2020 China Data Management Solutions Market Report 2020年中国数据管理解决方案市场报告 2020年中国ビッグデータ管理市場研究 Tags: Big Data, Data Management Solutions, Data Lake, Data Warehouse 2021/04 Any content provided in the report (including but not limited to data, text, charts, images, etc.) is the exclusive and highly confidential document of LeadLeo Research Institute (unless the source is otherwise indicated in the report). Without the prior written permission of LeadLeo Research Institute, no one is allowed to copy, reproduce, disseminate, publish, quote, adapt or compile the contents of this report in any way. If any behaviour violating the above agreement occurs, LeadLeo Research Institute reserves the right to take legal measures and hold relevant personnel responsible. LeadLeo Research Institute uses “LeadLeo Research Institute” or “LeadLeo” trade name or trademark in all business activities conducted by LeadLeo Research Institute. LeadLeo Research Institute neither has other branches other than the aforementioned name nor does it authorize or employ any other third party to carry out business activities on behalf of LeadLeo Research Institute. LeadLeo Research Institute Frost & Sullivan (China) Sullivan Market Report| 2021/4
Transcript
1
2020 China Data Management Solutions Market Report
2020年中国数据管理解决方案市场报告
2020年中国ビッグデータ管理市場研究
Tags: Big Data, Data Management Solutions, Data Lake, Data Warehouse
2021/04
Any content provided in the report (including but not limited to data, text, charts, images, etc.) is the exclusive and highly confidential document ofLeadLeo Research Institute (unless the source is otherwise indicated in the report). Without the prior written permission of LeadLeo Research Institute, noone is allowed to copy, reproduce, disseminate, publish, quote, adapt or compile the contents of this report in any way. If any behaviour violating theabove agreement occurs, LeadLeo Research Institute reserves the right to take legal measures and hold relevant personnel responsible. LeadLeo ResearchInstitute uses “LeadLeo Research Institute” or “LeadLeo” trade name or trademark in all business activities conducted by LeadLeo Research Institute.LeadLeo Research Institute neither has other branches other than the aforementioned name nor does it authorize or employ any other third party to carryout business activities on behalf of LeadLeo Research Institute.
LeadLeo Research InstituteFrost & Sullivan (China)
Sullivan Market Report | 2021/4 China：Data Management Series
InstructionFrost & Sullivan hereby releases the annual report"China Data Management Solutions Market Report
2020" as part of the China Data Management SeriesReport. The purpose of this report is to analyze theconcept definition, application prospects, technology
trends and development trends of data managementsolutions in China, and identify the competitionsituation in the market of data management
solutions in China, and reflect the differentiatedcompetitive advantages of the leading brands in thismarket segment.
Frost & Sullivan and LeadLeo Research Instituteconducted downstream user experience surveys on
data lakes, data warehouses and traditionaldatabases. Respondents are of different sizes and indifferent segments in each of its industry that
includes finance, consumption, media, operators,manufacturing and logistics.
Trends in data management solutions presented in
this market report also reflect trends in the databaseindustry as a whole. The report's final judgment on
market ranking and leadership echelon are onlyapplicable to the industry development cycle of thisyear.
All figures, tables and text in this report are based onthe surveys from Frost & Sullivan China and LeadLeoResearch Institute. All data are rounded to one
decimal place.
n Market Demand is Expected to Expand
The market of data management solutions is expectedto continue to expand due to the continuouspromulgation of favorable policies, the innovativeintegration of big data technology and more dataapplication scenarios gradually landing. Enterpriseusers will increasingly invest in Data ManagementSolutions to have the advantage of improvingdecision-making and operational efficiency.
n Policies Improvement and Enhancement
The legislation upon personal information protection,cross-border data flow and national data securityarouse the public attention of data rights, data privacyand data security. Other than the quantity, type, speedand value of data, the security of data will become aserious element that vendors need to consider todevelop.
n Cloud Deployment will become the trend
Based on the separation of memory and computation,cloud service meets requirements of elastic expansion,flexible iteration, cost control and so on. It reasonablyallocates resources in the scene of differentiatedresource demands.
n Data Lakehouse is urged to emerge
Avoiding loss of data value and extracting greatersupport from data for decisions making become twomajor demands for Data Management Solutions. DataLakehouse satisfies these by integrating the features ofData Lake and Data Warehouse to enable enterprisesto extract more value of data.
n Expanding and Deepening application
The application of DMS in the industry is graduallyextending to the core business in various fields as theapplication scenarios expand and deepen.
Alibaba MaxCompute Alibaba Data Lake Analytics Alibaba Alibaba Cloud Lakehouse
Huawei GaussDB(DWS) Huawei MapReduce Service Huawei FusionInsight Lakehouse
n Data warehouse and data lake constitute the core module:
Data Warehouse (DW): focus on structured data and processing efficiency, offeringpromotability.
Data Lake (DL): compatible with unstructured data, focus on storage of massive real-time rawdata, offering agility.
n Industry value of data management solutions
The DL and the DW provide the basis for the data capitalization of all industries, and the datacapitalization will reconstruct the enterprise value chain from the key-value nodes of marketing,research and development, supply chain and so on.
DMS utilizes computer hardware and software technology to effectively collect, store, calculate,analyze and apply massive amounts of data, aiming to extract and deduce valuable informationfrom the original data to support enterprise decisions.
The purpose of DMS is not simply to organize and store data but to enable advanced dataanalysis that directly provides the enterprise with more timely decisions and observations. DLand DW accelerate the value creation of enterprise data by connecting DMS elements andproviding the foundation of advanced data analysis to support enterprise decisions in real-time.
Data Lakehouse
Database Management Solutions DefinitionFrost & Sullivan define DMS or Database Management Solutions as the effective one-stop data management systems provided by service vendors to organizations.
§ Real Time Decision• RTDSS• EIS• Business Intelligence
§ Machine Learning• Data Intelligence
§ Data Sandbox
q Apply Knowledge Convert Intelligence
q Collect PhenomenonProduce Data
§ Structured Data• csv./json.
