Data science and business have become a perfect duo from the time of AI’s inception. With the rise of natural language processing, computer vision and other AI technologies innovations, market players have received powerful instruments to build products that transform industries, change the way we do business and approach our daily routine. Automated business operations, data-driven decision-making, accurate predictions and insightful analytics are just a few takeaways brought by data science to business.
In this article, we will explore some critical data science trends for 2023 and discuss what you should focus on this year. One of the most significant trends in data science that we expect to continue is the rise of machine learning and artificial intelligence. These technologies are becoming more accessible, affordable and powerful, allowing organizations of all sizes to leverage their capabilities to drive innovation and growth.
To prepare your company for the economic rival and ensure you’ll lead the market, read about the top trends in data science that will dominate the software development landscape in 2023.
Why Is Data Science Important?
As the world becomes increasingly data-driven, businesses and organizations must adapt to keep up with the latest new trends and advancements in data science. Thanks to new technologies and tools, data scientists can now extract insights from vast amounts of data more efficiently and accurately than ever before. As we look to future trends, it is essential to keep up with the latest data science trends to maximize our potential and stay ahead of the curve.
Data Science has emerged as a crucial field for organizations across various industries as they seek to make data-driven decisions. With the exponential growth of data, businesses are in a constant search for novel and innovative ways to utilize this data to gain insights that help improve operations, enhance customer experience and increase profitability. Data science trends refer to the emerging technologies, tools and techniques used to manage and analyze data.
In recent years, data science innovation has advanced greatly, and this trend is set to continue as the world becomes increasingly data-driven. With advancements in Artificial Intelligence (AI), Machine Learning (ML) and Big Data, organizations can now leverage their data to make accurate predictions, automate decision-making, and gain a competitive edge. The need for data science professionals who can harness the potential of these emerging technologies has never been higher.
One of the most significant new trends in data science innovation is the democratization of data. As more data is collected, individuals and organizations outside the traditional data science field are seeking to gain insights from it. Self-service Business Intelligence (BI) tools have made it easier for non-technical users to access and analyze data, allowing organizations to make data-driven decisions across departments. Another trend is the integration of AI and ML into data science workflows. These technologies have the potential to automate repetitive tasks and augment human decision-making, leading to more efficient and effective data analysis.
As organizations continue to invest in data science, there is a growing emphasis on ethics and transparency. Privacy and security have become critical issues, and there is a need for data scientists to ensure that the information they use is ethical and transparently sourced. Explainable AI is also gaining traction as organizations seek to understand how AI models make decisions.
Trend #1 Smarter and Faster AI
Artificial intelligence is widely applied across all business sectors, helping companies address their business challenges. If 10 years ago, you might have been wondering how Netflix knew your favorite movies, today, you’re pretty aware of its smart recommendation system and AI algorithms standing behind it.
In 2023, artificial intelligence promises to become even smarter and faster than it was before. Companies are expected to shift from piloting to operational AI, which will significantly increase streaming data and improve analytics infrastructure.
In the context of the current COVID-19 crisis, AI solutions, along with machine learning and natural human language processing, can provide vital predictions about the spread of the virus. Technology industry experts have already found a way to use AI innovations to detect infected individuals, spot patterns and develop possible solutions to complex issues. See our AI case study to find out how LITSLINK helped build a people counter software that assists businesses in implementing preventive measures and keeps a limited number of people in the building.
Trend #2 Graph Technology and Analytics
Graph technology and analytics refer to the use of graph databases and algorithms to analyze relationships and connections between data points. These data banks are a type of NoSQL database that stores information in nodes and edges, where nodes represent entities and edges represent the relationships between them.
Graph analytics, on the other hand, involves the use of algorithms to discover patterns, insights, and anomalies within data analytics. These algorithms are designed to take advantage of the graph structure to analyze the interrelationships between data points.
Graph technology and analytics are increasingly used in various industries, including finance, healthcare, and social media, to identify patterns in data and make better-informed decisions. They enable businesses to gain more profound insights into customer behavior, optimize supply chain management, and improve fraud detection.
What is graph technology?
Many companies mistakenly believe that the more data analytics they gather, the better result they’ll get in the end. By accelerating computer power, they strive to process more information in less time, believing it will provide them with more insights and help find the competitive advantage that will make them leaders in the world of tomorrow.
However, in most cases, it doesn’t matter how much valuable and structured data analytics you process if you lack an understanding of context. If the relationship between data patterns is missing, you won’t be able to turn information into meaningful insights.
This is where graph technologies are introduced to map data sets and understand relationships between them. This approach might help you find common patterns, drive contextualization, and develop more valuable products to meet your business challenges.
How can businesses leverage the power of graph technologies?
In the next 3–4 years, we’re going to witness rapid pace growth in the application of graph technologies. More than 30% of organizations will apply graph analytics software to drive greater contextualization in decision-making by 2024.
