Data science can be defined as an interdisciplinary field that uses scientific computing, statistics, processes, scientific methods, systems, and algorithms to extract knowledge and insights from structured, unstructured, and noisy data.
If data science is a chemist, it would mix a combination of math and statistics, specialized programming, advanced analytics, Artificial Intelligence (AI), and Machine Learning, along with specific subject matter expertise to unearth actionable data insights that is hidden in plain sight. Together, they are helping organizations understand their customers and the way their business functions in a data-driven manner in real-time — something that was difficult or even impossible in the past.
The Interrelationship between Data Science, AI, and Big Data
From the definition given above you should be able to gauge that data science is a bigger analytical practice. AI and Big Data fall within its precincts. There is a symbiotic relationship between data science, AI, and Big Data.
Data is the fuel that powers AI. Big Data enables Machine Learning applications to learn and acquire skills. AI handles Big Data effectively, and the latter improves the accuracy and effectiveness of AI. Together, data science, AI, and Big Data help to drive the ongoing progress in cognitive computing and its resultant technological advances.
Further, there is another subset to data science and Artificial Intelligence – Machine Learning. Big Data provides the raw material for Machine Learning systems to derive helpful insights. Machine Learning systems rely on data-driven algorithms and statistical models that constantly evolve to analyze and find patterns in data. Combining Big Data and Machine Learning could lead to a multiplier effect in your data analysis function.
Similarly, AI becomes better with more data intake. On the other hand, Big Data is simply useless without software to analyze it. When these tools are combined, it is easier to leverage advanced analytics capabilities such as augmented or predictive analytics and crystallize actionable insights from huge volumes and varieties of data.
It is no secret that data science is progressing by leaps and bounds.
But what kind of a digital future is it building?
Let us focus our lens on the major trends that shape the future of data analytics and cognitive computing.
Data Science Trends to Watch Out for in 2023 and Beyond
Here is a list of the major data trends you ought to watch out for in 2023:
- The boom in cloud migration
- Growth of predictive analytics
- Cloud-native solutions will become a must-have
- Augmented consumer interfaces
- Better data regulation
- AI as a Service
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The boom in cloud migration
Cloud migration involves transferring an organization’s digital assets from on-premises infrastructure to a cloud-based environment. The benefits of cloud migration are manifold:
The benefits of cloud migration are manifold:
- It allows businesses to scale resources on demand.
- It reduces infrastructure costs.
- It enhances org-wide collaboration and coordination.
- It improves data control and accessibility.
Many companies are migrating their data and analytics operations to the cloud to take advantage of its scalability, flexibility, and cost-effectiveness. According to Q4 2022, 33.4% of IT leaders plan to migrate from legacy enterprise software to cloud-based tools.
The extensive adoption of multi-cloud and hybrid strategies empowers businesses to leverage the capabilities of different cloud providers. Most importantly, it helps them own, manage, and analyze their data easily, thereby pushing the envelope for data science practice.
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Growth of predictive analytics
Predictive analytics uses data, statistical algorithms, and Machine Learning techniques to identify the likelihood of future outcomes, based on historical data. The goal is to go beyond knowing what has happened, to provide the best assessment of what will happen in the future.
According to Reports Insights, the global predictive analytics market is anticipated to expand at a compound annual growth rate (CAGR) of 18.5% between 2023 and 2030.
This growth is primarily driven by the widespread implementation of advanced analytics and Big Data in key industries such as healthcare, finance, manufacturing, and retail, where forecasting the future with maximum accuracy is crucial for business success.
Predictive analytics can be used for a wide range of applications, including:
- Forecasting future market trends.
- Predicting customer behavior.
- Identifying potential risks or opportunities.
By using predictive analytics, companies can make well-informed decisions and take proactive steps to improve their operations and achieve their goals.
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Cloud-native solutions will become a must-have
Unlike hybrid solutions, cloud-native solutions are designed specifically for end-to-end cloud environments. They take full advantage of its capabilities and ensure seamless functioning, which may not be the case with some hybrid systems.
Further, cloud-native solutions take a modern approach to software development that leverages microservices, containers, and dynamic orchestration to improve agility, scalability, and resilience.
Since the future of digital is going to be led by cloud computing, we can be certain that cloud-native applications will be crucial investments for companies that want to maximize their use of data and analytics.
Some examples of cloud-native solutions include:
Containers: Containers are portable, integrated operating environments that encompass an application and all the software components needed to run it. They are small, reusable, quick to deploy, and portable. Example: Docker
Microservices: Microservices are loosely coupled software services that can be strung together to create an application. Applications composed of microservices are patched together like Lego blocks, with negligible custom coding, enabling quicker and more dependable development. Example: Netflix, which has over 600 services in production and deploys updates 100 times per day.
