Taking into consideration the positive trends of Data Science from previous years, there lies an immense well of possibilities that awaits us in the future, that is, the upcoming year 2020. Some of these Data Science Trend Forecast for 2020 can be foreseen as follows:
Complicated code and extensions will no longer be required to get deep insights from data. The augmented analysis helps layman users/analysts (in machine learning/data science) to make use of AI to analyze data. This will change the way data is consumed, created and shared across all data-intensive fields. Already several BI and analytics tools are trying to implement AI assistance full force in their platforms.
Continuous/ Real-time Intelligence
There is intensive activity ongoing every second in real-time platforms. If through some method, one can plug into this data, real-time user experience can be enhanced manifold. Continuous or real-time intelligence aims to do just that by analyzing data in real-time so that instant results can be provided to the user while he is still surfing the platform. It can also help increase profit margins by re-aligning the platform as per the observed interaction of the user.
Natural Language Processing is a very important segment of Artificial Intelligence since most real-world data are in text or voice format. To process such data, advanced NLP techniques are required which are being innovated with each passing data. Today, we can read, understand, classify and even create unique text documents with the help of machines. Further developments like intelligent summarization, entity recognition and task management using text input and much more are expected to happen, owing to the intense research and increasing data-science experts choosing NLP specialisation.
There has already been a visible surge in the performance of voice assistants in 2019. In 2020, it is expected to further improve such that the conversational systems become more sensitive to the human language and also more humane in their response. By more humane, it will mean that the systems can keep track of previous responses and questions (which is not a very developed feature in any voice assistant in the market to this day). Also, most client interactions are expected to be taken over by conversational technology, thus, increasing response rate and efficiency.
The last decade has seen massive growth in AI aided decisions for sure, but it has been a persistent problem to be able to explain these decisions or why the AI wants to go a certain way instead of another. Recently, however, a lot of research has increased the scope of explainable AI. 2020 can further be invested in understanding problems like say, how and why a certain neural network arrived at a certain decision. This will indefinitely increase the faith of clients on adolescent technology.
Persistent memory/ In-memory computation
In-memory computing or IMC can deliver extremely high-performance tasks due to optimized memory architecture. It also has become more feasible due to the decreasing expense of memory which owes credit to constantly emerging innovations.
Data Fabric helps in the smooth access and sharing of data in distributed environments. It is usually custom made and helps in the transfer and store of data, data pipelines, APIs and previously used data services that have a chance of being re-invoked. Trusted and efficient data fabric can help to catalyze data science pipelines and reduce delays in customer-client interaction/iterations.
Advances in Quantum Computing
The research in Quantum Computing has a very high momentum at the moment. Even though the whole architecture of Quantum computing is at a very basic stage, increased investments and research are helping the field to grow by inches every passing day. A quantum computer is said to perform calculations which will take general computers a few years, in just a few seconds! As remarkable as it sounds, it can bestow superpowers to mankind! Imagine munching on years and years of historical data to arrive at conclusions about the future in just a few seconds. A whole lot of astonishing things await us, and we must be blessed to be a part of this century.
It is expected that India’s job openings in the analytics sector will double to about 200000 or two lakh jobs in 2020. Here is what 2020 for job seekers in data science will look like:
Fields like finance, IT, professional services and insurance will see a boom in demand for data science and analytics.
Having analytics skills like MapReduce, Apache Pig, Machine learning and Hadoop can provide an edge over other competitors in the field. The most fundamental in-demand skills will be Python and Machine Learning. Statistics is an added advantage.
Vacancies for roles like data developers, data engineers and data scientists will go over 700,000 by 2020.
The most promising sectors that will tend to create increasing opportunities include Aviation, Agriculture, Security, Healthcare and Automation.
The average salaries in India in development roles like Data Scientist or Data Engineer will range from 5 to 8 Lakh per annum.
The average salaries in India in management/strategizing roles like data architect or business intelligence manager will range from 10 to 20 Lakh per annum.