§ Unstructured Data• text/img/video
§ Semi-structured Data• xml./html.
n Pack the process of data transformation, export by one-click
Due to various kinds of data sources, complex types of data, a large amount of data, fastgeneration speed, enterprises need to ensure the reliability and efficiency of datatransformation on the one hand and control the operation cost on the other hand in theprocess of data processing. DMS can provide a cost-effective, fast, accurate data conversioneffect through professional software and hardware technology and implementation plan.
n Accelerate the formation of competitive advantage
In the future industry competition, demand insight, manufacturing, marketing, user trackingand other key functions are inseparable from the enterprise system, machine system, Internetsystem, social system that generate massive data. The application of DL and DW breaks thephysical barriers between systems, sorts out the industry data, business data, content data,online behavior data and offline behavior data and master the first-hand knowledge. It helpsdecision-makers to firstly occupy the strategic high ground of the emerging market in theindustry, provides the latest perspective throughout the industry competition dynamics, andimproves the enterprises' decision-making flexibility.
DMS utilize computer hardware and software technology to effectively extract and deduce valuable knowledges from the original data to support enterprise decisions
The strengthening Data governance of DL and the Expansibility of DW gradually weaken their boundary. The emerging integration of DL and DW joins the agility and promotability.
OLAP
OLTP
DevelopmentHistory
Stage of Traditional Database
Data Lakehouse• It reduces the redundancy of data warehouse and data lake when they exist
independently and transforms the unstructured data of data lake layer into the structured data of data warehouse layer.
Data Lake• Cost-saving: Using relatively cheap PC servers can build a big data cluster, break the
physical boundary of the database itself, and connect the isolated data islands.
Stage of Cloud Deployment Data Warehouse• Cost-saving: Advantages of pay-as-demand, scale-as-demand, high availability and
storage integration can be realized.
Stage of Traditional Deployment Data Warehouse• Costly：Originally tight coupling compute and storage, architected by independent
hardware and its corresponding software.• Low Scalability: When additional database nodes are added, the data in the cluster must
be “rebalanced” that requires the physical shipping of data across nodes of the cluster.
Business Oriented
TechnologyDriven
• It takes up a lot of storage space to store data by data blocks. As represented by row-based database, queries without index will consume a great deal of computing power.
n Cost Driver
The fundamental functions of database are storage and query of data.
In the traditional database stage, storage and query are faced with huge cost and difficulty; In the data warehouse stage oftraditional deployment, the ability of data governance is improved to reduce the cost and difficulty of query, but the limitation ofscalability determines its lower bound that it is capable to reduce the cost. Cloud deployment of data warehouse greatly boostsits scalability by eliminates hosting, operation, maintenance, software investment and other costs while attaining a high level ofresource utilization by pay-as-you-go. However, data warehouse cannot solve the incompatibility of unstructured data. Thepractice of data lake has achieved the leap in storage performance. It is compatible with real-time, massive and various types ofdata, and truly breaks the physical barrier between databases. The emergence of the integration of the lake and warehouseabsorbs the advantages of the data lake in storage and the advantages of the data warehouse in query, this further lowers thethreshold of big data application.
n Demand Driver
The requirement for agility and promotability will continuously evolve as the enterprise users develop.
At the start-up stage, the data period from generation to its consumption is still very long, often only online transactionprocessing (OLTP) system to record business events is needed, which is the application of traditional database; For datacentralized analysis of different services, the data need to be cleansed and stored in the data warehouse to provide OLAPanalysis. When the business grows to a certain scale, the local deployment database and data warehouse will be incorporatedinto the process of cloud deployment because of the cost. The analysis methodology for the increased amount of data can beextended to Data Mining, to access decision support system (DSS) and executive information system (EIS) analysis for morevaluable information and knowledge which help to build business intelligence (BI).
In the application scenarios such as marketing and operation of Internet firms, operation analysis of telecom industry, risk controland management of financial industry, data lake's ability to store massive data and data warehouse's ability to extract highlystructured data become significant. Due to the concept of data gravity, the huge cost of data transmission has put the actualbusiness under heavy pressure. It is in the demand of data business that draws forth the Data Lakehouse.
Large storage area on multiple databases for business and transactional data recording and querying functions
Source: Websites, Product brochure, Annual report of typical big data firms, Collected by F&S
Data Warehouse Architecture
Ø Data
Ø Quality
Ø Schema
Ø AnalysisØ User PortraitØ Cost-performance ratio
Advantage
Quality Management
Data Access
Access Control
Processing
Metadata Management
Batch Processing
Stream Computing
Interactive
Machine Learning
Computing
Data Governance
Data Source
-Structured
Data Only
TypicallyBusiness
IntelligenceApplication
Data Migration
Asset Catalog
StructuredData
Storage
Ø A built-in storage system where data is provided abstractly (such as in a Table or View) without exposing the file system
Ø Data needs to be cleansed and transformed, usually in the form of ETL/ELT
Ø Focus on modelling and data management to support business intelligence decisions
Characters
Description
Ø Understands the data deeply, optimize storage and computation
Ø Data life cycle management, equipped with relational system
Ø Fine-grained data management and governance
Ø Complete metadata management ability, easy to build enterprise-level data platform
DL and DW are two mainstream architectures to realize formal data management solutions. Data warehouse focus on the efficiency of big data processing and benefit organizations’ promotability.
DMS Typical Application- Data Warehouse
Relational data from business systems, operational databases, and line-of-business applications
Highly regulated data that can be used as an important factual basis
Design Before Data Warehouse Implementation (Write Mode Schema)
A centralized storage area that can store all types of data and enable in-depth analysis of unstructured data
DataSource
-Structured
And Unstructured
Data
Task Management
Quality Management
Data Access
Access Control
Process Orchestration
DataGovernance
Data Migration
Asset Catalog
Metadata Management
Structured Data Storage
Processing
Unstructured Data Storage
Batch processing
Stream Computing
Interactive
Machine Learning
Centralized Storage
Computing
TypicallyData
ScienceApplication
Data Lake Architecture
Advantage
Characters
Description
Ø Unified storage system
Ø Stored raw data
Ø Collect and ingest all data sources to obtain the entire isolated database set
Ø ETL (extraction-transpose - load) function is supported for real-time and high-speed data streams
Ø Scalability and agility
Ø Advanced analytics with artificial intelligence
Ø Abundant computational models/paradigms
Ø Not equal to cloud deployment
Data lakes is compatible with unstructured data and is advantageous in mass-data storage and it focus on storing mass-data to benefit organizations’ promotability.
