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Importance Of Building Data Analytics For Enterprises In Today’s World

Data is everywhere and all the things you do create new data. All kinds of electronic messages that you send or receive and even visiting a website contributes to data storage. And this is why data is amongst the most valuable commodity. Big Data refers to the voluminous and vast data sets that can be unstructured or structured.

Data Analytics is examining raw figures and facts to obtain valuable insights from it. Currently, #Data Analytics for business is being used with Big Data in most of the industries. The concept of Big Data has been there for several years now and many firms have realized with time that if they are able to capture relevant data into the business, the companies can easily apply analytics and derive important value out of them. However, some businesses choose to avail services of Big Data Analytics companies because they may not have enough funds or expertise.

Why Does Business Need Data Analytics?

Data is increasing aggressively every day. Data generation happens through many industries, businesses, and users. So, the future of Big Data Analytics is more challenging. Also, amalgamating the data which has been generated via businesses is crucial because if the data gets wasted, valuable information would be lost.

In the past, processing the data needed skilled analysts but now, tools are employed for high-speed data processing, which helps in incorporating Data Analytics during decision-making times. Big Data Analytics is important because it helps in tackling the data and using it to discover new opportunities. As a result, higher profits, smarter business moves, happy customers, and efficient operations are seen.

Once a business adopts a data analysis method, it can analyze the causes of specific events on the basis of data, understand directives and objectives, and have technical insights using a simple language. A business should build Data Analytics due to the following reasons.

Innovation

Data Analytics gives a rough idea about future trends in customer behavior that will allow you to create futuristic inventions of the products. This way, your business can make products and make services that make you stand ahead in the competition in your industry. Also, with these innovations, your business can maintain a high position in the industry. You can also patent those innovations and yield the best out of them while also accumulating high amounts of profits. You can accordingly carry out your enterprise app development as well.

Read Also – Data Analytics vs. Big Data vs. Data Science – A detailed comparison

Segmentation

Segmentation includes dividing the customer data on the basis of shopping habits, product usage, location, age, gender, etc. This information can help you in creating messages that resonate will all the segments individually. Whether it is the people belonging to the demographic group, staying in a specific area, or demonstrating interest in a particular activity or hobby, you can customize your marketing to directly appeal for their individual interests and needs.

Through segmentation, you can also assess which groups are highly profitable for your business, letting you identify your best customer segments as well as avoid wasting money on the segments that would not yield conversion. Many companies have begun assigning ratings to the customers using CLV (customer lifetime value), which determines the worth of the customer for the company as compared to other customers. Knowing which customers you should focus on as well as invest in is important for maximizing your profit.

Product Development

For being competitive in the customer-centric market, your business’s service or product needs to be customized according to the customer. Collecting data from surveys or from using A/B testing in order to experiment with disparate approaches can be a great way to ascertain what works and what does not. You can use the customer feedback to enhance your product’s quality or perform your enterprise app development and identify the right opportunities for innovation which will distinguish you from your competition.

Read Also – How Big Data Analytics Can Create a Billion-Dollar Mobile App UX?

Supports Agility

It has become very important for your business to be agile in this dynamic market. As acquiring new consumers gets increasingly complex, more companies are funneling their resources towards keeping the current customers engaged instead of finding new ones.

And this is not an easy part because successful customer retention implies constantly adopting the changing needs of the customers, anticipating problems, providing solutions, and then being able to adjust your strategy quickly according to it. But if businesses use the services of efficient Big Data Analytics companies, this entire process becomes easier because of the efficient use of Data Analytics.

Operational Efficiency

Through Data Analytics for business, companies can identify potential opportunities to maximize their profits and streamline operations. It helps find potential problems, remove the procedure of waiting for those problems to take place, and then take relevant actions. This allows the companies to know which operations have reaped the best results under varying conditions and find which operational are is error-prone as well as needs to be improved.

Read Also – How to Build Successful Mobile Apps using Big Data?

Saves Money

If you know what the customers know beforehand, you can make your marketing campaigns highly customer-oriented. This enables a business to customize its advertisements for targeting a particular segment of its customer base and yield better results. So, it saves the business’s money they would otherwise spend on advertising to their entire customer base.

For example, if a skincare brand wants to introduce an age-reverse serum meant for women above 50 years of age, advertising the serum to women in their 20s would not yield them many profits. So, through Data Analytics, the brand can reach its potential customers only.

Better Decision Making

When it comes to decision making, analytics and data are invaluable, as they provide insights about whether the business is moving in the right direction or not. It helps find market needs and trends while finding why a particular product is doing well while others are not.

Such data can aid in taking vital decisions including pricing policies, market expansion, customer care, widening its product range, etc. A business that takes decisions on the basis of Data Analytics usually enjoys an upper hand over its competitors. This is because instead of ambiguous information and guesswork, it can base the decisions on real and genuine data.

Summing Up

The future of Big Data Analytics is extremely wide and important. Big Data is increasing exponentially each day and if it is interpreted correctly, it can prove to be a blessing for the business. If the business is able to understand and interpret data rightly, it can exercise numerous benefits like better decision making, innovation, product development, targeted advertising, and improved operational efficiency. As a result, a lot of money can be saved. This is why Data Analytics for business is highly crucial.

How Artificial Intelligence, Data Science And Technology Are Being Used To Fight The Coronavirus Pandemic?

The novel coronavirus from its origination in China has spread and affected more than 800,000 people all over the world in the span of 3 months from January 2020. Moreover, the disease has taken the lives of almost 40,000 people as of 31st March.

Maintaining so much data, which is ever-increasing is not possible without the help of technology. AI or artificial intelligence is adept at analyzing patterns from big data. China’s success with artificial intelligence as the crisis management tool shows its utility and justifies all the financial investment this technology has used to evolve over the years.

Advancement in the AI application like speech recognition, data analytics, natural language processing (NLP), deep learning, machine learning, and others like facial recognition and chatbots have been used for diagnosis as well as for vaccine development and contract tracing.

Spurred by the gains of China in this area, other countries are also sharing their expertise to expand the current capability of AI and make sure AI can emulate its role in aiding China in dealing with coronavirus pandemic. The list mentioned below will be throwing light on how AI has been applied to solve the pandemic.

Chatbots

Several healthcare app development companies have developed chatbots using artificial intelligence and NLP so that people can get the right information through these apps. People can also chat with medical professionals using the apps and can clear their doubts. This is feasible for people as well as doctors and health practitioners because people are provided with the necessary information by simply tapping on their phones.

