Published Date : 01 Feb 2023
The global data science platforms market size was evaluated at USD 112.12 billion in 2022 and it is expected to hold around USD 378.7 billion by 2030, growing at a CAGR of 16.43% during the forecast period from 2022 to 2030.
A data science platform is a pre-packaged software program that offers the capabilities necessary for a data science project's whole life cycle. Platforms for data science are essential tools for data scientists. It makes model creation, model dissemination, and data exploration possible. It offers a large-scale computer infrastructure and makes data preparation and visualization easier.
Data science platforms offer a centralized platform that encourages user collaboration. Data science platforms act as a one-stop shop for data modeling since they include the APIs necessary for model creation and testing with little help from outside engineers.
Every business has been touched by the COVID-19 (Coronavirus Disease) pandemic. The data science sector has also been impacted by the epidemic. Due to the quick changes in internet traffic or purchasing trends, the models that were previously utilised for forecasting or segmentation are no longer working.
The supply chain has been disrupted, and the borders have been secured. As a result, businesses are now concentrating on developing short-, medium-, and long-term data-driven plans in order to make wise decisions.
Data Science Platforms Market Report Scope
|Market Size in 2022||USD 112.12 Billion|
|Market Size by 2030||USD 378.7 Billion|
|Growth Rate from 2022 to 2030||CAGR of 16.43%|
|Largest Market||North America|
|Second Largest Market||Europe|
|Fastest Growing Market||Asia Pacific|
|Forecast Period||2022 To 2030|
|Segments Covered||By Component, By Application, By Industry Vertical, By Organization Size and BY Deployment Mode|
|Regions Covered||North America, Europe, Asia-Pacific, Latin America and Middle East & Africa|
North America is forecasted to have the largest market share in the data science platform market. Most businesses and industry sectors in North America rate data discovery and Data Science platforms as highly successful. However, Europe is progressively integrating these cutting-edge technology into its businesses. Due to growing digitization and the desire for centrally controlled systems, APAC is seeing a significant increase in the usage of data science platforms.
Due to the growth of social media, IoT, and multimedia, which have generated an overwhelming flow of data in both organised and unstructured forms, the amount of data that enterprises collect is constantly rising. For instance, just the previous two years have seen the creation of about 90% of the world's data.
The growth rate of machine-based and human-generated data is often 10 times greater than that of traditional corporate data. For instance, the growth rate of machine data is exponential and 50 times quicker. The majority of data is consumer driven and focused. The majority of data produced worldwide is produced by today's "always-on" customers.
Nowadays, the majority of individuals spend 4-6 hours each day creating and consuming data via a range of gadgets and (social) applications. Every time you swipe, click, or send a message, fresh information is added to a database somewhere in the globe. Since everyone now has a phone, enormous volumes of data are being created.
Massive amounts of unstructured and structured data are eventually produced as a result of the rising corporate data volume, quickening technical advancements, and falling average sales prices of smart gadgets. More than 80% of the information that businesses gather is not stored in a typical relational database.
It is instead stifled in unstructured texts, postings on social media, machine logs, pictures, and other sources. The enormous growth in data presents chances for businesses to learn new things, which has led to a rise in the need for fresh approaches. This in turn significantly influences the market for data science platforms.
Businesses should research the issues they intend to use the data science platform for. Prior to having a good understanding of the business problem to be solved, using the mechanical technique of finding datasets and executing data analysis turns out to be less productive. When businesses use the data science platform to make wise decisions, this is extremely unhelpful. Even with a defined aim in mind, efforts are ineffective if firms' expectations from the installation of the data science platform are not in line with the objectives.
Businesses are embracing the data-intensive strategy swiftly and taking tremendous efforts to make sure they can compete in the digital age, when consumers are more aware than ever before and rivals are doing all in their power to win over them. They are using various data science tools, technologies, and industry best practises to find the best answers to their complicated business challenges, get more understanding of the behavior and needs of their consumers, and come up with innovative solutions to meet all of their various business needs.
Organizations may use data science to properly forecast potential outcomes and make better-informed decisions based on real-world situations. Businesses may follow their consumers in real time, their behaviour patterns, their purchasing habits and preferences, and their social networks using the vast volumes of data generated by customers through mobile applications and other practical solutions. With the help of cutting-edge data science tools currently available, organisations can analyse this crucial data and modify their business strategies for success.
A recent study found that 33 percent of the businesses that used data-driven choices outperformed their competitors in terms of profitability by 6%. Organizations are more oriented toward making important decisions based on previous and real-time data analysis rather of depending on expert views as a result of the introduction of sophisticated advanced technologies including big data, Machine Learning, the Internet of Things (IoT), and the cloud.
Nowadays, businesses use sophisticated analytics methods including streaming analytics, machine learning, and predictive analytics. These methods are difficult and need in-depth analytical expertise. Technical expertise as well as analytical and critical thinking abilities are necessary to develop an ML model. Many end users lack the personnel with the necessary abilities and information.
The majority of an organization's effort is spent collecting and correcting data that is generated from numerous sources. Not every person that handles data must be knowledgeable in data science. To create a culture of data-driven decision-making, business expertise and the accompanying training are also necessary. Therefore, one of the largest difficulties that the majority of company end users may have is a shortage of trained staff.
Major Key Players
By Industry Vertical
By Organization Size
By Deployment Mode
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