, which is where analytics come into play. Your data doesn’t have meaning without analytics, and internal statistics are crucial to the data puzzle.
There are plenty of effective analytics software choices available today, and your data or development teams can help you choose the right one for you. The proper analytics layer for your data stack is vital to how you interpret your data, so select your software with care.
Observable Data
Because modern data is so complex, there has to be a certain level of observability for your data team to determine whether the information presented is trustworthy. Your organization does not have the time to deal with partial or incorrect data.
Through effective data observability, your teams can fully comprehend the health of your data. You’ll apply what you’ve learned from your experience with Development Operations to your data pipeline, focusing on usable and actionable data.
Check your data for freshness, proper formats, completeness, schema, and lineage. The right observability software will connect seamlessly to your data platform. This level concerns security, compliance, and scaling mass amounts of observable data.
The Discovery Level
Finally, you need real-time access to your data, and data catalogs and warehouses no longer cut it. Consistent access to reliable data is necessary to running a successful business in any industry, period. Data discovery picks up the slack where lack of support for unstructured data falls short.
The presence of data discovery offers a real-time glance into the health of your data and supports data warehouse and lake optimization. Data discovery will authorize your team to trust that their assumptions regarding your data match the reality of what that data presents.
Utilizing the Data Lakehouse
Each of the steps mentioned above will lead you toward a data lakehouse architecture. The data warehouse paradigm enables data storage in an organized hierarchical structure. The data lakehouse is a piece of that structure that can transform unstructured data into something you can use to establish your business and better your brand.
The level of business intelligence that runs the data portion of your company is an imperative component of your success. It would help to leverage your data software and services into actionable insights every moment your data team is on the job.
There has never been a more crucial time to put the best (and most modern) practices into place to ensure that you’re making reliable data-driven decisions every day. Instant and organized access to your data is crucial for you and those on your team who benefit from that access. Consider building that modern data platform, beginning today.
Automating the Automation: The Silver Bullet for QA ROI
Quality assurance is crucial in every business within every industry on the planet. It’s impossible to survive as a company, especially in the oversaturated markets we’re experiencing today. Many companies rely on technology to effectively handle repetitive tasks, including quality assurance.
Heated debates could go on for hours regarding quality assurance and whether it’s suitable to automate the way we test it, and some points serve both sides of the argument well. Because QA plays a crucial role in our businesses and product delivery, it needs to work well within agile development methodologies and highly compressed test execution styles.
There’s no doubt that QA engineers face many challenges, most revolving around the automated changes with QA coding. So often, code that worked in previous test periods, or sprints, is affected by features and bug fixes from following sprints. All of this increases the risk of automated QA not working correctly.
Because of this, tech teams need to consider automating QA testing. Without it, it’s hard to provide real-time feedback and system analysis, but is it too much to automate within automation?
Knowing When to Automate
Before deciding if automation is a silver bullet in the chest of quality assurance, we must understand when we should embrace automation and when we shouldn’t. If your application is ready for automation, then, by all means, automate your heart out.
However, there are instances where applications are not ready for automation. There are specific criteria an application should meet before rushing into QA tests.
Determining Application Readiness
It can be tricky to determine whether your application is ready for automation. These days, everything feels a bit rushed, primarily when we’re working with deadlines or working toward a product launch.
Be incredibly careful if you’re working with a graphic user interface. Take care never to begin automation at the beginning of a project, or you might have to rewrite those automation scripts, maybe even more than once.
Automation requires functional features that are ready for testing. You’re better off beginning your automation process when you’re sure that the elements of your application are not going to change.
Suppose you plan on changing them, or they evolve naturally as part of the development process. In that case, early automation is fruitless, taking too much precious time and cutting into your return on investment.
The automation planning process cannot overlook script design. We have the knowledge that QA can fail with automated test scripts when we create a new version of the product or service. It’s vital to devise scripts to require very little maintenance in the future.
A complete dismantling and rebuilding of new scripts will cause setbacks in time and money, which is one reason why many consider it unwise to automate automated QA. To avoid potential roadblocks, ensure that your app is fully prepared for automation and don’t move forward without a plan.
Team Skill Sets
QA engineering requires a particular skill set. There are plenty of technological tools available to assist in effectively executing automation, but skills within the field are a crucial component. It goes without saying that challenges will arise, and your team has to be prepared to handle them without batting an eye.
Time is money in any field, but particularly in technology and engineering. The fast we move through testing and automation, the faster we get our services to the public. However, we cannot expect to automate every single process flawlessly. Human intervention is necessary, and this is often the case for automated testing for QA.
Most of the tools we use to automate processes (like quality assurance) mandate coding experience. These tools typically provide the resources necessary to teach those without experience using them how to use them properly.
Sometimes, this built-in guidance is enough, but you might want to consider bringing in an outside expert. Not only will your team come away with a ton of learned knowledge and hands-on experience, but fewer future mistakes will lead to an improved ROI.
There’s no question that test automation is a long-term investment. From frameworks to automated scripts, a lot of time and money goes into developing and maintaining your team and their work. Outsourced expertise makes for a better team overall, reducing the need for constant rebuilding and script rewriting.
Testing Test Automation Tools
So, you’ve decided to automate what’s already automated by automating tests for automated programs. It sounds complex because it is complex. Before you jump in with both feet, have you stopped to consider whether or not you’re using the right tools?
