Data Science Platform Market Size, Industry Analysis Report, Regional Outlook (U.S., Germany, UK, Italy, Russia, China, India, Japan, South Korea, Brazil, Mexico, Saudi Arabia, UAE, South Africa), Application Development, Competitive Landscape & Forecast, 2016 - 2024
Report ID: GMI1284
Data Science Platform Market size is predicted to witness significant growth during the forecast timeframe. Businesses have transitioned their focus from profit-centric to customer-centric approach. This has caused a rising integration of predictive modeling and advanced analytics into business processes to optimize decision making in order to provide value to customers. An ineffective flow of information across disconnected tools has given rise to the necessity of a data science platform that allows smooth functioning between the various software.
This software provides highly efficient tools to integrate and explore information acquired from various sources ensuring simplicity in data organization, transparency and scalability of the work performed. This has, consequently, led to the advancements in big data technologies which is a major driving force in the data science platform market. Enterprises that have increased their focus on ease of use methods to propel the growth of their business are driving the data science platform market. Application of such software has enable the data scientists to analyze collected information, build models and uncover concealed information.
Several government regulations pertaining to the security and privacy of the customers are restraining the data science platform market growth. Data governance is a quality control discipline which has hindered the industry demand. It ensures the formal management of essential data assets throughout the enterprise and enforces transparency and accountability. There is a lack of reliability on data science in organizations. The cost of investing into these tools is very high. Furthermore, the reliability of the data acquired is unstable and challenges the adoption of these platforms into an organization. To benefit from this software and to stay ahead, constant updating of the software is a necessity to cope with advances in data sources and tools which may provide growth prospects for the data science platform market.
Inclination of organizations towards business strategies that are data intensive are creating avenues to streamline the data science platform market growth. Deep learning, a sub-set of artificial intelligences, is a growing trend that is increasing the availability of data-sets. An increase in awareness and investment in this will lead to the emergence of new tools and technologies to manage big data and will consequently provide an opportunity aiding market growth. An efficient software can implement various functions such as marketing, logistics, human resources, operations, sales, and risk management and aid in the data science platform market demands.
Functional diversity in the implementation of this software will allow data science tools to gain a foothold in the marketplace. End-to-end platforms ensure smooth functioning of the data science project cycle, from collection and exploration to model deployment. It ensures swift action by scientists and are predicted to have a high ROI. The IoT technology and machine learning is predicted to experience exponential growth in the data science platform market. The emergence of unified cloud-based development environments has further assisted data scientists to enhance analyzing skills and collaborate machine learning and analytics into core businesses.
Based on application, the industry is segmented into BFSI, healthcare, manufacturing, media and entertainment, information technology and telecommunication, retail and consumer goods, government and defense, transportation and logistics, energy and others. The BFSI segment is predicted to dominate the industry over the forecast period. The data science platform market helps financial institutes to manage risks that arise due to poor quality of information collected. Ease of fetching of customer information from various channels, for instance ATMs and POS terminals, have increased the adoption of this software. The manufacturing market will also grow at a rapid rate owing to such technologies facilitating real-time analytic streaming acquired from sensors and devices located at the factory floor.
The deployment model segment is bifurcated into on premise and cloud-based deployment model. On premise segment will hold a significant industry share in the data science platform market due to confidentiality and privacy of organizational data. Cloud based systems are gaining in prominence across organizations, which will further propel the industry growth.
Based on business function, the data science platform market in segmented into marketing, logistics, human resources and operations. The logistics vertical segment is predicted to grow at a steady rate owing to easier information collection and ability to answer business queries with inputs derived through various sources. Effective network planning and optimization achieved through historical information insights of transportation routes is enabled.
The U.S data science platform market is expected to acquire a large industry share attributing to the presence of many capital-intensive industries across this region. Growing awareness of the benefits of these platforms, enterprises will be predicted to integrate the systems into their operational system to enable them to gain a competitive advantage in the marketplace.
APAC revenue is driven by India, which is predicted to encounter a high growth rate due to exponential increase in foreign direct investments, lenient government policies encouraging the development of industrialization. Furthermore, digitalization, and smart city initiatives will intensify the adoption of these platforms in the region
Players operating in the data science platform market include IBM Corporation, Google, Domino Data Lab, Sense Inc., Wolfram, Datarobot, Inc., Continuum Analytics, and Rapidminer, Inc. They are adopting business strategies such as agreements, partnerships, collaborations, software developments and upgradations. In September 2016, Microsoft and Adobe entered a strategic partnership to boost its cloud capabilities that help enterprises to offer personalized experiences through various phases of customer relationship management. This partnership will aid businesses to empower their brands through Microsoft Azure, Microsoft Dynamics 365, and Adobe Marketing Cloud. In October 2016, IBM released the Watson platform to allow data scientists to incorporate artificial intelligence into businesses.
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