As exciting as all of it sounds, there is always a bag of unforeseen advancements that are bound to take us all by surprise, as has always happened with Data Science and AI in the past. So, hold tight for yet another mind-boggling ride through the lanes of technology this 2020!
Data Science has seen a massive boom in the past few years. It has also been claimed that it is indefinitely one of the fastest-growing fields in the IT/academic sector. One of the most hyped Trends in Data Science this year was that the sector saw a major hike in jobs as compared to the past years!
Such an unprecedented growth owes all its dues to the unimaginable benefits that artificial intelligence has brought to the plate of mankind for the very first time. It was never before imagined that external machines could aid us with such sophistication as is present today. Owing to this, it is imperative that an individual, irrespective of his/her calling, must have at least a superficial knowledge about the past advances and future possibilities of this field of study. Even if it is the job of scientists and engineers to figure out solutions using machine learning and data science, the solutions, undoubtedly is bound to affect all our lives in the upcoming years. Moreover, if you are planning to plug into the huge well of job openings in data science, exploring the past and upcoming trends in this field will surely take you a step ahead.
Looking back on the achievements of the year 2019, there is much which has happened. Here is a brief glimpse of what Trends in Data Science of 2019 looked like:
The once-popular belief that AI technology was only meant for high-scale and high-tech industries, is now an old wives’ tale. AI has spread so rapidly across every phase of our lives, that sometimes we do not even realize that we are being aided by AI. For instance, recommendations that we get on online forums are something we have become very used to in recent times. However, very few have the conscious knowledge that the recommendations are regulated by AI technology. There are also several instances where a layman can use AI to get optimized outputs, like in automated machine learning pipelines. We even have improvised AI-aided security systems, music systems and voice assistants in our very homes! Overall, the impact of AI in everyday lives saw a massive boost in 2019, and it is only bound to increase.
The rapid growth of IoT products
As was already forecasted, the number of machines/devices which came online in 2019 was immense. Billions were invested in research to back the uprising IoT industry. Today it is nothing out of the ordinary to control home appliances like television and air conditioners with our smartphones or lock our and unlock our cars from even the opposite end of the globe. Bringing devices online not only makes the user experience far smoother but also generates crucial data for analysis. With such data, several unopened gates can be explored across several domains. The investments and count of IoT devices are expected to go up at an increasing rate in the upcoming years.
Evolution of Predictive Analysis
The concept of predictive analysis is to use past data to learn recurring patterns, such that it can predict outcomes of future events based on the patterns learnt. Today, with increasing data it becomes extensively important to make use of optimized predictive solutions. Big data comes into picture here and significant advancements have been made in 2019 about it. Tools like PySpark and MLLib have helped scale simple predictive solutions to extensive data.
Migration of Dark Data
Dark data is very old data which has probably been sitting in obsolete archives like old systems or even files in storage rooms! There is a general understanding that such unexplored data can show us the way to crucial insights about past trends which can help grab useful opportunities and even avoid unwanted loopholes. Therefore, there has been visible initiatives to make dark data more available to present-day systems with the help of efficient storage and migration tools.
Implementation of Regulations
In 2018, General Data Protection Regulation (GDPR) brought in a few data governance rules to emphasize the importance of data governance. The rules were laid down so fast that even at the year-end, several companies dealing with data are still trying to comply wholly with all the principles laid down. These principles have not only created a standard for data consumption and data handling domains but are also bound to shape the future of data handling with great impact.
DataOps is an initiative to bring in some order in the way the data science pipeline functions. It is essentially a reflection of agile and DevOps methods in the field of data science. In 2019, it has been one of the major concerns of management in data science to integrate DataOps into their respective teams. Previously, such integration was not possible since the generic pipeline was still in making or under research. However, now, with a more robust structure, integrating DataOps can mean wonders for data science teams.
