If you have read our previous article, “Why You Need a Data Warehouse in Dental Clinics & DSO”, you already have an understanding of data warehouses. Just as a refresher, a data warehouse is something that stores heaps of data of an organization and provides analytical insights along with business intelligence solutions. In this article, we are going to dig a little deeper and understand what types of data warehouses are available, and which one is the best option for a DSO or dental practice.

Types of Data Warehouses 

Data warehouses are an extremely efficient way of getting analytics and displaying product data, but they come in all shapes and forms. You will find 3 types of data warehouses with certain differences in functionalities. Each of them caters to the requirements of certain businesses and users. Now, those 3 types of data warehouses include the following.

• Enterprise Data Warehouse (EDW) 

• Operational Data Store (ODS) 

• Data Mart 

As mentioned earlier, each data warehouse type will bring in different features and benefits. Let us understand those data warehouse types and how they help a particular organization.

Enterprise Data Warehouse (EDW) 

An enterprise data warehouse is a centralized database that helps you get a clear picture of the entire organization. It offers a foundation for decision support across an organization and assists with the visualization and analysis of the data collected.

You can classify your information according to departments or locations, allowing for quick access to relevant information in one central location. It also offers the largest single database, which makes it a perfect choice for businesses.

Components of Enterprise Data Warehouse (EDW) 

An EDW typically includes 

• Sources of information from transactional and operational systems (ERP, CRM, finance apps, IoT devices, mobile and online systems). 

• A location for gathering and cleansing data. 

• An interactive summary of key metrics like querying, reporting, and sharing in a presentation or access space. 

• A variety of APIs or tool integrations for data (BI software, ingestion and ETL tools, etc.) 

If you are wondering why EDW is the best choice for a DSO or dental practice, there are multiple reasons. One simple reason is that it allows the extraction of data from multiple locations. Want to know another best thing?

We, at veltris, provide DSOs and solo dental practices with an industry-best enterprise data warehouse to get business intelligence reports. Using such information, an organization can make data-driven decisions to improve efficiency and overall growth. Our data warehouse will bring in multiple PMS under one platform to automate it all. The best feature is that it works with the no-touch deployment of PMS.

Operational Data Store 

An operational data store (ODS) is another kind of data warehouse. An ODS is an added element to your enterprise-wide data warehouse (EDW) that touches almost every business aspect of your company. One of its purposes is to serve as a central database for operational reporting and business intelligence tools, acting as the backbone for consistent reports across the entire company. With that, a company can make the right decisions.

Storing employee records, maintaining them, and other reports is easier in ODS as it is constantly updated in real-time.

Data Mart 

The third kind of data warehouse is a Data Mart. A data mart is derived from a data warehouse (an actual subdivision of it), and is designed to be used by a specific team, such as finance or sales. It is subject-oriented, meaning it will provide the relevant data that these individuals need and also act as a ready reference for them so that they don’t have to go through an entire massive data warehouse to find what they’re looking for.

The real-time availability of all this relevant information means that the people involved in the processing and internal operations can rapidly act on the new information available instead of getting bogged down trying to find variables from across the data pool.

Data Warehouse Architecture 

As familiar as you may be with the term “data warehouse,” you may want to think of data warehouse architecture as a specific type of product in its own right. Architecture, as we all know, is the design of a structure. This is true for a data warehouse as well.  

No matter what structure your company chooses for its ultimate product, it is still necessary to go through the laborious process of organizing information and making sure it ends up easily digestible by your visiting customers. The architecture of a data warehouse is determined by the organization’s specific needs.  

A common data warehouse architecture includes the following things. 

Simple Architecture  

Almost all data warehouses have a central repository where metadata, summaries, and raw data are stored. Data sources feed the repository, and end users access it for analysis, reporting, and mining. You can call this single-tier architecture.  

Simple Architecture, but with a staging area 

It is necessary to clean and process operational data before storing it in the warehouse. It is possible to do this programmatically, but many data warehouses also include a staging area so that data can be prepared before entering the warehouse. 

Hub and Spoke Model 

Organizations can customize their data warehouses in a hub-and-spoke model to serve different lines of business by adding data marts between their central repository and end users (acting like nodes between multiple spokes). Data is moved to the appropriate data mart when it is ready to be used. 

