Remote work is here in force, and here to stay for many workers. Depending on mission, this can extend to Federal agencies, many of whom are now focusing on low-code and no-code application development platforms to support remote work for developers. According to Business Wire, low-code/no-code platforms are expected to grow at 44.4% by 2022, reaching $27.23 billion (up from $4.32 billion in 2017). IDC estimates that more than 500 million apps could be created with these platforms by 2023. But how can they best support the Enterprise?
Large Enterprises rely on software applications to build and run core business functions (sales, marketing, supply chain, logistics, business intelligence, and others). They can be integrated or interconnected with other applications to create an overall Enterprise system. Even though Enterprise software is created to support large-scale organizations, the reality of implementation can take up more technical support resources than they’re supposed to spare.
Low–code and no-code advantages, for Enterprise and beyond:
Bandwidth Issues: Internal cross-Enterprise IT teams may have little time to manage client or Enterprise-level applications.
Cost-Constraints: Purchasing semi-custom apps or hiring a mobile/web application development firm can rack up a huge bill quickly.
A faster time to market: The most significant benefit is that the development duration is cut between weeks and months or days. In addition to bringing your app to market quicker, it is also possible to quickly take feedback from users and create the latest features.
Multiple Deployments: These development platforms allow businesses to create applications deployed on several platforms simultaneously. No-code, low-code mobile application development makes deploying an app to a platform much simpler.
Reduced Errors: Less code equals fewer errors, equals less dev time.
Lower Development Costs: Due to the reduced development process, the faster speed and less resource requirements development costs for low-code and non-code applications are lower.
Low-code and no-code devallows companies to build applications with visual development techniques, thus eliminating other development methods that require the writing of many lines of code.While low-code applications and no-code software development work best when combined, there are fundamental differences between low-code and no-code:
No-code platforms let teams that do not know about software development or coding use functional, reusable components to build applications.
Low-code platforms require a certain level of programming, but they allow developers to create applications developed in faster turnaround times.
Top Low–Code and No-Code App Development Platforms:
Siemens Mendix
Microsoft PowerApps
Appian
Out Systems
Airtable
Amazon Honey code
Salesforce.com Lightning Platform
Zapier
Google AppSheet
Great care must still be given to ensure thenecessary governance of the full business process while using such platforms. When done thoughtfully, developersare able totackle workflow and process issueswith greater speed, even remotely. Rest-assured there is stilldemand for traditional programming techniques for complex applications – butexpect to see big increases in corporations and agencies investing funds and strategies in these low-code and no-code platforms.Have you used any of the platforms? Which is yourfavorite?
The demand for Machine Learning for use in different areas is growing because the quantity of data increases with time. Machine learning provides a wealth of methods for separating information from data and converting into a set of goals.
Machine learning algorithms can enhance the information and automate functions mostly connected to optimization and regulation. In addition, computer vision and machine learning have expanded many fields of study, medical diagnostics statistics, statistical data analysis, algorithms, and scientific research. ML is being implemented in mobile applications and computer devices, online websites, cybersecurity, and other areas.
The expanded data has a significant impact on a variety of disciplines. The ability to extract valuable knowledge and inferences from data has emerged as the newest research and commercial applications model. On this page, we will look at some of the applications of machine learning that are being implemented into our everyday practices.
Machine Learning Applications in Daily Life
1. Commute Predictions
Predicting traffic: We all use GPS services to guide us while driving. ML, in such scenarios, assists us in our everyday routine to get around traffic jams and get to our destination in time. GPS is a system that we use to locate and save our location and speed in the server central to manage traffic. The data is used later to create the map of present traffic. ML also analyzes congestion; the one drawback is that it can be untrue if only a few vehicles use GPS when driving.
Online Transportation Networks: When booking a taxi using the app, the app determines the cost of the journey. If sharing these services, what can they do to minimize detours? Machine learning is the answer. In an interview, Jeff Schneider, the engineering director of engineering at Uber ATC, reveals that they employ machine learning to identify price surge hours by predicting the riders’ demand. Through the entire life cycle of these products, ML plays an important part.
2. Email Spam and Malware Filtering
There are several spam filtering methods that email clients utilize. Ensure that the spam filters are continually up-to-date and driven by machine learning. If rule-based spam filtering is implemented, it cannot keep up with the latest spammers’ techniques. Multi-layer Perceptron, C 4.5 Decision Tree Induction, and C 4.5 Decision are just a few methods used to filter spam powered by the ML.
More than 325 000 malwares are identified every day, and each bit of software is between 90 and 98 percent like its predecessors. Security programs for the system that runs on machine learning comprehend the code pattern. Therefore, they can easily identify new malware with a 2-10% range and provide security against them.
3. Social Media
Many of us are obsessed with social media today, and for a good reason. Social media can be fun and informative in all aspects, from teaching DIYs and other new techniques via videos to news and social networking. ML technology plays a significant part in creating web-based social media platforms that are friendly to users and applications.
Recommending Friends: Social network websites like Facebook maintain a record of people we have connected with, the profiles we check often, shared groups, and our work, and interests. Based on ongoing education, Facebook suggests people with whom we can form friendships.
Face Recognition: Facebook and other social websites and apps like Facebook and Instagram instantly recognize our friends when uploading photos to media. They then send notifications to add them to our profiles. While the interface is easy to use and appears seamless on the front, the complete process on the back end can be quite complex.
4. Medical Diagnosis and Healthcare
Machine Learning incorporates a soup of methods and tools that tackle diagnostic and prognostic concerns in the various medical fields. Machine learning algorithms are widely employed for
the study of medical data to detect patterns in the data,
managing inappropriate data,
explaining the information produced by medical units
also, to ensure the effective surveillance of the patients.
Machine learning is also helpful in estimating breakthroughs in diseases and generating medical data to research outcomes, planning, and aiding the treatment process, and overall management of patients. Alongside the machine-learning process, AI is utilized to ensure efficient monitoring.