Chatbots are programmed to answer the questions based on the information their developers add and through artificial intelligence, the information is updated automatically. Chatbots are highly useful in providing clear guidelines and reliable information, check and monitor symptoms, recommend protection measures, and advise users whether they need to practice self-isolation at home or require hospital screening.

Read Also –  How Digital Health Technology Can Help in Managing The Coronavirus Outbreak?

Disease Surveillance

Surveillance is crucial for a disease like coronavirus. Human activity, specifically migration, has been responsible for spreading the virus all over the planet. Apps have been developed in several countries that leverage NLP and machine learning to track, recognize, report the spread of coronavirus faster than the WHO.

In the coming future, technology like this might be employed to know about zoonotic infection risks to humans considering the variables like human activity and climate change. The combined analysis of clinical, personal, social, and travel data including lifestyle habits and family history gathered from sources like social media may enable more precise and accurate predictions of individual healthcare results and risk profiles.

Facial Recognition, Fever Detector

To detect fever in people, thermal cameras are used for some time now. The only drawback is that for using this technology, human operator is required. However, with AI, this issue has been resolved and this technology has proved to be very useful in detecting people with fever at airports, hospitals, sea ports, railway stations, etc.

This technology detects people with fever automatically and tracks their movements, detects whether they people are wearing face masks, and recognize their faces.

Read Also – How Apps And Artificial Intelligence Are Being Used To Tackle Coronavirus Scare?

Diagnosis

Immediate diagnosis implies response measures like quarantine can be employed in time to curb further spreading of the infection. A hindrance to rapid diagnosis is shortage of clinical expertise needed to interpret diagnostic results because of volume of cases. Major of the countries are struggling to diagnose the disease in time because the symptoms may take several days to show. Furthermore, because coronavirus spreads so easily, the hospitals have to diagnose thousands of patients in a day.

AI has sped up diagnosis process for coronavirus by detecting the infection within a minute with extremely high accuracy. This was made possible due to open source artificial intelligence model that analyzed CT scans and identified lesions as well as quantified in terms of volume, number and proportion.

Usage of Drones and Robots

Because coronavirus spreads through physical contact, drones are being used in some places to deliver medical supplies. Drones are also being used for tracking people who are not using the facemasks in public, broadcasting information to large audiences, disinfecting public spaces, and checking if people are breaking quarantine.

Robots are also being used for cleaning and sanitizing the rooms of the patients, serving them food, and giving the medicines. Healthcare workers face the highest risk because they deal with infected patients directly. This is why using robots in hospital rooms and isolation wards is a great move. Many companies are donating free robots to hospitals for the same purpose.

Read Also – Coronavirus Scare gives mhealth an opening to Redefine Healthcare

Development of Drugs

Various companies are employing robots and artificial intelligence to develop drugs, decode the virus, and know more about the disease. A British startup known as Exscienta became the first firm to present AI designed drug molecule which has proceeded for human trials. It took only a year for the algorithm to make the molecular structure while with traditional research methods, it takes five years or more for the same.

AI can also lead the charge for development of vaccines and antibodies for the Covid-19 either designed through drug repurposing or completely designed from scratch. The AI company of Google is developing structure models of proteins which have been linked with coronavirus in a bid to help the science in understanding the virus.

Verification of Information

The uncertainty and incomplete knowledge about the coronavirus disease have resulted in propagation of myths and misconceptions on social media platforms. There has been no qualitative assessment done so far to evaluate the amount of misinformation present already but it is certainly a huge figure.

Facebook and Google are battling to control the waves of the conspiracy theories, misinformation, malware, and phishing. A search for the disease yields the alert signs along with links to the verified sources of information. On the other hand, YouTube directly links its users to credible organizations and WHO for information. Videos which misinform are hunted for and taken down as soon as AI detects them.

Conclusion

Coronavirus is causing panic amongst people because until now vaccine or medication for this disease has not been developed yet. Moreover, coronavirus spreads very easily and its carriers may not even realize they have the disease until they are diagnosed and by then, they may infect other people.

Human brains along with AI has helped control the spread of disease in China and can prevent its outbreak in other countries as well. Early detection, proper monitoring of the infected, and using drones and robots for minimal human contact can help prevent the disease from spreading. Monitoring the infected and people who have come in their contact is the only measure being used by several nations to control the spread of the disease. However, without AI, managing such elephantine data would be impossible.

healthcare apps development company

Reasons to Employ Virtual Reality Technology at the Workplace

Interacting with technology and working online both within and outside the office has become more of a necessity than a choice for most of the modern workplaces. Besides, with all the advancements of new-age technology, the companies get to truly expand their boundaries of opportunities and boost their productivity.

Speaking of new-age technologies making the mark in the modern businesses, a mention has to be made of virtual reality: the innovation that is transforming the workplaces and the work processes.

If you go back a decade, the mention of the word virtual reality would have simply meant something straight out of science fiction. However, nowadays it has some actual practical usages in the workplace, and it can offer a range of benefits to a company when employed correctly.

With that being said, let’s take a look at some of the reasons why employing VR in the workplace is a good idea.

  1. Virtual reality is the catalyst for better collaboration

Think of it like this: you are sitting miles away from the customers or your colleagues, and yet it seems as if you are in the same office and the same conference room. Virtual reality acts as a catalyst in ensuring this for you. All you need is to plug in those noise cancellation headphones and put on the headset, and you will get the immersive and collaborative virtual environment that you want.

There are sensors in devices like Oculus Rift that are able to pick up and read body language, along with other such non-verbal interactions, which you would not have read with the traditional modes of communication, such as Skype. With virtual reality app development focusing on the ability to translate languages in real-time, even those barriers are done away with.

We are living at a time when a majority of the interactions we have are all done online. There is a sense of isolation among people because of the lack of personal contacts. In those remote offices, employees find it hard to stay focused, and the constant isolation from the colleagues can affect the cohesiveness and productivity of the team.

Thus, when it comes to ensuring better collaboration within an organization, virtual reality truly holds tremendous potential.

Read Also –  How is Virtual Reality(VR) going to change the modern world in 2020?

  1. Virtual reality to help in hypothetical and real-world training

Virtual reality is already being used in many businesses for the purpose of training. The best example of this is NASA. Using virtual reality, NASA ensures that the ones sent into space have the necessary experience in getting detached from the shuttle, use their backpack for finding the way back, and carry out difficult tasks even with the absence of gravity. NASA basically stimulates all of these using virtual reality.

Don’t let this give you the idea that you’ll have to be an astronaut for getting trained using virtual reality. Take the example of customer service training, which is so important for many modern workplaces. It involves training the employees to react, act, and understand the nuances of customer satisfaction, including the use of body language, greeting, tone of voice, and the likes, to deal with the customers in the best possible way.

