In this article, we shall discuss the important themes to watch out for in data science.
As data generation is increasing exponentially, new themes and trends are evolving in the data science domain. These trends are changing the way companies are processing data. With the ever-increasing need for data analysis, the demand for data scientists is also increasing rapidly. According to IBM, there will be an increase in data science job openings in the coming years. If you want to make a career in data science, then you can start with this data analytics courses or data scientist certificate.
In the coming years, data science combined with AI technologies will play a crucial role in preparing, analyzing, and predicting important insights from the data. In recent times, in response to the COVID-19 emergency, over 500 clinical trials of potential COVID treatments began worldwide. The trials used a living database that collects data from trial registries. This helps medical health experts in predicting disease spread and finding new treatment methods.
Here are a few essential themes and trends that data industry leaders should focus on.
AI is Becoming More Smarter, Faster and Responsible
With the help of various data science techniques such as reinforcement learning and distributed learning, organisations are creating more adaptable and flexible AI systems. These more innovative, faster, and responsible AI systems are capable of handling complex business problems effortlessly.
Source: DNA India
Gartner predicted that 75% of enterprises would shift from piloting to operationalizing AI by the end of 2024. This will result in a fivefold increase in streaming data and analytics infrastructure.
In the context of the current pandemic, AI techniques such as machine learning and natural language processing provide powerful insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. AI and machine learning techniques are critical for realigning the supply chain to fulfil the new demands.
Research-based corporations are investing significantly in new chip architecture, which will accelerate AI and ML computations and reduce workload. Eventually, this will lead to smarter, faster, and more scalable AI solutions that will significantly affect all humans.
Reduction of the Dashboard Systems
With the help of advanced data science techniques, dynamic data stories – with automated and consumerized experiences – will replace visual, point-and-click authoring and exploration. This will lead to a reduction in the time users spend on predefined dashboards. The shift to in-context data stories means that the pertinent insights will stream to each user based on their previous context. These dynamic insights leverage technologies such as augmented analytics, NLP (Natural Language Processing), anomaly detection and collaboration.
Source: PixelPlex
Data industry leaders need to evaluate their existing analytics and business intelligence tools regularly to enhance NLP driven user experience beyond the predefined dashboard. The dynamic insights result in better human-machine interaction and trust for greater adoption and alignment of decisions in an organisation.
The emergence of Decision Intelligence
In data science, decision intelligence plays a crucial role in developing AI-based applications. Decision intelligence is the combination of decision management and decision support and encompasses applications in complex adaptive systems. It brings multiple traditional and advanced disciplines together and improves the learning abilities of the AI system.
Source: What After College
According to Gartner, more than 33% of big organisations will have analysts practicing decision intelligence by 2023. It provides a framework to help data and analytics design, compose, model, align, execute, monitor, tune decision models, business outcomes and behaviour processes.
Augmented Data Management
In data science, augmented data management optimizes and improves operations with the help of ML and AI techniques. It converts metadata from being used in auditing and lineage to powering dynamic systems.
Source: Blockgeni.com
Augmented data management enabled AI systems to examine large operational data samples, including actual queries, performance data, and schemas. In future, AI systems will be able to simplify and consolidate data management tasks.
Blockchain in Data Science
Blockchain technology is a structure that stores digital assets in several databases in a network connected through peer-to-peer nodes. The goal of Blockchain is to allow digital information to be recorded but not edited.
Source: Entrepreneur
In data science, blockchain technologies address two challenges. First, it provides the complete lineage of assets and transactions. Second, it ensures the transparency of complex networks.
Blockchain technologies are currently becoming supplementary to their existing data management infrastructure in the data and analytics industry. Today, data scientists use blockchain technologies to ensure authenticity and track data at every point of the process.
Data Marketplaces and Exchanges
According to Gartner, 35% of large organisations will either be the seller or the buyer of data via formal online data marketplaces by 2022.
Source: Medium.com
Data marketplaces and exchanges offer a single platform to transact third-party data. These marketplaces provide centralized availability and access that create economies of scale. Data and analytics leaders should establish a transparent methodology by defining data governance rules, thereby improving the overall data marketplaces.
Final Thoughts
Data science is an ever-evolving field, and the new trends, tools and technologies will help develop it further. We have discussed some of the essential themes, trends and technologies in this article. In the future, these trends will play a crucial role in the data science domain.
Frequently Asked Questions (FAQs)
Q. What is data science, and what is its impact?
Ans: Data science refers to processing the data and getting valuable insights using various tools and techniques. Data science is used every day to help businesses drive efficiency, getting deeper operational insights and ultimately improving the product or service.
Q. What is the future of data science?
Ans: Data is the new oil; therefore, the data science field is proliferating. Data science will play an important role in the future in every aspect of human life. From businesses to healthcare, transportation to telecommunication, data science will be used in almost every field.
Q. What are the important themes and trends in data science to watch out for?
Ans: The new technologies such as AI and ML enable data scientists to explore uncharted territory. With this, AI-based applications are becoming smarter, faster and more responsible. Blockchain technologies combined with data science provides data security to the users.