5. Personal Smart Assistants
We have seen a significant increase in personal smart assistants such as, Siri, Cortana, and Google Assistant, as well as Amazon Alexa and Google Home.
By implementing AI to its fullest extent and integrating it into the home devices and personal assistants, follow instructions, such as setting reminders and searching for online information, controlling lights, etc.
Personal assistants and devices that include ML chatbots rely heavily on Ml algorithms to gather information, learn about users’ preferences, and provide a better experience based on previous interactions with people.
Conclusion
It is not hard to see how artificial intelligence and machine learning have transformed our lives by making them more straightforward and efficient. With the emergence of AI and ML trends, we take advantage of smart technology. We have reviewed a variety of apps here. Machine learning technology is utilized in the field to affect our daily lives. It can also help us make decisions in business, improve operations, and boost productivity in industries that stand out in the marketplace.
All software enterprises are adopting the most emerging technologies for software development to stay abreast of the competition. It is anticipated that the use of artificial intelligence for software development can increase the efficiency of the entire process.
Artificial Intelligence is poised to alter software development fundamentals in profound ways, the first since the advent of FORTRAN and LISP. It will be the first serious challenge to the traditional methods of programming. What do these changes do to affect those millions currently working in software development? Are we likely to see job loss and layoffs? Or will programming development become distinct, even focusing on providing users with a satisfying experience? Let us try to determine the impact of AI (Artificial Intelligence) on software development in general by looking at its various factors.
Requirement Gathering:
The primary phase of SDLC (software development lifecycle) and the one in which the most human involvement is needed. AI provides a wide range of tools and techniques, such as Google Machine Learning (ML) Kit and Infosys Nia, to automate certain processes cutting down on the need for constant human involvement. The presence of some automation during this phase aids in detecting loopholes before the development phase.
It is an AI technique known as natural language processing (NLP) which helps computers comprehend users needs in natural languages by automatically influencing the higher-level software models. Although this technique is a vast area for advancement and refinement, it is one of the most studied areas in AI.
Design:
Designing and planning projects require experts with specific knowledge and expertise to provide an effective solution. Making the best plan for each stage is an endeavor that is prone to errors. Retracts and forward investigation plans make it necessary to change the plan until the user arrives at the ideal solution. Automating some intricate processes using artificial intelligence tools allow you to use the most effective methods to safeguard your designs.
For instance, using AIDA (Artificial Intelligence Design Assistance), designers can learn about the customer’s requirements and preferences and apply that understanding to develop the right project. AIDA is a web-based building platform that looks at the various combination of design software and presents an appropriate design that is customized by the client’s needs.
Automated Code Generation:
It is well-known that developing a business concept and creating the code for a huge project is time-consuming and labor-intensive. To cut down on time and money, many developers are now experimenting with an approach that involves an option that creates code before starting with the development. But this approach is not a good idea because it is a risky approach with no certainty about the goal of the code. Collecting this information can take the time that could be utilized to write code from scratch.
Intelligent programming assistance using AI can cut down on the workload in terms of automating the creation of code and identifying the flaws in the code. Simply said, if you choose an example of a project which has your concept written in your native language, your computer will comprehend it and convert it to executable program code.
AI-Oriented Testing:
One of the important stages of software development is testing, which can ensure the quality of the software. If testing for a specific software is repeated regularly, then the source code must be modified by repeating the tests, which can be time-consuming and add a significant cost as well as increasing the time to production.
A broad range of software test tools use AI to create test cases and conduct regression tests. Each AI tool assists in automatizing the process to guarantee error-free testing. For example, Testim.io, Functionalize, and App Vance are just a few machines learning and artificial intelligence testing platforms.
Deployment Control:
In the software development paradigm, the deployment phase is where developers typically upgrade their programs or apps to more recent versions. If developers do not execute the upgrade process correctly and deploy, they will risk failure in the program’s execution. AI will protect developers from vulnerability during upgrades and minimize the risk of failure during deployment. Another benefit that artificial intelligence has is it allows developers to track the deployment process using algorithms that learn from machine learning.
The benefits of AI in software engineering:
Artificial development is making software smarter. Here are some of the benefits AI brings to software development.
Improved Security of Data: The security of software cannot be neglected during the process of development. The system typically collects data from sensors in the network and software on the end of the customer. AI allows you to analyze the data with machine learning to discern anomalies from normal behavior. Additionally, Software Development Companies adopting AI to develop their software will also avoid delayed warnings, false notifications, and alarms.
Error & Bug Identification: The integration of AI tools helps code be more efficient and more efficient. This means that testers and developers don’t have to be concerned about analyzing the many executable files that contain bugs and errors. It’s much simpler for them to identify errors immediately and fix them.
Decision Making: Increased decision-making capacity is another notable benefit of using artificial intelligence in software development. It is not easy for engineers to determine what features should be included in their product and how best to design an interface that meets the end-user’s requirements. With AI, developers can make quick and effective decisions. This helps companies grow and increase their influence in the marketplace.
Intelligent Assistants: Programmers spend a considerable amount of time reviewing documentation and solving code issues. Developers can reduce their time by using intelligent programming assistants that offer immediate guidance and suggestions like the best practices codes, examples of code, and other relevant documents.
Accurate Estimates: Software development often exceeds the budget and deadlines that were set. For reliable estimates, you need an advanced level of knowledge and a complete understanding of the environment, and an understanding of the implementation team to manage this. Machine learning can assist in the process of training information from prior projects. These projects may comprise user-generated stories, features descriptions, and estimates to predict work and money.
There is little doubt that artificial intelligence can help with technology development and is a great way to automate business operations.
Today, cloud computing can be considered an essential part of business technology that provides many different hosts and services to choose from. A staggering 94% of businesses are estimated to be using at least one cloud service, according to the latest State of the Cloud report.