These are not the kind of skills that can all be learned hypothetically. However, using virtual reality enables the employers to expose the employees to real-life situations of dealing with customer dissatisfaction, compliant, and the way to bring a customer over to your side.

Perhaps medical professionals can benefit the most using virtual reality tools because it helps them to avoid human testing and go for unrealistic simulations.

  1. Virtual reality to help human resources find the perfect fit

The present workforce consists, by and large, of millennials. It is the generation that looks for greater mobility, flexibility, and higher importance being given to work-life equilibrium. Thus, the millennials pay a lot of importance to the company culture at the time of their evaluation and consideration of new employers.

Virtual reality can actually come to the aid of human resource departments in more ways than one. First of all, the applications of virtual reality help the employees to get as much flexibility and mobility that they want by letting them access the office space virtually. Thus, virtual reality helps in giving employees the kind of autonomy they want in terms of deciding how, where, and when to work.

And, what about the use of virtual reality for helping the potential candidates to take the right decisions? Well, virtual reality is useful for letting a potential employee get a tour of the office, and witnessing a typical day in the life of the existing employees. Enabling it can be beneficial to the human resource department as well because it can reduce employee turnover and raise retention rates this way.

Read Also – How AR & VR are redefining retail?

  1. Virtual reality and its contribution to customer service

One of the busiest corners of modern businesses is customer service. You will always have some or other new methods or theories being tested by the professionals here to test its effectiveness at improving customer satisfaction and sales. There are things like social media interaction, customer service texting, and instant chatbots, which can connect support staff and customers.

Virtual reality can work wonders when it comes to enhancing this connectivity. With this innovation of technology, the service side can be massively elevated to the office. The customers get a sense of direct connectivity this way, which was not possible previously.

Businesses are constantly striving for improving customer services because it is something that impacts every area of a company. So, investing in virtual reality in this regard is a great idea.

  1. Virtual reality and the way it aids in interviews

Connecting virtual reality and interviews might sound quite futuristic. However, at some point in the very near future, this will become a major part of company practices. Interviewing candidates over video calls, even though it has its advantages, is nothing out of the world anymore. People might have been too excited when it started becoming a thing, but that excitement has long died down.

Interviews involving virtual reality can transform the entire experience to a great degree. You can speak with the applicants face to face, without the need to travel anywhere. With the world becoming smaller, and the companies extending their branches over miles, this can be an important addition to their abilities.

  1. Virtual reality and its use in designing and prototyping

Businesses, when developing products both small and large, use virtual reality for developing a prototype. Every element of the product is put in its place for testing safety, durability, and so on, under all conditions. There are upfront costs of platforms and tools, but it all comes under-investment, at the end of the day.

For instance, a plane and automakers save millions by eliminating the need to make working prototypes. Similarly, construction materials are tested against all kinds of difficult weather conditions.

Wrapping up

So, as you can see, employing virtual reality can do a world of good when it comes to improving the potential of modern businesses. However, the technology is still expensive, which is why the adoption rate is so slow. But, even though the adoption is slow, it is steady and a VR transformation is clearly underway.

VR Company

How Big Data Analytics Can Create a Billion-Dollar Mobile App UX?

In the past couple of years, there has been a constant evolution of the technical aspects of the procedures of app development, and this has also been instrumental in bringing a major change in the non-technical side of it. And, big data needs to be thanked for all these changes.

There is nothing new about the fact that more and more people use multiple mobile apps at present, which is exactly what makes it a possible multibillion-dollar business in the future. And, for you to make sure that the apps help you earn the billions, you need to deliver the best possible user experience. The role of big data is undeniable in that regard.

With the modern smartphone users generating a huge volume of data on a daily basis, it gives developers a chance to make better mobile apps through improved Mobile App UX. The apps developed this way not only meet users’ expectations to the optimum, but also help businesses earn the bucks that they were aiming for.

So, how can big Data Analytics for Mobile Applications assure a major breakthrough in user experience? How does it help in creating that billion-dollar mobile application UX? Read on to find out the answers to all that.

The value of user experience in building the finest mobile application

What makes an app truly amazing? Of course, it has to be fast, attractive, and easy to use, but most importantly, it has to make sure that the users’ needs are met to the optimum. This is the reason why it is important to study user experience in as much detail as possible. After all, it helps the developers come up with better mobile apps because they get a clear and precise idea about what the user looks for in an app.

Think of user experience as this rich source of information that helps in identifying the right ideas needed to build new apps. Analyzing the ways in which the users interact with the mobile apps and their actions at the time of using it help the Mobile App Development Companies to pinpoint the solutions that can help in improving and upgrading the existing apps, along with garnering enough user-based ideas for the new applications they come up with.

Read Also –  How to Build Successful Mobile Apps using Big Data?

Detailed analysis of user experience through big data

As already mentioned before, developing a mobile app requires a thorough analysis of the users’ experience and understanding it deeply. Big data comes as a blessing in that regard because it covers all that you need to know about users’ experience using a mobile app. It gives you a precise idea about the important pain and focuses points present in the users’ experience.

As the entirety of crucial analysis gets completed, knowing the collective preferences of the users can help in establishing a complete list of wants and needs that the users have in terms of a mobile application.

Furthermore, it can be of huge help to the developers in terms of the new ideas and apps that they are coming up with. Analyzing big data related to the other top apps that belong to the same niche as the new one, the app developers can improve their latest creations. They can find out new and improved ways to make sure that their new apps are more in line with the expectations and needs of the users.

Read Also – How Artificial Intelligence is Driving Mobile App Personalization?

Let’s understand it further with the example of fitness apps. Building a great fitness app leads the developers to go through the other popular applications in this particular niche, like Fitstar, Runkeeper, or Argus, and find out exactly what works for these apps, and what makes users prefer these apps over the other.

Now, a detailed analysis reveals that there are features like a calorie counter, pedometers, along with many other unique aspects that make these apps gain an edge over those other in the market. So, now the mobile app developer understands the features that he can include in his new app, and the already popular features that he can do better. You can guess how big data analytics helps you in edging past the competition.

Understanding the use of big data for mobile app UX

Big data is helpful in delivering a complete user experience because it is larger than life. The amount of data that users generate has already moved way past the petabyte level into the zettabyte level, and even then it continues to increase at a brisk rate every single day.

In the next couple of years, the amount of data stored all over the globe is expected to reach yottabyte levels. In fact, it will not be an exaggeration to say that the volume of unstructured data made only in the last couple of years has gone way over the total amounts of data that was produced right before it.