As stated by Gartner, Inc. cloud computing and edge computing has evolved to become a complementary model in 2019. Edge computing goes by the concept of “more the proximity (or closeness to the source of computation), better is the efficiency”. Edge computing allows workloads to be located closer to the consumers and thus, reduces latency several-fold.
There is, however, a huge recurring gap when it comes to the need and availability of skilled people who can launch and contribute to these developments significantly. India contributed to 6% of job openings worldwide in 2019, which scales to around 97000 jobs!
The job trends of 2019 looked as follows:
BFSI sector had a massive demand for analytics professionals, followed by the e-commerce and telecom sectors. The banking and financial sectors continued to have high demand throughout.
Python served as a great skill to attract employers to skilled job seekers
A 2% increase in jobs offering over 15 Lakh per annum was observed
Also, 21% of jobs demanded young talent in data science, a great contrast to all previous years. 70% of job openings were for professionals with less than 5 years of experience.
The top in-demand designations were Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultants, Statistical Analyst and Hadoop Developer
Big data skills like Hadoop and Spark were extremely in demand due to the growing rate of data.
Telecom industry saw a fall in demand for data science professionals.
The median salary of analytics jobs was just over 11 Lakh per annum.
Artificial intelligence uses data science and algorithms to automate, optimize and find value hidden from the human eye. By one estimate, artificial intelligence will drive nearly $2 trillion worth of business value worldwide in 2019 alone. Hence, that’s an excellent incentive to grab a slice of the AI bounty. Also, fortune favors those who get an early start. Therefore, the laggards might not be so fortunate.
Artificial Intelligence (AI) is the rage now, but like all things tech, it is in a continuous state of evolution. Here is how Artificial Intelligence is expected to play out in 2019.
Trends in Artificial Intelligence
1. Automation of DevOps to achieve AIOps
There’s been a lot of attention in recent years about how artificial intelligence (AI) and machine learning (ML). Furthermore, DevOps is all about automation of tasks. Its focus is on automating and monitoring steps in the software delivery process, ensuring that work gets done quickly. AI and ML are perfect fits for a DevOps culture. Also, they can process vast amounts of information and help perform menial tasks. They can learn patterns, anticipate problems and suggest solutions. If DevOps’ goal is to unify development and operations, AI and ML can smooth out some of the tensions that separate the two disciplines in the past.
Moreover, one of the key tenets of DevOps is the use of continuous feedback loops at every stage of the process. This includes using monitoring tools to provide feedback on the operational performance of running applications. Additionally, this is one area today where ML is impacting DevOps already. Also, using automation technology, chatbots, and other AI systems, these communications channels can become more streamlined and proactive. Also, in the future, we can see AI/ML’s application in other stages of the software development life cycle. This will provide enhancements to a DevOps methodology or approach.
Furthermore, one area where this may happen could be in the area of software testing. Unit tests, regression tests, and other tests all produce large amounts of data in the form of test results. Applying AI to these test results could identify patterns of poor codes resulting in errors caught by the tests.
2. The Emergence of More Machine Learning Platforms
People are yet not done figuring out machine learning, and now there is a rise of a new advanced term on the market for machine learning, and, i.e. “Automated Machine Learning.” Automated machine learning is a more straightforward concept, and it makes things easier for developers and professionals. Furthermore, AutoML is a shift from traditional rule-based programming to an automation form where machines can learn the rules. Also, in automated machine learning, we offer a relevant and diverse set of reliable data to, in the beginning, to help automate the process of decision making. The engineers will no longer have to spend time on repetitive tasks, thanks to AutoML. The growth in the demand for machine learning professionals will get a massive boost with the rise of AutoML.
We’re in a golden era where all platform mega-vendors providing mobile infrastructure are rolling out mobile-accessible tools for mobile developers. For example:
Imagine a world where you can sit next to your customers and have a one on one conversation about their expectations from your brand with every interaction, and deliver on their expectations every single time. As we move forward in the digital era, this might be the reality for the brands, where businesses get the opportunity to win their customers’ heart with every single interaction. Artificial Intelligence and Augmented Reality are two such technologies, which will show the most potential in connecting with consumers in 2019 and will rule the technology landscape. The key reason behind this trend is that, compared to virtual reality, which needs a hardware device like Oculus Rift, it is fairly simple to implement augmented reality. It only needs a smartphone and an app.