Sandboxes 

The purpose of a sandbox is to provide companies with a secure and private space to look for new data sets as quickly as possible. Along with that, data analysis methods cannot be governed by data warehouse protocols and restrictions. 

Well, so far, we have talked about data warehouse types and architecture. While discussing all these things, you have heard the database quite a few times. This might lead you to wonder, what’s a database then? This is quite a common question that many people ask when they talk about a data warehouse. We can provide you with a simple one-line answer, but that’s not useful now, is it? So let us discuss the difference in a bit more detail.  

Difference Between a Data Warehouse and a Database 

In simple terms, a database is nothing but a collection of various kinds of data placed in storage. It could be cloud storage or a physical one. This may sound a little like a data warehouse, but one key difference makes the two poles apart. The difference is analytics.

Data warehouses provide insights and business solutions from various historical data, whereas a database is a storage space. A data warehouse uses Online Analytical Processing (OLAP), whereas a database uses Online Transactional Processing (OLTP). When it comes to usage, the database uses ER modeling techniques for design. Again, this isn’t in a data warehouse; rather, data modeling techniques are used.

Just like data warehouse types, there are a few different types of databases. The downside of using a database is that it is restricted in storage to one application only. You will not find this restriction in a data warehouse, as multiple applications’ data is stored here. This is a significant reason why many organizations prefer data warehouses over simple databases.

A data warehouse is also better optimized and organized for a business requirement. They offer more flexibility and options. That’s the reason you will also find them in dental practices and DSOs.

Difference between a Data Warehouse and a Data Lake 

A Data Lake is a storage archive that can store large amounts of data from multiple sources. Unlike a data warehouse that holds only structured data, a data lake can also store unstructured data. It is also a centralized storage archive, but it is flexible in terms of the kind of data it can store. Other data types include semi-structured and structured data. In simple terms, a data lake is a more advanced version of a data warehouse.

The key advantage of using this type of technology is that you can easily connect multiple systems. This makes it much simpler to create complex reports and analyze them – preserving your valuable time. Now, let’s talk about some key differences between a data warehouse and a data lake.

In terms of data quality, a data lake contains raw data as it takes all kinds of data, whereas a data warehouse has organized data. We know that a data warehouse’s main function is to provide analytics to make the right decisions for a business, so what is the main function of a data lake? Well, it is to store huge amounts of data, but more cost-effectively. This is another major difference between a data warehouse and a data lake.

A Few Other Differences

Another question may pop into your head about how data lake users can access data and utilize it when it is unstructured or semi-structured.

There’s one simple answer. Machine Learning, Big Data, exploratory analytics, operational analytics, etc. So, it’s more than one, I guess. While data in a data warehouse is in tabular format, which is easily accessible by SQL queries, a data lake uses machine learning and Big Data analytics techniques to access and use the data.

As listed in the table, another difference between a data warehouse and a data lake is the Schema. A data warehouse is in write model for a schema. Usually, this means schemas are written and designed before the implementation of a data warehouse (sometimes also at the time of analysis). Still, for a data lake, the schema is on read, so it is usually written at the time of analysis.

Interestingly, many data warehouses are now being updated with all the advantages that a data lake carries. So, there is not much reason to choose between either option when both can do the same job.

Factors to Consider While Evaluating a Data Lake or a Data Warehouse

As mentioned earlier in the table, there are various data warehouses and data lakes in the market. So, here we are going to list out a few factors (in no specific order) that will help you evaluate a data warehouse and a data lake.

1. Data Loading
2. Querying
3. Query Performance Scaling
4. Pricing
5. Deployment

Data Loading

The data loading in a data warehouse or a data lake is one of the major factors to determine how good a data warehouse/data lake is. Data loading tells us whether it is easy or difficult to load data into a data warehouse and a data lake. Similarly, it also tells us about the querying difficulty. 

Formats like CSV, Parquet, ORC, AVRO, and JSON are the supported & popular formats for a data lake/warehouse. Most of the time, data ingestion is done from the cloud storage where raw data is kept. The data loading part is different for each data warehouse/lake. Some use APIs to load the data (like Redshift), while others use an external table (like Snowflake & Hive). Then there’s BigQuery, which loads data directly from the Google Cloud storage.