In this situation, it is important for high-end analytics to come into the picture if these massive amounts of important data have to be put to good use. But, the efforts definitely make a lot of sense because it can make relevant data, which can be utilized for maximizing the success of present applications and developing more effective and inventive new apps.

Read Also –  Reasons Why Everybody is Using Cloud Mobile Apps

Big data: the recipe for the multibillion-dollar app industry

Going by the recent studies, the mobile app market has already hit the 365 billion US dollar mark, and it is expected to go up to over 935 billion US dollars by the year 2023. This is no surprise given the fact that most of the global users have completely transitioned from desktops to smartphones and tablets. In this situation, creating better mobile apps is clearly the next important step in the world of digital development.

The thing is computer apps are not as volatile as mobile apps. The mobile applications are much more inclined to be simplistic in their displays and features. Thus, it is of absolute importance that those specific but simple features catch the attention of the users.Big data analytics comes here as the finest way of collecting this kind of accurate and relevant information, which is slated to make big data just the ingredient you need for creating a billion-dollar mobile app UX.

Thus, it is clearly understood that the future of mobile apps goes hand-in-hand with big data. The analytical specialists come up with new and improved strategies for better interpretation of the massive amounts of unstructured data for discovering the important features of the next generation of applications. With that, more and more apps will end up being perfectly in sync with users’ expectations.

Summing up

Of course, one cannot know everything going on in the users’ minds, but one can know enough using the data that is available. It will not only help in improving the apps you have delivered already but also help you with the apps that are still in the process of being developed.

It is all about taking advantage of this huge storehouse of information in front of you and delivering the finest user experience. And, big data can be just the tool you need in this regard to propel you into that billion-dollar club.

What the Future of Digital Health Looks Like in 2020?

With billions of dollars being poured into it, the last decade was pretty phenomenal for the digital health sector. Things like interactive apps, increase adaption of technologies, and greater customization of digital applications to patient’s conditions have become the norm for the past decade.

So, it’s time to look at the next stage of changes that are about to hit the digital health sector. What are the innovations coming up? And, what are the implications of it? Let’s find out!

  • The solutions that meet the unmet needs

There is no denying the fact that engaging digitally with patients is a remedy to many challenges. However, in 2020, the approach is slated to be more focused on particular areas of concern in order to determine whether digital health initiatives are the right solution to that issue.

With a number of factors being the determinants of the success of a study, there is going to be a stepwise approach that validates potential solutions individually to provide absolute clarify in terms of affordability and effectiveness.

The REMOTE trial by Pfizer can be a good example in this regard. It tried to imitate a conventional trial by using a couple of smartphone and web-based technologies at the same time. So, it included a web-based consent process, a web-based recruitment portal, and an e-diary where patients recorded about their health.

Though the trial didn’t reach its completion, the e-data capturing tools and the web-based consent ideas used for the study were both lauded and used for other important clinical trials. So, though the REMOTE trial didn’t successfully showcase the possibility of a completely virtual study, but the patient-centric solutions it had for particular problems were advanced and validated.

Read Also –  How app development can empower the healthcare industry in the coming years?

  • Putting more focus into actionable information

The market for wearable health technology has grown substantially already, and it will grow further in the years to come. What this means is that the healthcare providers, patients, and the industry in general now has greater opportunities for easily gathering health-related information. In 2020, the focus is going to be on making sure that the information gets relayed and collected in a more useful way to bring about the much needed change.

Honestly, the world has enough apps already in 2020! So, the aim for custom healthcare app development companies this year and in the future is to deliver better apps that can help people in supporting their healthcare goals. The aim now is to develop apps that can combine clinical support with real-time information.

The focus in more on creating apps that collect your health information and offers you educational coaching and condition-specific information. These apps would also be able to connect to your healthcare provider to make sure clinical decision supports are not limited by traditional clinic visits. In fact, there are already apps that ask for your physician’s prescription before starting to work.

  • The realization that one size cannot fit all

For the medical device manufacturers and biopharmaceutical companies involved in digital health initiatives, the first consideration in terms of design objective is the ease of using. After all, the digital divide is still very much real, especially when you think of the seniors, which limits the widespread acceptance of digital health initiatives and devices.

Even in this new decade, there is a section of population that suffers from poor digital literacy, which makes the configuration of an app or a wearable device really challenging. And, in future digital health has to take care of this aspect.

In a study related to use and utility of fitness trackers for older adults with serious medical conditions, some insightful facts came to light. The outcomes showed a clear improvement in clinical results, and even proved that the perception about wearable health devices and the acceptance of these are also quite positive. However, what was needed was proper support and training.

In future, to ensure that people of all age groups embrace wearable health tech, there will be an added support offered to set it up. Of course, no one will come and do it for you, but rather the instructions and the user interface would be made so easy that you can do it for yourself.

Read Also – Why this is the Best Time to Launch a Secure Healthcare Chat App?

  • An increased focus on taking behavior change into account

One of the best developments that the world of digital health has seen in the recent years is the increased focus on taking behavior change into account. Intrinsic motivation is getting prioritized these days, which is a good thing.

The thing is, people understand the things that are healthy for them, in terms of lifestyle choices, exercises, and diet, but embracing those good habits is still not as easy as it seems.

With the idea of sustaining, creating, and withholding healthy behavior, Aetna recently announced an initiative that will track the activity of the members, offer customized health recommendations, and along with that, reward the members for inching closer towards their fitness goals.

  • Gamification and its role in digital health in future

Gamification is all about using game design mechanics, techniques, and style to the non-game applications for engaging the users, solving their problems, and making their mundane tasks more engaging and fun. When you think of it in terms of digital health, it is being used in health and wellness apps for disease prevention, self-management, adherence to medication, and improving medical know-how.

In most of the health and wellness apps that deal with weight loss, adherence to medication, or self-management, gamification will work in one of the three given ways:

  • Using progress bars for measuring success, thus, the evident values of the services is amped by using progress-related mental biases, for instance, in health education or weight loss apps, and so on.
  • By letting the users share their results and progress with other users or their friends on the same app or on social media. A competitive feeling is brought about to ensure better and more use of the services, which you see with weight loss and fitness apps.
  • By offering virtual gifts like stars, medals, or badges during every stage of using the app. Thus, a feeling of achievement is ensured for increasing the level of motivation. This is, again, something that is found in chronic health issues management apps, and fitness and weight loss apps.

Wrapping up

The multifaceted usage of digital health initiatives, in 2020 and beyond, showcases the potential held by modern technology in terms of improving patient care remarkably. It also proves its greater potential in terms of generating crucial real-world patient experience information to support formulary discussions and regulatory submissions.

healthcare apps development company

MVC vs MVVM vs MVP vs VIPER: Which design architecture is suitable for iOS?

