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### 数据分析系统能力成熟度评估模型与优化策略 #### 背景介绍随着科技的发展以及数据量的爆炸性增长，数据已经成为当今社会中的重要战略资产。如何建立合适的体系来管理和分析这些数据变得越来越重要。本文针对美国大学生数学竞赛（简称“美赛”）中的一个获奖论文进行了详细解析，该论文探讨了如何实现数据的全面潜力。 #### 论文概览这篇论文主要关注于数据分析系统的成熟度评估，并提出了一系列方法来最大化数据资产的潜力。通过构建模型分析影响数据分析系统成熟度的因素，揭示了如何更好地利用数据资源，并制定计划以实现数据资产的最大化利用。 #### 数据分析系统能力成熟度评价模型(DAMM) 在论文的第一部分(Task 1)，作者基于扎根理论，综合考虑了ICM公司（论文案例中的企业）的需求及题目给出的成熟度模型文献中的因素，筛选出了影响数据分析的指标，并建立了数据分析系统能力成熟度评价模型（Data Analysis Maturity Model, DAMM）。具体步骤如下： 1. **指标筛选**：根据ICM公司的需求以及成熟的模型文献，筛选出影响数据分析的关键指标。 2. **权重分析**：采用层次分析法（Analytic Hierarchy Process, AHP）与熵权法（Entropy Weight Method, EWM）相结合的方式对相关指标的权重进行组合分析。 3. **系统成熟度划分**：引入隶属函数的概念，将系统成熟度分为五个等级，并计算得到系统成熟度指数得分0.8173分（满分1分），处于相对成熟的第四级（最高为第五级）。 #### 系统优化在第二部分(Task 2)中，作者首先利用线性规划模型进行定量分析，在有限的优化成本下，将问题转化为最大化系统成熟度指数。接着，运用系统动力学进行模拟，获取不同成熟度水平对应的指标值，通过比较模拟结果与第一部分的评估模型来验证模型的有效性。结合定量与定性分析的结果，基于第四级与第五级之间的差异，提出了ICM公司需要在团队协作和技术管理方面加大投资的建议。 #### 协议制定在第三部分(Task 3)，为了制定协议，作者进一步探讨了如何在资源有限的情况下提高系统的成熟度。这部分可能涉及到了具体的合作框架、资源分配策略等细节。虽然具体内容未在提供的部分中明确给出，但可以推测，这部分讨论了如何在现有的资源条件下，通过有效的资源配置和管理，提升数据分析系统的整体表现。 #### 结论与启示这篇获奖论文通过对数据分析系统的成熟度进行深入研究，不仅构建了一个实用的评估模型，还提出了一系列优化方案。对于企业而言，这不仅是理论上的指导，更是实际操作中的重要参考。它提醒企业在面对海量数据时，不仅要注重数据的收集与存储，更要重视数据分析系统的建设和优化，以充分挖掘数据的价值，提升企业的竞争力。此外，论文的研究方法也为其他领域的数据分析提供了借鉴意义。

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How Can We Realize the Full Potential of Our Data?

Summary

With the development of technology and the explosive growth of data, data is becoming an

important strategic asset in today's society, and it is more and more important to establish a suitable

system to manage and analyze the data. In this paper, we build a model to analyze the factors

affecting the maturity of data analysis systems, revealing how to make better use of data, and for-

mulate plans to maximize the potential of data assets.

In Task1: First, based on the grounded theory, we comprehensively consider the ICM company

requirements as well as the factors of the maturity model literature given in the title, screening out

the indicators that affect data analysis to establish a data analysis system capability maturity eval-

uation model (DAMM). Then we use the Analytic Hierarchy Process combined with the Entropy

Weight Method (AHP-EWM) to perform a combined weighting analysis on the weight values of

the relevant indicators. Finally, we introduce the concept of membership function, dividing the

system maturity into five levels, and calculating the system maturity index score of 0.8173 points

(the highest score is 1), which is in the relatively mature fourth level ( the highest is the fifth

level).

In Task2: First, we use a linear programming model to quantitatively analyze, and in the case

of limited optimization costs, we transform the problem into maximizing the system maturity

index. Then, we use system dynamics to simulate and obtain the index values corresponding to

different maturity levels for qualitative analysis, verifying the validity of the model by comparing

the difference between the simulation results and the evaluation model of task 1. Finally, we com-

bine the two methods, and based on the differences between the fourth and fifth levels of maturity,

we give suggestions that ICM companies need to invest in teamwork and technical management.

In Task3: In order to formulate an agreement to measure the effectiveness of the system, we

divide different index levels from the perspectives of people, technology and process, using the

Apriori to analyze the rule association between the three groups of indicators, combined with

system dynamics simulation in the way of simulation, we give the relevant agreement of the ICM

data analysis system in the connection mechanism between people, technology, process and the

three together.

In Task4: First, we evaluate the model based on the differences in data analysis systems of

seaport companies with different scales, and conduct multiple sets of simulations based on the

model proposed in task 2 simulation. Then, we compare the errors between the simulated maturity

index and the results obtained by the DAMM model, which are all smaller than 5%, showing the

good applicability of the model to seaport companies of different sizes. Finally, combined with the

International Standard Plan of "Quality Management System Requirements" (ISO9001), we ana-

lyze the reasons for the universality of DAMM, and give three benefits for customers to adopt

DAMM for ICM.