DMS Typical Application- Data Lake
Source: Websites, Product brochure, Annual report of typical big data firms, Collected by F&S
Ø Data
Ø Quality
Ø Schema
Ø Analysis
Ø User Portrait
Ø Cost-performance ratio
Relational and non-relational data from devices, websites, applications, media, etc
Any data that cannot be regulated (such as raw data)
Write at analysis time (Read Mode Schema)
Machine learning, predictive analytics, data discovery and data analysis
Data Scientist, Data Developer, and Business Analyst
Faster query results require only lower storage costs
12
Value Creation Analysis ofData Management Solution
• Provide scientific support to judge the hot market andinvestor confidence
• Automatically analyze the solvency of enterprises andjudge whether to give loans to enterprises
• Process customer information and understand customersto the greatest extent
• Build customer churn early warning model to reducecustomer churn rate
• Infrastructure construction optimization and networkoperation management and optimization
• Customer profile, relationship chain research and precisionmarketing
• Call centre service optimization and customer life cyclemanagement
• Business operation monitoring and business analysis• Data external commercialization and independent profit
• Traffic planning, comprehensive traffic decision-making,cross-departmental collaborative management,personalized public information services, etc
• Identification and prediction of road traffic conditions,assists in traffic decision-making and management,supports smart travel services, and speeds up theinnovation of transportation big data services
Governmment
Healthcare
Technology& Energy
• Build a comprehensive service platform
• Integration of multi-source government
database
• Build a comprehensive big
data service platform
• Integrate multi-source data
and build a large database
• Improve the efficiency of diagnosis
and treatment
• Reduce the cost of patient care
• Big data integration and interoperability among different
government departments and affiliates.• Government organs at all levels have accumulated a large
amount of data in their daily management, but they havenot fully excavated the value of these data
• standardization construction of large-scale generalhospital informatization system
• the establishment of nationwide e-health archives• Build a regional medical informatization platform
• Adjustment and transformation of energy structure,
coordinated development of various energy sources• Provide energy status identification and prediction to
support energy decision making
• Provides demand forecasting, energy early warning andother functions to realize coordinated decision-making ofenergy development and consumption
Source: Alibaba Cloud, Huawei Cloud, Tencent Cloud, Amazon Web Services, IBM Websites, F&S
The DL and DW empower data capitalization of all industries, and the data capitalization will reconstruct the key value factors along marketing, research and development, supply chain and so on
Changes of Value Nodes due to DMS
n Value Nodes Change
DMS changed the practice of value nodes in business, marketing, and research and development across industries. The value of data is embodied in showing the development process of the phenomenon, describing the nature of the phenomenon and forecasting the trend of the phenomenon development.
Transform data to deduce prediction, trend analysis, cross-selling strategy recommendation, customer profile matching in relevant directions, and then master the elements of products and services that customers really care about. This application of data intelligence will use data as a means of production to drive business reinvention.
n Value Chain Change
The core idea of data cloud service is "everything serves", liberating productivity and allowing enterprises to focus on their most core areas.
The capitalization of data not only brings changes to marketing and R&D personnel but also provides optimization suggestions for other functions in the value chain, such as storage, production planning and personnel management, through the monitoring and insight of data downstream. At the same time, remote synchronization and real-time accessibility of data enable any part of the value chain to adjust functional decisions anytime, anywhere.
The potential of distributed innovation is unleashed, which improves the customer experience creating a new space for value creation at every node in the value chain and driving all kinds of participants to foster new efficiencies.
n Benign Cycle in Data Ecosystem
Data connectivity in the horizontal dimension, on the one hand, it can gather a series of customers from different industries externally; on the other hand, it can promote close collaboration of different section in the value chain internally. On the vertical dimension, let the participants of the ecosystem enhance their experience and even play a leading role in it.
Through network effects, ecosystems can provide products and services that an individual enterprise cannot provide on its own, thus attracting new customers and generating more data.
Data management has an obvious amplifying effect in various industries specifically in insurance and finance. Timely decision and observation is one of the major source of benefits from data management.
The increase in ROA per 10% input of DMS in each industry [Percentage]
Business Practice Analysis
Source: The University of Texas at Austin, F&S
Proportion of revenue streams from DMS
DMS generate revenue by 3 means：
n Improve traditional performance indicators
ü Accelerate growth rate
ü Improve productivity
ü Improve risk control and management
n Explore new sources of growth
ü Discover new value from unstructured data
ü Extract value from structured data
n Launch data driven new products or services
ü Implement advanced analysis of data
Commonality of financial institution：n Labor intensive n Databases BarrierØ In marketing section, each subsidiary is running
on an autonomous track. Ø In R&D section, the data needed in the
development of financial derivatives is procured from providers rather than collected within the system.
Ø The continuity of its own business and cross-department collaboration are weak
n Large proportional to Customer businessn Massive and complicated data Ø Inconsistent Data Type
• Personal credit data (Semi-structured)• Personal consumption behavioral data
(Unconstructed)Ø Large data volume
• A depositor's credit report contains up to 10GB of data
Achieve growth by applying computing power：
n Effective data governance discover value nodes from the jumbled and massive data
n Effective and fast data computing with low latency provides data for advanced analytics
n Advanced analytics directly offer enterprises with more timely decisions and observations
Business Practice AnalysisDL and DW accelerate the value creation process by connecting the five key elements and construct a solid basis for the advanced analytics which is critical to enterprise decision making.
DL and DW directly accelerate data collection, storage, calculation, analysis and invocation
DW and DL serve as accelerators in data management
Data Lake (DL)Storage Location for massive structured or unstructured data from random sourcesThe enterprise processes and analyses data according to its demand.
Centralized data warehouseSome data is of large volume and often require post-processing, they are transferred to the high-performance memory.
Data is collected from everywhere
Data in DL to be processed and called Advanced analytics call the data
Structured DataCredit card numberDataAmountPhone…
Unstructured DataWebsiteE-mailsSocial media contentAudio，Video…
Data applicationData Scientist/ Data Analyst perform advanced analytics from the processed data.