Service meshes: Service meshes comprise configurable infrastructure layers for microservices applications that enable flexible, reliable, and fast communication between service instances. Example: Istio
Immutable infrastructure: Immutable infrastructure refers to an approach that involves managing IT software deployments and services, where components are substituted rather than altered. Example: Amazon, which uses Amazon Machine Images (AMIs) to create pre-configured virtual machines that can be launched quickly and consistently.
Declarative APIs: Declarative APIs allow developers to declare what kind of outcome is expected instead of writing code to lead to that outcome. Example: Kubernetes API.
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Augmented consumer interfaces
One of the challenges in data science is that non-technical users find it difficult to access and analyze data. Augmented consumer interfaces are a remedy for that. They use technologies such as Augmented Reality (AR) and Artificial Intelligence (AI) to provide users with an enhanced, interactive experience. For example, an augmented consumer interface might use AR to overlay virtual information onto a user’s view of the real world, providing him with additional context and insights.
Some examples of augmented consumer interfaces include:
Virtual try-on: Retailers are using AR to allow customers to virtually try on clothes, makeup, and accessories before making a purchase. This could help the eCommerce industry reduce product returns, which burns a huge hole in their operating margins.
Interactive product demos: Companies are using AR to provide customers with interactive product demos that allow them to explore features and functionalities more engagingly. A great example is Tesla’s design your own model service.
Virtual home design: Home design companies are employing AR to allow customers to virtually place furniture and decor in their homes before making a purchase. IKEA’s app already has this feature that helps customers choose the right-sized furniture for their homes.
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Better data regulation
We now know that the whole ambit of data science consisting of AI, Machine Learning, and Big Data requires large volumes of data to improve their offerings. That has also brought to light the uncontrolled behavior of some mega corporations in mining user data — sometimes with explicit user content, most often without it.
As concerns about data privacy and security continue to grow, companies need to ensure that their data practices comply with relevant regulations.
In 2023, there will be a global rise in data privacy regulations. Companies will invest more in privacy technologies to ensure a cookieless future. According to Gartner, by the end of 2024, it is expected that about 75% of the global populace will have their personal information covered under privacy norms.
Data-driven organizations will have to go the extra mile to ensure that they are compliant with user data security and privacy protection guidelines. They will have to invest in ramping up data encryption, access controls, and data retention policies. Further, there would also be a rising need to ensure transparency in data collection and processing practices, and communicate them to the end users.
As more countries implement data protection regulations, we can expect to see companies placing a greater emphasis on data regulation compliance in the coming years.
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AI as a Service (AIaaS)
AIaaS is the delivery of Artificial Intelligence capabilities through cloud-based platforms. It comprises a plethora of AI services such as Machine Learning, Natural Language Processing, and computer vision. It aims to integrate AI competencies into various applications used for core business operations. Many companies use AIaaS to streamline their operations and gain a competitive advantage. For example, an AI system can be easily integrated with the knowledge base of a support portal.
To start using AIaaS, a company typically creates an account with an AIaaS provider and then uses the latter’s APIs or other tools to integrate AI capabilities into their applications. The AIaaS provider would handle all the underlying infrastructure and maintenance, allowing the company to focus on using the AI capabilities to improve their products and services.
Some examples of AIaaS providers include Google’s TensorFlow, Amazon’s SageMaker, and Microsoft’s AzureML.
Tomorrow’s Intelligence: The Exciting Future of AI In Data Science
The future of AI is incredibly exciting with many potential advancements on the horizon. Some of these advancements include:
Generative AI
If AI has become such a consumerist tool, the whole credit goes to generative AI. All the manipulated images and videos that are setting social media ablaze are created using generative AI.
We can describe generative AI as a type of Artificial Intelligence that allows systems to generate new content such as text, images, and music. For this purpose, these systems are fed with text inputs or in some instances, image inputs. Generative AI then deploys Machine Learning algorithms to analyze the input data and identify patterns and relationships within existing data. The algorithm then uses this information to generate new content that meets the input criteria but is not its exact copy. For example, if you give text input to create an image or provide an image as a reference, the algorithm will not recreate the reference image as it is.
There are several examples of generative AI such as DeepDream and Midjourney. These are computer vision programs that use neural networks to generate new images based on a set of text or image inputs.
Generative AI has many potential applications including creating new art, music, and literature, generating realistic virtual environments for video games and simulations, and improving the realism of computer-generated characters in movies and TV shows.
