10 Top Software Development Trends in 2020
Change is the only constant on this planet. There is no industry from education to healthcare which can resist changes. Every sector welcomes innovations that bring changes in the ongoing IT trends. Similar is the case with software design & development trends as well. In fact, the software industry is one of those sectors of contemporary times that witnesses a constant change in its practices because of the growing software technologies. Custom software developers are used to keeping themselves updated with the latest software and IT trends of their industry. Same is with the businesses that get the software development services from software development companies. Now let’s come to the moot point — What will be the top software development and IT trends in 2019? Undoubtedly, 2018 was the year of discussions about the various trending software technologies from Artificial Intelligence to Blockchain. The IT industry has seen tremendous growth in recent years. In a report by Gartner in 2018, it is predicted that the spending in the IT industry will witness the growth of 8.3% in 2019. The more findings of the research are represented below: These figures are the outcomes of constant research and introduction of innovations by full-stack developers. The dramatic growth is motivating the developers to put in more hard work and bring some amazing trends to the software development industry. Every year advancements and restructuring in software product development technologies are changing existing trends. The business of new software technologies like cross-platform app development, blockchain development, machine learning, etc. is getting enhanced worldwide constantly. If you talk about blockchain technology specifically, then it alone is predicted to bring 2,312 million investment globally in 2021 according to a study by Statista. Here is a graphical representation of their findings: The large finances invested in these trending software technologies have made them a hot topic for discussions today. Here are the top software development trends in 2019 that need your focus. So, let’s explore them in detail: Software Trend 1: The Mixed Reality Mixed reality is the combination of twin technologies of virtual reality (VR) and augmented reality (AR). The market size of mixed reality worldwide in 2018, 2019 and 2025 is estimated and predicted by Statista. Here is the graphical representation of their findings: AR has won a great amount of growth over the years and the credit for this solely goes to its integration on smartphone apps. The popularity of smartphones is the only reason why companies are scrambling to invest in their own AR application. These companies are also hiring AR developers in large numbers. Unlike its twin technology; VR, AR apps do not need hardware with the latest specifications. Many people assume that the scope of Virtual Reality does not extend beyond gaming and entertainment. But the efficient utilization of this technology by Walmart and the US army do not hold this belief true. The people who lost their trust in VR app developers have started finding the amazing uses of this technology. Microsoft’s HoloLens’s mixed reality tech is used by the US army for military training purposes. Apart from them, Walmart is also planning to use VR in 2019 for the purposes of employee training in customer compliance and service. Software Trend 2: Artificial Intelligence With the onset of Artificial Intelligence, the machines are programmed to conduct the tasks that were restricted to the human mind. The artificial intelligence-based software can think intelligently like humans. The subsets of artificial intelligence like machine learning and deep learning are gaining constant popularity among businesses. More and more companies are taking machine learning and solutions services as a necessity. Artificial intelligence improvises business tasks and makes them simple. It has provided the web app developers a brilliant support to experiment. This has made AI to reach healthcare, banking, education, mathematics, etc. Software Trend 3: IoT Internet of things covers the broad categories of devices that are connected to the internet. These applications have spread to both the customer and industrial domains. In the area of safety and customer experience, IoT is witnessing exponential demands. Every second over 127 new devices will be connected to the internet, according to David Evan’s calculations (former researcher of CISCO). This gives an idea about the constantly increasing reach of IoT. Over 90% automobiles by 2020 will be IoT enabled as per PWC estimates. Moreover, according to the data found by Statista, there were around 23.14 billion devices in 2018 and the number will reach to 26.66 billion by 2019. Software Trend 4: Progressive web applications The hybrid of mobile and web applications are known as progressive web applications. They are completely different from regular mobile applications. Their script called service worker is an integral part upon which they majorly work. They are easy to develop and maintain which has attracted many mobile app development companies to primarily focus on them in the past few years. These applications can be loaded very fast even with low internet speed. Software Trend 5: Blockchain Blockchain technology is undoubtedly one of the most talked about technologies in the software world. The digital currencies like bitcoin and Ethereum would have not come into existence with the introduction of this technology. Although it is extensively used in the finance sector, it is finding increasing applications in media and publishing, banking and finance, and healthcare software development services. We have already seen the growing graph and predictions of this highlighted technology in the contemporary world. The secured and simplified recording of transactions in a decentralized ledger with the help of blockchain technology services make it strategically important for businesses in all industrial domains. This is the reason why a large number of blockchain developers are required these days as more companies are extending their arms to blockchain development services. Software Trend 6: Language Trends With the introduction of software technologies, languages and frameworks are also introduced simultaneously to program them. Today, JavaScript and its various frameworks are widely used by Full Stack Developers. It has excellent adaptivity for