MicroservicesData is configured into tiny modular components that quickly transmit real-time information to data users.
Internal reportSupport internal information user decision making
External reportDirect business value transformation
Identifyvaluesources
CreateDataEcosystem
Build Insight Models
ImplementInsightSolutions
PracticeAndVerify
1
2
3
4
55
Market InsightsBusiness demands
Internal EcosystemExternal Ecosystem
Data ModelingGet Inspired
Process ReengineerTechnique Practice
Establish CapabilityTransform Managment
Case study of digital transformation of banking by :User operation-Service innovation-Value transformation
GBC Integration： Construct an integrated business model of the Government, Business organizations and Customers. Promote mutual attraction among different customer groups so as to form an external ecology.
Regional Linkage：Construct linkage mechanism between head office and branches.
Front/Middle/Back Office Agility：Promote rapid collaboration across business and functions.
eMarketing：Customer Acquisition and Activation through innovative service by referencing the operation idea of Internet enterprises.
Business sharing Platform： Build enterprise-level sharing capability to support the rapid development of business.
Technical capability sharing:Sharing fintech capabilities to business partners, creating products/services with technology partners, so as to accelerate the integration of technical innovation and traditional business.
Server(Hardware) Storage(Hardware) IT Service Big Data Software Business Service
Market Size AnalysisThe market of data management solutions is expected to continue to expand due to the continuous promulgation of favourable policies, the innovative integration of big data technology and more data application scenarios gradually landing.
q DMS Marketq Big Data Hardware Market q Big Data Software Market
DMS Market Size in China，2015- E2024
GAGR：19.0%
GAGR：23.7%
[Hundred Million USD]
Big Data ServiceBig Data Hardware
数据来源：沙利文
n Expanding market demand
Enterprise users will increasingly invest in DMS. Data's desire to improve decision-making and operational efficiency in order to will rapidly drive up the market demand for DMS. Data management talent is becoming a leader in digitally transformed enterprises, further expanding and solidifying the demand for data management solutions.n Data management solutions technology is maturing and cost-effective
There are abundant and matured DW and DL products in the market. Safe and stable products with complete functions meet differentiated needs for each industry and enterprise. Data Lakehouse combines the strengths of two mature products to meet enterprise-class features such as flexibility, cost, performance, safety and governance, and it further reducing overall costs.n Promulgation of favourable policies
As the national policy on personal information protection, data across borders, national information security are on the table of legislation, the data rights, data privacy, data security become the valuable attributes that the market care about along with other attributes such as data volume, data type and transmission speed.
Source: F&S
• The scale of China's big data hardware market is estimated to be US$ 11.10
User Demand InsightsEnterprise users will increasingly invest in DMS to have the advantage of improving decision-making and operational efficiency
Willingness of Chinese enterprises invest in Big Data Service in 2019
n Primary demand of enterprise users
Improving decision-making efficiency and operational efficiency are the major drivers of demand.
Risk Control, Business Innovation, Production and Customer Service also matter to Enterprise User.
n High Willingness to invest in DMS
Up to 55% of enterprise users plan to invest more in data management solutions.
n Three Logics that drive the DMS market
1. Improve financial performance: Labour productivity, ROE, ROI, ROA
2. Improve customer relationships: enhance innovative capabilities to generate revenue growth in new product lines and expand customer base
3. Improve operations management: Resource Utilization Level, Forecasting & Production Planning, Delivery Cycle, Service Terms
n Marginal inputs produce extra gain
Significant gains in basic data access and data quality will be achieved first. The attributes of the data, including quality, ease of use, intelligence, accessibility, and flexibility, all gain upgrades proportionately as the level of investment increases. Companies build a competitive advantage in the industry by adjusting their investment to different levels and strategies of advanced analysis.
The enterprise's perception of data management talents has evolved from specialist to leader. With the change of perception, the role and leverage of data management talents is improving
Analysis on Enterprises' Perception
The course of change in Perception of Data Management Talents
Specialist Perception
LeaderPerception
Geek1995
Manager2015
Evangelist2005
Specialist2000
Assistant2010
Leader2020
Transforming math and computer science into strategic practice to gain a competitive advantage
Help companies build professional data teams and use data science as a professional skill
Companies are setting "Chief Analytics Officers" to help them understand and share the value of data through analytics
Helps companies establish a clear vision and road map to build a data-driven business
They don't necessarily have a technical background, but they are methodical and focused on helping the company deal with problems that are holding back growth at an organizational level
Taking the leadership in building a partnership between the organization, and IT to ensure practical approaches are effective to business outcomes
n Transit from the IT era to DT era
With the popularity of cloud services and mobile Internet, data output is dispersed among smalland medium-sized enterprises and consumers. Competition in all industries requires a fineoperation. Through big data analysis, we can gain insight into demand and generate situationalpredictive knowledge to establish differentiated competitiveness.
n Big data further integrated with the LoT and AI
In this new era, leaders should redesign workflows with the application of hybrid cloud, 5G,Internet of Things, edge computing capabilities etc. to enhance enterprise resilience. Developdata-based AI strategies, consider data as the key evidence of business decision, develop a clearbusiness plan, and build a cognitive enterprise.
n Data decisions require leadership recognition
Data Decision: A complete predictive support decision loop includes historical data input, modeltraining, data prediction, decision making, execution, result collection, and data feedback. Dataanalysis supports the management decision, the premise of which is to refine the purpose of thedata. For example: after a new function goes online, what modifications need to be made toincrease the users' activity; According to new product sales ratio and regional performance, howto adjust regional sales strategy in the later stage; how to push promotional informationaccording to the purchasing power analysis of members, etc.
Only when a series of purposes are clear, data can be collected and used properly. Data leadersare required to make a comprehensive plan to ensure the effectiveness of the data loop.
Issuing Authority Policy Name Issued Date Key Points
the Standing Committee of Shenzhen People's
Congress
《Data Regulation of Shenzhen (Draft)》
2020-12 Public data belongs to new state-owned assets, and the data right belongs to the state.
the Standing Committee of the National People's
Congress
《Law of the P.R.C on the Protection of
Personal Information (Draft)》
2020-10
Article 2: The personal information of natural persons is protected by law, and the personal information rights of natural persons must not be infringed upon by any organization or individual.
the Standing Committee of the National People's
Congress
《Data Security Law (Draft)》
2020-07
Article 10: The state is to actively carry out international exchanges and cooperation in the data sector, participate in the formulation of international rules and standards related to data security, and promote the safe and free flow of data across borders.
