Research on Big Data Information Processing Model of Management

Communication Under the Background of Big Data

Hongtao Mao

School of Economics and Management, Wuhan University of Engineering Science, Wuhan, Hubei, China

Keywords: Big Data Technology, Information Processing, Communication Model.

Abstract: Management under the background of big data is faced with the application of big data technology and the

change of thinking mode, and how to better integrate the two is the purpose of the research. Through the

analysis of relevant theoretical models, the qualitative method, inductive method, deductive method and

comparative analysis method are used to analyze the significance of management communication

effectiveness in the context of big data, and focus on providing the effectiveness strategy of management

communication in the context of big data. In the context of big data, the effectiveness of management

communication is not based on a simple upgrade of technology, but more based on the upgrade of

management thinking in the context of big data, with the ability of big data information processing.

1 INTRODUCTION

Big data, also known as big data, refers to the

amount of data involved that is so large that it

cannot be captured, managed, processed, and

organized into more active purposes for business

decisions within a reasonable time through

mainstream software tools. "Big data" requires new

processing models to have stronger decision-making,

insight and process optimization capabilities to

adapt to massive, high-growth and diversified

information assets. Big data requires special

techniques to efficiently handle large amounts of

data that tolerate elapsed time. Technologies for big

data, including massively parallel processing (MPP)

databases, data mining, distributed file systems,

distributed databases, cloud computing platforms,

the Internet, and scalable storage systems. Of course,

big data is a kind of appearance or feature of the

development of the Internet to the current stage,

there is no need to myth it or maintain awe of it, in

the cloud computing represented by the

technological innovation curtain, these originally

difficult to collect and use data began to be easy to

use, through the continuous innovation of all walks

of life, big data will gradually create more value for

human beings. (Huang,2022)

Management communication in the context of

big data is based on the basic knowledge of

management, mathematics and computer technology,

and the systematic use of big data management

technology and methods to communicate in the

context of big data, cloud computing, artificial

intelligence and other emerging technologies under

the background of big data storage, big data analysis

and optimization management, big data governance

and assisted decision-making. This means that

management communication in the context of big

data not only emphasizes the impact of big data

technology on modern management, but also

emphasizes the importance of big data thinking.

(Zhang, 2022)

2 THE PROCESSING PROCESS

OF BIG DATA INFORMATION

The strategic significance of big data technology is

not to master huge data information, but to

professionalize the processing of these meaningful

data. In other words, if big data is compared to an

industry, then the key to the profitability of this

industry is to improve the "processing capacity" of

data and realize the "value-added" of data through

"processing". (Liu,2021)

Technically, the relationship between big data

and cloud computing is as inseparable as the two

sides of a coin. Big data must not be processed by a

Mao, H.

Research on Big Data Information Processing Model of Management Communication Under the Background of Big Data.

DOI: 10.5220/0012074100003624

In Proceedings of the 2nd International Conference on Public Management and Big Data Analysis (PMBDA 2022), pages 299-307

ISBN: 978-989-758-658-3

Copyright

c

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

299

single computer, and must adopt a distributed

architecture. It is characterized by distributed data

mining of massive amounts of data. But it must rely

on the distributed processing of cloud computing,

distributed database and cloud storage, and

virtualization technology. The term big data is

increasingly being used to describe and define the

massive amounts of data generated in the era of

information explosion, and this era of massive data

is known as the era of big data. (Xu,2022)

With the advent of the cloud era, big data has

also attracted more and more attention. Big data is

often used to describe the vast amount of

unstructured and semi-structured data created by a

company that takes too much time and money to

download into a relational database for analysis. Big

data processing generally includes four steps:

collection, import/preprocessing, statistics/analysis

and mining.

2.1 Big Data Collection

Big data collection refers to the use of multiple

databases to receive data from the client (Web, App

or sensor form, etc.), and users can use these

databases to perform simple query and processing

work.

