The instant growth and discovery of computational algorithms in the rapidly growing fields like Artificial intelligence (AI) and specifically machine learning (ML) have changed the world in a glimpse. Recent developments in the aforementioned areas pushed the companies to adopt modern changes and launch themselves for fast, cost-efficient, and innovative ways to utilize the potential of big data. But in order to effectively station these technologies, organizations must stay up to date on the contemporary trends in data science.
AI, ML, Neural Networks, and Deep learning are the backbone of Data Science. In other words, Data Science is a combination of a data modelling, algorithm, analysis, and technology that assists organizations in curbing complex business challenges and to make precise predictions to implement calculated solutions.
Data is almost doubling in size every two years, showing rapid exponential growth. The never-ending need for data is fueled by the ever growing strategic advantages it offers once translated. It is helping organizations automate and streamline complex business processes linked with extraction, analysis, and presentation of information from raw data using advanced algorithms and technologies. With the rapid expansion of data science technologies, it is critical to stay on top of the best practices and predict data science future trends.
In this article, we will discuss the topmost data science trends that any organization should be aware to stay ahead of the competition.
According to International Data Corporation (IDC), the global technology expenditure on the Internet Of Things (IoT) would cross $1 trillion in the year 2022.
It has become the new normal that our smartphones are being used to control domestic appliances such as TV, AC, and other smart gadgets. It has become common as IoT. Smart devices like Google Assistant and Microsoft Cortana are being used to automate the daily routine essentials, the expanding magnetism of IoT is attracting the attention of most businesses to invest in technology development, especially mobile application development that supports in harnessing IoT.
This technological revolution is opening gateways to collect a huge amount of data, and along with the methods to manage and analyze useful insights being extracted.
Businesses in this era are focusing more on acquiring the latest devices that are more intelligent in gathering, analyzing, and processing data.
Access to Artificial Intelligence
Artificial Intelligence (AI) is now mainly utilized to assist both small and big businesses to grow rapidly and improve the overall business processes. AI can solve more complex problems in a faster and more precise way than human beings. The more convincing reason to deploy AI is that it minimizes or almost eliminates the possibilities of getting an error expected during the process and improves the overall workflow of the business.
By deploying the AI with the right knowledge of data science trends in business models and streamlining the processes along the way, employees can focus on more critical jobs and improve the quality of service.
Evolution of Predictive Analysis
Another data science trend the businesses must know to have a competitive edge is Big data analysis. It serves as a central strategy to mark victory in a competition rich world. Nowadays, organizations are using a variety of tools to analyze big data and uncover the meaning behind apparently obscure events that are happening at the moment. This is exactly where the Predictive Analysis comes in as it can unleash the realities of the future with high precision.
This is how predictive analysis helps in using the collected data to extract the patterns from the customers’ behavior and businesses can have a clear insight into the market and they can develop efficient strategies for targeting the right customers with the right tool.
Dark Data on Cloud
Information that is yet not available in the digital format is called dark data. The notable point of this data science future trend is that dark data is a huge ocean that is yet to be discovered. For now, Dark data is acquired through specific methodologies but not used in any way to extract insights for decision making and predictive analysis. This dark data is expected to be migrated to the cloud so that it can be used for smart decision making that will help the organizations to explore another hidden treasure of data.
By the end of 2021, it is expected that around 40% of the data science tasks will be automated. After the rebirth of machine learning thanks to the recent advancement in computer architecture, the continuously expanding machine learning technology is playing a vital role in automation and machine intelligence. With the intelligent combination of useful machine learning methodologies and automation tools, businesses can derive crucial and deep insights that were not possible to extract by skilled statisticians or analysts.
Rules, Laws, and Regulations
The General Data Protection Regulation (GDPR), a regulation in EU law on data protection and privacy in the European Union helped to throttle the process of data regulation in a way so fast that a lot of businesses are struggling to meet the regulatory demands.
This set of laws and regulations cast a notable effect on data handling, consumer profiling, and data security. It has become a compulsory practice for organizations not only to follow these regulations but also to understand its impact on contemporary and upcoming events. Data science experts with rich knowledge of these regulations are going to be a huge asset for organizations.