Ministry of Industry and Information Technology
of P.R.C
《Guiding Opinions of the Ministry of Industry
and Information Technology on the
Development of Industrial Big Data》
2020-04
In view of the current stage of industrial big data, the comprehensive layout and systematic promotion will be made in 6 aspects: accelerating data convergence, promoting data sharing, deepening data application, improving data governance, strengthening data security, and promoting industrial development.
Cyberspace Administration of China
(CAC)
《Administration of Data Security
(Consultation Paper)》2019-05
For the purposes of safeguarding national security and the public interest, protecting the lawful rights and interests of citizens, legal persons and other organizations in cyberspace, and maintaining the security of personal information and important data,
Policy AnalysisWith the acceleration of the construction of digital economy, the government gradually attaches greater importance to the development of the big data industry
n Emphasizes personal information protection and explores the legislation of data rightsData, as a new factor of production, is written into it for the first time, keeping pace with other factors of production such as land, labour, capital and technology. A natural person has the right of his personal data according to law; Public data belongs to new state-owned assets, and the data right belongs to the state. Factor market entities also have the right of data, which no organization or individual may infringe. It has laid the legal basis for the development of the digital economy and directly requires the standardization of the data circulation market. In the future, data regulation, privacy security and ownership of rights will be a new digital economy track for solution providers.n Strengthen legislation to safeguard national data securityChina is setting up a cross-border flow data management system to balance the interests of national security, personal privacyprotection and industrial competition, and to meet the requirements of data flow required by the globalized economy as well asthe monitoring and control of data required for security. International relations or political sensitivity will be the key dimensionthat requires all relevant enterprises to consider, which will directly affect the strengthening of the regulation of cross-borderdata flow faced by foreign data management solution providers in the Chinese market. In addition, the improvement ofconsumers' or enterprises' knowledge of data will also directly affect their preference for similar solutions. Domestic datamanagement solution providers will grow by capturing the market share of some foreign companies that have withdrawn fromthe Chinese market.n Emphasizing the combination of big data technology and specific application scenarios”Big data" has long been deeply rooted in the hearts of consumers, enterprises and local governments. Effective datamanagement solutions that are suitable for the current period of enterprises is the foundation of the development of the digitaleconomy. The cooperation between government and enterprises, the service of people's livelihood issues, the establishment ofinterdepartmental data exchange, the acceleration of the approval process, and the realization of the application of big datathat benefits the people will become the hot spot of service providers and developers, and the application potential of big databurst during the anti-epidemic period will be reappearing in many other fields.
Source: Standing Committee of People’s Congress, MIIT, CAC，F&S
22
Development Prospect of China Data Management Solution Market
uKey Milestones
uCloud Deployment
uIntegration of Data Lake and Data Warehouse
uDeepening Application Scenarios of Data Management Solution
The previous data management solutions focused on the quantity, type and speed of data, while the current data management solutions focused on the value of data
Enterprise Resource Planning
Customer Relation Management
Network
Other data sources
Sensor /RFID devices
The mobile network
User Clickstream
Sentiment analysis
User-generated content
Social interaction and push
Space and GPS coordination
HD video, audio & images
Product/service log
Instant messaging
Web logs
Product record
A/B testing
Dynamic fixed order
Alliance network
Search marketing
Behavior orientation
Dynamic funnel
Customer segmentation
Product details
Customer contact
Support Contact Information
Procurement details
Purchasing records
Payment records
MBGB
TB
PB/ZB
Data Categories
q 15MB• Human resource
database of a global bank
q 500MB• Weekly annual sales
data of a single product in a category of a retail enterprise
q 2TB• Membership data
for a global coffee company
q 2.5PB• Transaction data of a
global supermarket
Source：Teradata，F&S
Evolution of data management
q The performance in data processing regarding to types, volume and speed experienced explosive development
q The increase in data magnitude and value density will generate the need for a new generation of databases.
Key differences in the database segmentation
DataVolume
Represents the major solutions of database products to big data
ü Trending to cloud: Cloud databases offer natural flexibility, cost-effective deployment, and pay-as-you-demand payment.
ü Increased proportion of non-relational data: Data growth is concentrated in unstructured data such as audio and video files and social information.
ü Memory database is more widely used: Compared with disk storage, memory can meet the requirements of information interaction, high concurrency, low latency, fast reading characteristics.
ü Streaming databases are growing rapidly: Combining transaction processing and real-time analysis, they can respond in real-time and with low latency when large amounts of data come in.
ü Open-source database environment is constantly improving: Such as MySQL, PostgreSQL, MongoDB and other open source databases are occupying the market of commercial databases in small and medium-sized enterprises with the characteristics of low price, equal performance and rich functions.
Cloud DeploymentBased on the separation of memory and computation, cloud service meets requirements of elastic expansion, flexible iteration, cost control and so on. It reasonably allocates resources in the scene of differentiated resource demands and thus cloud deployment will become the trend
Data management system deployment
Business Strategy• Critical to Business• Product Life Cycle• Target
Marketing Planning• Business Priority• Marketing Scheme• Sales Distribution
Human Resource• Skills required
R&D• Characteristics
• Brings to Market ASAP• Medium and long term• Goodwill and customer relationship
Traditional Deployment
• Sales first, Products second• Relational Marketing• Direct distribution
• Communication and demand satisfaction
• Customized products that meet differentiated needs
• Brings to Market ASAP• Short term• Market Share
Cloud Deployment
• Product first, Sales second• Scale Marketing• Direct and Indirect distribution
• Creativity and Programing Techniques
• Generic products that meets general needs
n Coupling data storage, computing, processing and analysis
In the past, in order to deal with the problems of insufficient network speed and a long timeof data exchange between nodes, the distributed framework of big data adopts the form ofstorage and computing coupling, so that data can be calculated at its own storage point toreduce interaction. This is a traditional deployment DMS.