Since the entry barrier is low, today’s tech-savvy consumers do not shy away from experimenting with the technology, and for enterprises, it only requires a thought-through AR-based app. Further, with tech giants like Apple, Google, Facebook making it easier for developers to build AR-based apps for their platforms, it has become easier even for smaller businesses to invest in augmented reality. Also, industries like retail, healthcare, travel etc. have already created a lot of exciting use cases with AR. With technology giants Apple, Google, Facebook etc. offering tools to make the development of AR-based apps easier, 2019 will see an upsurge in the number of AR apps being released.
4. Agent-Based Simulations
Agent-based modelling is a powerful simulation modelling technique that has seen a number of applications in the last few years, including applications to real-world business problems. Furthermore, in agent-based modelling (ABM), a system is modelled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviours appropriate for the system they represent — for example, producing, consuming, or selling.
The benefits of ABM over other modelling techniques can be captured in three statements: (i) ABM captures emergent phenomena; (ii) ABM provides a natural description of a system; and (iii) ABM is flexible. It is clear, however, that the ability of ABM to deal with emergent phenomena is what drives the other benefits.
Also, ABM uses a “bottom-up” approach, creating emergent behaviours of an intelligent system through “actors” rather than “factors”. However, macro-level factors have a direct impact on macro behaviours of the system. Macy and Willer (2002) suggest that bringing those macro-level factors back will make agent-based modelling more effective, especially in intelligent systems such as social organizations.
The Internet of Things is reshaping life as we know it from the home to the office and beyond. IoT products grant us extended control over appliances, lights, and door locks. They also help streamline business processes; and more thoroughly connect us to the people, systems, and environments that shape our lives. IoT and data remain intrinsically linked together. Data consumed and produced keeps growing at an ever expanding rate. This influx of data is fueling widespread IoT adoption as there will be nearly 30.73 billion IoT connected devices by 2020.
Data Analytics has a significant role to play in the growth and success of IoT applications and investments. Analytics tools will allow the business units to make effective use of their datasets as explained in the points listed below.
Volume: There are huge clusters of data sets that IoT applications make use of. The business organizations need to manage these large volumes of data and need to analyze the same for extracting relevant patterns. These datasets along with real-time data can be analyzed easily and efficiently with data analytics software.
Structure: IoT applications involve data sets that may have a varied structure as unstructured, semi-structured and structured data sets. There may also be a significant difference in data formats and types. Data analytics will allow the business executive to analyze all of these varying sets of data using automated tools and software.
Driving Revenue: The use of data analytics in IoT investments will allow the business units to gain insight into customer preferences and choices. This would lead to the development of services and offers as per the customer demands and expectations. This, in turn, will improve the revenues and profits earned by the organizations.
6. AI Optimized Hardware
The demand for artificial intelligence will increase tremendously in the next couple of years, and it’s no surprise considering the fact it’s disrupting basically every major industry. Yet as these systems do more and more complex tasks, they demand more computation power from hardware. Machine learning algorithms are also present locally on a variety of edge devices to reduce latency, which is critical for drones and autonomous vehicles. Local deployment also decreases the exchange of information with the cloud which greatly lowers networking costs for IoT devices.
Current hardware, however, is big and uses a lot of energy, which limits the types of devices which can run these algorithms locally. But being the clever humans we are, we’re working on many other chip architectures optimized for machine learning which are more powerful, energy efficient, and smaller.
There’s a ton of companies working on AI specific hardware
Google’s tensor processing units (TPU), which they offer over the cloud and costs just a quarter compared to training a similar model on AWS.