Keywords: data analysis; maturity evaluation model; AHP-EWM; system dynamics; Apriori

Problem Chosen

2022

MCM/ICM

Summary Sheet

Team Control Number

2215444

Team # 2215444 Page 2 of 22

Contents

1 Introduction ...................................................................................................... 3

1.1 Problem Background ....................................................................................................... 3

1.2 Restatement of the Problem ............................................................................................. 3

1.3 Literature Review ............................................................................................................. 3

1.4 Our Work .......................................................................................................................... 4

2 Assumptions and Justifications ....................................................................... 4

3 Notations ........................................................................................................... 5

4 TASK 1: DAMM Based on AHP-EWM ......................................................... 6

4.1 Indicator selection ............................................................................................................ 6

4.2 Weight determination ....................................................................................................... 9

4.2.1 EWM ...................................................................................................................... 9

4.2.2 AHP ...................................................................................................................... 10

4.2.3 Combination weight ............................................................................................. 10

4.3 The Solution of Task 1 ................................................................................................... 11

5 TASK 2: Linear programming and System Dynamics ............................... 12

5.1 Model Overview ............................................................................................................ 12

5.2 Establishment of Model ................................................................................................. 12

5.2.1 Linear programming ............................................................................................. 12

5.2.2 System Dynamics ................................................................................................. 13

5.3 The Solution of Task 2 ................................................................................................... 14

6 TASK 3:System Effectiveness Measure Model Based on Apriori ............. 15

6.1 Model Overview ............................................................................................................ 15

6.2 Data Processing .............................................................................................................. 15

6.3 Establishment of Apriori ................................................................................................ 16

6.4 Analysis of The Result ................................................................................................... 17

7 TASK 4: Sensitivity Analysis and Model promotion .................................. 18

7.1 Sensitivity Analysis ........................................................................................................ 18

7.2 Model promotion ........................................................................................................... 19

8 Model Evaluation and Further Discussion .................................................. 20

8.1 Strengths ........................................................................................................................ 20

8.2 Weaknesses .................................................................................................................... 20

References .......................................................................................................... 21

Team # 2215444 Page 3 of 22

1 Introduction

1.1 Problem Background

With the completion of the information revolution in the 1950s, the main body represent-

ing advanced productivity has been banned by informatization. Nowadays, data and infor-

mation have become strategic materials for enterprises and companies to compete. Only by

properly managing and making full use of information can cold data be transformed into visible

wealth. Therefore, mature data analysis capabilities have become the top priority to enhance

the competitive advantage of enterprises. The complete cognition of the data analysis system

can undoubtedly clarify the direction of e

nterprise optimization and development. As a conse-

quence, a reasonable assessment of its own data analysis capabilities has become the key for

enterprises to improve themselves and enhance user loyalty.

1.2 Restatement of the Problem

Considering the background information and restricted conditions identified in the prob-

lem statement, we need to solve the following problems:

 Provide indicators to measure the maturity of Intercontinental Cargo (ICM) D&A

systems (including talent, technology and process).

 Establish a model for Intercontinental Cargo Company (ICM) to evaluate the maturity

of its own D&A system, and provide the modification method that ICM needs to make

to the system to fully realize the potential of data.

 Give the protocol that ICM needs to make to measure the effectiveness of the D&A

system.

 Apply the model to ports of other sizes and different industries, analyze the adapta-

bility of the model, point out the superiority of the model indicators, and analyze the

possible benefits to ICM companies if ICM customers use the evaluation model.

 Write a letter to ICM Collaborative users describing our approach and increasing con-

fidence in ICM Collaborative D&A systems.

1.3 Literature Review

In 1979, Nolan proposed the growth stage model to study the development of enterprise

information data system, and it became the prototype of the maturity model. In 1986, Carnegie

Mellon University in the United States developed the Capability Maturity Model (CMM) [1]

based on the Nolan model. Its main contents are divided into the qualitative analysis of capa-

bility maturity and the evaluation index system of capability maturity model. After the capa-

bility maturity model is established, many scholars begin to improve the maturity model and

apply it in different fields according to the characteristics of different fields. For example, hu-

man resources maturity model, customer information quality management maturity model, en-

terprise information maturity model.

In the research of various scholars, Pekka Berg [2] constructed the R&D quality maturity

model, by constructing the dimensions of the quality maturity model, the maturity level and

Team # 2215444 Page 5 of 22

the background and basis for our subsequent analysis.

 Assumption 1：It is assumed that ICM's data analysis system is comprehensive in

terms of people, technology, and processes.

Justification: Although the general description given in the title is not necessarily all

the indicators concerned by ICM's data analysis system, in order to make the indicator

analysis clear and controllable, we assume that other indicators that affect data anal-

ysis found later are not considered by the system and belong to the part of maturity

that needs to be improved.

 Assumption 2

：It is assumed that the literature we have checked is relatively com-

prehensive and scientific.

Justification: In order to make the DAMM model scientific and reasonable, it is as-

sumed that the literature we have checked is comprehensive and scientific, which

means that our analysis is consistent with objective cognition and facts.

 Assumption 3

：It is assumed that ICM clients are limited to collective companies,

ignoring a small number of self-employed individuals.

Justification: In real work, intercontinental freight companies may also trade with a

small number of self-employed individuals, but the vast majority of customers are of

little value and significance because of individual people, technology and processes.

 Assumption 4：Assume that the estimated costs that ICM can use to improve the

data analysis system are known.

Justification: This is done to analyze how to use the data assets to their fullest po-

tential.

3 Notations

The key mathematical notations used in this paper are listed in Table 1.

Table 1: Notations used in this paper

Symbol Description



Factors considered score



Initial score for the factor of interest





Comprehensive weight





Sbjective weight





Objective weight





People's Scores for Different Indicators





Scores for different indicators of technology





Scores for different metrics of the process

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