n Coupling storage and computing generate unnecessary costs
In the practice, the demands for data storage space and computing capacity vary respectively,making the demand ratio of the two types of resources unpredictable. When a resourcebottleneck occurs in one of them, the horizontal expansion of resources will inevitably lead tothe redundancy of storage or computing capacity, and the migration of data will also causeadditional costs.
n Separation between storage and computation effectively control cost
Storage and calculation are separated to form two independent resource sets, which do notinterfere with each other but they can fully cooperate. The scale aggregation reduce the costper unit resource as far as possible while it has sufficient elasticity for horizontal expansion.When resources are scarce or rich, we acquired or recycled the resource respectively andutilize specific resource ratio to reduce redundancy and achieve reasonable allocation ofresources in the scene of differentiated resource demands.
n On-demand cloud-based services have significant advantages
On the basis of the separation of memory and computation, Serverless and Cloud Nativeenable data processing no longer requires a complete platform, which greatly shortens thedevelopment. At the same time, the service application is operated and maintained by theprovider, and it charges dependent on users' demand, thus it is cost-saving.
Source: CAICT, F&S
Choose among Local, Cloud and Hybrid DeploymentChoose among Public Cloud, Private Cloud and Hybrid Cloud
Balance between data security and cost-effectiveness
Avoiding loss of data value and extracting greater support from data for decisions making become two major demands for DMS. Data Lakehouse satisfies these by integrating the features of DL and DW
Integration of Data Lake and Data Warehouse
The benefits of the integration
Trend: Data Lakehouse enters the stage Approach: Integrate the Scheme Design
Integration of Cloud Native
Provide structured data computing and analysis capability
Unified Data Management System provide the basis of data capitalization.
n Improve the efficiency of DMS
The data processing flow is optimized by applying Cloud Native Scheme.
Reasons why it is difficult to extract data value：
n Isolated Data Islands
Due to technology and management, data is scattered in each business system. Thus,Enterprise lacks a unified view of data value.
n Poor Data Quality
Data quality determines the value of data assets. Continuously improving data quality will becritical to developing business decisions analysis.
n Lack of a secure data environment
Data security risks include data leakage and data abuse. Once data security incidents occur,they will cause losses to the enterprise and infringe on users' privacy, which will greatlyconstrain the extraction of data value.
n Lack of data value management system
Enterprises have not established an effective data management system and applicationtemplate, including data value evaluation, data cost management, etc., also lack complianceguidance for data service and application.
Reduce logistics costsImprove customer service levelInventory forecastEquipment repair forecast
n Big Data Industry
The big data industry takes data and its value as the core production factor to drive the data-enabled industry through data technology, data products, data services and other forms. Bigdata is applied in various industries and fields, providing application software and overallsolutions which are closely related to the industry. Increasingly, the demand for data-enablingis shifting from perceptual applications to predictive and decision-based applications.
n Deepening the application of industrial big data
Edge-cutting technologies such as 5G, LoT, edge computing and blockchain are graduallyintegrated into the industrial domain. These technologies, integrated with big datamanagement solutions, support agility management and refined operation. Further, they alsopromote the transformation, upgrade the manufacturing speed, support higher qualityindustrial data through a closed-loop: collection, gathering, distribution, analysis andapplication. Eventually, DMS enhance the efficiency of developing new industrial techniquesand products.
n Financial big data has a broad prospects in application
Financial institutions stores a large amount of structured data about customers, accounts,products, transactions and a large amount of unstructured data including voice, image, videothat reflect customer preferences, social relations, consumption habits and other information.DMS constantly benefits in the application of specific business such as transaction fraudidentification, precision marketing, black money prevention, credit risk assessment, supplychain finance, investment market prediction. Additionally, breaking the data barriers betweenfinancial institutions will become the next trending improvement.
Source: Big Data Industry Ecological Alliance, CAICT, F&S
27
Competition Analysis of China Data Management Solution Market
China’s DMS market is in a steady growthstage. The conclusions of this report about thecomprehensive competitiveness of DMSproducts and services in each of the competitivesubjects are only applicable to the marketdevelopment of DMS at this stage.
Frost & Sullivan will continue to monitor themarket of DMS to capture competitive trends.
n X-axis represents “Innovation Index”：
• To measure the innovation ability of competitors in data management solutions,the more to the right of the position, the greater the innovation ability is.
n Y-axis represents “Growth Index” ：
• To measure the growth ability of competitors in data management solutions, thehigher the position is, the greater the performance growth potential is.
n Color depth represents “Foundation Index”:
• To measure the foundation ability of competitors in data management solutions,the darker the color is, the stronger the fundamental attributes are.
Note: The circle corresponds to thecomprehensive score from low to highaccording to the logic of increasing from insideto outside. Competitiveness is represented bycombining "innovation index", "growth index"and "foundation index" (the circle is onlyapplicable to competitors in the first quadrant).
Comprehensive Assessment in the market of DMS in China——Frost Radar TM
The Leadership Region
Vendors in this region are the leaders in DMS market in China
The market of DMS in China is in a steady growth stage. The major competitors have their own competitive advantages in three dimensions: Innovation ability, Growth ability and Foundation ability
Machine learning algorithm capabilityText semantics understandingMultilingual semantics understandingStorage resource utilizationData resource utilizationCold vs Hot data managementUsability ((easy to use and manage)Data model flexibility (multi-dimensional analysis)Data Query Degree of Freedom
Support Capability Mass Storage Capability
Data Acquisition Capability Data Source Acquisition Capability to obtain full/incremental data
Data Call CapabilityPush results to the right storage engineReal time data analysisParallel Query
Data Integration Capability Data Mart integration abilityService Ability Service Level Agreement (SLA)
External Compatibility
Support major cloud platform (Amazon Web Services, Alibaba Cloud, Huawei Cloud etc.)Support traditional database（Oracle, MongoDB, DB2, Redis, MySQL, etc.）
Open Source Support Capability
Support open source communities（Hadoop, Spark, etc.）
Support BI tools（Tableau, SAS, Zeppelin etc.）Support open source scheme（Tensorflow, Pytorch, MXNet, etc.）Support open source real time event processing system（Kafka, flume, etc.）
Data Lakehouse Capability
Support ACID transactionsVisible and searchable Global dataUnified data governance systemUnified data development systemUnified integration of data lake and warehouseCorrelation computing of multiple DL and DWData VirtualizationUnified data directory of DL and DWUnified data storageUnified data invocationData movementData opennessAccess to the third-party interface layerData access control
Assessment Criteria（1/2）In this report, growth index, innovation index and foundation index are set to evaluate the competitiveness of different DMS vendors
Security of data storage, use, encryption, desensitization, etc.