The architectural patterns of design are the ones that aid in de-cluttering and organizing the code. And, regardless of whether you are developing an iOS app yourself, or you are hiring an iOS App Development Company to do the job for you, it is important for you to understand the options that you have in this regard.

There was a time, not too long back, when designing an iOS app meant relying on one of the three design patterns, namely, the singleton pattern, the decorator pattern, and the bridge design pattern. But, since these patterns started throwing up issues of interactions among client and server, iOS moved towards the newer and better patterns of MVP, MVVM, MVC, and Viper.

So, which of these design architecture is the best suited for iOS? Let’s run a thorough analysis of it all, and see for ourselves.

The MVC pattern

MVC, short for Module View Controller, was introduced by Trygve Reenskaug, a Norwegian computer engineer. Given the fact that it has been around since 1970, makes it pretty much the grandfather of the other patterns on this list. It was, in fact, the first approach towards object-oriented programming.

View displays everything for the user of the system. Model handles the business entities, databases, and the rest of the data. Controller has the responsibility to handle the work of the model, provide data to database, and bring the data from the view to the database and vice versa.

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Now, the problem is that Apple has such a tight link between Controller and View that both of these are actually united, while the Model stays separate. Thus, the testing process suffers, as only the Model can be tested, and the tight link between Controller and View prevents them from being tested.

The strong connect between Controller and View is not healthy for software, and thus, in the mvc vs mvp debate, the latter wins by a margin.

The MVP pattern

MVP, or the ‘Model View Presenter,’ has a couple of points working for it, which makes it vastly different from MVC.

The Model

  • View and Model are loosely linked. The Presenter has to bind the View to the Model.
  • The interactions with view take place through a proper interface, which makes it easier for unit testing.
  • Generally Presenter to View happens on a one-to one basis, though complicated views might come with multiple presenters.

The MVC Pattern

  • Controllers depend on behaviors. It can get shared across the View
  • Handles the responsibility of determining the right view for displaying

The functions of the Model stay the same in this case. Business logic is handled by the Presenter. The interesting part of it is the View, which has two components, namely, the View Controller and View, which handle the interaction.

In the mvvm vs mvc debate, the former solves the issues of heavy connection between Controller and View modes that are there in the MVC patterns. The testing troubles also get solved this way, as everything, from Presenter, user interaction, View, and Model can be tested.

The major inconvenience lies in the Presenter because it is still too huge and it still considers all the present business logics.

The MVVM pattern

John Gossman, an architect from Microsoft is credited to have created the ‘Model View-View Model’ pattern in the year 2005. There are three main components of this model:

Model is all about implementing the domain model of the application to include the data model, validation, and business logic. Instances of the Model objects are DTOs (data transfer objects), business objects, POCOs (Plain Old CLR Objects), proxy objects, generated entity, and repositories.

View is all that the user can see, like the structure, the layout, and how everything comes up on screen. It is the app page within the application. View receives and sends out updates to only the View-Model, except the communications that take place between the Model and this part.

View-Model is the chain between the Model and the View components. The logic behind view is handled by this component. The model classes are used by the View-Model to interact with Model. The View-Model then takes the Model data in the form that View can put to use.

Read Also –  Swift vs Objective-C: Which is Ideal for iOS App Development in 2020

Difference between MVVM and MVC in iOS

The distribution pattern of MVVM is much better than MVC, but the problem is that it is also immensely overloaded. Testing is a significant aspect to keep in mind in here. Just writing the right code is not a guarantee that the project will work as expected. You need the right kind of testing to know that it will actually work.

The Viper pattern

While the search was still on for the right architectural solution, the developers got hold of this clean piece of architecture on the Clean Coders. Clean Coders is a renowned platform that arranges training sessions aimed at software professionals of the world.

This new architecture was all about splitting the logical structure of the application into a number of responsibility levels. This splitting is helpful in increasing the testing capacity of the different levels and solves the tight connection problems.

Viper for iOS app design

Viper is the realization of a cleaner architecture for building the iOS applications. Even this one is an acronym for ‘View-Interaction-Presenter-Entity-Routing.’ Each of these parts handles the responsibility of a particular element, like:

  • View mirrors the actions which the user has for an interface.
  • Entities actually corresponds the most with the Interactor. So, the Interactor is informed by the Presenter regarding the developments in the View. The next up on the contact for the Interactor is the Entity. The data it gets from the Entity is sent to the Presenter. The View, then, mirrors it for the user. All the sites, entities, and data models remain linked to the Interactor.
  • Presenter has, comparatively, limited responsibilities. It just gets the updates from the Entity, with sending data to it.
  • Entity has the objects which the Interactor controls, like the content and the titles. It can never interact with the Presenter without the intervention of the Interactor.
  • Routing, also called the Wireframe, helps in navigating between the screens, and thus, does the job of routing. Routing handles the objects of UINavigationController, UIWindow, and the likes. UIkit is the framework upon which it gets built in an iOS app design architectural pattern. UIkit has all the components of MVC, minus the tight link that makes lives difficult for the codes.

Viper is great in terms of unit testing because the amazing distribution of the patterns allows you to run thorough tests on the available functions. This solves the main issue that developers had while using MVVM, MVP, and MVC software pattern. However, Viper has multiple nuances that are difficult to generate. So, it’s not like Viper is a picture of perfection for the developers.

Wrapping up

So, who gets the crown for the best design architecture for iOS? Actually, no one! This is because each of these has its underlying issues and none of them are perfect enough to be universally used for every project you take up.

They say ‘fortune favors the brave.’ So, perhaps it’s time to take the bold step of playing with a mix of two or three patterns!

Charging the Utilities Industry with New Technology

The utilities sector represents a category of those companies that offer basic amenities. Such as electricity, water, dams, sewage services, and natural gas. Although they make profits, utilities are elements of the public service landscape as well as are therefore heavily regulated.

Just as all business models change, utilities industry is no exception. Utilities companies are conducting business processes and operations differently currently than they did twenty years ago. The industry is welcoming the introduction of the latest technologies and innovative products. Increasing customer expectations are leading the industry to be more customer-centric.

Customers now have many alternatives and may switch providers in case they are dissatisfied with the company’s services. Therefore, utilities firms are adopting varying strategies to compete and build customer’s confidence. The latest technologies aim to improve procedures and enhance customer service by offering a more personalized experience, self-service solutions, and lower costs.

But for now, let us focus on recent trends, including the rise of grid modernization, facilitating new workforce, implementing digital tranformation, and intelligent automation.