As of Today, What Are The Benefits of The Cloud?
Employing technology with the cloud offers many benefits for virtually any size business, which influences the increase in use of this technology since it has been introduced as a business tool. According to the IDG 2020 study on Cloud Computing in companies, which bases its results on a survey of more than 500 IT professionals, 81% of companies have at least one application or part of their computing infrastructure in the Cloud. This data represents an increase of 73% compared to 2018.
Many cloud computing services are available on-demand and are comparatively inexpensive to alternative options of data management. If the scalability or data collection varies from month-to-month, the cost of service is normally correlated. Traditionally, there was the risk of buying an expensive computer network and realizing that it was not scaled for effective use. However, cloud providers may not require a long contractual obligation and it is highly customizable, so no cloud space goes to waste.
Cloud computing can offer services to both public and confidential business functions. A cloud-based email account is an example of a public cloud computing service. However, many companies use virtual private networks (VPNs) to access secure private clouds, such as those only accessible to people who work in a particular company or department.
Downsides of The Cloud: 4 Things You Should Not Overlook
There are many advantages of cloud such as accessibility from anywhere on any device, flexibility, etc. Nevertheless, there are potential drawbacks of the cloud that should not be ignored:
Security and Privacy Issues: Attacks on organizations around the world remind us that a good infrastructure is necessary to increase protection for sensitive information from both external and internal attacks. Although cloud service providers (CSPs) have their security procedures, there is always risk of unauthorized access to the heart of these informational assets. However, the major CSPs such as Azure, AWS, Google, and others spend large amounts of money to ensure that their services are secure. A breach of security would bring significant financial losses to a provider.
Interruptions: This is a symptom of when the cloud server is down, which can occur either when high-volume traffic causing the server to overload, or if there is any planned maintenance activity. This is referred to as server downtime. Since all the data is stored in the cloud, it can not be accessed during the downtime period, thereby causing delay in response.
A reliable internet connection must be available with enough resources and capabilities to deal with slowdown, frequent outages, or prolonged service downtime.
Conditions of Service:It is not yet easy to migrate cloud infrastructure from one vendor to another as cloud technology has not yet seen a simple solution for migration. This may be because of differences in technical framework and network criteria from the vendor which can influence how migration takes place, and what can be salvaged. This might lead to additional costs and complexities in the migration.
Transparency: Many organizations are unclear on who owns the data that is hosted in the cloud. The ownership of data is a critical issue which can have legal repercussions. Therefore, it is necessary to know if once the data is uploaded to a system, the cloud becomes the property of the cloud service provider. At the same time, the terms and conditions of the agreement regarding the management of these assets are not always known exactly.
To overcome the limitations of cloud computing and to build more efficient solutions; cloud computing is moulding itself into a newer approach, Edge computing. Distributed computing infrastructure, or edge cloud computing, can facilitate applications to come closer to data sources like the Internet of Things ( IoT) – connected objects and devices equipped with sensors, software and other technologies that allow them to transmit and receive data – to and from other things. More companies are testing this model and have found positive impacts from both a technical and optional standpoint.
What is Edge Computing?
Edge computing refers to a distributed computing model that allows business applications access to data and direct actions. This model can be supported using IoT devices or a local perimeter server.
Edge computing offers lower latency, more efficient communication, and a variety of other improved capabilities for new applications. The lower latency allows much faster communication speed which can connect systems within a few milliseconds. With this level of speed, it can push new applications to be able to operate in real time more often which is particularly useful with technology such as virtual reality or autonomous driving. As a business tool, this can elevate a company’s analysis capabilities and internal operation functions.
Another great perk is that cloud infrastructure can be stored locally, which heightens the security capabilities and the privacy of the data. Instead of depending on a large but minimal cloud space that is maintained in another location, businesses can host the data in close and secure locations under control, along with any hardware used in tandem.
These new capabilities will have a significant impact on devices. Edge computing will allow some of them to be unloaded with processing capacity since this will be able to take place on the network, closer to the user and in real-time. This will lead to a reduction in their cost (simpler devices) and their energy consumption (less process), with which the number of connected devices will increase and will also generate new growth opportunities in the connectivity business.
How Edge Computing is Used in Companies
Data Storage: All companies have continuous data that they want to use and, due to efficiency and risks of loss, proper storage space needs to be provided. Edge computing can host the data to make it as accessible as if it were in the local network and replicated, but at a higher speed.
Artificial Vision: The decreasing price of components and advances in artificial intelligence have increased the functional use of cameras and sensors. These cameras have the potential to create spectacular value in public security, automatic stores, and robotic warehouses. However, there are also increased risks for non-stakeholder privacy and cyberattacks. Edge computing enables a secure, efficient, and privacy-friendly deployment, where raw images never leave the local realm.
Industrial Internet: Production and deployment processes are rapidly being digitized. This requires being able to control information to the millisecond to ensure efficiency, quality, and safety of workers. This level of stability and scalability can only be done with Edge computing and 5G networks in business environments.
Video and Augmented Reality: Virtual and augmented reality are also becoming part of the production and operational processes of companies. Edge computing creates and environment where VR and AR technology can be functional at peak capacity. This can impact training capabilities, client facing technology, and physical security measures.
Even with recent advancement in technology such as Edge computing, the current cloud framework remains a necessary business tool for today’s environment. With the increasing digitization of the market and everyday life, it is important to have the right technology to respond as an enterprise. While there are a variety of obvious benefits to the cloud, it is imperative that users pay attention to the risks/liabilities of adopting this technology. It is also beneficial to follow the development of new cloud technology, with the biggest developing being Edge computing. This is an opportunity to obtain further competitive advantage and so that enterprises do not fall behind the data management technology wave. At this rate, Edge computing could be a common business tool used both in the private and public sectors for various business functions and operations.