Permission security system
Cyber-security protection
Data life cycle storage capability
Ability to store raw data, analyze intermediate results, and analyze procedures
Data management capability
Data management optimization ability for specified compute enginesCoverage of metadata, master data, data model, data standard, quality standard, data security, data sharing and data visualization management
Data lake multi-generation coexistence
Data warehouse multi-generation coexistence
Analysis or optimization in Data warehouse (e.g. based on CBO, RBO optimization model)
Platform and Business Ecosystem Degree to which the vendors empower partners
FoundationIndex
Data storage capacity (for self-developed
products)
Structured data storage capacity
Unstructured data storage capacity
Infrastructure capacity
Private Cloud
Public Cloud
Self-developed server
Assessment Criteria（1/2）In this report, growth index, innovation index and foundation index are set to evaluate the competitiveness of different DMS vendors
31
Sullivan Market Report | 2021/4 China：Data Management Series
Leader - Amazon Web ServicesAmazon Web Services is the leader in DMS in China providing technological innovation, global business practices, flexible data management, cloud security and strong business ecosystem
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL
Amazon Elasticsearch Service：Fully managed, scalable, and secure Elasticsearch service
• provides support for open source Elasticsearch APIs, managed Kibana, integration with Logstash and other Amazon Web Services services, and built-in alerting and SQL querying.
Amazon EMR：Cloud big data platform
Easily run and scale Apache Spark, Hive, Presto, and other big data frameworks
• Scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters.
Amazon Aurora： Relational database built for the cloudPerformance and availability of commercial-grade databases at 1/10th the cost.A distributed, fault-tolerant, self-healing storage system
• a fully managed, multi-region, multi-active, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications.
Amazon SageMaker：Machine learning for every data scientist and developer
• helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
Amazon Redshift：Analyze all of your data with the fastest and most widely used cloud data warehouse
• Make query and combine exabytes of structured and semi-structured data across your data warehouse, operational database, and data lake using standard SQL.
Lake House Architecture Overview
Amazon Web Services Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development.
Amazon Web Services Lake Formation is a service that makes it easy to set up a secure data lake in days.
AmazonS3
Amazon Athena
Data Gravity Data Lake
Amazon DynamoDB：Fast and flexible NoSQL database service for any scale
n Technological Innovation: Data lakehouse architecture § Integrating AI technology with data management functions: the high-availability
architecture of data management services, security authentication and fine-grained monitoring, storage and computing separation, and automatic resource expansion.
§ Amazon QuickSight Q（A machine learning powered capability that uses natural language processing to answer your business questions instantly）
n Global Business Practice: Customized data management service§ Amazon Web Services combines the best of the global practices with the market conditions
in China, provides customized data management services to different industryn Flexible data management: High Usability
§ Amazon Web Services is capable of node configuration, software configuration, automated indexing and extraction, data isolation and security, industry compliance, cluster sizing, automatic patching, alarm and detection, and hardware maintenance
n Cloud Security: Shared responsibility model§ Amazon Web Services responsibility “Security of the Cloud”: hardware, software,
networking, and facilities that run Amazon Web Services Cloud services.§ Customer responsibility “Security in the Cloud” : operating system, network, firewall
configuration, application, identity & access management and customer datan Business Ecosystem: Customer Enablement
§ Migrate and build faster in the cloud with Amazon Web Services Customer Enablement services. Augment your team’s cloud skills with deep Amazon Web Services expertise where, when, and how you need it.
32
Sullivan Market Report | 2021/4 China：Data Management Series
n Technology Innovation：MRS combines three types of Data Lake§ Offline Data Lake: Open format storage engine, Diversity engine, Support multiple
analysis workloads, Data lakehouse realization§ Real-time Data Lake: Real-time integration, batch stream fusion, real-time update
and delete, real-time analysis service, T+0 timeliness§ Logic Data Lake: Data Virtualization implemented unified access and collaborative
analysis of data inside and outside the data laken Abundant Industry Practice: Business acumen in demand
§ Government affairs (digital transformed Shenzhen’s Longgang District; Enabling access via one website), Operators (Smooth Migration), Finance (Collaborate across data warehouses)
n Comprehensive Security: From infrastructure to application access§ Network isolation (support for multi-section network security), Host security
(operating system kernel security reinforcement, etc.), Application security (user-level access control, etc.), Data security (multi-copies backup for disaster recovery guarantee) and security authentication (unified authentication system)
n Ultimate data management: fulfill the requirement in each functional scenarios§ Abundant data management functions, data lake, data warehouse multi-generation
coexistence§ Ultimate performance in data collection, collation, desensitization, analysis and
management, securityn Business Ecosystem: Customer Enablement
§ A cloud ecology that adhere to openness, cooperation and win-win benefits. Fertilize the partners quickly integrate into the local ecology as the “black soil” in the “intelligent earth”
§ Open community contribution （Ranked 2nd in Hadoop, 4th in Spark)
Leader – Huawei Cloud
Real-time
Entry into the Lake
Incremental
Update
DWS数据仓库
FusionInsight
Data Source
Transactional System
Web/Mobile
3rd Party
Social Media
IoT
…
DLC Unified Metadata | Unified Security
GESGraph Engine
ServiceHetu
Engine
MRS Cloud Native
ModelArtsAI Platform
DGCData Lake Governance Center
Data Catalog
Data ServiceData GovernanceData integration, development, scheduling
Storage（OBS）
Computing（BMS、PM、VM、Container）
Huawei Cloud
MRS MapReduce ServiceMRS combines three types of Data Lake（OfflineData Lake, Real-time Data Lake and Logic Data Lake ）GaussDB Cloud Data WarehouseAn fully-managed and out-of-the-box analyticdatabase service （ employed Shared-NothingArchitecture, massively parallel processing (MPP)engine and cross- AZ disaster recovery）DGC Data Lake Governance CenterA one-stop data lake operations platform(Functions with Data Integration and DataDevelopment etc.） Rapidly grow your enterprise'sbig data Operations (Build industry knowledgelibraries with intelligence etc.)ModelArts AI PlatformA one-stop AI development platform thatenables developers and data scientists (data pre-processing, semi-automated data labelling,distributed training, and automated model buildingcapabilities.)