If you want to succeed in test automation, you’ll need skilled testers and the correct tools for those testers to utilize. Test automation tools are readily available, but some are not as good as others, like most things in life. Also, some are free, while others are pretty pricey.
Automation tools are an aspect of technology in which you’ll have to decide for yourself what will work for your business. You’ve got to choose a reliable automation tool, but you’ve also got to stay true to your budget. Things can become tricky here because it’s essential to continue to consider your return on investment.
Using an automation tool that isn’t of high-enough quality for the job you need it to do can cause severe damage in the long run. At the same time, overspending at this stage might be unrecoverable. Before you select your automation tool or tools, consider the following:
When it comes to QA testing, the bottom line is knowing your bottom line! Understand your risks and if you’re willing to take those risks. One of the best pieces of advice we can offer is to start with a tool you can afford and apply it to a pilot project.
Once you know what the tool is capable of and if it’s a good fit for your upcoming workload, you can commit. There isn’t a single application on Earth that’s perfect. We always wish it had this feature or that we could get rid of that feature.
Tools for automation are similar in that way. The app you choose won’t be complete perfection, but it has to prove that it can provide purpose and a great ROI.
What Should You Automate?
Let’s talk about what you should automate and which aspects of your applications you should leave alone. You now know when to automate and what to consider when choosing automation test tools, but what exactly should you automate regarding your QA (and everything else in your business model)?
The Golden Rule: If It’s Repetitive, Automate It
Testing software and QA includes a whole lot of repetitive tasks. Doing these tasks manually leaves too much room for human error, and you can easily avoid that by implementing an automation tool.
Humans tend to do the same task differently every time they do it; sometimes, in a desperate attempt at breaking up the monotony, machines repeatedly do one job the same way. Automated tools are superb at producing the same results when assigned a specific task, supplying your team with consistent answers and feedback.
Automate Difficult Tasks
The amount of time you’ll save by automating complex quality assurance tasks is immeasurable. Technically not, because there’s likely an automated tool to measure it.
If your team is consistently bogged down by tasks that take an extremely long time to complete, such as syncing thousands of email and contact accounts to a new application, it’s time to automate.
Automating application testing can be a time saver for a quality assurance engineer and a money-saver for you. Your team’s effort into testing app features that you can (and should) test automatically is directly lowering your ROI.
Automation and the Future of QA
Everyone in the technology industry knows that automation is our future. We’ve struck a pretty good deal with AI and machine learning, and there’s no reason we can’t utilize it for tasks and tests that can thrive under automated processes.
Automation will never replace human testers. It’s impossible, as humans can analyze and notice nuances in ways that computers do not. However, it’s good to have computers on your side when it comes to QA automation and the possibilities it has to increase your ROI.
Without question, automated tools are here to assist testers, not replace them entirely. Manual tests are still just as necessary as those that are automated, and the combination of the way we use these testing methods will look very different for every business. Automation increases efficiency as long as you use it in the right place at the right time.
Sailing in the Speed boat of Scrum
Software development projects demand high productivity and adaptability to meet the ever-changing requirements—this is where methodologies like Agile come into play. Inside the Agile methodology, there are frameworks like Scrum that help to structure teams and their functions. The Scrum Framework is crucial within Agile Software Development as it significantly increases productivity by delivering the product in an incremental and iterative method.
Here are five best Scrum practices we follow in our projects at TVS Next:
Let’s look into each Scrum practice in detail:
1. Conduct Scrum ceremonies consistently
The Scrum Master needs to make the team understand the importance of Scrum ceremonies within a Sprint. The main Scrum ceremonies are Sprint Grooming & Planning, Daily Scrum, Sprint Review & Sprint Retrospective.
Regular meetings help the team stay updated on the work status and help to give and receive valuable feedback. A successful Scrum Master makes the team see the value in these ceremonies.
2. Plan and prioritize
Plan a new Sprint only when the backlog has enough items for the next two sprints. Ensure not to let scope creep happen.
A minimum of two Sprints backlogs must be planned and prioritized by the product owner, where the scope of the project and the Sprint goal must be clearly defined. Unless the goals for each Sprint are clearly laid out, it could become challenging to prioritize the tasks in the backlog.
3. Build a cohesive team
A Scrum team has five to nine people and three prominent roles: Scrum master, product owner, and the development team. Everyone in the team is equally important and must be treated as such.
The team’s learning is a continuous process that happens through experience developing the product and working as a team. The Scrum master’s job is to apply the Scrum framework successfully, lead the team’s meetings, empower the team to become cross-functional and self-reliant.
4. Deliver viable product
A Scrum master must encourage the team always to meet the requirements and deliver a viable product to the customer. For every Sprint, the team can show a demo to the principal stakeholders and receive feedback. They can then adjust and improve before delivering the final product for that Sprint.
5. Conduct Sprint retrospective
Even an excellent Scrum team can always improve. A Sprint retrospective is an integral part of Scrum to develop, deliver and manage complex projects. This meeting is held at the end of every Sprint. The agenda of this meeting is to discuss what the team did right and wrong and identify what they can change. The team can then decide on a plan of action to incorporate the findings in the next Sprint.
As a takeaway, I firmly believe that we can successfully implement the Scrum framework in any project if we follow the above best practices.
Following some golden rules such as being open to providing and receiving feedback, criticizing the work and not a person, and making decisions as a team further strengthen the implementation of Scrum in Projects.