For example, e-commerce companies use

traditional relational databases such as MySQL and

Oracle to store every transaction data, and NoSQL

databases such as Redis and MongoDB are also

commonly used for data collection.

In the process of collecting big data, its main

feature and challenge is the high number of

concurrency, because there may be thousands of

users to access and operate at the same time, such as

train ticket sales websites and Taobao, their

concurrent visits reach millions at the peak, so it is

necessary to deploy a large number of databases at

the collection end to support. And how to load

balance and shard between these databases does

require deep thinking and design. (Wang,2022)

Figure 1: Important components of Flume.

For example, Flume, a tool for data collection,

can ensure that data is delivered to the big data

platform safely and in a timely manner, no matter

what the company or how large the data comes from,

no matter what the data comes from, and users can

focus on how to understand the data. Flume supports

customizing various data senders in the logging

system for data collection. Flume provides the

ability to perform simple processing on data and

write to various data recipients.

Simply put, Flume is a data acquisition engine

that collects logs in real time (Figure 1). Flume has

three important components, Source, Channel, and

Sink.

Figure 2: Flume architecture.

Components in the Flume architecture (Figure 2):

An agent is essentially a JVM process that controls

the flow of Event data from an external log producer

to a destination (or the next Agent). A completed

agent includes three components: Source, Channel,

and Sink, Source refers to the source and way of

data, Channel is a buffer pool of data, and Sink

defines the way and destination of data output.

Source is responsible for accepting data to the

Flume Agent's component, which can process

various types and formats of log data, including avro,

exec, spooldir, netcat, etc. A channel is a buffer

located between Source and Sink. Channel allows

Source and Sink to run at different rates. The

Channel is thread-safe and can handle write

operations from multiple sources and read

operations from multiple sinks at the same time.

Commonly used channels include: Memory

channels are queues in memory. Use in scenarios

where data loss is allowed. If data loss is not

allowed, you should avoid using the Memory

Channel, as program death, machine downtime, or

reboot can cause data loss. File Channel writes all

data to disk. Therefore, there is no data loss in the

event of program shutdown and machine downtime.

The sink constantly reincarnates events in the

channel and removes them in batches, and writes

these events to the storage or indexing system in

batches, or sends them to another Flume Agent.

Sinks are completely transactional. Before deleting

data from a channel in batches, each sink initiates a

transaction with the channel. Once the bulk event is

successfully written out to the storage system or the

next Flume Agent, the Sink commits the transaction

using the Channel. Once the transaction is

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committed, the Channel removes the event from its

own internal buffer. Sink components include hdfs,

logger, avro, file, null, HBase, message queues, etc.

An Event is the smallest unit of data streaming

defined by Flume.

2.2 Big Data Import/Preprocessing

Big data preprocessing. Although there are many

databases on the collection side itself, if you want to

effectively analyze these massive data, you should

import these data into a centralized large-scale

distributed database or distributed storage cluster,

and at the same time, complete data cleaning and

preprocessing on the basis of import. Some users

will use Storm from Twitter to stream data when

importing to meet the real-time computing needs of

some businesses.

In the real world, the data is generally

incomplete, inconsistent "dirty" data, can not be

directly data mining, or the mining results are

unsatisfactory, in order to improve the quality of

data mining, data preprocessing technology is

produced. Data preprocessing mainly includes steps

such as data cleaning, data integration, data

transformation and data reduction.

Figure 3: Big data preprocessing process.

