DaaS(Data as a Service) 시장 : 기업, 산업, 공공, 정부용 DaaS(2019-2024년)
Data as a Service Market by Enterprise, Industrial, Public and Government Data Applications and Services 2019 - 2024
상품코드 : 317506
리서치사 : Mind Commerce
발행일 : 2019년 10월
페이지 정보 : 영문 296 Pages
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DaaS(Data as a Service)는 데이터를 유용한 정보로 변환하는 서비스 모델을 말합니다. DaaS 시장은 XaaS(Everything as a Service) 클라우드 컴퓨팅 기반 서비스 모델의 일부이며, SaaS, PaaS, IaaS 등이 포함됩니다.

세계의 DaaS(Data as a Service) 시장에 대해 조사 분석했으며, 기술, 시장, 전략, 애플리케이션, 시장 전망과 향후 미래 등의 정보를 정리하여 전해드립니다.

제1장 주요 요약

제2장 DaaS 기술

제3장 DaaS 시장

제4장 DaaS 전략

제5장 DaaS 애플리케이션

제6장 시장 전망과 DaaS의 미래

제7장 DaaS 시장 분석과 예측

제8장 지역별 DaaS 시장 분석과 예측

제9장 결론과 제안

제10장 부록

LSH 18.10.19

이 페이지에 게재되어 있는 내용은 최신판과 약간 차이가 있을 수 있으므로 영문목차를 함께 참조하여 주시기 바랍니다. 기타 자세한 사항은 문의 바랍니다.

영문 목차


Data by itself is useless. Data needs to be managed and presented in a manner that is useful as information. Data as a Service (DaaS) represents a service model in which data is transformed into useful information. DaaS is one part of the larger Everything as a Service (XaaS) cloud computing based services model, including the traditional three horizontals of SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). It intersects with all three and derives value from a number of different horizontals and verticals.

There is considerable competition in the market, happening at a variety of different levels, with feature highly variable between vendors. This causes confusion for the enterprise and causes them to often choose two or more providers. Barriers to enterprise adoption of the DaaS model include security concerns, reliability, regulation, vendor lock-in/interoperability, IT management overhead, and other costs.

However, the reasons for implementing DaaS far outweigh the concerns, especially when it comes to IoT data, which must have flexible and scalable platforms for storage, processing, and distribution. Accordingly, enterprise organizations are five times more likely to implement DaaS for machine-generated IoT data than for static data located in corporate repositories or data lakes. The DaaS market must support both static and dynamic data, but the latter will benefit significantly more, especially as edge computing is implemented and real-time data is available.

A surprising number of enterprises, do not realize they have options for solutions that involve combinations of different data types including (1) their own data, (2) other companies' data, (3) public data, or a combination of all three. Accordingly, it was not surprising for Mind Commerce to find confusion even for many of those enterprise organizations already considering Data as a Service, or already with DaaS in place.

Another important opportunity area for DaaS is enterprise data syndication, which is the opportunity for companies of various sizes to syndicate (e.g. share and monetize) their data. This is one of the biggest opportunities for the Data as a Service market as whole. However, there remains challenges above and beyond the core adoption barriers, which include specific security, privacy, and care of custody concerns.

Data as a Service Market Segmentation

The Data as a Service market is broadly divided by Data Structure into Structured Data and Unstructured Data, with the latter always requiring Big Data technologies, and the former often requiring the same tools and techniques due to factors other than structure such as data volume and velocity.

The Data as a Service market is also segmented by Sector including Public Data, Business Data, and Government Data.

The Data as a Service market is also segmented by Source Type. As it is prohibitively difficult to identify all of the sources and source types, Mind Commerce has broadly segmented Source by Machine Data (consumer appliances, vehicles [ cars, trucks, planes, trains, ships, etc. ], robots and industrial equipment, etc.) and Non-machine Data (everything else including people texting/talking/etc., enterprise data collected by humans, etc.).

It is important to note that the DaaS also includes data sourced from a machine (such as from a jet engine) that is not "Internet-connected" and thus limited in utility without the Internet of Things (IoT) to collect, relay, and provide opportunities for feedback loops. Accordingly, Mind Commerce has also segmented the Data as a Service market by Data Collection Type, which includes IoT DaaS data and Non-IoT DaaS data. Machine Data that does not use IoT, by definition, will not be streaming data or allow for real-time analytics.

This research covers all of the aforementioned DaaS market segment. To summarize, the report covers:

It is also important to note that there are three core types of data from an overall perspective:

Raw Data: This is data in its unchanged form. It is un-manipulated, but may be formatted Meta Data: This is data about data. Meta data defines data attributes/categories such as Raw, Machine, Business, etc. Value-added Data: This is data that has been changed/manipulated with the intend to add some value

In addition to leveraging Big Data Analytics, another approach to transform data into useful information is through use of Artificial Intelligence (AI). One useful AI technique is Machine Learning, which may further convert Value-added Data into actionable decisions. Mind Commerce covers the use AI in Big Data and IoT in various report including Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2019 - 2024. One of the important growth areas for the Data as a Service market is to leverage AI to offer Value-added Data in a "Decisions as a Service" model.

This Data as a Service market report evaluates the technologies, companies, strategies, and solutions for DaaS. The report assesses business opportunities for enterprise use of own data, others data, and combination of both. The report also analyzes opportunities for enterprise to monetize their own data through various third-party DaaS offerings.

The report evaluates opportunities for DaaS in major industry verticals as well as the future outlook for emerging data monetization. Forecasts include global and regional projections by Sector, Data Collection, Source, and Structure from 2019 to 2024. All direct purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

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Table of Contents

1.0 Executive Summary

2.0 Data as a Service Technologies

3.0 Data as a Service Market

4.0 Data as a Service Strategies

5.0 Data as a Service Applications

6.0 Market Outlook and Future of Data as a Service

8.0 Regional DaaS Market Analysis and Forecasts 2019 - 2024

9.0 Conclusions and Recommendations

10.0 Appendix



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