Top executives have realized that the use of data science platform is extremely important and data analytics is going to shift the competitive landscape globally. Hence, organizations look at data science investments as ROI-based valuation, wherein they allocate budget for existing strategies and decide whether to invest in data acquisition, in developing a complex model or in both.

Portland, OR -- (SBWire) -- 01/23/2018 --According to a new report by Allied Market Research, titled, Data Science Platform Market by Type and End User: Global Opportunity and Forecast, 2017-2023, the data science platform market was valued at $19,621 million in 2016, and is projected to reach at $183,688 million by 2023, growing at a CAGR of 39.6% from 2017 to 2023.

The phrase "Data Science Platforms" is the most talked about topic in data science conferences, meet-ups and top publications these days. However, the concept of data science platforms is not new in the big data space but still many do not know what is a data science platform, why a company needs a data science platform, what are the best data science platforms out there in the market. Data science platforms are the buzzword of 2017. This blog walks you through answers to the following questions –what is a data science platform, what are the features of a good data science platform, why a company needs a data science platform and list of some of the best data science platforms available today in the market.

What is a Data Science Platform?

The easiest way of defining a data science platform – "A data science platform is a framework of the entire life cycle of a data science project.

Data science platform contains all the tools required for executing the lifecycle of the data science project spanning across different phases –

1. Data ideation, integration and exploration
2. Model Development
3. Model Deployment

In the present scenario, it has become a critical investment choice that has significantly contributed to the growth of the smart and digital industry. The report does not consider open source platforms such as R and Python and it only evaluates commercial data science platform vendors. A data science platform helps data scientists enhance their analysis by helping them run, track, reproduce, share and deploy analytical models faster. Usually, all these tasks require lot of engineering effort and hassle to build and maintain analytical models but a data science platform gives you the extra "power tools" to speed up analysis.

Get the PDF of Data Science Platform Industry Insights @ https://goo.gl/9mg2RJ

Why your company needs a data science platform?

Every team in an organization uses some kind of a software platform to support their workflow – just like the engineering team of a company uses source code control systems, the sales team of a company uses CRM systems and the customer support team uses ticketing system. Similarly, to perform data science at scale, organizations need to rely on data science platforms. It's time for companies to bid adieu to data science processes that depend on disjointed tools and widespread engineering effort to perform data science. Data science platforms bring everything that a data science team needs at a centralized place so that data scientist can pool resources and team up effectively speeding up the process of deploying models instantly.

Challenges Data Scientists Encounter in the Lifecycle of a Data Science Project

> Data science process begins with exploring the data to understand what is on the plate for analysis. Ideation and exploration can be a time consuming process if you do not know what other team members have already accomplished as you might be redoing the same thing.
> Data scientists run experiments to test different ideas, review the output and make changes. This phase of the data science workflow is likely to slow down in the absence of a data science platform if the experiments performed are computationally intensive.
> It is necessary to operationalize data science work to gain value from the outcomes of analysis. This requires engineer resources incurring additional costs and increases the time to market.

Market Dynamics

1. Data Explosion

Data is growing in an exponential manner. About 90% of the data that currently exists in the world has been created in the past few years. The massive increase in data creates opportunities for organizations to gain new insights, for which the demand for new techniques and methods has also increased. This in turn plays a very crucial role to drive the data science platform market.

2. Realization of Importance of Data Science Platform by Organizations

Data science platforms help organizations to be proactive and predictive. It allows them to take actions to optimize outcomes rather than being reactive. Many organizations and start-ups have been benefitted using data science platforms. Some examples include PayPal using Big Data analytics to detect fraud, the pharma industry building better medicines at a faster pace using data science tools, and many retail companies customizing individual customer preferences and building products that meet their expectations using data analysis. Organizations have become data driven and are investing in infrastructure, people, and processes to initiate a data science journey.

3. Adoption of Cloud-based Solutions and Services

Cloud-computing, an exciting and nascent, yet the fastest-growing technology in the data science platform industry, enables significant value addition to prominent players. For instance, the innovative cloud model allows data scientists to perform complex analyses by linking significantly large volumes of data. This in turn enables companies to reduce non-productive time, operational costs, uncertainty, and risks. Furthermore, the adoption of cloud-based solutions and services is anticipated to drive the industry.

4. Target Untapped and Emerging Markets

Data science platform industry has created the opportunity to transform in all sectors. There are many untapped markets such as telecom industry, video analysis, etc., which has a promising future. Emerging markets such as Latin America, the Middle East, and Africa are expected to present significant growth opportunities for prominent players. Along with this data science platform, there is an opportunity of growth for other technologies such as Internet of Things (IoT), Artificial Intelligence (AI), machine learning, etc.

Do Purchase Inquiry @ https://www.alliedmarketresearch.com/purchase-enquiry/2307