Querying

When you are done loading the data, it needs to be used for querying, and only then is the raw data useful for analytics. Various data warehouses/lakes use different techniques to run queries on the data. Various data warehouses/lakes use different query engines for this. Redshift uses ParAccel, Hive uses OSS Apache Hive, and Athena uses Presto. 

Query Performance Scaling

A data warehouse/lake should be able to scale independently, which is one of the other evaluation factors. Snowflake, Athena, and Hive are some of the data warehouses that can scale independently whereas BigQuery and Redshit don’t do that. The better the independent scalability, the easier it is to manage them.

Pricing

The pricing factor is different for every data warehouse or data lake. For example, Snowflake pricing starts at $2 per hour and has a 1-minute-based billing. Redshift has an hourly pricing that depends on the provisioned server’s capacity. Similarly, each one has a different pricing model.

Deployment

This is probably the most important evaluation factor for a data lake/warehouse. You’ll see 3 kinds of deployment modes: Serverless, Cloud/On-premise, and SaaS. Many businesses choose between these 3 deployment models based on their requirements and business model. 

If you use a SaaS-based deployment model, you can get space through a subscription-based model, and there’s no need to provide any space manually on a server. When it comes to the Serverless model, it is scalable on its own without any management. Cloud and On-premise, though, require management and provision of space.

Wrapping Up 

In this article, we have covered a lot of things. From types of data warehouses to data warehouse architecture, and their differences with databases and data lakes. We have discussed all these things, some briefly and others in a bit more detail. You might be looking for some answers to questions like which one is better for a dental practice. A data warehouse, database, or data lake. Well, the answer depends on your business requirements, but most of the time it’s a data warehouse. 

Again, an enterprise data warehouse is the best option for a DSO or any solo dental practice. Since you have come this far, you might as well connect with us so we can talk business.  

Before we talk about a dental DSO data warehouse, let’s go back a little in time. When the world was just getting used to computers, there was another interesting discovery about to happen. It’s Data Warehouse, which was first introduced as a concept by Paul Murphy and Barry Devlin. These two researchers from IBM in the 1980s were playing with the idea of the business data warehouse to provide a centralized data management system. 

Well, this is something most people know, but they aren’t the first ones to talk about data warehouses. It’s in fact, Bill Inmon who was first to use the term data warehouse in the 1970s. That’s the reason he is called the father of the data warehouse. Inmon worked on a high-level data flow architecture, known as the Corporate Information Factory. 

Well, that was a small history lesson. Let us discuss the topic at hand, which is the Data Warehouse. 

What is a Data Warehouse? 

The term data warehouse is self-explanatory as a place that stores large amounts of data, but that’s not it. While that’s one of the key features of a data warehouse, it’s much more than cloud or traditional storage. A data warehouse’s major feature is to provide data analytics.  

In a data warehouse, data is collected and stored from multiple sources in a centralized way. This data is then analyzed to apply various business intelligence strategies to make informed decisions. Making informed and strategic decisions for an organization is a crucial feature of a data warehouse, be it a dental practice, a dental DSO (Dental Support Organizations), or any other organization.  

What Data Warehouse Does for Dental Clinics and Dental DSOs? 

When it comes to implementing data warehousing for a dental practice or a DSO, many people ask the question of what a data warehouse can do for them. While the whole process of how it works is the same for a dental DSO or any other organization, except for the data part. A huge amount of PMS (Practice Management System) data can be integrated into a data warehouse, which makes the whole patient data management a walk in the park.

Now, when you have a data warehouse for a DSO or dental practice, a single API (Application Programming Interface) is enough to access all the data. The ONE API application lets users access various insights from heterogeneous PMS data. There are also various types of data warehouses, some of which are PMS agnostic – like Veltris’s Data Warehouse. 

In case you are unfamiliar with what a PMS agnostic data warehouse means, it means that the data warehouse works with any PMS. This is one of the best features to have when you are looking for a data warehouse for a DSO or dental practice. Apart from being PMS agnostic, having Machine Learning and Data Engineering capabilities makes it even better.   