Grid Modernization and Microgrids

The utility industry is transitioning towards more scattered energy sources. This is why the companies face a crucial choice, consider this opportunity to adapt and improve or take it as uncertainty and collapse.

Favorably, the most prominent firms are considering to modernize power grids by implementing microgrids. With microgrid implementations and grid modernization, the advanced system can:

  • Offer revenue to utility during grid outages.
  • Save on transmission as well as fuel costs
  • Distribute power effectively
  • Give predictive analytics along with maintenance

By definition,

microgrids are energy generation systems, which have the ability to break apart from the main grid and go on to power isolated facilities in the absence of a larger electrical grid. These microgrids offer reliable, continuous energy to community service to critical business, schools, first responders, community service organizations, and other necessary facilities post a natural disaster.

The grid modernization is already taking place. This trend does not follow the norm, as these changes are happening already. In fact, it is not unreasonable to envision customers possessing up to 5 autonomous grid devices at once – a home battery, a rooftop solar system, a smart water heater, an electronic vehicle charger, and a smart thermostat. These devices would function of a microgrid to offer efficient resources whenever needed, during days as well as nights.

grid modernization

For instance, PG&E or Pacific Gas and Electronic has almost four million users in the San Francisco Bay area. Post getting set-up on the contemporary grid, PG&3 would possess more than 20 million devices to conduct on a single grid. These advancements can be administered with ease with smart technologies and autonomous grid enhancements although they are so many in number.

Recently, the NGA or National Governors Association said it will offer assistance to New Jersey, Maryland, Rhode Island, and New Mexico for modernizing its electric grids along with addressing topics like electrifying the heating sector, improving system resilience, and integrating electric vehicle charging networks and clean energy. Because of these modifications, governors are looking for solutions to upgrade their state’s structure.

Automation will go along with it, with grid adaptations. AI or Artificial Intelligence, predictive analytics, ML or machine learning will play a huge part in these modernizations. However, a fully autonomous grid possibly would not happen for a while. The reason being humans still make more efficient judgments than robots.

But, predictive analytics and AI are now being regarded as decision support. Smart algorithms and AI, in a supportive role, can lay out several scenarios and results, letting the human-controlled make the most suitable decision moving forward.

Read Also – Six critical mistakes that are pushing digital transformation far from reality

Facilitating New Workforce

Companies need to know how they are interacting with new customers and meeting system demands while offering safe, affordable, and reliable energy, as the utilities industry develops. For this, they will need experts who enable the companies to compete in the digital arena. However, businesses are finding it challenging to hire such experts.

Facilitating New Workforce

New advancements in digital and technology transformations demand that the employees learn completely new skill sets because everything from planning to customer and field service is changing quickly. This has formed a pronounced skill gap between the aging workforce and expertise required to make and administer grids of the future. Therefore, the companies much adapt as well as offer new infrastructure, technologies, and business processes.

So, utilities companies must take action to get new tools and invest in their workforce. These investments allow the companies to educate and train the current workforce. This will not only keep the existing workers up-to-date and happy but also appeal to the newer generation of employees. It will also bridge the workforce skill gap.

Digital Transformation

The digital twin is an augmented model of a product, process, or service. This pairing of the physical and virtual worlds allows the monitoring of systems and analysis of data to head off problems even before they occur, develop new opportunities, prevent downtime, and even plan regarding the future by employing simulations.

Currently, digital twin market is booming, with the possibility of reaching $35.8 billion by 2025. The utilities firms are among several asset-intensive industries set to benefit a lot from these technologies.

digital transformation

A digital twin is a virtual model that is used to attain both predictive and real-time insights on performances. The leading companies are putting digital twins into practice in order to test analytics both predictive and real-time. While running such tests virtually, the digital twins are maximizing companies’ successful results, while trying to attain tasks in digital world, without any fear of real-world hit and miss.

Through these digital trials, companies save a massive sum of money, as the trials are carried out in digital space. As digital twins test and predict solutions, firms can adjust the algorithms to ensure success even before launching them in the real world.

Read Also – A Guide to Digital Transformation and Cloud Migration

Intelligent Automation

intelligent automation

It is the automation of the processes of a company, including general corporate-level and specific task-level processes. Through intelligent automation, a company will boost its productivity and improve its employee and customer experience because it will help make intelligent decisions through rigorous case studies and analysis and avoiding duplication of data.

Conclusion

With so many changes taking place in the utilities industry, firms looking forward to leading the transformations need to remain open and nimble to new technologies, thoughts, and processes.

Customers anticipate the best in the current service-oriented world. Moreover, if they are not acquiring what they desire with one firm, they can go to another service provider fairly quickly. This is why it is becoming increasingly necessary for the entire utility industry to embrace digital transformation.

Major cloud security challenges for enterprises in 2019

Cloud computing, cloud security in 2019

With SaaS anchoring the pole position of increasing market value till 2021, it is going to be a $113.1 billion affair that will be followed by IaaS at $39.5billion and PaaS at $18.8 billion. These numbers are enough to paint the canvas of the global IT industry with the dominating color of cloud computing. Cloud App Development companies have been one of the top earners and is surely going to continue its Midas touch as the importance of data increases, while we approach 2020.

It is interesting to note that cloud computing platforms and comprehensive solutions generally form the backbone of information processing systems or even simple data warehousing. In an era where everybody is looking beyond electronic data for complete digitization, cloud computing becomes inevitable to remain out of this scene.

Here are the most lethal cloud security challenges that can upset a digital enterprise in 2019

Direct data breaches

Direct data breaches are one of the most dangerous threats that can occur in any cloud computing environment. With continuously increasing trends and awareness about analytics, data stored across the cloud and transactions made through gateways are becoming extremely vulnerable. People with malicious intent may gain control of the situation and thus compromise the most critical, behavioral and personal data of individuals and organizations on a broader scale. There are several other loopholes in the deployment process, content delivery networks and encryption of tokenized data which is stored across the cloud computing platforms.

Scarcity of identity management infrastructure; internal compromises

2019 has been the year when conventional businesses have aggressively taken steps to transform themselves into a genuinely cognitive enterprise. When a business is going completely digital, the identity management of their employees and related roasters, database all comes at stake offs cloud computing platform. Security of critical personal data of employees and their organization related sensitive data stored in their employ account become highly vulnerable because of:

  • Larger transitions
  • More wait time
  • Insider threats
  • Compromised company operations

Unsecured APIs & app interfaces

Recently there was a little data breach that compromised classified bank details of a multitude of account holders in the UPI payment system. It was mainly because of super exposed unsecured APIs and app interfaces which become a crack for cyber attackers to invade the environment. An environment that contained highly critical information and bank details of millions of people.  Encrypted gateways for data transmission and loosely held mobile app interfaces are softer targets for cyber attackers to invade and compromise the most critical information like bank statements, personal details, and PAN.

