Get Armed With Future Technologies – Blockchain, AI, Data Science
“In order to share your true brilliance, you have to take risks that seem odd initially.”
Undeniably, the introduction of dynamic technologies such as Blockchain, Artificial Intelligence (AI), and Data Science is revolutionizing the whole world. The myriad applications of these technologies are aiding the ailing industries from the health sector to the financial sector. According to Statista, the size of the blockchain technology market worldwide will increase 10 times by 2023 to the present. A similar trend is shown for worldwide artificial intelligence market revenues that will grow about 12 times by 2025. Whereas the global big data and business analytics market is forecasted to rise with a compound annual growth rate of 13.2 percent.
These are capable of catering to the overall market by natural language processing, robotic process automation, machine learning, decentralization, cryptographic principles, and more. Adopting these emerging technologies has become a necessity for an organization’s success and survival. Though most of the businesses are struggling to find a balance needed to deploy new technologies with speed and agility with risk management. The inability of assessing new technology risks is due to the lack of awareness and understanding. Therefore, technology risk management should be included in strategic business planning.
Blockchain technology is very promising with the immense scope of its adoption still, a few challenges are associated with it. Privacy and scalability are the two major obstacles to widespread acceptance. Furthermore, inefficient technological design, regulation, lack of adequate skills, and public perception are some other issues that need to be addressed. From the digital currency of Bitcoin, it is accurate that companies are not able to figure out what it is capable of.
To understand opportunities, a leader should find the answer to these questions:
- Where can the new blockchain approaches be piloted on the edges of business?
- Who will be most affected by these implementations?
- How will it impact the organizational structure and strategy, business processes, governance, talent, and legacy systems?
- What are the required common standards?
The answers will help to devise a practical approach to closely adjust the services and get the results. Despite the evolving need for technological improvements, Blockchain can still transform the interbank payments, replace existing mechanisms for exchange, and maintain financial information and agreements.
All the autonomous things like robots, vehicles, drones, appliances use AI to interact more with their surroundings, only the degree of intelligence, capability, and coordination varies. Artificial Intelligence has a wide array of applications from facial recognition to language translators and assistants. It radiates enthusiasm and skepticism collectively. Through its innovation and growth, it is providing real value to organizations.
However, many businesses are still unable to find their way into it. The main issue is that AI solutions are built on the right data, not just any type of data. Obtaining large and comprehensive data sets for supervised learning is always tough. The black box complexity of deep learning techniques poses an explainability challenge. Approaches like Local Interpretable Model-Agnostic Explanations(LIME) are showing positive hope to increase the transparency of models whereas Cloud computing to speed up the complex calculations.
The scarcity of use cases of AI implementation, hiring the AI experts, difficulty in assessing vendors are some of the other limitations. For a remedy to these problems, a shift to the platforms that permit AI-driven work “as a service” will be beneficial rather than starting from scratch. It will provide the enterprises the readymade solutions to plug in their own data.
Data science is another one of the most fascinating technologies. The adoption of analytics is increased but has its own set of challenges. The first and foremost is a steep learning curve for the engineers and scientists who do not program full-time, due to the continuous evolution of cutting-edge new techniques. The solution is to enable them to run quickly with a programmatic interface to fine-tune analytics with improved robustness and accuracy. The next one is the most common problem of finding the right volume and velocity of data. The use of data cleaning makes sense here for the accuracy of models and to drive profitable business decisions. Identifying the appropriate analytics use cases, maintaining agility, and planning periodic updates in advance are the other key points that will help in coping with the common problems.
With major developments occurring regularly, it’s really exciting to speculate what is coming in next? Blockchain, Artificial Intelligence, and Data Science technologies are transforming business models radically. Nonetheless, tons of other technologies will surface in the near future. As of now, these are top picks to leverage the existing customer base to bring the best experience. It is the core of the business to treat the customer as a boss who decides the fortune of the company by spending money. For a booming business, one should be future-ready to meet the growing demands by adopting emerging technologies!!
———————————————————————————————————————————————————————————————————————————-
This post was authored by Munish Jauhar. If you want to get featured on our website please reach us at advertising@alltechevent.com
Author Details:
Munish Jauhar
Founder and CEO