Usually, when we get the data, it is difficult for

the data to reach our expectations, such as: missing

data, accuracy problems, too many indicators, and

so on. It always has to go through a series of

analysis, and only through data manipulation can we

get the data we want. Therefore, at this time, an

important step - data preprocessing is particularly

important. Big data preprocessing is very important,

and data quality is the life of data. Data

preprocessing is the key to data quality. The above

big data preprocessing flow chart (Figure 3) shows

that data preprocessing is mainly divided into five

steps: data exploration, data cleaning, data

integration, data specification, and data

transformation. (Wang,2022)

2.3 Big Data Statistics/Analysis

Statistics and analysis mainly use distributed

databases, or distributed computing clusters to

perform general analysis and subtotals of massive

data stored in them to meet most common analysis

needs, in this regard, some real-time requirements

will use EMC's GreenPlum, Oracle's Exadata, and

MySQL-based columnar storage Infobright, etc.,

while some batch, or semi-structured data-based

requirements can use Hadoop. The main feature and

challenge of this part of statistics and analysis is that

the amount of data involved in the analysis is large,

which will greatly occupy system resources,

especially I/O.

Figure 4: Student's t-test.

For example, the statistical method used for

evaluation, the hypothesis test, the Student's t-test

(Figure 4), assumes that the distributions of the two

populations have equal but unknown variances. And

assume that each population is normally distributed.

In this case, T (t-statistic) follows a t-distribution

with degrees of freedom (df) (n1 + n2 - 2). The

farther T from zero is to the point that it is

impossible to observe such a T-value, the greater the

difference between groups. If T is too large, the null

hypothesis will be rejected. According to the

formula, the greater the difference in the mean, the

larger the T. When the variance of the population is

larger, the smaller the T. Significance level - α:

The likelihood that the null hypothesis will be

rejected when it is actually TRUE. For a small

probability, such as α=0.05, look for the value of

T* such that P(| T| ≥ T*) = 0.05。 After sampling

and calculating the observations according to the

formula, if | T| ≥ T*, the null hypothesis (μ1 =

μ2) is rejected. A student's t-test that considers the

probability of μ1 > μ2 and μ1 < μ2 is called a

bilateral hypothesis test, and the sum of the tail

probabilities of the two t-distributions should equal

the significance level. For the most part, it is

customary to evenly divide the level of significance

between the two tails. (0.05 / 2 = 0.025) −t and t are

both t-statistically observed values. p-value: is the

sum of P(T <= -t) and P(T >= t). If the null

hypothesis is TRUE, the p-value provides the

observed | T| >= the likelihood of t. In general, the

p-value represents the probability that a sampling

result will lead to the null hypothesis. Therefore,

when p-value < significance level, the null

hypothesis can be rejected. The opposite is true.

Confidence is an interval estimate based on the

population parameters of the sample data.

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2.4 Big Data Mining

Different from the previous statistical and analysis

process, data mining generally does not have a

pre-set theme, mainly on the existing data based on

various algorithms of calculation, so as to play the

effect of prediction, so as to achieve some high-level

data analysis needs. Typical algorithms include

Kmeans for clustering, SVM for statistical learning,

and NaiveBayes for classification, and the main

tools used are Hadoop's Mahout. The characteristics

and challenges of this process are mainly that the

algorithms used for mining are complex, and the

amount of data and computation involved in the

calculation are large, and the commonly used data

mining algorithms are mainly single-threaded.

With the development of the times, the data

generated by humans has grown exponentially, and

the open application of data and the value of data

mining are getting higher and higher. Behind a series

of hot words such as big data precision marketing

and big data insight, it is data mining and analysis

technology that plays an important role. Data mining

technology has not only become an important means

for government departments to improve their

governance capabilities, but also the key to

enhancing the core competitiveness of all walks of

life. Data mining is the process of extracting hidden

information and knowledge from large, incomplete,

noisy, fuzzy, and random data that people do not

know in advance, but have potentially useful

information and knowledge (Figure 5). It can be

seen that data mining is the name of the result of a

process, that is, the main goal is to mine hidden

information from the data. It is an interdisciplinary

field of science influenced by multiple disciplines,

including database systems, statistics, machine

learning, visualization, and information science.

Figure 5: Big data mining process.

Therefore, the general process of the entire big

data processing should meet at least these four

aspects of the steps in order to be regarded as a

relatively complete big data processing.