DoS Attacks

Competitors and cyber attackers are often keeping up their plans of sabotage the cloud computing resources with denial of service (DoS) attacks. Denial of Service (DoS) attacks is highly potent and cause a great panic attack in all the digital products and online resources. It is a mechanism that replicates bots which start sending a fake service request to your server and engage all slots of the cloud server communication. Consequently, it blocks genuine data and service requests that are to be rendered from the data stored over the cloud. It is a great hindrance in outbound productivity of the product and can directly turn up into a monetary loss if not covered and mitigated beforehand.

Non-compliance

Outright compliance with regulatory mandates is extremely important for all stakeholders in walled in the cloud computing environment. Whether it is the cloud service provider or a client hosting the product over the platform, everyone is concerned about regular compliance of their regulatory product. A major issue is not one-time compliance but renewal and consistent upgrade with several changes in the regulatory mandates.

Often companies tend to focus on compliances as a one-time investment but they forget to gauge its importance in the long run and they remain on bear conscious of the fact that the resources are no longer compliant which strikes them as a reality in the form of a cyber attack or a data breach. Only digital enterprises extensively looking for cloud service providers wish to seek compliance of regulatory mandates like:

  • PCI DSS
  • HIPAA/HITECH
  • GLBA
  • FISMA
  • FERPA
  • EU data protection

Disaster recovery, fault tolerance

Cloud computing is extremely important for successful digital products and they form an integral part of a comprehensive digital solution for any problem of acute degree. Often, it is realized that once services are a move to the cloud or data it is stored within a content delivery network (CDN), operations tend to behave alienated with no real control from the owner. The ultimate control lies in the hands of the cloud service provider. There has been a healthy debate on the matter since long and that doesn’t seem to have an end. Just like a coin, there are two sides to everything.

Imagine yourself running reputed news, media blog that stores and retrieved a lot of multimedia data from the content delivery network (CDN). One fine day the operation stops due to the failure of server cloud connection and your services stay at a halt for a long downtime. There has been no slack at your end but the consequences have to be borne by the owner of the digital product. In such a situation where there is no control of the owners and, cloud services may stain the reputation or cause acute inconvenience to your precious viewers.

Conclusion

Cloud security challenges are generally overlooked by cognitive enterprises unless they face harsh consequences or they seek compliance to enter a new league of businesses. It is highly inappreciable and should be taken care of seriously. Sometimes, company leadership ends up thinking that it is justified to cut corners of the overall budget by inadvertently curbing the cloud security budget head and using it somewhere else.

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What Is Better Java or Kotlin – Pros, Cons and the Conclusion

Java and Kotlin are the most-widely used development languages when it comes to Android App development. Professionals from any Android App Development Company, who have been making use of Java since very long, have another option now in Kotlin. Having been announced by Google as the second official language for developing Android Apps at the I/O conference, Kotlin has now emerged as one stellar programming language. No wonder, Kotlin offers to be compiled successfully and run effectively on the Java Virtual Machine. Java, having reigned as the most preferred language for developing Android apps, has found its match in Kotlin.

The Open-Source Programming Language of the Recent Times – Kotlin

While it is not compatible with Java, Kotlin, the powerful statically-typed programming language, runs on Java Virtual Machine. Code written using Kotlin works fine with Java as well as it natively does. Using aggressive inference for determination of the types of expressions and values – when they have not been stated clearly – Kotlin, as a programming language, is more distinctive than Java that at times may require repetitious type specifications.

After having been announced officially as a first-class Android Development language, Kotlin certainly has grabbed the attention of global Android App Development Companies and developers.

Java or Kotlin – Making the Choice Becomes Tough

Regardless of whether you are a beginner or expert, you are faced with the dilemma of deciding between Java and Kotlin today. With the additional choice, it is common that programmers and business owners alike find it a difficult task picking the right one for developing their Android Apps. It is by going through the various pros and cons related to both the programming languages that one will be able to arrive at the right decision in this connection.

Pro-Java Factors – What Goes in Support of Java

Android itself having been written in Java, there’s no wonder that Java has been many developers’ favorite with regard to Android App Development. This object-oriented programming language claims the title of the second most active language on GitHub. It has been around for nearly 20 years with its popularity only appearing to grow.

  • Java proves easy to understand and learn
  • Android App Development using Java is swift
  • It offers great flexibility – allows reuse of code and regular updating of software
  • Cross-platform apps can be conveniently developed using Java
  • It has a large open source ecosystem
  • Java apps tend to be compact and lighter; complex computing that is faced in Kotlin is avoided
  • Java is highly adaptable with virtual machine or browser window, rendering itself to be smooth during updates and code reuse
  • The app building process is considerably faster in Java than Kotlin
  • Large projects can be easily handled with Java, thanks to Accelerated assembly with Gradle

Glitches in Using Java for Android App Development

  • Limitations in Java may sometimes cause issues with Android API design
  • Java, when compared with certain other programming languages, is slower
  • It requires more memory
  • More coding needed in Java renders it prone to bugs and errors
  • Java faces certain issues such as endless try-catch blocks and Null Pointer Exception
  • Null safety concerns and not being extendable are certain other issues
  • Being an old version is a snag with Java

What Makes Kotlin Gain Momentum in Contemporary

Being positioned as the first-class language for developing Android apps, Kotlin is considered to be the ‘enhanced Java’ by programmers across the globe. Kotlin provides several improvements over Java, its 2-decade old predecessor. Not without reason, Kotlin, the modern programming language offers a number of advantages, especially for building Android apps.

  • Complete Java interoperability and compatibility with existing code is one major strong point of Kotlin
  • Being clear, compact, and efficient, Kotlin has an intuitive and concise syntax – this leads to increased productivity
  • Allowing for more stable code and reduced errors in production, Kotlin involves fewer bugs and requires reduced QA efforts and time
  • Java to Kotlin switching is easy- it requires just installing Kotlin plugin and adding it to Gradle for conversion
  • Smart extension functions available in Kotlin enable Android App Developers to build clean APIs
  • Access to Anko Library is a major advantage offered by Kotlin
  • It is easy to learn even if you have no prior mobile app development knowledge
  • Kotlin is fully supported by Android Studio
  • Reliable and precise codes enhance the security
  • Kotlin excels over Java in regard to data classes

There are Some Limitations as well, with Kotlin

As with any programming language, there are certain catches in using Kotlin also.