84% of companies around the world consider Artificial Intelligence as a key factor of competitiveness. It is estimated that the Artificial Intelligence market will reach 126 billion USD by the year 2025 on a global scale.
Undoubtedly, Artificial Intelligence (AI) will be the key to selling in 2021. More than 60% of high-performance companies have increased investment in AI in the last year to respond to the situation derived from the crisis caused by the COVID-19 pandemic. The primary sectors that have led this investment are healthcare, automotive, and financial services.
Thanks to the application of Cloud Computing and Big Data technologies, in recent years, AI has developed rapidly based on the imminent arrival of 5G networks and the entry into a hyper-connected world. It is estimated that by 2025, customer service organizations that incorporate Artificial Intelligence into their multichannel platform, will increase operational efficiency by 25%.
Artificial Intelligence allows you to analyze purchasing habits, influence strategic direction by extracting data on trends, or help identify and track inventory accurately while being efficient.
Areas such as customer management, marketing, or sales are taking advantage of these initial AI applications, such as the creation of simulation models or propensity to purchase. AI is also used as well for the personalization of the purchase process using Machine Learning technologies. Out of a sample of current AI users, 87% said they were using or considering using artificial Intelligence for sales forecasting specifically.
Artificial Intelligence helps companies that are users, have a greater organizational flexibility by adopting best practices in models, tools, technology, and use of performance data.
Artificial Intelligence can be implemented directly as a tool to improve performance levels and production safety coefficients in the company. You can even automatically recognize and catalogue employee invoices, saving labor costs and improving efficiency.
In addition to the entire application to analyze data and perform calculations, Artificial Intelligence can directly influence the process of so-called “real-time marketing”. Analytics and Artificial Intelligence will be the emerging technologies that will have the greatest impact on marketing techniques. This technology helps companies better understand customer behaviors and purchasing trends. It also acts as a tool to predict future changes in customer wants and needs, based on mature and new data points.
AI applications also make it possible to increase efficiency and social welfare while promoting the protection of the environment. For example, the application of a smart heating solution that uses reinforcement learning technology, can reduce up to 10% fuel consumption.
Artificial Intelligence will be used in all fields and sectors such as medicine, energy, transportation, education, scientific research, and logistics systems. Also, AI can address critical problems in different traditional and specialized industries. In recent years, an example of this is “smart agriculture”. Smart agriculture is operated by farmers who can have total control over their entire plantation, through AI integrations. A software interface that correctly analyzes the data can warn about potential red flags in real-time so that farmers can take necessary actions that will protect the crops. This kind of process can help make sure the products are the most profitable and ready for sale. This process can also decrease the room for human-error and reduces potential costs of having to hire additional farm hands.
The flexibility of a great organization is exhibited by reporting the impact of AI on the income statement as it facilitates the best practices of tools and technology, data usages, and models. Hence, the corporate strategy and finance departments, along with the supply chain, will be the ones that perceive the greatest impact of the adoption of AI in economic terms.
With AI, comes the massive collection and management of data points. We are fully entering the fourth industrial revolution, which is expected to generate a world in which virtual and physical manufacturing systems can cooperate with flexibility on a global level. We can extract the best knowledge value from data through Big Data technologies and AI application. The compelling reason is that traditional methods cannot process such large and especially varied volumes of data generated at high speed, in real-time. This also helps eliminate the possibility of human marginal error, when conducting these kinds of analysis.
Using AI daily will become one of the main recurring trends for the evolution of future ecosystems. At this point, it is likely that Artificial Intelligence will drive industrial development and help companies achieve long-term, environmentally friendly, and profitable growth, at a global scale.
“The big question that organizations must ask themselves in the digital age is how to respond effectively to the increasing digitization of society, not only in terms of how to avoid becoming obsolete in the face of competition but also how to adapt and lead the way. Digital disruption”.
Digital transformation is a continuous process over time, in which many factors beyond the technological ones are involved. It is of little use to digitize a company if employees are not empowered to adopt digitization in their work. Therefore, an organization’s cultural change is considered the most complex challenge of digital transformation for all companies.
Sustainable digital transformation focuses on carrying out a progressive digital immersion, divided into a series of phases or steps so that the next step in projects are not addressed until the different initiatives of the common step have been completed. To carry out the most complex digital initiatives, it is necessary to go through the following degrees or steps of maturity or digital immersion, the so-calledImmersion ladder.
Phases of Digital Transformation
Digital Foundations: In the first phase, what we call Digital Foundations is established. In this step, initiatives are carried out to establish a base on which the digitization of a company will be built on. The architecture and strategies that coordinate the different digital actions to be carried out include a social media plan, CRM execution, and different digitization processes.
Digital Expansion: The second step is the stage that we call Digital Expansion. In this phase, symptoms begin to appear outside the organization, known as digital contact points. Employees are also being empowered in digital skills, and the results of actions carried out in digital media are being analyzed.
Digital Optimization: We identify the third stage as Digital Optimization. At this point, the actions of the previous step are delved into. The companies that are in this echelon have a true digital culture. They can analyze digital information in an advanced and predictive way, and with self-learning processes. They also interact with their customers through the channels they always prefer, collect their feedback in an advanced way, and can enhance the innovations derived from co-creation.
Digital Maximization: The last step, which few companies reach today, is Digital Maximization. At this stage, automated processes are seen in which authentic artificial intelligence is applied. Large amounts of data, both internal and external, are analyzed, allowing for robust personalization of the customer experience. This allows creating new business models based on the digital world, such as virtual reality or augmented reality.
Pillars of Digital Transformation
A company that wants to start a digital transformation process must consider key elements that help drive the process forward:
Leadership: The success of digital transformation in a company does not depend only on the degree of digitization but also on its managers and leaders’ ability to drive change. This implies adopting agile management styles that facilitate the evaluation and implementation of new models, sources of income, and opportunities.
Customer Experience: Digital Transformation is closely linked to Customer Experience. It must aim to use technology to create new ways of communicating, predict customer needs and behaviors, and improve omnichannel strategies.