Components of Data Warehouse  

A data warehouse is built up of multiple components due to its huge structure. The access tools, metadata, ETL (extract, transfer, load), and the central database are the components that make up a data warehouse. Each of these components plays a unique role in its functioning. Let us understand each component in a bit more detail. 

Central Database 

Imagine a base infrastructure for the data warehouse, and that’s what a central database is, which is sort of like a foundation. Depending on the type of data warehouse, this database might be on a cloud or just on a traditional RDBMS (Relational Database Management Systems).  

While some may argue that RDBMS is not the right choice for data warehousing since it isn’t optimized for that, the issue can be fixed when the said databases are deployed in parallel. The reason for using a parallel development of the relational database is to provide scalability and shared memory. Similarly, using new index structures and multidimensional databases are a few ways for the RDBMS in data warehouses. 
 

Data Integration 

ETL (Extract, Transform, Load) and ELT (Extract, Transform, Load) are the two common processes used in data warehousing for data integration. Out of the two, ETL is again the most used one for data extraction and integration. Using the ETL tools, one can transform the data in a way that works with your data warehouse. 

Some of the functionalities of ETL tools are 

• Derive and summarize data from multiple sources. 
• In case data needs anonymity and restricted access, then this tool can do that. 
• Adds default values to missing data. 
• Undesirable data in the operational databases can be removed if not required for loading into the data warehouse. 

If you want to learn the differences between ETL and ELT, check out this qlik article on the same. 

Metadata 

Metadata helps to understand the data in your database, which basically is data that talks about, well, data. This tells you the source of data, along with its value, features, and usage of data in a data warehouse. When you have all that information, you can use that data to build, maintain, and manage your entire data warehouse. 

Let me make it even simpler for you. For example, there is raw data, something like 5873 UY009 2.445. Can you understand what this data is all about? No, right. With metadata, you will know its source, value, usage, and pretty much everything you need to know.  

In the case of the above data, it means something like this. 5873 is the model number, UY009 is the workforce agent ID, and the rest is the sales amount. It also tells you to reload the count of data in the data warehouse, attributes, keys, and tables in the data warehouse, etc. One other thing to know is that there are two kinds of metadata. 

1. Business Metadata (useful for administrators and designers) 

2. Technical Metadata (useful for end-users) 

Access Tools for Data Warehouse 

There are quite a few tools that help users access data from a data warehouse. Each tool brings in different functionality. These functionalities include interacting with the data and reporting tools for analysis or operational reports, among a few others. Tools like reporting tools, query tools, data mining, OLAP, and application development tools are the access tools for a data warehouse. 

Query Tools 

A query tool allows users to interact with the data from the data warehouse to make informed business decisions. Most of the time, it is useful for businesses as it helps them with strategies. There are also managed query tools that help users fix issues with the database in the data warehouse.  

Reporting Tools 

As the name suggests, this provides reporting on the data from the data warehouse. There are two types of these. One is the report writer tool, and the other is the production reporting tool. The former helps with the analysis of data for the end-user, while the latter helps organizations to develop organizational reports.  

Application Development Tools 

This helps to generate custom reports of the data using the graphical charts and reports. 

Data Mining Tools 

Extracting the data isn’t enough when you cannot draw any conclusion from it. Data mining tools help understand the patterns and trends in data from multiple sources to correlate them and generate insights. 

OLAP Tools 

Multidimensional data is a complex thing to analyze; fortunately, OLAP tools help in that aspect. 

What Does a Data Warehouse Allow Dental DSO Organizations to Achieve? 

Since we are talking about data warehouses and their role in the dental industry, let’s have a look at their benefits. 

• Integration of multiple PMS and a centralized view of all the data. 
• An effective result for your dental practice or DSO with increased business intelligence. 
• A standard data model for multiple PMS. 
• PMS agnostic 
• The data from various sources follows conformity, making it easier to understand for analytics.  

• You get operational reports, data trends, and other insights to make the best decisions for your DSO/dental practice based on accurate data. 
• Data is secure and accessible by authorized users to follow HIPAA compliance. 
• Increased efficiency for your business as you don’t have to work on the data management part.   