  • Compilation speed of Kotlin is slower than Java
  • One drawback is the extra runtime size involved with Kotlin
  • In contrast to the verbose but clearly spelled out Java coding, Kotlin involves concise syntax that might prove difficult to decipher initially
  • Being a relatively new language, the community of Kotlin is smaller presently, making only less help available
  • A few of the Android Studio features such as auto-complete and compilation are found to be slower in Kotlin than in Java
  • Kotlin developers are not so freely available as Java developers as of now—experienced Kotlin developers are few
  • Official support is not as great as in the case of Java

Which one is Better – the Inference

Although Kotlin proves to be far better than Java in certain aspects, it has not reached being the perfect language yet. It is in a way in an experimental stage, although it appears close to outshining Java. Kotlin is about to be adopted on a wide level in future, no doubt. Java, with its years of use and reputation, will take a long time to be replaced, but Kotlin is fast becoming the choice of startups and those seeking innovative technologies.

Android Development Companies had better experiment with Kotlin slowly and get to know how their team fares with it and whether they are able to reap the intended benefits.

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What Happens When Machine Learning And DevOps Join Hands?

The Role of Machine Learning and DevOps in the Present Times

Machine learning, the Artificial Intelligence application that facilitates systems to learn as well as improve from experience, without having to depend on being programmed manually for each of such instance is gaining momentum in recent times. There has been a noticeable rise in machine learning and related capabilities such as artificial intelligence and predictive analytics. This demands contemporary organizations to go for exploring implementation of mathematical algorithms-based data analysis model.

DevOps, the exclusive software engineering practice that aims at unifying software operation and software development, has been instrumental in exponentially improving software development related to increasing both productivity and quality. The relationship between DevOps and Machine Learning is evident, and is capable of improving the ability of organizations to analyze and manipulate huge volume of data in a rapid and accurate manner than any human resource. Here’s where app development companies find the combo of machine learning and DevOps effective.

The Machine Learning and DevOps Connect

There exists a strong synergy between Machine Learning and DevOps that extends to certain related aspects such as Artificial Intelligence, Predictive Analytics, and IT Operations Analytics. Quite a few DevOps methodologies are seen to surge high and generate huge variety and volume of data throughout the project lifecycle, starting with development through deployment through performance management. The ultimate goal of DevOps, automation, can be accomplished by harnessing the data by using a robust and reliable analysis system.

A huge volume of data is generated by successful DevOps practices; such large volume of data has the capability to draw insights that are helpful in workflow streamlining, production monitoring, and issue predictions. The voluminous data produces predictable result. Generally, teams do not involve in reading the data that is populated directly but they do ensure a threshold of some specific activity to declare that as problematic. It is customary that DevOps teams, rather than looking for data individually, look for the exceptions that arise. Here’s where machine learning plays a vital role in analyzing such data and drawing meaningful insights.

How Does Machine Learning Fit into DevOps Methodology and Help

Implementing machine learning in DevOps results in two distinct benefits: reduction of noise-to-signal ratio and replacement of reactive mode with proactive approach that is based on accurate predictions. Most teams are using the threshold approach that is based on habit, gut feelings, and conventional wisdom for monitoring.

Compared to this, the machine learning approach, that is more mathematical, is grounded. Here, models and methodologies such as classification, linear and logistic aggression, and deep learning are being used for scanning huge sets of data. Identifying correlations and trends and making predictions are enabled. Threshold defining is based on what is logically sound and statistically significant.

Benefits Achieved by Gelling Machine Learning with DevOps

  • Root Cause Identification: Machine Learning helps in discovering the root cause, enabling the teams to fix performance issues at one strike.
  • Learning from Mistakes: Issues caused by mistakes committed by DevOps teams can be located and rectified by Machine Learning systems that help in analyzing the data and portraying what has taken place.
  • Development metrics can be Viewed Differently: Collecting data about aspects such as bug fixes, delivery velocity, and continuous integration systems is enabled.
  • Fault Prediction: Machine Learning application helps draw meaningful insights from the data produced by monitoring tools during failure generation.
  • Orchestration Measurement: Orchestration process monitoring becomes easy; team performance can be evaluated effectively with the help of Machine Learning.
  • Going Beyond Threshold Setting: With voluminous data, DevOps teams are held up with setting thresholds rather than analyzing the entire data –Machine Learning applications help with predictive analytics.

Key Areas of Machine Learning Use

  • Production failure prevention
  • Triage analytics and troubleshooting
  • Production management
  • Application delivery assurance
  • Alert storms

The Need for Combining Machine Learning with DevOps

An increased number of next generation tools related to DevOps have started supporting machine earning to varied extents. Coordinating machine learning with the current business demands additional knowledge from the part of programmers.

Today’s DevOps engineers have to be aware of how to code, know how the infrastructure works, and learn how DBaaS can be utilized in the cloud. Most of the contemporary DevOps engineers not being mathematicians, it is a huge challenge to add machine learning skills to the skill sets mentioned here.

Challenges Faced by DevOps Engineers in Adding Machine Learning

  • Gap in Machine Learning Skills: For understanding machine learning that is based on applied mathematics, developers are expected to have a clear understanding of calculus, logarithms, linear algebra, linear programming, trigonometry, infinite series and sequences, statistics, and regression analysis.
  • Organizational Challenges: Machine learning is mostly data science that has to be divided across varied skill sets. Putting together a multi-disciplinary team consisting of Big Data programmers, Big Data engineers, and Data Scientists is one obstacle faced by organizations.

What Does the Future Hold?

Regardless of the obstacles and challenges faced, adoption of machine learning will only be growing as it is lucrative and is certain to attract more and more IT professionals and engineers with factors such as high income. With algorithms becoming easily understandable and convenient to implement, thanks to proliferation of frameworks, in future, what was once the domain of PhD scholars will be feasible to Big Data programmers and data scientists.

With several huge benefits that can be reaped by enterprises by using the machine learning-driven DevOps infrastructure, App development companies and managers have already started ways to boost machine learning among their teams.

How Does Machine Learning Help Optimize DevOps?

Machine Learning helps accomplish the following:

  • Looking for trends becomes possible
  • Fault can be predicted at fixed point of time
  • Specific goals or metrics can be optimized
  • Correlation across varied monitoring tools is enabled
  • Historical context of data can be provided
  • Effective analysis of data

Although it may take time, with the network architecture and algorithms chosen appropriately, Machine Learning system is sure to produce great results. Developers and App development companies are about to be benefited in several ways by combining machine learning and DevOps!