Consolidate the data foundation in the data center

In the next five years , The state accelerates the construction of digital transformation , The business model of an enterprise must change in response to changes in the market environment , How the traditional management mode drives the change of the new mode will be the main external factor of digital transformation , At the same time, driven by the enterprise operation mode and new technology , Enterprises are on the way to digital transformation , Finance is the key link of value chain management , It is crucial to build the ability to match the digital transformation of enterprises . Financial digitalization transformation is based on “ Digitalization of financial operation 、 Intelligent management and analysis ” Target , Use emerging technologies such as big data to reconstruct financial capabilities 、 Reengineering the financial process , Establish a data map covering the enterprise value chain , Form a data base for refined management , Strengthen business control and business empowerment , structure “ The whole scene 、 Perception is fast 、 Insightful and accurate 、 Decision making spirit ” Digital decision-making system , Assist enterprises to improve their insight 、 Prediction ability and value creation ability .

Yuannian technology has many years of in-depth research in the field of finance , around Digital transformation 、 how Building The world-class financial management system has done a lot of research , Write the 《 Research and case practice of financial digital transformation in the first year 》 white paper , In the white paper, the financial digitalization of the first year was proposed innovatively “1234567” Solution , Include a goal , Two principles , Three characteristics 、 Four abilities 、 Five entry points 、 Six key technologies 、 Seven key applications describe the digital solution of the first year .

First year Technology Think , Consolidating the data foundation is one of the paths for the construction of enterprise financial digital transformation , First, carry out data governance , So as to provide guarantee for the construction of upper Digital Application .

The low level of enterprise data governance is usually reflected in the following three levels ：

1. Metadata ： The source and destination of data and the definition of responsibility are unknown , Unclear data attributes and calculation logic
2. Master data ： name 、 dimension 、 Definitions such as classification structure are not unified or missing , The maintenance is not standardized or timely
3. Trading data ： Data consistency 、 correctness 、 timeliness 、 Integrity does not meet business or management needs

As a result, it is difficult to mine the value of data , It is difficult to implement digital applications 、 High abandonment rate 、 produce very little effect , Even doubt the digital transformation strategy or lose confidence in the promotion of the strategy . therefore , Through financial data management , Build data middle platform , Integrate global data , Connect all internal and external information of the enterprise , Precipitate into enterprise data assets , Refine data operation and data control , Then expand data intelligence 、 Data service scenario Application , Form the core driving force of enterprise data management , Is a feasible path .

Free material download ： The white paper of the ark data in the first year ; Ark low code platform white paper in the first year ; First year · Ark enterprise digitalization PaaS Platform white paper

Analysis of promoting the integration of industry and finance in the data , Realize the digital transformation of data-driven finance

Integrate industry and finance data , Realize the analysis of the integration of industry and finance , Finance leads all business management teams to achieve management efficiency driven by data , Budget through financial data 、 forecast 、 actual 、 Comparative analysis of historical business and financial data , Quickly locate the causes of business problems , Formulate countermeasures for business problems , Improve business management performance , Integrate business and financial data , Realize the integration of management and financial statements, expand the analysis content 、 Enhance analysis / methods 、 Break through the data decision barrier , From the analysis of past oriented results , Turn to future oriented prediction and trend analysis .

Data center helps enterprises build “ Data brain ”, Empowering enterprises N Financial management scenarios , Establish management closed loop , In particular, the launch of the new generation of data platform of science and technology in the first year will redefine the application architecture of the new generation of business decision-making system in the first year , Help enterprises build a data base of digital intelligence operation center and a more comprehensive and timely monitoring center 、 A more intelligent and insightful Decision Center 、 A more agile and real-time command center 、 More effective and comprehensive strategy center .

The data center will continue to innovate around the financial digital transformation , For example, based on years of management accounting research and consultation, Yuannian Technology , R & D has formed a business decision-making platform 、 Business platforms such as finance 、 purchase 、 Taxation 、 Business travel and other products , It greatly improves the enterprise's ability in data collection 、 Simulation calculation 、 Business analysis 、 Management ability and decision-making efficiency in supporting decision-making , Realize the combination of Finance and business , Create a platform for the integration of industry, finance and taxation , And realize the effective connection between the business analysis platform and the professional system .

The data center provides data processing and data mining capabilities for financial data

Data center is very important for the construction of enterprises' digital operation ability . Its impact on the digital operation of enterprises The key The function is mainly reflected in the data Handle 、 Algorithm analysis 、 Intelligent application Other aspects ：

The core of data center is to get through the data in various fields , Including financial data 、 Business data 、 internal data 、 External data 、 Structured data and unstructured data , Form a unified data platform , Turn data into knowledge 、 Insight . Data itself is also a business , It comes from business , Also be able to empower business . Data center has a strong ability to process massive data , The data center collects massive data through distributed computing technology 、 Calculation 、 Storage 、 machining . No matter the enterprise produces 、 operating 、 Customer traceability 、 Supplier maintenance 、 External public data and other different kinds of data , The data center realizes the timely and accurate sharing of enterprise data , Improve the timeliness and foresight of enterprise management , Let data create value , Provide timely and accurate business decision-making data for enterprise decision-makers and employees .

