We recently hosted the webinar on Data Science in Practice Across Three Industries, presented by Ronald van Loon. Ronald is one of the foremost thought leaders in Data Science and Digital Transformation, named by Onalytica as the world's #1 influencer in Data and Analytics, Automation, and the Future of the Economy (Tech). He is an author for leading Big Data & Data Science sites including Datafloq and Data Science Central, and he is a member of Simplilearn's Advisory Board.
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The Three Key Industry Sectors
"The need for data science has become so great that it has created an industry-wide data science vacuum," Ronald says. "Organizations are really struggling to find the talent they need to get the insights they want." In particular, there is significant demand for data scientists in three key industry sectors.
For each, Ronald looked at how the sector leverages data science, the role of the data scientist, significant use cases, and drew a picture of where data science is having the most significant impact.
1) Manufacturing and Industrial
The Manufacturing and Industrial sector is increasingly reliant on robotics and automation. The proliferation of AI (artificial intelligence) into decision-making in almost all aspects of the sector has prompted what is being called the Fourth Industrial Revolution, also known as Industry 4.0.
Manufacturers also face pressures to improve sustainability, energy efficiency, and corporate responsibility, and other continual disruption from cost volatility and the need for more operational agility to respond to constantly shifting customer demands. Robotics, automation, and AI enable smart manufacturing systems and smart supply chains and is allowing companies to digitize the value chain for greater transparency and responsiveness to markets that are rapidly changing. Sensors and the Internet of Things (IoT) enable manufacturers to have visibility across their suppliers, partners, distributors, and customers, as well as their factories. AR/VR (Augmented Reality and Virtual Reality) and 3D printing have entered the sector to create new methods of training employees and users and designing, prototyping, and making products.
Data science is an integral part of Industry 4.0. IoT and sensors generate massive amounts of data, structured and unstructured, from a wide variety of sources such as production equipment, suppliers, transportation, and customers. To remain successful and competitive, companies must leverage data science for AI, data mining, and predictive analytics to gain a competitive edge and increase production efficiency.
Ronald reviewed the application of data science in the Manufacturing/Industrial sector through the following use cases:
- Predictive analytics
- Supply chain management
- Computer vision/machine vision
- New product design
2) Consumer Service
Companies in the Consumer Service sector are in a perpetual battle to offer the best, the most relevant, and the most compelling customer experience to gain an advantage over their competitors. There's a race between companies in this sector to innovate and adopt new technologies to come up with unique services that provide more customization, more immersion, and more real-time experiences, all while continuing to earn profits.
Consumer Service covers a broad array of domains: healthcare, medicine, travel, hospitality, education, retail, insurance, telecommunications, and many more. Leaders in customer experience like Netflix, Amazon, and Apple have set a high bar for the levels of customer experience. On the other hand, consumers also expect personalization, more immersive experiences, responsive customer service, and better integration across all the platforms they use.
And here's where data comes into play. Data science gives companies a better understanding of their customers. It gives them insights into what customers are thinking, how they are feeling, and what they want from their brands. It lets companies examine how customers interact with their brands. It also gives them the ability to scale consumer service across all their departments.
Part of how companies stay competitive in the current business environment is to deepen personalized engagement with their customers. Companies can draw on vast quantities of internal and external data from and about their customers, but they have the challenge of figuring out which of this data is useful. They also face concerns over privacy, data ownership, lack of channel integration, and data accessibility. Compliance with government regulations on handling and use of data is another emerging issue.
The use cases Ronald chose to show how Consumer Service providers leverage data science included:
- Personalized marketing
- Sentiment analysis
- Human resources
- Conversational AI, chatbots, and virtual assistants
- Biometric authentication
Finance as an industry sector has shown a tremendous eagerness for innovation. Many companies in this sector are early adopters of emerging technologies for digital transformation. Because of the high value of data science solutions in Finance, some data scientists in this sector can earn salaries above US$100,000.
Finance was one of the first industries to use data analytics to innovate and to introduce new products. The value of having data in modern systems where it can be used for innovative applications is very high, but migrating data from older systems and storage types is equally challenging.
Another challenge is the sheer complexity of financial data (such as stock and commodity markets) and new financial and investment products. The development of Fintech (financial technology) to deal with this complexity applies new technologies like machine learning and blockchain to disrupt traditional financial products and applications. Where financial companies once looked for new hires with specific financial industry training, they now look more and more for people with data science and data analytics skills. Their shift to seeking data science skills has created a severe imbalance between the supply of and the demand for data science talent.
Ronald described how data science is applied in several use cases in the Finance sector :
- Fraud detection
- Algorithmic trading
- Customer management
- Personalized services
- Smart contracts
You can also watch the webinar video to know more about these industries and the role and influence of data science.
Ronald identified key takeaways from the webinar for each of the industry sectors he discussed:
- Industry 4.0, robotics, automation, and continual disruption are generating new applications and use cases in manufacturing
- New customer expectations for personalization and greater industry competition accelerate innovation in consumer services
- Technology innovation in finance is booming, leading to increasing demand for data science specialization
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One additional takeaway that applies across all these industry sectors and many more is the value of gaining skills and certifications in data science and related fields. The career opportunities for data scientists, data engineers, data analysts, and roles in related areas like AI and cloud computing are growing in number and in salaries offered. So fast track your data science career today, and get into the field with the challenges and opportunities of tomorrow.