Business Model: A business model is a way a company creates, delivers, and captures value. Within the Digital Transformation, companies must be willing to evaluate and modify:
Value proposition: that is, the products and services it offers.
Value delivery: these are the distribution channels, customer segmentation, and the relationship with them.
Value creation: the resources and alliances to create the products.
Value capture: An adequate Digital Transformation also implies a revolution in costs and sources of income. Therefore, the need to have the support of the directives in this function.
Organizational Culture and Agility
Technology within the Digital Transformation is the great pillar within the path of change. However, technology could not act in the desired way if it is not accompanied by processes of practice and relationships between human agents. In this way, technology is linked and shaped by the culture and the organizational context; thus, Digital Transformation will be a more complex process that involves all the actors in the organization.
Digital transformation requires investment in human capital and culture. Also, for a company to respond adequately to rapid changes in society, it must become an agile company.
To learn more about how agile is changing the way we work, listen to this podcast on agile culture and how companies are beginning to adopt it to improve their processes and make them more efficient in the fourth industrial revolution.
Benefits of Digital Transformation
An adequate Digital Transformation will have 7 positive impacts on:
1. Competitive advantage: Digital transformation allows a company to create new products and services according to customer needs, and this undoubtedly allows diversifying services, making better decisions, and driving growth versus versus its competitors.
2. It promotes the culture of innovation: Agile innovation management in companies is one of the most important tools to adopt new methodologies that deploy the creation of new products, solutions, and business opportunities.
3. Improves productivity: When automation processes are adopted in companies, employees’ better performance is guaranteed, who see digital tools as the ally to achieve the objectives.
4. Greater brand presence: When we talk about a company having an omnichannel strategy in its digital and physical channels, it is because the said company has understood the importance of providing the best service to its customers, and the digital transformation allows these channels to communicate and flow with each other, avoiding setbacks in customer service processes.
5. Give importance to data: A company’s databases must be converted into stocks to have a greater impact on the market. Digital transformation allows better decisions based on the big data generated through all areas of the company.
6. Reduce costs: When methodologies such as Agile or DevOps are adopted to develop technological products in an agile way, errors in their production are reduced, which gradually lowers the company’s costs. In addition to the automation of processes and cloud computing for information storage, avoiding the purchase of physical servers and their maintenance.
7. Customer satisfaction: It is perhaps the greatest benefit that digitalization brings to companies. Knowing customers allows providing a better experience, more agile and secure services, and direct communication to help the company attract, convert, and retain loyalty more appropriately.
Technology applied to Digital Transformation
Big Data: Big Data is the tools and techniques that allow the real-time processing of large amounts of data collected from different organization’s internal and external sources.
The processing of this data and its subsequent analysis will make decisions based on predictions, anticipate people’s needs, and distribute budgets more intelligently.
Cloud Computing: Cloud computing has great advantages for digital transformation.
Cloud services allow computer tools such as databases, servers, analysis, networks, and software, within a flexible and low-cost infrastructure.
The cloud also facilitates access to different technologies, deploys services almost immediately, and has architectures based on microservices, delivering greater agility and scalability.
Mobile: With the arrival of smartphones, mobile technologies’ development has been escalating rapidly, promoting apps that open a new form of relationship between organizations and their customers, suppliers, and workers.
Artificial Intelligence: AI is a set of techniques that allows machines to perform actions rationally. In this way, companies can understand customer needs, suggest the perfect product to the right person and streamline the sales process.
For companies’ benefit, artificial intelligence is used to automate processes and capture and analyze information, bringing benefits such as cost reduction and optimization of services and products. This type of service is widely used in the financial sector, which has pointed to banking’s digital transformation for a few years.
Conclusions
The digital transformation process in the company is not something that is not trivial and must be implemented with strategic planning. It involves profound changes in the company at all levels and must necessarily encompass all departments from senior management to operators. The digital transformation methodology will help the company develop a digital strategy that will improve the company’s processes, which will translate into an increase in productivity and an increase in the efficiency of the processes.
One of the most important tools for any business to use is a CMS (Content Management System)
CMSs are a fundamental asset that allows us to create, develop and maintain a web page, which is why it is important to choose according to the digital content of the website.
WordPress vs Drupal are two of the most common CMS seen on the web. They present different characteristics such as security, the manager’s simplicity, variety, optimization, or quantity of plugins.
General terms of WordPress and Drupal
Without a doubt, WordPress is the most user-friendly and intuitive blog builder available today. This is possibly one reason why users tend to prefer it, and it is that its popularity makes it easier to work since there are tutorials, courses, and virtual academies where you can learn to work with this CMS.
The many uses of the services offered by WordPress: e-commerce, blogs, or professional business websites are some of the most general examples of users who use this platform. There are also websites developed in this great CMS for forums, portfolios, events and even E-Learning. Some examples of WordPress-based sites are Mercedes Benz, The New Yorker, Whitehouse.gov, etc.
Drupal, on the other hand, corresponds to one of the best-known CMS worldwide.
Companies such as Harvard, Twitter, BBC, and NBC News are some of the most popular sites that work with Drupal and it covers a large series of web pages such as blogs, personal or corporate websites, forums, E-commerce, or social networks.
Now: Why choose one CMS over the other?
As we have seen, both WordPress and Drupal contain a wide range of very useful features that provide many advantages to design a professional website. Financial benefits, such as adjusting cost, flexibility on production, and freedom to select features, must be considered.
At this point, you may be wondering: WordPress vs Drupal, which one should I choose? Next, we will detail all the characteristics that will help you make this decision.
WordPress and Drupal interface
The interface within a CMS is one of the most critical points since it corresponds to most content creation. Both WordPress and Drupal have a virtual interface, and for both managers, these interfaces present a facility within the platform.