Wrapping Up 

With so much data, there’s always going to be a need for a good dental DSO data warehouse. Veltris offers data warehouse solutions to DSOs, dental practices, and any business to help them centralize their data. Our services are the best on the market, and something that no other organization offers. Check out Veltris’s services and give us a call to take things further. 

We are seeing new software roll out almost every day. It could be a company’s SaaS product or any app for Android or iOS devices. The bottom line, there are a lot of such products on the market and many more on their way. This might make you think it’s an easy job, and not much goes into developing such products. On the contrary, there are a multitude of processes and phases involved in software development. One such phase is the Software Testing Life Cycle (STLC).  

What is Software Testing Life Cycle (STLC)? 

The Software Testing Life Cycle, or STLC, is a series of test phases to ensure a product/software meets the set standards. After a product goes through the development phase, the next phase is the testing phase. In this phase, some predefined standards are set for the software/product quality, and the testing team analyzes the product against those standards. 

This process is basically a verification process to check the product quality and make sure that it is free from bugs and ready to release. The Software Testing Life Cycle (STLC) and the Software Development Life Cycle (SDLC) might sound similar, but they are not the same. STLC is a part of the SDLC, which is the development process of a product. It’s all about testing the product, but in multiple phases. To find out the difference between the two, take a look at this javapoint article. 

One of the crucial things about STLC is the software requirement document, which defines the standards for testing a product. Now, there are a few other terms one should be familiar with when it comes to STLC. These terms include test cases and entry & exit criteria. Also, there are deliverables for each phase of the testing cycle.  

The test cases, entry, and exit criteria are applicable for each level of a testing cycle. 

Entry Criteria 

Before starting the testing process, a few essential items/processes are necessary to begin. Once those are met, the entry criteria for an STLC phase are also met.  

Exit Criteria 

It’s the same thing as the entry criteria, except it’s for the exit part of a test cycle. 

Test Cases 

A test case in the software testing life cycle is nothing but a document that contains all the conditions for inputs, executions, desired results, and the testing procedure. The test is something that is obtained from the Software Requirement Specification (SRS). The shareholders usually share the SRS with the testing team.  

Now that we have covered all the basics in STLS, let us get to the main part, the phases in the testing cycles. 
 

Software Testing Life Cycle (STLC) Phases

Any kind of STLC has 6 different phases to make sure that the product meets the customer’s requirements. Each phase of this life cycle is to test various aspects of the product. Also, each phase is not always as important as the others and is sometimes avoidable depending on the product testing. Let us see what these phases/steps are. 

1. Requirement Testing Phase 

2. Test Planning 

3. Development of Test Case 

4. Test Environment Setup 

5. Test Execution 

6. Closure of Test Cycle  

A product has to go through these six different phases in the software testing life cycle. We shall now discuss each phase in detail. 
 

Requirement Testing Phase 

The very first step involved in the testing process of STLC is requirement testing. It’s self-explanatory as to what this phase entails. The testing team will determine all the criteria/requirements for testing every aspect of the software. This is after the testing team collaborates with stakeholders to understand all the prerequisites.  

This phase includes business, architecture & design, and system & integration requirements. Each of these types provides a base for the requirement testing of a product. Apart from understanding the types of requirement testing, testers must also decide on the entry criteria, activities, and final deliverables. 

Entry Criteria 

This is something we have already discussed, which is the requirement type for testing. Other than that, you also need a requirement specification document to start. 

Activities  

Here, the activities will involve you making a list of queries to get answers from the other teams and stakeholders involved in this project. It includes defining test types, environmental details, testing priorities, RTM (Requirement Traceability Matrix), and automation feasibility analysis. 

Deliverables 

The automation feasibility report, RTM, and the testable requirements are your deliverables in this phase. 

Test Planning 

Test Planning or the Test Strategy is a phase where the team devises a strategy for the testing life cycle. This means making cost estimates, resources, objectives, test environment, scope, limitations, and efforts required for the whole project.   

Entry Criteria 

In this phase, all you need is the requirement document. A requirement document is something that contains information on test conditions, parameters, support requirements, etc.

Activities  

Make a strategy and decide on the type of testing, along with a document for the same. Then choose the test tool, estimation of test effort, resources, and the training requirement.   

