7 Ways Artificial Intelligence and Machine Learning Will Impact Data Science Operations in 2019

Data science is emerging as one of the biggest arenas in data analytics in 2019. Data science will contribute to data analytics in a big way in the coming years by offering new techniques to apply to analyze trends in big data. Based on core scientific methods, data science is also embracing machine learning and artificial intelligence to improve the quality of data science training. For this reason, some of the biggest advancements in data science in 2019 will be driven by machine learning and AI, two fields that have been around for a while but have matured now to lead the fourth industrial revolution.

1. Generating Big Data for Businesses

Businesses have now woken up to the fact that data science can revolutionize the way they search for new consumers, markets and products. In 2019, data science will leverage machine learning and AI to generate the findings and insights that businesses need to develop long-term strategies. Predictive analytics is one way in which machine learning will extract meaning from big data without having to rely on human judgment and intuition to make decisions. The quality of decisions made by these systems will improve dramatically as the scope for error, and human bias is minimized.

2. Automated Data Quantification

Data science is all about scientifically managing data to extract relevant meaning so that they can be used to make decisions. Artificial intelligence offers a unique way to scan and convert data into a quantifiable form to make objective decisions. This is going to be particularly useful in the retail industry where advertising investments are significant. Applying AI methods to retail data will help marketers to identify the most critical and lucrative areas for advertising spend. In this way, AI will enable data science to inform management decisions on advertising and positioning.

3. More Robust Security

AI is also set to change the rules of the game as far as online security is concerned. In 2019, online security issues are gaining prominence globally which is why help is being sought from machine learning and AI experts to revolutionize this field. As long as security issues are not addressed, the growth of ecommerce in emerging markets is going to remain limited. Thankfully, data science is now developing techniques to apply granular monitoring which has made traditional signature-bound methods of monitoring redundant. In one swift move, AI has placed data science at the heart of online security management.

4. Improving Medical Diagnosis

The medical field is another area where surprisingly data science and AI are changing the traditional ways of working. In the area of medical diagnosis, traditional reliance on doctors as interpreters of radiological images is giving way to image recognition technologies that are AI-enabled. As with business based applications, this technology removes the element of human bias and error that can cost the loss of health and life for human beings. At the very least, such technologies will complement the interpretation of human doctors in 2019, if not replacing it entirely. 

5. Opportunities to Learn

While AI and machine learning are hugely complementing data science in 2019, this has not always been the case. In fact, none of these technologies have ever enjoyed center-stage as they are experiencing it now. Thus, 2019 is the year when these technologies will develop greater co-dependencies and learn from each other. Huge investments are expected in developing the infrastructure that can support large volumes of data that need to be managed to get solutions based on machine learning. Also, the way people think about data is also undergoing a shift which underlies the need for data science training.

6. Limited Expertise Available

The growth in data science driven by machine learning and AI has caught most stakeholder unawares. The result is that the field is experiencing an acute shortage of trained and qualified professionals who can develop the solutions the technology is capable of. Even though companies like AWS and Microsoft have developed AI-based services for HR and customer service functions, there is a need for more universal data science training so that a workforce can be raised to work in the new industry. 2019 seems to be the year when companies will step up investment in this area. In the coming years, it is likely that an army of data scientists will emerge who will be able to work on complex algorithms and models of data science.

7. More Formal Training Programs

In addition to corporate investment in training for AI and machine learning, it will be interesting to see how universities respond to the need by developing courses and programs that develop these skills in future professionals. Some universities have already started to offer courses and programs in data science which is an encouraging sign for the growth of this industry. However, the benefits of such IT training may not be realized until the next 4 or 5 years. Until then, the industry will have to make do with professional data science training and certification offered by other companies like Google and AWS.

Conclusion

Data science training should receive the most attention from human resource developers and professionals looking to make a career in this field. Even though a lot of literature is available on the potential of machine learning and AI for big data science operations, the key lies in understanding how managers can make use of this data for decision making and strategy formulation. As with every new technology, there is likely to be some confusion and resistance. However, competitive pressures may just be enough to make managers take the leap of faith in 2019 and start taking data science seriously.