The essence of data-driven operation is to establish an analytical model by collecting data 、 Find problems in operational decisions , And adjust the strategy 、 Make plans to improve operations . The core of multidimensional algorithm model is to form a service-oriented data application based on data and analysis data model , Its essence lies in modeling enterprise business , That is to establish a quantitative model to simulate the business model and business model of the enterprise . Data modeling is the core capability of the data center , Online data modeling can be carried out based on intelligent data research and development 、 Unified portrait and public data model based on Intelligent Algorithm . With powerful modeling and computing engine , Enterprises can establish business models and financial analysis models according to different themes , Discover the relationship between data , Make inferences based on data , Meet the performance requirements of the system for digital operation .

To a large extent, the value of digital operation for business needs to be reflected by Scenario Oriented Intelligent Applications . Intelligent application based on Data Center , Covering the automation of information systems 、 Intelligent 、 Online 、 Real time and digitalization of business processes , And based on the understanding of business knowledge , Scientific prediction 、 Reasonable control 、 Intelligent analysis , Truly become the intelligent assistant of managers . future , With the development of artificial intelligence The in-depth development of intelligent technology and its high-level application in the business field , Have advanced The digital business platform of human brain intelligence will be based on the understanding of business knowledge , Families, Academic prediction , Reasonable control , Intelligent analysis , Even directly replace managers to make automated decisions .

The data center provides financial data Data governance Ability

In the process of financial digital transformation , The data center provides a solid data base for transformation , Ensure the smooth progress of digital transformation , And in the construction of data center , Data governance is the top priority . Generally speaking , Data governance products or tools mainly include the following components ： Data standards management 、 Metadata management 、 Data quality management 、 Data security management 、 Data asset management .

The key to data standard management is to sort out the scope of data standards , Define data standard items . Data standard management is formulated and released through unified data standards , Combined with system construction 、 Management and other means , Resolve inconsistencies in standard definitions 、 The calculated caliber is inconsistent 、 Inconsistent data sources lead to low data reliability 、 Problems that seriously affect analysis and decision-making . The key of metadata management is to sort out technical metadata and business metadata , Establishing enterprise level metadata warehouse can help enterprises manage data assets efficiently , Perspective data assets through panoramic view , Using full link impact analysis and kinship analysis , Link the upstream and downstream data , Provide efficient metadata retrieval 、 Display and analysis functions . Data quality management provides the definition of data quality indicators , Data quality task management , Data quality audit , Analysis of data quality problems , Data quality problem statistics and other functions , From the integrity of data 、 Uniformity 、 Uniqueness and other dimensions evaluate data quality , Build global data resources 、 Online configuration check rules 、 Perform quality inspection tasks periodically , With a normalized workflow , Constantly improve the data quality of the data center . Data security management mainly includes user rights 、 Three mechanisms of Data permission and encryption desensitization . Ensure data storage 、 transmission 、 Safe to use , Avoid unreasonable use of data assets , Or the leakage will bring economic losses to the enterprise 、 Legal disputes and other serious consequences .

Data asset management provides a directory of data assets , Data Asset Services , Data asset usage monitoring , Data asset query and other functions , Realize the visibility of data 、 Understandable 、 You can use 、 Operational . so ： Through a comprehensive inventory of data assets , Form a map of data assets . For data producers 、 managers 、 Different roles such as users , Share data assets in the form of data assets Directory , Users can quickly 、 Accurately find the data assets you care about . Understandable ： Through metadata management , Improve the description of data assets , Process and organize data into human beings Understandable 、 Unambiguous data assets . You can use ： Through unified data standards 、 Measures to improve data quality and data security , Lower because data is not available 、 Communication costs and management costs caused by unreliability . Operational ： By establishing a set of data-driven organization management system, process and value evaluation system , Improve the data asset construction process , Improve the level of data asset management , Increase the value of data assets

It is suggested to strengthen data governance during the construction of financial data center , In the data standard 、 Data quality 、 Metadata 、 Data security , Continuously apply the tools and methods of data management , Promote data governance , And combine data governance with data middle office operation management process , Effectively and continuously improve the data quality of the data center , Strengthen the service capacity of data center , Realize the value of enterprise financial data , Support the digital transformation of enterprise finance .

In the first year of ark data, the platform can realize the unified management of business and financial data , The basic master data of finance 、 Business data of each system 、 Operational data 、 financial ERP data , Through data integration 、 Data cleaning 、 data mining 、 Data services and other processes create a solid data base for enterprises , Form the data assets of the enterprise . Digitalize the business , Data capitalization , Break the isolated island of financial data , Lower the threshold of using data services , Build a bridge between finance and business , Promote the integration of data and industrial and financial scenarios . At the same time, clarify the data architecture , Data integration specification , Data standards , Data quality specifications , Data security and other data governance content can be achieved through both data governance system and data asset management platform , Comprehensively ensure high-quality data results , Transforming massive data into high-quality data assets , Enable business innovation and development with data , Provide more personalized and intelligent data services .