Deliverables 

The deliverables for this phase are various documents on effort estimation and test strategy. 

Development of Test Cases 

The third phase in the software testing life cycle (STLC) is test case development. You already know what test cases are, as we discussed in the earlier sections of this article. Well, in this phase, the team works on developing those test cases for the product, along with the test data.  

Once those test cases are done, the quality assurance team checks them. In this phase, the team also works on test scripts and verifies them. The team shouldn’t repeat a test case, and the test cases should be based on the end-user requirements. 

Entry Criteria 

It’s the same as the other phases, as it needs a requirement document. 

Activities  

Creation and review of test cases, test data, and scripts. All these need to be clean and easy to understand. Don’t repeat any test cases, and they should cover all the software requirements. 

Deliverables 

The resulting deliverables are the test cases, test data, and test scripts. 

Test Environment Setup 

One of the most crucial aspects of STLC is the test environment. Only with the right environment can a team carry out the testing. Here, the test environment means the hardware and software criteria. Most teams carry out the test environment phase after/along with the test case development phase. 

Entry Criteria 

Documents of test cases, test strategy, and test plans. 

Activities 

The test environments you will see in this phase are the setup of test servers, network, bug reporting, test data for the test environment, and test PC setup. Also, performing smoke tests (checks the stability of the built software) on the build. 

Deliverables 

Executions, smoke tests, and defect reports are the deliverables.  

Test Execution 

You have developed test cases, plans, and scripts; now is the time to execute those things. The testing team will execute the test scripts and perform maintenance for the test strategy.  

Entry Criteria 

Test plan/strategy, test data, and test cases are the entry criteria for this phase. 

Activities 

Creation, execution, and mapping of test cases. Documents on the results (passed, failed, etc.) of corresponding test cases, along with tracking defects. Any defects found are mapped to test cases in RTM (which is also prepared in the same phase). 

Deliverables 

Defect reports, the finished execution status of RTM, and the test case report after execution.  

Since we are talking a lot about defects, you can learn a little more about defect logging from this Tutorialspoint article. 

Closure of Test Cycle  

This is it; you are at the end of the STLC life cycle with this phase. All the results of various phases and processes from the last 5 phases are verified here. Various testing teams involved in the testing process check all the test completion reports and documentation. After this, the team analyzes the whole process in terms of quality, test coverage, time, and objectives met.  

A final report on test closure is also published at the end of this phase. This report is also useful for future reference and helps with improvements in areas where needed.  

Activities 

Documentation and reports on the project and a final test closure report are generated. A test metrics report based on the cost of the test, test coverage, quality, time taken, and a few other parameters is also published.   

Deliverables 

A test metrics and test closure report are something you see as a result of deliverables.  

Conclusion 

The whole software testing life cycle (STLC) is pretty lengthy and takes a good amount of time. Sometimes teams have to go back and forth to make things right and fix certain issues. Then again, not every team does that. In any case, we have broken all the phases in the testing life cycle into easily digestible information. 

Each phase deals with an essential aspect of software testing, as you may have learned by now. Many industries and businesses are employing automated testing to reduce manual work and reduce errors in the testing process. This Test automation article will help you understand why test automation is important, now more than ever. 

That’s why we offer automation, IT solutions, and business solutions to improve businesses and make them reach their full potential. Our expert team of developers can also help you with the testing and quality assurance of a product. All you have to do is hop on a consultation call with us. 
 

Agile product development has gained tremendous popularity over the past few years. Its demand has increased due to its vast range of benefits in the current world of software. The reason isn’t surprising at all. It makes your work easier and at the same time, it skyrockets the quality and efficiency. This, in turn, reduces the time consumed and accelerates the delivery of the product.

Who wouldn’t wanna have all these benefits for their organization? Many companies have now started using agile methodology in their product development. While some organizations still prefer the waterfall method, it is strictly based on the kind of product you want to develop. In case you want to learn the difference between waterfall and agile, check out this article: Agile vs Waterfall Methodology.

In case any of their employees aren’t very familiar with the agile methodology, many companies even train them.

Agile Product Development

Veltris, being one of the fastest-growing software development firms in the IT sector, also uses an agile product development methodology. Agile product development defines the creation of products with agile processes and techniques. The whole development process is divided into multiple phases, which makes it easier for product development.