hybrid applications also. In order to avoid repercussions at the end of a software development project, a business should select the most optimum language for programming them at an initial stage. You can judge the popularity and benefits of various languages through statistical data. Stack overflow survey that takes the input data by developers to find the popularity of programming languages and frameworks is a good source for this purpose. In their latest survey, Node.js was found to be the most popular framework followed by Angular and React. These are the frameworks of JavaScript that clearly depicts its preference. Another survey by the Stack overflow survey found the popularity of programming languages. Here also JavaScript topped the list giving one more piece of evidence of its amazing popularity. You can easily make out which languages are gaining a vast market and use them according to your needs. Businesses can utilize this information to plan their annual custom software development projects. Software Trend 7: Low Code development Low code development can be related to an approach of building lego blocks by web and software development companies. It serves as a savior as it eliminates the requirement of complex codes. This do not require expertise and help clients to grasp their software project conveniently. This, in turn, allows them to customize their software in their own way, on their own. It acts as a crucial tool for companies that are planning for digital transformations. But this does not mean that its an ideal coding practice. In cases where businesses require structured and complex solutions. This approach would simply not work. Software Trend 8: Code Quality With the constant evolution of technology, the quality of codes trends become the area of primary focus. One has to keep a strict eye on programming techniques as an essential task in the year 2019. As discussed in this blog, low code development is an important trend in the contemporary software development industry. This has allowed information technology companies to hire non-technical employees for programming the software. The type of task for software development should be the foundation to decide the code quality and structure in order to deliver the optimum outcomes. Every software development company must have a strong strategy for development to ensure the best coding is done for their softwares. Software Trend 9: Cybersecurity Can you imagine your life without security at your home or road or workplace? Probably no because security is an essential parameter of human life. This issue spreads to the business world as well. The software loss and threats are becoming the primary concern to check by custom software developers. In a report published by Wipro in 2018, the stolen or lost data was determined industry wise in 2017. Here is the image showing their findings: Software Trend 10: Outsourcing Software Development The outsourcing market is growing tremendously every year worldwide. In a survey by Statista, the market size of outsourcing services was determined in U.S billion dollars for the whole world. Here are their findings: This graph is a clear piece of evidence to the growing market of outsourcing globally. IT industry also has a large number of outsourcing services as companies resist the hiring of in-house developers to save cost and resources. The software development requirements are increasing in all industrial domains. Businesses are looking for full-stack developers in almost every field like financial software development, healthcare software development, e-learning software development. Final words: These evolving software technologies provide a clear idea about the ongoing development that takes place in the information technology industry. Full stack developers are working at a fast pace all around the globe to enhance the comfort level of humans. It is essentially vital for companies to match the pace of these trending software technologies to outshine their competitors. Software consulting companies can provide greater insight into these technologies and help businesses to become a software leader.
Data Science Trends for 2019
This year can be considered the booming of Artificial Intelligence (AI). Just look at the number of startups with the term AI in their taglines; where acquisitions from big companies focused on; and the topics at the biggest tech conferences. AI is everywhere — even if just as a buzzword. But what actually is AI? That’s a rather difficult and controversial question to answer. Hype bag-of-words. Let’s not focus on buzzwords, but on what the beneath technologies can actually solve. What is clear is that data science is solving problems. Data is everywhere, and the uses we are making out of it (science) are increasing and impacting society more and more. Let’s focus on Data Science, while other philosophize on the best definition of AI. While other buzzwords keep thriving, how’s data science? Interest for “data science” term since December 2013 (source: Google Trends) The interest is not bad at all! I keep making my stand that data science is not a buzzword. Even for people now joining data science — and there are a lot of them — you just need to make a quick job search on LinkedIn and you’ll be amazed by the number of offers. Let’s start by taking a look at what has happened in 2018 and then focus on hot topics for 2019. Today Last year, I published an article on my expectations on Data Science trends for 2018. The main developments I mentioned were: automation of workflows, explainability, fairness, commoditization of data science and improvements in feature engineering/cleaning tools. Regarding automation, the data scientists’ job is, very often, the automation of their own work. Companies open sourcing their own automation pipelines is common nowadays. Others, just keep selling it, but every day with more competition (e.g., Microsoft Azure, H2O, Data Robot, Google). Fortunately, data science is a transversal discipline and the same algorithms that are used in healthcare can be used, with some tweaks, in agriculture. So, if a company fails in a vertical, its developments can be quickly adapted to another field. These tools are becoming regular commodities that you don’t even need to know how to code to use them. Some of them were born out of the scarcity of data science talent some years ago and were turned into profitable products afterwards. This recalls one of the principles of Rework