New advancements in graph technologies can comb through large amounts of documents, research papers, surveys, statistics and other pieces of information to build connections between data analytics sets and draw relevant conclusions. When applied to business administration, graph analytics might help companies spot issues early, develop accurate predictions and make data-driven decisions based on the insights they get.
In the context of the current coronavirus crisis, graph technologies can assist healthcare professionals in finding common patterns in the spread of the virus, which will contribute to a better understanding of the disease. Thus, a data-driven approach to the issue might help develop better capacity plans, find new treatment methods and come up with effective preventive measures.
It is also predicted that prescriptive graph analytics combined with artificial intelligence software will become a top data science instrument for identifying and predicting natural disasters. Graph technologies can cover the whole solution development cycle from planning to implementation, helping specialists deliver better results and improve the public health system.
Trend #3 Blockchain in Data Science
Blockchain technology has the potential to revolutionize the data science industry by enhancing data security, transparency, and privacy. In the context of data science, a blockchain is a distributed and immutable ledger that enables secure and transparent transactions between parties without the need for intermediaries.
Unlike fintech and healthcare, where blockchain has already become a household term, data scientists only started to explore the whole potential this industry technology can deliver to the industry.
Blockchain can address the two major challenges data scientists face today.
First, decentralized ledgers provide a brand-new way of managing and operating big data. As a rule, you need to structure the information in a centralized manner, where all data sets are brought together for further analysis. This process takes some time as you need to gather and structure the information properly before getting down to “science”.
Data scientists can use the blockchain’s decentralized structure to conduct analysis from individual devices. As blockchain ascertains the origin of data, you can always track where it comes from and validate it.
Second, blockchain infrastructure guarantees transparency for complex networks of participants. When you must have a helicopter view of all the operations, decentralized infrastructure can help you track all the operations, see relations between data analysis sets and check their origin.
Gartner estimates that the majority of permissioned blockchain applications will be substituted by data management systems (DMBS) by 2021. Since DMBS provides a more comprehensive way of operating big data sets, companies will be able to see more opportunities in utilizing data science and uncover at least a fraction of its potential.
Apart from already existing blockchain applications, this technology industry can provide an appealing opportunity for small and medium-sized enterprises. It can be used to audit existing data sources, build a reliable data-based infrastructure where all participants will be able to track the origin of information and have access to available data resources. Such an approach builds transparency and enhances the security of the stored data.
This refers to the ability of blockchain networks to handle large volumes of transactions while maintaining performance and security. Interoperability refers to the ability of different blockchain networks to communicate and work together seamlessly. Governance refers to the need for clear rules and protocols to ensure that blockchain networks are secure, transparent, and accountable.
Another challenge is the lack of standardization in the development of blockchain technology. There are many blockchain platforms and protocols, each with its unique features and capabilities. This makes it difficult for businesses and organizations to choose the right platform for their needs and integrate blockchain technology into their existing systems.
Overall, blockchain technology offers significant potential to enhance data security, transparency and privacy in data science applications. However, it is important to note that the technology is at the early stages of development. There are still many challenges to overcome before it can be widely adopted in data science applications.
Thus, we may conclude that blockchain is an inexhaustible resource for data science, which, when handled well, can lead to innovations in artificial intelligence.
Data Science Lifecycle
The data analytics lifecycle is a framework for conducting data science projects. It is a process-oriented approach outlining the steps required to create a successful data science project. The lifecycle is an iterative process that involves multiple stages, each of which builds on the previous one.
Keeping an eye on new industry trends in data science is vital, but it is only a fraction of what you should know to build a data science product. Understanding the data science lifecycle is essential if you strive to develop a solution that will be in demand in the global market.
The Data Science Process provides a clear outline for the development of your DS solutions. It includes the core milestones your team should follow to build quality DS software that will meet your business goals and needs.
The lifecycle described below is applied to deliver smart applications, which utilize artificial intelligence and machine learning to build smart predictive analytics models. It can also be customized for exploratory data science projects or for building improved analytics software. However, some steps might be missing in this case, so you’ll have to adjust the data science project lifecycle to your particular business case.
Build Your Next Data Science Product with LITSLINK!
Data science is a rapidly evolving field, so staying up-to-date with the latest trends in data science and technology is crucial for professionals and organizations. To succeed in this field, data analytics scientists should focus on developing a strong foundation in mathematics, statistics and computer science, as well as keeping up with the latest tools and techniques in the industry. Additionally, soft skills such as communication and teamwork are becoming increasingly important in the data analytics field.
Knowledge is power. Especially when it comes to business, being aware of industry trends can provide you with a competitive advantage, which will set you apart from your rivals.
To capture the full potential of data analytics science and successfully apply it to your business, read our insightful white paper catered to you by the top industry experts.
If you are planning to build robust data science software, contact us, and our team of experienced engineers will create a solution perfectly tailored to your needs.