Each phase is for a week or 2, and sometimes more. These development time periods are called sprints. During a sprint, the scrum team decides on a few things that they want to complete, and then they finish them during the assigned time. Here, we are first going to talk about agile product development, which includes both hardware and software products.

These can both be interrelated, as hardware contains software and software is embedded in the hardware. Previously, agile was considered useful only for software, but this has changed now, and we see how agile technology has its uses in hardware development too.

The Main Focus of Agile Product Management

• Hardware product: Deliverables oriented towards developing specifications for a product that needs to be manufactured.
• Software product: Deliverables oriented towards building the real product. Example: an online store.
• Hardware embedded with software: Embedded software can evolve within the bounds of a static hardware environment. Example: Cell phone, network equipment, and also analytical equipment for chemistry and biotech.
• Hardware with associated software: Associated software can evolve over multiple devices. Example: laboratory equipment added to locally hosted or web-based software controls multiple devices.

This isn’t to say that agile is only used for these cases and in software development only. Many industries and companies have started to integrate agile methodologies into various processes. It’s not just the IT sector now, but you will find it in the automotive industry, marketing campaigns, universities, and a few other sectors.

The reason for agile integration in other sectors is pretty simple. It makes every process easier and less time-consuming, without compromising on the end product. Sometimes, even improving the end result.

Product Management

Product management is said to be an organizational function that tends to guide every step of a product’s life cycle, starting from development to positioning and pricing. The focus is on the product and its customers first. To build the best possible product, product managers need to advocate for customers within the organization and make sure that the voice of the client is heard and heeded.

Product management can be described as the intersection of business, user experience, and technology.

Business

It helps teams to achieve business objectives by bridging the communication gap between designers, customers, and the business.

UX

Product management in UX has its focuses on user experience and represents the customer inside the organization.

Technology 

This happens regularly in the engineering department. A thorough understanding of computers is necessary.

If you want to learn about various phases in agile methodology, then check this workfront or MarsDD article.

Why Agile Product Development is Needed?

We have seen that agile development allows for the prevention of time wastage and useless designs. The iterative nature of the philosophy creates an environment of constant progress and innovation. With that said, there are a vast number of benefits that are required in today’s world

1. It tends to force engineering teams through the DMU cycle at a faster pace for smaller sections of the design.
2. Along with the establishment of cross-functional teams, workloads can be managed entirely autonomously within each of the smaller tasks.
3. Work can constantly be accomplished without a bigger meeting that would involve the whole engineering team.
4. It induces active thinking and preventative action.
5. In a typical design process, we create a blueprint of tasks that need to be accomplished and followed as much as possible. This “instruction-based” workflow might work well, but it encourages engineering technologies to work better.
6. By utilizing iterative agile techniques, we can be deeply involved with the problem-solving structure throughout the creation process, perhaps even foreseeing possible issues.
7. It creates an environment for innovative ideas.
8. The goal of every engineer or engineering team is to progress a project through constant innovation, finally reaching the best possible deliverable product for the client that would help them work according to their requirements.
9. Static waterfall processes can be said to be the best possible solution imaginable when the initial specifications and the workflow blueprint are imagined. Agile allows for constant change throughout the entire workflow of the creation of designs and creates a setting that would be effective for innovative growth.

Why Should You Avail Our Services?

Veltris gives you detailed knowledge regarding agile methodology before we start working on a project with you. Our agile product management team provides a clear view regarding the launch of the product and its management aspects. We let our clients decide on the deliverable and the features while we work on the time and keep the shareholders updated. This is done in many sprints.

In case any feature or changes are missed in one sprint, we finish them in the next one. All this without compromising on the release date or quality of the final product. Veltris gives a platform to further explore agile skills and technology. Hence, for any further details, feel free to contact us and register.

The need for software in almost every field is increasing every day, and there are more potential risks associated with software malfunctions. Hence, companies need to test their software in every possible use case. This is to extend to the customers their Quality Assurance (QA) Automation pledge.