book — sell your by-products. Ways to make humans trust machines are being paved (image by rawpixel) Explainability and fairness saw great developments in 2018. There are now many more available resources. Tools that were just Python alpha versions have matured (e.g., SHAP). Also, you can easily find structured and supported books on the topic, such as Interpretable Machine Learning book, by Christoph Molnar. Understanding highly complex models are going in the right direction by decreasing barriers — Google’s What-If Tool is a great example. Feature engineering is still one of the main secret sauces of Data Science solutions — take a look at the description of the winning solution for Home Credit Default Risk in Kaggle. While much of the best features are still manually created, Feature Tools became one of the main feature engineering libraries this year, for the lazy (smart?) data scientist. The problem of these tools is that you need to have data standards across your business, i.e., if one of your clients delivers data in one format, you should make sure the second client follows the same procedure — otherwise, you’re going to have a lot of undesirable manual work. Finally, if we delivered Oscars to programming languages, Python would probably receive some of them. It is today the fastest-growing major programming language and the most wanted language for the second year in a row, according to Stack Overflow. At this rate, it is quickly becoming the most used programming language. Tomorrow So, what’s next? What can still be done? There is plenty to be done in the above topics. And they will continue to be some of the main focus of data scientists in 2019, and the following years. The focus will be on maturing technologies while answering the questions: How can we minimize the time spent, by data scientists, on data cleaning and feature engineering? How can we define trust in the context of machine learning? If we say that a machine model is fair, what are its characteristics? What are the principles according to which we can say that we trust a robot? (image by Andy Kelly) But, besides these meta-questions, that are difficult to answer, what are the promising topics? Reinforcement Learning might have gone through a lot of winters during its life. However, it looks like we are approaching another spring. A great example is the fantastic performance in Dota 2. There is a lot to be done, and a lot of computational power will be needed… But, anyway, reinforcement learning is the most human-like learning behavior we currently have and it’s exciting to see its applications. We’ll most probably start seeing these proof-of-concepts turned into actual products. If you have the time, take a look at them and use OpenAI gym to develop them. GDPR’s Recital 71: The data subject should have “the right… to obtain an explanation of the decision reached… and to challenge the decision.” General Data Protection Regulation (GDPR) is in effect in EU since 25th of May 2018 and directly affects data science. The problem is: companies are still understanding the limits of this new regulation. Two of the main open topics are: Data Privacy. Companies that mishandle personal data are now threatened by huge fines. Does this mean that access to data will become more difficult for research? Will we see new developments in data synthetization? Can we truly anonymize data? Right to explanation. Fully automated decisions must be explainable. Well, that is great… But what does it actually mean “explainable”? Will we see the standardization of a machine learning interpretability algorithm? There isn’t an answer from EU entities on this — we’re just probably waiting for the biggest fine ever to be executed. Trustworthy AI has two components: (1) it should respect fundamental rights, applicable regulation and core principles and values, ensuring an “ethical purpose” and (2) it should be technically robust and reliable since, even with good intentions, a lack of technological mastery can cause unintentional harm [EU AI Ethics] As algorithms affect society more, we are entitled to make sure biases are mitigated, and their use is towards the benefit of the whole and not just a few. Fortunately, companies and institutions are working on this. The EU AI Ethics draft and the Google AI principles are perfect examples. There’s still a long way forward for ethics, but it’s now a recurrent discussed topic — and that’s good. EU’s draft on AI ethics is an example on how governmental institutions are tackling the subject. As algorithms become more complex, and more data is readily available (every gadget now generates data, right?), fewer people will be just using their laptops to do data science. We’ll use cloud-based solutions, even for the simplest projects (e.g., Google Colab). Time is scarce, GPUs are not… Laptops are not evolving fast enough to keep the pace with the required computational power. Google Colab: making it easier to share notebooks and using more computational power. Now, imagine you see a company with an open vacancy for the position of “Engineer” — just that. That’s great… But there are like 100 types of engineers nowadays. Is it a mechanical engineer? Aerospace? Software? “Engineer” is too generalist. One or two years ago, companies would just publish a job vacancy as “Data Scientist”. Well, it is starting to feel incomplete. And if you’re just starting in this field, becoming a general data scientist might be too overwhelming. After having a grasp on this field, you better focus on a particular topic. Take for instance Netflix, which has nine Data roles: Netflix data roles (source: Netflix Tech Blog) There are a lot of specializations areas that didn’t exist before and itis becoming more important for data scientists focus on one to make a stand. It’s time to find your own if you haven’t already. From my point of view, Data Engineering skills are the most interesting ones for next years. If you don’t have them in your team, you’re probably just playing data science in Jupyter notebooks. And companies are realizing that. 2019 is going to be an amazing year, again. There’s a lot to be done, and it is not just techy and nerdy! Real problems to be solved are awaiting. As a concluding remark, remember that time is our biggest asset. Every second you spend doing worthless is a second you just lost not doing something great. Pick your topic, and do not consider your work business as usual.
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