Microsoft is investing in field programmable gate arrays (FGPA) from Intel for training and inference of AI models. FGPA’s are highly configurable, so they can easily be configured and optimized for new AI algorithms.
Intel has a bunch of hardware for specific AI algorithms like CNN’s. They’ve also acquired Nervana, a startup working on AI chips, with a decent software suite for developers as well.
IBM’s doing a lot of research into analogue computation and phase changing memory for AI.
Nvidia’s dominated the machine learning hardware space because of their great GPU’s, and now they’re making them even better for AI applications, for example with their Tesla V100 GPU’s.
7. Natural Language Generation
The global natural language generation market size will grow from USD 322.1 million in 2018 to USD 825.3 million by 2023. A necessity to understand customers’ behaviour has led to a rise in better customer experience across different industry verticals. This factor is driving organisations to build personalised relationships based on customers’ activities or interactions. Moreover, big data created an interest among organisations to derive insights from collected data for taking better and real-time decisions. Thus, NLG solutions have gained significance in extracting insights into human-like languages that are easy to understand. However, the lack of a skilled workforce to deploy NLG solutions is a major factor restraining the growth of the market.
8. Streaming Data Platforms
Streaming data platforms bring together are not only about just low-latency analysis of information. But, the important aspect lies in the ability to integrate data between different sources. Furthermore, there is a rise in the importance of data-driven organizations and the focus on low-latency decision making. Hence, the speed of analytics increased almost as rapidly as the ability to collect information. This is where the world of streaming data platforms comes into play. These modern data management platforms bring the ability to integrate information from operation systems in real-time/near real-time.
Through Streaming analytics, real-time information can be gathered and analyzed from and on the cloud. The information is captured by devices and sensors that are connected to the Internet. Some examples of these streaming platforms can be
Spark Streaming/Structured Streaming
Azure Streaming services
9. Driverless Vehicles
Car manufacturers are hoping autonomous-driving technology will spur a revolution among consumers, igniting sales and repositioning the U.S. as the leader in the automotive industry. Companies like General Motors and Ford are shifting resources away from traditional product lines and — alongside tech companies like Google’s Waymo — pouring billions into the development of self-driving cars. Meanwhile, the industry is pressuring Congress to advance a regulatory framework that gives automakers the confidence to build such vehicles without worrying whether they’ll meet as-yet-unspecified regulations that might bar them from the nation’s highways.
Supporters say the technology holds immense promise in reducing traffic deaths and giving elderly individuals and other population groups access to safe and affordable alternatives to driving themselves. Achieving those benefits, however, will come with trade-offs.
10. Conversational BI and Analytics
We are seeing two major shifts happening in entire BI/Analytics and AI space. First, analytic capabilities are moving toward augmented analytics, which is capable of giving more business down insights and has less dependency on domain experts. Second, we are seeing is the convergence of conversational platforms with these enhanced capabilities around augmented analytics. We expect these capabilities and adoption to quickly proliferate across organizations, especially those organizations that already are having some form for BI in place.
Many technology experts postulate that the future of AI and machine learning is certain. It is where the world is headed. In 2019 and beyond these technologies are going to shore up support as more businesses come to realize the benefits. However, the concerns surrounding the reliability and cybersecurity will continue to be hotly debated. The artificial intelligence trends and machine learning trends for 2019 and beyond hold promises to amplify business growth while drastically shrinking the risks. So, are you ready to take your business to the next level with Artificial Intelligence trends?
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2018 has been where AI and analytics surprised everyone. A large amount of time spent on research and experimentation has given us a clearer picture of what AI can do. But in 2019, organizations and professionals are going to invest in how these innovations can be used for real-life value-creating applications. In this article, we discuss AI and Analytics trends in three areas
It is clear that companies are realizing how valuable data can be and how it can drastically change the decision-making process, for better. This led companies to collect more and more data about consumers. Fun fact – Facebook even keeps track of where your mouse pointer moves on the screen. Fine movements of ours are being recorded and processed to make our experience better. But it’s not so easy for these companies.