GES Graph Engine ServiceFacilitates querying and analysis of graph-structure data. Specifically suited for scenariosrequiring analysis of rich relationship data.
FusionInsight Lake House Overview
Huawei Cloud is the leader in DMS in China providing technological innovation, abundant industry practice, comprehensive security, ultimate data management and strong business ecosystem
GaussDBCloud Data Warehouse
33
Sullivan Market Report | 2021/4 China：Data Management Series
DB级
元数据透视
MaxCompute Built-in optimized storage
n Technology Innovation：Data Lakehouse Architecture
§ Own PrivateAccess network connectivity technology, ability to connect across the integrated network, faster access
§ One-key database metadata mapping technology, unified metadata service
§ Provide unified development environment , highly compatible with Hive/Spark
§ Intelligent cache technology; identify cold data and hot data; Automatic data warehouse
n Cloud Native Practice ：Enable cloud native transformation for millions of enterprises
§ In 2009, Alibaba launched the core middleware system for the first time
§ In 2011, Taobao and Tmall began to use container scheduling technology, and then launched self-developed cloud native hardware Shenlong server and cloud native database PolarDB
§ In 2019, Double 11 Festival, Ali e-commerce core system is 100% on the cloud, which is also the largest cloud native practice in the world
n Ultimate data management: fulfil the requirement in each functional scenarios
§ Enterprise-class high-performance data warehouse, high flexibility and agility at a lower cost
§ Complement elasticity resources and EMR cluster resources
§ Based on PAI, encapsulated many algorithm services that are close to business scenarios
n Business Ecosystem：City Brain 3.0§ All urban elements, such as farmland, buildings and public transports will be linked through the
urban space gene pool
§ Perform intelligent decision-making of all urban scenes, such as traffic, medical care, emergency response, people's livelihood, elderly care and public services
Leader – Alibaba CloudAlibaba Cloud is the leader in DMS in China providing technological innovation, cloud native practice, ultimate data management and strong business ecosystem
Alibaba Cloud Lake House Overview
IDE Task Scheduling Data Security Asset Management Data Service
Offline Computing Service
Interactive Computing Service
Machine Learning Service
Deep Learning Service
Real-time Computing Service
MCSQL
MCSpark PAI TF PAI GNN
MaxCompute Meta Service
Cache
Hive Spark Flink Presto
Hive Meta Service
Structured Semi-Structured Unstructured
MaxCompute Data Warehouse Cluster Data lake Cluster
uFrost & Sullivan has conducted in-depth research on the market changes of 10 major industries and 54 vertical industries in China with more than 500,000 industry research samples accumulatedand more than 10,000 independent research and consulting projects completed.
uRooted on the active economic environment in China, the research institute, starting from data management and big data fields, covers the development of the industry cycle, follows from the enterprises’ establishment, development, expansion, IPO and maturation. Research analysts of the institute continuously explore and evaluate the vagaries of the industrial development model, enterprise business and operation model, Interpret the evolution of the industry from a professional perspective.
uResearch institute integrates the traditional and new research methods, adopts the use of self-developed algorithms, excavates the logic behind the quantitative data with the big data across industries and diversified research methods, analyses the views behind the qualitative content, describes the present situation of the industry objectively and authentically, predicts the trend of the development of industry prospectively. Every research report includes a complete presentation of the past, present and future of the industry.
uResearch institute pays close attention to the latest trends of industry development. The report content and data will be updated and optimized continuously with the development of the industry, technological innovation, changes in the competitive landscape, promulgations of policies and regulations, and in-depth market research.
uAdhering to the purpose of research with originality and tenacity, the research institute analyses the industry from the perspective of strategy and reads the industry from the perspective of execution, so as to provide worthy research reports for the report readers of each industry.
uThe copyright of this report belongs to LeadLeo. Without written permission, no organization or individual may reproduce, reproduce, publish or quote this report in any form. If the report is to be quoted or published with the permission of LeadLeo, it should be used within the permitted scope, and the source should be given as "LeadLeo Research Institute", also the report should not be quoted, deleted or modified in any way contrary to the original intention.
uThe analysts in this report are of professional research capabilities and ensure that the data in the report are from legal and compliance channels. The opinions and data analysis are based on the analysts' objective understanding of the industry. This report is not subject to any third party's instruction or influence.
uThe views or information contained in this report are for reference only and do not constitute any investment recommendations. This report is issued only as permitted by the relevant laws and is issued only for information purposes and does not constitute any advertisement. If permitted by law, LeadLeo may provide or seek to provide relevant services such as investment, financing or consulting for the enterprises mentioned in the report. The value, price and investment income of the company or investment subject referred to in this report will vary from time to time.
uSome of the information in this report is derived from publicly available sources, and LeadLeo makes no warranties as to the accuracy, completeness or reliability of such information. The information, opinions and speculations contained herein only reflect the judgment of the analysts of leopard at the first date of publication of this report. The descriptions in previous reports should not be taken as the basis for future performance. At different times, the LeadLeo may issue reports and articles that are inconsistent with the information, opinions and conjectures contained herein. LeadLeo does not guarantee that the information contained in this report is kept up to date. At the same time, the information contained in this report may be modified by LeadLeo without notice, and readers should pay their own attention to the corresponding updates or modifications. Any organization or individual shall be responsible for all activities carried out by it using the data, analysis, research, part or all of the contents of this report and shall be liable for any loss or injury caused by such activities.
![THE GLOBAL MOBILE WEIGHT LOSS MARKET 2020 · Figure 2: Mobile Weight Loss Market Solutions: Narrowing the Scope [Type here] 8 Global Mobile Weight Loss Market Report 2020 Physical](https://static.documents.pub/doc/80x56/5fd9aa418cb17d76d448fca8/the-global-mobile-weight-loss-market-2020-figure-2-mobile-weight-loss-market-solutions.jpg)