The WordPress interface is one of the most intuitive, which makes it easy to learn to use. It is simple, with floating buttons with which you can modify the blog or website.
Being one of the most used CMS, there is a community of many followers, ready to offer you help if you need it. There are also options to learn how to handle it through blogs or portals made by the people who use it and are part of this “forum”.
Although you use programming languages to develop web pages, you do not need to know how to program with code, so almost anyone can start WordPress.
On the other hand, Drupal has a very welcoming and intuitive interface. Like its relative, you will not need to know programming languages to develop and modify your web page.
In conclusion, you can publish new content frequently without changing code lines, adding resources, or managing your website’s configuration and appearance that you like the most.
How do WordPress and Drupal work?
WordPress is the most popular CMS on the internet. As we mentioned before, its interface makes it easy to work for beginners or experts. This is a great advantage since you can start a personal project without having to look for an expert in programming or website development, and it is also at a more professional level, companies or organizations that decide to work with this CMS would save time and money in finding a web developer.
Drupal is a more modern content manager (CMS), so not everyone knows how to use or work with it. Its installation requires a little extra knowledge, especially if we compare it with the installation of WordPress.
In conclusion, in WordPress, you can start from scratch and with the free themes, you will be able to present a web page of good quality and with good material, while in Drupal, you will have to go to a web developer since it presents highly personalized or coded themes.
Which is more customizable between WordPress vs Drupal?
Both CMS have a wide variety of customization options, although WordPress has a greater number when we talk about plugins. WordPress brings thousands of pre-established templates with which you can change the website’s appearance to give it the most customization according to your website model.
What is the most secure between WordPress vs Drupal?
Even though all platforms can be vulnerable, we can say that both have teams made up of developers to address the lack of security within any website within their power.
Drupal has better security and content management. Some companies have a series or group in charge of the different facets of creating and maintaining a website. This makes the page more vulnerable, as more people have access to it, which can cause more risk against the website.
Due to a lack of skills or personal reasons, a person can add, remove information, or content that leads to the website’s deterioration or breakdown. Drupal reduces this risk by only allowing certain users (such as administrators) access to make changes. For some content to be uploaded, it must go through the review of the website’s managers or administrators.
In Drupal, the content is previously uploaded to the website, and once it is approved, it is there that it becomes visible to the community or users in general.
WordPress is considered a bit more vulnerable than Drupal. It relies heavily on third party plugins. That is why Drupal is used by government brands that require security and control over their pages.
What are the costs of these CMS?
Whichever manager best suits your needs, you can start both for free!
Drupal is open-source software and is completely free to download, use, and customize your website.
WordPress is a free service where you can start a project from scratch. Although the professional themes and plugins have costs, they have more features: security patches, maintenance, and other extra options. Such is the case of the language options in both managers.
Drupal presents multilingual functions by default, while in WordPress, you must purchase plugins to acquire such a function.
SEO in WordPress and Drupal
Currently, Search Engine Optimization (SEO) positioning is an important factor for any web page. With Drupal, you can generate URLs based on each content. Also, you can connect your website with the profiles of your social networks. WordPress offers an ideal foundation for SEO. Remember that having a good SEO will exponentially improve the traffic on your website.
How does file management work in these CMS?
Some web pages have a large amount of content, from organizations, businesses, entertainment, or government websites. All of these will need management of the content of the web page. Drupal offers content collaboration; that is to say, a group or a series of managers can divide the work since this manager allows it.
Functions or positions such as preview, editing, or programming are the most important and useful tools you can have in this content manager (CMS), ideal for companies that formwork teams for the organization and manage their web content. WordPress also has a service that offers a library where you can upload files such as images or videos.
Which CMS should I choose between WordPress and Drupal?
Creating a web page must go through a series of procedures: Having reliable hosting and owning a web domain corresponds to this process.
The choice of a content management system is essential in this process, and which one that will give life to our websites in every way. The customization, security and loading speed are some of the features and benefits that must be considered when choosing our CMS.
What are the best factors between WordPress vs Drupal?
Although they are platforms with characteristics of great advantages for the design, creation, customization, and maintenance of a website, you must study your website’s needs and your availability of resources. Criteria such as financial flexibility, company objectives, and scalability.
For more similarities that Drupal and WordPress have in this context, the managers present marked differences. For example, in WordPress, although the ease of creating content is greater, to carry out a more professional website, you will have to have paid services, such as plugins and themes.
While in Drupal, even if you do not need to invest such an amount of money, you will have to find an expert in this area; since its complexity is greater, although it has characteristics such as content and information management.
Both CMS are useful, and each one has its advantages, so everything will depend on you and the work you will do. Now tell us! In this WordPress vs Drupal which one would you choose? Have you already used one?
Conclusion
WordPress is a common platform for creating small websites to customize and expand with thousands of awesome themes and plugins. If you plan to go with the functionality, you cannot build on your own. It would be far easier to find support and recruit professionals.
On the other hand, Drupal is suitable for more complex websites and other web ventures that WordPress cannotmanage. However, to take advantage of all that strength, you will need to be familiar with HTML, CSS, and PHP. You should be a competent programmer if you want to create complex websites with Drupal.
Both Drupal and WordPress have benefits and drawbacks but choosing between them should be quickly based on your objectives.Which option did you choose?
A new trend that we see in DevOps teams is the adoption of microservices, where big and complex applications are broken down into independent and small processes and services. These microservices can communicate with each other through application programming interfaces (APIs). By breaking a monolith into microservices, developers are able to handle applications better, isolate problem areas without shutting the whole application down and focus on completing singular tasks.
While switching to microservices seems like a rather easy task, many developers can underestimate the complexity of the migration process, and that can eventually lead to disastrous results. That is why, before transforming your application’s monolithic architecture into microservices, it is important to set out best practices to avoid any challenges which might arise during the process.