Consider the Equifax data breach that happened not too long back. For those of you who do not know, Equifax is a credit reporting company. In our case, this means that it holds very sensitive information about citizens. More than 800 million of them. In the data breach, over personal data of 143 million Americans were compromised. Some of the information over which hackers were able to gain access to included name, address, and even social security number. The company’s security department said it “was aware of this vulnerability at that time, and took efforts to identify and to patch any vulnerable systems.” Equifax admitted it was aware of the security flaw a full two months before the company says hackers first gained access to its data.
There are many such examples of data breaches. Consequently, people are becoming more and more aware of their digital footprints. Government and lawmakers are realizing that data is no more just bunch of files on a computer. It has the potential to do good and to do the worst. This leads to the question – How safe are we digitally? What measures are being taken by the companies to safeguard our privacy?
Analytics is seen as a potential way to tackle this problem. Tools like user analytics try to understand steps a hacker takes when he crawls through the security walls of any company. It tries to understand if any user activity is different from the usual. For example a sudden spike in money withdrawal from a particular bank account. Deep Learning, arguably the most advanced form of analytics available to use is used on similar lines. Like user analytics, it focuses on anomalous behavior. The advantage is that it does it at a very micro level and is faster at detecting any such hazard, which is very important to take preventive measures.
Today we hear about deep learning as if it is Harry Potter’s, Elder Wand. Deep Learning has given some surprising results. Consider the following painting created by AI, which got sold for $432k at an auction.
Image Source: Christie’s website
In case you are wondering what kind of signature is at the bottom right, it is the objective function of the GAN (Generative Adversarial Network) that produced this art. Now we know why Math is often called ‘Art’.
But as awesome it may seem, deep learning is still considered as a black box method. Meaning, we are not perfectly clear on what’s going under the hood. Consider the use of Deep Learning in Cancer Detection. The algorithm, out of 100 cancer positives, might detect 90 of them. But wrongly predicting even 10% is fatal. Moreover, we don’t know why did the algorithm make those 10 errors. This puts a limitation on deep learning. In addition, company executives cannot trust major decisions of a company with some algorithm that might be accurate, but can’t explain why. This is where explanable AI comes into the picture. So that we can go from ‘may be’, ‘should be’ and ‘can be’ to ‘is’ or ‘isn’t’. Explanable AI is an active research area.
It’s a cliche to say AI will transform every possible industry on the earth. So let’s skip to the part where we specifically talk about a few selected industries and the ways.
In this nation of 1.25 B, where people are increasingly going online to shop, logistic is something every e-commerce focuses on the most. By forecasting demand and analyzing shopping patterns, companies can optimize their logistics. Thanks to real-time GPS data, weather data, road maintenance data and fleet and personnel schedules integrated into a system looking at historical trends, the most optimised routes and time are selected for delivery. Warehouse automation is something that has reduced the mundane repetitive work and has made processes faster and seamless.
Personalization or the ability of the algorithms to go to the micro level details and offer the best recommendation could be very well used in education. When you and I studied, we probably sat in a classroom where we had 50 classmates who. Now some of us understood what was being taught and some of us didn’t. It was impossible to teach each one of us in a way we could understand. Because there wasn’t any unique way. AI can open doors to this kind of personalization and enhance our learning experience with multiple folds.
We all know how dreadful disasters are. Life and property get destroyed in seconds when n earthquake or Tsunami strikes. In other cases like famine, a farmer’s livelihood gets disturbed. What if analytics can be used to predict the next earthquake or predict what crop would get the best yield? Startups like Cropin technologies provide smart solutions to farmers.
In 2015, researchers at the Vrije University Institute for Environmental Studies in Amsterdam published a research paper exploring how analyzing Twitter activity can help to detect flood events. The researchers found that by combining data from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity, disaster relief organizations can “gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods.”
If you liked this article and are trying to make your mark in this industry, do check out the following article –