Natural Language Processing (NLP)
NLP allows machines to understand, interpret, and generate human language, enabling more natural and meaningful interactions between humans and machines. In our everyday life, we interact with NLP in the form of voice and speech recognition. Apple Siri, Google Assistant, and Amazon Alexa are all NLP at work.
Explainable AI
As AI becomes more embedded in critical decision-making processes, there is an increasing need for interpretability and transparency. Explainable AI refers to the ability of AI systems to provide clear explanations for their actions and decisions, ensuring accountability and building trust among users.
Companies like IBM and Google are investing in research and tools that enable explainable AI. For instance, Google’s Explainable AI Project aims to develop techniques that shed light on the decision-making processes of AI models. This advancement will have profound implications in domains such as healthcare, finance, and autonomous vehicles, where understanding the reasoning behind AI prediction is crucial.
Federated learning
Data privacy concerns and the increasing amount of sensitive data have paved the way for federated learning, an emerging approach in AI. Federated learning allows models to be trained across multiple decentralized devices or servers while keeping the data localized and secure, thus addressing privacy concerns.
For instance, healthcare organizations can collaborate on training a medical diagnosis model without sharing patient data. Google’s Federated Learning framework has demonstrated successful applications in mobile keyboards and on-device language models. As this technology matures, federated learning has the potential to revolutionize industries that deal with sensitive data, while still benefiting from the collective intelligence of a distributed network.
Ethical AI and bias mitigation
One of the key challenges and concerns that global IT leaders raised about the extensive use of AI is the bias involved in it. As we know, AI is fed with historical data from previous decades into which biases of several kinds are baked in (unfortunately!). Recognizing this, researchers and organizations are actively working towards developing frameworks and techniques for ethical AI and bias mitigation.
For example, Microsoft has launched the Fairlearn toolkit, which provides developers with resources to assess and mitigate unfairness in Machine Learning models. Similarly, IBM’s AI Fairness 360 toolkit offers a comprehensive suite of algorithms and metrics to detect and reduce bias in AI systems. These initiatives signify a growing commitment to building AI systems that are fair, unbiased, and inclusive.
Big Data, AI, and Data Science: A Combination for Big Impact
In the rapidly evolving field of data science and Artificial Intelligence (AI), Big Data plays a pivotal role in driving innovation and shaping the future of AI applications. As we explore the trends in data science in 2023, it is crucial to understand the significant role that Big Data plays in advancing AI and the challenges and opportunities it presents.
Training data-intensive AI models
AI models such as deep learning and neural networks require vast amounts of data for training. Big Data enables the collection and storage of diverse and extensive datasets, allowing AI models to learn and generalize patterns effectively. With more data available, AI algorithms can uncover hidden insights and make more accurate predictions.
Improved AI model performance
Larger datasets enhance the performance of AI models by reducing bias and increasing the robustness of predictions. Big Data acts as a huge pool of data that enables the AI system to learn widely about diverse scenarios and conditions. This enhances the AI model’s ability to provide accurate and reliable predictions. For instance, in the healthcare industry, analyzing a large volume and variety of patient data can help identify patterns and early-stage disease symptoms. This would help in improving patient outcomes and enhancing preventive measures.
Real-time insights
If AI’s predictions and output is not based on real-time data, then it is not useful for real-world applications. Thankfully, the volume, velocity, and variety in which Big Data is created enable AI systems to provide real-time insights and predictions. The rise of edge computing and IoT devices has also helped generate massive amounts of real-time information. By leveraging Big Data analytics, AI systems can analyze this data on the fly, and empower several industries such as transportation, finance, and cybersecurity with real-time decision-making.
Needless to say, all these points indicate that Big Data is fundamental to driving advancements in the field of AI. It acts as the supplier of raw materials for training AI models, based on which AI systems deliver real-time insights and predictions.
However, leveraging Big Data is not without challenges. The primary challenges that need to be addressed include ensuring data quality, privacy, scalability, and ethical considerations. By effectively managing big data and embracing responsible practices, organizations can unlock the full potential of AI and drive transformative changes in various industries.
In a Nutshell
We do not need reassurance that data science and its subset files are growing rapidly. There is full evidence to show that while they might remove certain jobs, they would also make jobs easier for almost every industry.
From predictive analytics and augmented consumer interfaces to state-of-the-art cloud-native solutions and improved data regulations, there is unprecedented growth in every aspect of data science.
As we step into the digital future, we can be certain that more exciting developments are on the way, as companies continue to leverage the power of data science and AI to drive innovation.
We offer a variety of analytics services to help you understand your data and make better decisions. Contact us at services@suyati.com for more information.