QA Automation and Test Automation

Most people in the software industry know the following things about QA testing tools. First, there are distinct differences between manual and automated testing. Second, there is too much work and too many errors a developer needs to take care of in the manual method. One can thus easily deduce the need for testing the software before it is put into use by the clients.

More than that, the inevitability of automated software testing. Veltris steps into the picture here as a provider of expert knowledge and automation testing tools. You can learn about various automation testing tools from this list of the 18 best automation testing tools for 2022.

How Inevitable are QA Automation and Automated Testing?

A manual testing engineer plays the role of an end-user and follows a written test plan, while maintaining records of his findings in log files and SQL. There are many such repetitive tasks to be performed by the inspector in a formal testing process. Those are avoidable if there are automation testing tools. Besides, the best QA Automation tools can give you a lead in the sense that they are critical in the following two software engineering approaches:

1. Continuous delivery, in which a software product is developed in short cycles.
2. Continuous testing aims to reduce developers’ waiting time for feedback.

We, at Veltris, help our clients in automating their existing manual test cases. Along with that, we also help them start all over in automation with our in-depth automation knowledge and innovative QA testing tools. If you are in a company doing manual Quality Assurance Testing, it means you are spending money to build QA resources. That, and a lot of time on some laborious tasks associated with it. Especially if the project’s scale is large.  Add to that the error-prone nature of the work since it involves high human interaction at all stages.

Such problems are nothing but a result of inevitable human involvement in doing some mundane tasks again and again. Whereas some of the best QA Automation tools allow for the reusability of test cases and solve the issue.

Automated software testing, while solving these problems, gives us the following advantages:

Framework Approach

QA Automation provides us with the possibility of going by a framework approach. This is an integrated system of building block components that simplifies the automation effort. The advantage is that you do not need to change the entire driver Script and startup script if there is a change initiated in any test case.

Time

Automation testing tools save the time spent on exploratory testing and tedious tasks( take low-level regression testing, for example). It is used in coding these tests and repeatedly making the needed adjustments. This way, time is spent on more important issues in your software, such as customer requirements, functionality, and improvements.

Cost

The initial cost of implementation is high if we compare it to manual testing. This, however, is justified in the long run since it reduces the necessity for multiple coding revisions. Again, it indirectly pays off by freeing you for more important issues, as said above, and by saving the time involved.

One common misconception is that the automated method is superior to manual means. While it is true to some extent, it highly depends on the applicability. QA Automation, however, has an unassailable lead in the automation of big projects in particular.

What’s Our Work Plan?

Veltris’s QA Automation is a proven model for automated software testing that has fetched us recognition in the IT industry. We employ our own unique framework to run tests. We have a 5-step approach which is shown below.

Identify System

We examine our client’s software and identify how it fits in our framework to proceed forward since it involves the selection of test tools based upon the technology under which the application is built on.

Understand the Need

We look for specific things and define the scope of automation, such as the areas of automated testing, the complexity of test cases under concern, etc.

Leverage Veltris’s QA Automation Framework

Our unique framework is to create an infrastructure where different tools can do their job in perfect sync to offer a solution.

Setup Framework

We define the format to provide results, execution, and reports of results with our framework. This is done with the help of innovative QA testing tools that we have developed in our lab.

Report Results

The reports are generated once the framework is run against the test application, and test cases are executed by it.

Why Are We the Right Choice for You?

Multi-Domain Expertise

Since we have a highly talented team specializing in multiple domains, from software development for physicians to large DSOs, we know everything about software development. It is, thus, highly helpful to design QA testing tools that seamlessly operate over different types of applications.

Experienced Hands

The team at Veltris consists mostly of engineers who have at least five to ten years of experience. This experience covers various technologies like Agile, analytics, and app development for the cloud, desktop, and mobile. That, apart from being well-equipped with the skill sets required for automated software testing, and using the best QA automation tools.

Business Ethics

We do not force our clients to sign long-term contracts as we believe in the quality of our work for client retention rather than making legal bonds. Hence, our customers are free to change their minds anytime as they get to see the updates and progress regularly.

Wrapping Up

Now, you have a basic understanding of what QA Automation is and how it helps in software development. We have also discussed Veltris’s QA automation techniques and its basic framework. If you want to know more, schedule a consultation call with us.