3 THE SIGNIFICANCE OF

MANAGING

COMMUNICATION

EFFECTIVENESS IN THE

CONTEXT OF BIG DATA

John Naisbitt, a famous American futurist, once said:

"The competition of the future will be the

competition of management, and the focus of

competition is on the effective communication

between members of each social organization and

with external organizations." Indeed,

communication is the soul of management, and

effective communication determines the efficiency

of management. Through effective communication,

managers can accurately convey information such as

the organization's vision, mission, expectations and

performance to the members of the organization, so

as to more effectively implement organizational

reform, improve management functions, coordinate

the behavior of organizational members, and adapt

the organization to changes in the external

environment. In the context of big data, modern

managers attach great importance to the role of

effective communication as the key to business

success. This not only shows the important position

and role of effective communication in management

activities, but also shows that it is not easy to

achieve effective communication, and requires the

efforts and efforts of both parties involved.

3.1 It Can Promote Employees to Form

a Consensus on the Goals and

Tasks Determined by the

Enterprise

In the context of big data, enterprise managers

should let employees understand the tasks and goals

of the enterprise in a timely manner, and understand

"What is the business (task) of the enterprise?" And,

"What does the business want to become?"

Enterprise managers should listen to (collect)

employees' reasonable opinions and suggestions on

tasks and objectives, conduct timely research, and

further modify and improve enterprise tasks and

goals. Then feedback the tasks and goals of the

enterprise to employees in a timely manner. Through

such effective communication, the company's tasks

and goals are shared among all employees. This can

not only make the enterprise goals reflect the will of

all employees, but also improve the scientific

decision-making. (Bi 2019)

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3.2 Under the Background of Big Data,

It is Conducive to Collecting Data

and Sharing Information to

Achieve Scientific Management

In the increasingly fierce market competition, in

order to successfully achieve the goal and form a

competitive advantage, enterprises must first fully

understand all aspects of information, in order to

make scientific decisions, in order to survive and

develop in the changing environment. Effective

communication can enable enterprises to understand

various external information and intelligence, such

as national guidelines, policies, the status quo and

development trend of similar enterprises at home

and abroad, the dynamics of the consumer market,

etc., and timely adjust business decisions to adapt to

changes in the external environment; It can enable

enterprises to understand their own resource

situation and ability to use resources; Can

understand the basic situation of employees, know

people well, and improve the enthusiasm of

employees; It can gain insight into the relationship

between various departments and improve the

efficiency of management. Therefore, only by timely

and comprehensively grasping all kinds of internal

and external information, intelligence and data can

we accurately control and direct the operation of the

entire enterprise and achieve scientific and effective

management. (Liu,2022)

3.3 In the Context of Big Data, It Can

Improve the Interpersonal

Relationship of Enterprises

Effective communication within a company, whether

between departments, between departments, or

between individuals, is extremely important. In

reality, some enterprises have tense relations

between departments and between people, fierce

contradictions and internal interpersonal relationship

imbalances, and the reason is nothing more than lack

of communication or improper communication

methods. If the information communication

channels of an enterprise are not smooth, it is

difficult for people to achieve effective

communication, which will make people depressed

and depressed. In this way, it will not only affect the

mental health of employees, but also seriously affect

the normal production of enterprises and reduce the

efficiency of enterprises. Therefore, if an enterprise

wants to develop smoothly, it must ensure the

smooth flow of various communication channels

within the enterprise, left and right, only in this way

can we stimulate the morale of employees, promote

the harmony of interpersonal relationships, and

improve management efficiency. This is true (Figure

6), relationship-oriented leaders are positively

correlated with high group productivity and high

satisfaction, while work-oriented leaders are

positively correlated with low group productivity

and low satisfaction. (Qiu,2021)

Figure 6: Task relationship model.