Here are the Microservices Best Practice You Should Know
1. Understand why do you want to migrate to microservices
Just switching to a microservice architecture because it is the latest technology may not do your organization any good. Switching to microservices can take months depending on your application’s size, and it can also be expensive since you will have to train your resources or hire new DevOps to handle the migration.
After all, if you have a working application that works just fine, then why disrupt that by changing the architecture? There has to be a driving force for the change.
Whether you are facing issues with your application or you want to make it faster, the reason has to be big enough for you to make the shift.
2. Define what a microservice is
Before planning a strategy for microservices, you need to define what exactly a microservice will entail in your application’s architecture. Depending on your business requirements, you might want to go for medium-sized services, if bigger services align with your business and engineering teams better.
One way to determine the size of a microservice is checking which pieces of code, when changed, create exponential test cases. It is crucial to have a clear idea about what microservices, services, and functions look like for your company, because if you do not, then you could end up with either of these problems:
Your application gets under fragmented, and you are not able to see any benefits of microservices
Your application gets over fragmented, and the weight of managing the numerous microservices takes away its value as a whole
3. Create isolation between microservices at several levels
By isolating microservices from each other, you are able to change them as quickly as possible. Isolation needs to be done at several levels, including:
Runtime processes: One of the most common ways of isolation is differentiating microservices according to runtime processes. This could involve various http management approaches, event architectures, containerization, service meshes, and circuit breakers.
Teams: By partitioning your application, you are able to partition work among teams in a more well-defined manner and give autonomy to the team members as well.
Data: Of course the biggest advantage of implementing a distributed computing technology like microservices is that your data gets partitioned and it re-integrates at the system level.
4. Decide how services will find and communicate with each other
As you are building and deploying microservices separately, you also need to remember that these microservices need to be able to communicate with each other to create a logical workflow and finished application, which from the user’s perspective should look the same as the monolithic application.
While many developers try to hard code the locations of microservices in the source code, it can lead to an array of problems when the location of any of these services need to change. Better alternatives to this problem include a centralized router or a service discovery protocol since both require registration, deregistration, scalability, and high availability.
With service discovery, automatic detection of services becomes possible, and the router is able to work between systems to direct one service towards another. It is the responsibility of service discovery to tell where things are, while centralized router proxies all the traffic.
5. Selecting the right technology
While many companies spend a lot of time selecting the right technology to implement microservices, the truth is, it is rather overvalued. That is because most of the modern computing languages are equally flexible and fast. Most importantly, almost any problem can be solved with any technology.
While all languages have their pros and cons, the decision really comes down to personal preferences and not technical reasoning.
Choosing a language for implementing microservices will become a hiring decision since you will need developers on board who are comfortable working with that language. That is why it is also recommended not to mix too many programming languages, as it could make hiring people rather difficult.
In conclusion
Switching to microservice architecture can lead to many challenging. Before you start the migration process, make sure you have real reasons for it, take an incremental approach, and follow all the best practices.
It can be rather challenging for legacy monitoring tools to monitor ephemeral and fast moving environments like Kubernetes. The good news is, there are many new solutions that can help you with this.
When you are carrying out a monitoring operation for Kubernetes, it is crucial to make sure that all the components are covered, including pod, container, cluster, and node. Also, there should be processes in place to combine the results of the monitoring and creating reports in order to take the correct measures.
Before moving forward, your DevOps team has to understand that monitoring a distributed system like Kubernetes is completely different from monitoring a simple client-server network. That is because monitoring a network address or a server gives much less relevant information as compared to microservices.
Since Kubernetes is not self-monitoring, you need to come up with a monitoring strategy even before you choose the tool that can help you execute the strategy.
An ideal strategy will have highly tuned Kubernetes that can self-heal and protect itself against any downtime. The system will be able to use monitoring to identify critical issues before they even arise and then resort to self-healing.
Choosing Monitoring Tools And When To Monitor
Every monitoring tool available for Kubernetes has its own set of pros and cons. That is why there is no one right answer for all types of requirements. In fact, most DevOps teams prefer to use a combination of monitoring tool sets to be able to monitor everything simultaneously.
Another thing to note is that many DevOps teams often think about monitoring requirements much too late in the entire development process, which proves to be a disadvantage. To implement a healthy DevOps culture, it is best to start monitoring early in the development process and there are other factors that need to be considered as well.
Monitoring during development – All the features being developed should include monitoring, and it should be handled just like the other activities in the development phase.
Monitoring non-functionals – Non-functionals like requests per second and response times should also be monitored which can help you identify small issues before they become big problems.
There are two levels to monitor the container environment of Kubernetes.
Application process monitoring (APM) – This scans your custom code to find and locate any errors or bottlenecks
Infrastructure monitoring – This helps collect metrics related to the container or the load like available memory, CPU load, and network I/O
Monitoring Tools Available For Kubernetes
1. Grafana-Alertmanager-Prometheus
The Grafana-Alertmanager-Prometheus (GAP) is a combination of three open source monitoring tools, which is flexible and powerful at the same time. You can use this to monitor your infrastructure and at the same time create alerts as well.
Part of the Cloud Native Computing Foundation (CNCF), Prometheus is a time series database. It is able to provide finely grained metrics by scraping data from the data points available on hosts and storing that data in its time series database. Though the large amount of data it captures also becomes one of its downsides since you cannot use it alone for long-term reporting or capacity management. That is where Grafana steps in since it allows you to front the data which is being scraped by Prometheus.
To connect to Grafana, all you have to do is add the Prometheus URL as a data source and then import the databases from it. Once that integration is done, you can connect Alertmanager with it to get timely monitoring alerts.
2. Sysdig
Sysdig is an open source monitoring tool which provides troubleshooting features but it is not a full-service monitoring tool and neither can it store data to create trends. It is supported by a community, which makes Sysdig an affordable option for small teams.