Competitive Edge & Technology
To handle a business model successfully and efficiently, it’s crucial to stay updated with the existing technology trends. Modern solutions and methodologies are rising at a faster rate than ever. A smart data analyst never relies on a single technology or platform or methodology. Data processing is another skill that requires instant updates with evolving contemporary trends, scenarios, and demands. The expert having rich experience and an updated skill set that can tackle complex data processes will be in more demand.
Developing Trustworthy AI Model
Automated decisions must be explainable. The question is what does “explainable” actually mean?
A trustworthy AI has two major compliances.
- It should be in accordance with the basic rights, required regulations, and central principles so that AI can go along with the ethical requirements.
- It should be technically robust and reliable because even with a positive objective, a small defect in machine intelligence can lead to unintentional catastrophes.
The Edge computing technology utilizes the potential of proximity by handling the information as physically close endpoints as much as possible. This is the main reason why it can lower the engagement and traffic on the network. Cloud computing is growing rapidly, not just getting installed in the distributed servers and edge machines. This methodology would not only reduce the traffic but at the same time, it would also lower the operational cost for a business to handle real-time activities on the server.
Another notable trend that is very common is data storytelling and visualization. Most organizations are transferring their traditional data warehouses to the cloud. With a sudden spike in the use of cloud-based data integration methodologies and applications, data would be more managed and centralized.
The newly emerged concept of DataOps is growing at a faster pace. The main reason is the data pipeline has become more complicated and requires even more attention and regulatory mechanisms. DataOps applies the Agile methodology and DevOps to the whole data analytical operations. The DataOps involves the processes like collecting sources required for analysis, automated testing, and delivery of improved data quality and analysis.
Blockchain & Cryptocurrency
In our times, Blockchain is the biggest ever technological revolution, a methodology that serves as a backbone for cryptocurrencies like Bitcoin and Ethereum. Blockchain is a very secure digital currency platform despite regulatory issues and has a variety of applications from electoral-voting to digital banking. When it comes to maintaining strong data security, Blockchain will unlock plenty of opportunities in the near future. Blockchain technology is a notable trend in 2020.
Quantum computing starts after the climax of conventional computing as millennials have ever seen. It is the greatest quantum benchmark since the invention of the computer itself. A quantum computer can handle 100 qubits to 200 qubits and can easily crack every single encryption methodology. With some rumors about quantum computing being developed, experts predict it could take around a decade to build a fully functional quantum computer that would unlock another dimension of possibilities.
Advancement in the area of natural language processing allows users to ask questions to businesses using speech and voice input. AI-driven machine learning algorithms can easily learn from data, extract insights and predict the unseen outcomes. With the use of a real-time system as a base tool, managers have access to more insights.
2020 and Covid-19
The World Health Organization declared a global pandemic as the coronavirus rapidly spreads across the world. With the emergence and spread of the coronavirus disease 2019 (COVID-19) across the globe, we have seen trends on how data science and intelligence helped in predicting and containing the spread of viruses.
The issues and domains addressed by data science technology involve:
- Visualization of online data to understand the impact of the pandemic outbreak
- Modeling and Forecasting the spread and transmission rate of disease with respect to different demographics
- Machine learning for tracking of virus spread using prediction models
- Simulation of expected outbreak events
- Predictive Analytics in COVID-19 risk profiling
Russian President has stated that whoever becomes the leader in AI will become the ruler of the world. Military officials of war capable countries are fully aware of ongoing AI trends and Chinese & Russian investments & objectives regarding AI. Their intelligence acknowledges that their military is at risk of remaining outdated in using this advanced capability on the battlefield. As the future of warfare depends more on AI and that of intelligence gathering on Data Science, it is crucial for the Army of every country to enhance the technological capability in accordance with the contemporary trends and occupy large & dynamic data sets to develop insights for (just in case) a real-time scenario.
The exponential growth of data, accelerated by sensor-based devices, is launching Data Science, Artificial Intelligence (AI), and machine learning as market differentiators in the global business-intelligence domain. With the surge in demand for Data Science and Machine Learning experts, the year 2021 will witness several data science trends.
Data science is one of the fastest emerging and transcending fields. That’s why it’s critical for organizations and individuals to adopt the changes. Staying up-to-date with the trend will help you analyze effectively where you need to improve your business and discard traditional practices in order to achieve maximum efficiency with minimal effort and cost.
Whether you are a data science expert or running an organization, with the right information about the aforementioned data science future trends and existing business intelligence trends of 2020 you can give an effective boost to the overall operational capability of your business.