Sysdig can implement role basedaccess control RBAC into tooling, it has an agent-based solution for non-container platforms, and it also supports containerized implementation. Since it already has a service level agreement implementation, it allows you to check and get alerts according to response times, memory, and CPU. There are canned dashboards that you can use.
The only downside to using Sysdig is that to use it you need to install kernel headers. Though, the process has now been made much simpler with Sysdig able to rebuild a new module everytime a new kernel is present.
While Sysdig is the free and open source, there is premium support available for enterprise customers through Sysdig Monitor, and there are two types of solutions available- SaaS full-service monitoring and on-premise.
3. DataDog
DataDog is a SaaS-only monitoring tool which integrates APM into its services. It provides flexibility with alert monitoring, and you also get access to dashboards through a UI. It can also provide APM for Python, Go, Ruby, and there will soon be a Java support as well.
You can connect DataDog to cloud provider environments, and it can consume data from sources like New Relic and Nagios. With its dashboards, you can overlay graphs, and it can process other APIs which expose data.
There is a service discovery option which allows DataDog to monitor dockerized containers across environments and hosts continuously.
In conclusion
As we mentioned above, monitoring Kubernetes is not an option but a crucial requirement, and it should be implemented right from the development phase.
73% of IT leaders believe that centralized/integrated technology systems must be a priority.
Economic activities are taking place digitally constituting great challenges for the IT department. The IT department has to not only oversee the daily computer operations, monitoring of communications and networks, keeping up with compliances etc., but work toward transformation and innovation too. For the same reason, IT modernization is slowly taking precedence for business and IT leaders to meet their business goals and keep ahead of the competition.
Why IT Modernization important:
With frequent changes in technology, the systems need to be changed/upgraded too. Systems that tend to just keep up, can be vulnerable and left behind fast. The need for IT modernization or integration of systems is for achieving goals, reducing costs, improving performance and operational efficiencies. With startups releasing products and applications with newer features in the market at a greater speed, it is no wonder that larger businesses and their business leaders have felt the need to push the efforts towards IT modernization and drive their businesses with agility, security, and efficacy.
Here are a few reasons which back IT modernization:
Efficiency: If IT infrastructure/data is decentralized, it is difficult to track, protect, supervise and manage. Cloud implies cost-effectiveness, innovation, and speed; hence must be integrated with the IT infrastructure augmenting connectivity and access. Integration of cloud in the IT infrastructure can readily ensue via the Integration Platform as a Service (iPaaS) while keeping security and efficacy intact. Through iPaaS, data can move securely and faster. This allows employees to concentrate, anticipate and solve issues with clarity due to them having operational recognition and control. It is advised to develop a data management strategy.
Security: It is vital to have complete control and visibility of data within the IT infrastructure. The movement of data within and outside the organization’s network and its usage by employees determine decisions. In decentralized structures, it is difficult to secure data leading to non-compliance at times.
Agility: The existing infrastructure must handle a responsive organizational environment. Data transfers must be speedy and efficient in organizations so they can stand out from competitors. In an enterprise data management strategy, a robust but flexible infrastructure is the key to working swiftly.
IT Modernization Steps
IT modernization includes a gamut of strategies such as planning, alignment with goals, and understanding loopholes while having a partner to make it a reality.
Assemble and modernize: It is imperative for organizations to take inventory of their applications and the infrastructure associated with it. Mobile users access data from anywhere at any time, and also generate data, hence organizations must deploy software-defined infrastructure and comprise a data center infrastructure to scale up.
Automate: Updating application infrastructure is a vital step in modernization. Manual steps which can stifle growth and increase delays/errors must be replaced with automation. Compliances must be followed with respect to provisioning, distribution, and scheduling. In this process, APIs must be mapped to pre-defined policies; and automatic allocation must occur in resources, tracking utilization etc. while ensuring standard repeatable processes.
Measure and examine: System parameters must be identified which can be used and metrics which should be monitored and reported. Through repeated monitoring of these metrics, deviations can be identified, vulnerabilities understood and rectified before any errors occur. These metrics ensure that the infrastructure and applications run smoothly adding to productivity. Proactive log analytics assists in determining issues which help the team in responding to failures before they occur.
Audits: When tools, technologies, and systems are introduced in the organization, audits are required. They are a must for security procedures, data lifecycle (available and recoverable at all times with minimal losses), and data governance. Modern data centers improve systems availability while lowering costs.
IT modernization’s implementation must be in phases with a partner – identification of new teams, technology, and new processes. Once successful, these services can then be scaled along with other new initiatives, and measurable benefits be gained.
Government’s Benefits From IT Modernization
75-80% of IT budget is spent on operations and maintenance, leaving very little room for innovation and modernization. The need is to push for modernization. Outdated infrastructure also gives the government a negative perception from its citizens. Hence, digital services must be enhanced while keeping costs low for citizens, for the services provided.
The federal government must invest in new applications/services for its citizens by using the latest technology. Using big data and analytics – programs in public safety, justice, reducing cybercrime, disaster response improvement, or streamlining other processes can be supported by an effective government. Automation through the artificial intelligence of simple questions can allow the agency employees to focus on other complicated matters.
So how can the government provide highly agile, secure and flexible services through a robust infrastructure? While data can assist in taking decisions, cloud can be used for focusing on mission related decisions. Agencies must move the applications to cloud (using DevOps and agile methodologies). Also, through application modernization, codes can be re-hosted, new programs developed, and agile development to join dissimilar systems together. APIs can be used and applied to data sets to assist in new development. These systems will ensure that needs of the citizens are met faster in conjunction with safety, while providing high quality of goods and services.
The Modernizing Government Act (MGT), passed by the bill is waiting for consideration from the Senate. “It would place $250 million fund managed by the General Services Administration and overseen by the Office of Management and Budget, that agencies can tap into for modernization for projects with cybersecurity challenges, can move to other shared services or are expensive to maintain.” It is time the government works with private partners to begin transformation, improve services and efficiencies.