3.4 Under the Background of Big Data,

It Can Enhance the Cohesion of

Enterprise Employees

Employee cohesion is an important asset for

enterprises. Strong cohesion indicates that the

enterprise is attractive to members, so how to

enhance the cohesion of employees? First, promote

communication and support among employees. It is

common for employees to have conflicts and

opinions in cooperation, and the key is what kind of

attitude to adopt to deal with the different opinions

between each other, and the best way is to

communicate, that is, take the initiative to exchange

their own views with each other. Second, adopt a

democratic management approach. In the process of

enterprise management, it is necessary to listen

carefully to the opinions of employees from all sides,

especially different opinions. Third, companies

should link performance and remuneration to

employees, including rewards for employees who

can work closely with others, which helps improve

employee satisfaction. Fourth, we must attach

importance to mutual trust among employees.

Promoting trust can be considered in terms of: open

communication from top to bottom and bottom up

within the organization, and the concept of honest

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communication; Involve employees in management;

Advocate employee self-management, so that

employees have a sense of trust in the enterprise and

colleagues. Fifth, carry out performance

management. The essence of the performance

management process is the communication process,

in the dynamic process assessment, creative

employees and outstanding performance employees

are rewarded; Employees with poor performance

should be adjusted in a timely manner; Penalties are

imposed for passive sabotage or disciplinary

violations. Improve the performance of each

employee through dynamic process performance

management, thereby driving the improvement of

the performance of the entire enterprise and the

achievement of corporate goals. These need to be

achieved through communication, so effective

communication can enhance employee cohesion.

4 MANAGE COMMUNICATION

EFFECTIVENESS

STRATEGIES IN THE

CONTEXT OF BIG DATA

4.1 Improve Managers' Understanding

of Big Data

In the context of big data, managers should raise

awareness of the importance of communication and

must effectively change their parent or authoritative

communication roles. Equal communication is the

foundation of good and effective communication.

Managers should not only treat subordinates equally,

but also treat themselves and subordinates equally,

and truly achieve rationality, respect and trust in

subordinates. Truly realize the one-way, top-down

communication method of the past to equal and

two-way communication, both top-down and

bottom-up communication methods. Effective

communication can only be achieved by equal and

active two-way communication. In addition, to be

fully prepared for communication, managers must

develop clear communication goals and develop a

clear communication plan before communication. At

the same time, the personnel involved in

communication are encouraged to negotiate and

collect and analyze information and materials, and

on this basis, publicity and explanation, to provide

employees with a good communication environment,

so as to fundamentally improve the efficiency of

enterprise communication, and then improve the

operational efficiency of enterprises. (Shen,2022)

4.2 Integrate Big Data Communication

Channels

In the context of big data, the integration of

communication channels is to adjust and combine

existing channels, delete some inefficient or

ineffective channels, and add some new, fast,

efficient and acceptable big data information

channels (Figure 7), while considering the

complementarity of various channels, the purpose is

to better perform communication functions and

achieve communication goals. In the process of

integration, the following principles should be

adhered to: First, the system principle. An

organization is a system, and changes or changes in

any part of the organization will have a cascading

effect on the entire system. The same is true of

communication channels, each type of

communication channel or method of

communication does not exist in isolation, there are

connections and influences between each other.

Therefore, in the process of integrating

communication channels, the connections and

influences of channels should be considered. Second,

the principle of openness. In the process of

management communication, enterprises are

required to disclose the channels of communication

for most management communication activities that

do not require confidentiality, and increase

communication channels as much as possible to

increase their awareness, transparency and openness.

In this way, communication blind spots caused by

information asymmetry can be reduced. Third, the

principle of timeliness. It is required that in the

process of communication, we must pay attention to

the saving of communication time and the

improvement of communication efficiency.

Therefore, it is necessary to choose and adopt the

shortest path to communicate as quickly as possible.

Its purpose is to increase the speed of information

transmission and reduce the possibility of

information distortion caused by layer by layer

transmission. Fourth, the principle of integrity. From

the perspective of the communication process, a

complete communication process includes the

transmission, reception and feedback of information.

The integrity principle emphasizes the integrity of

the communication process. If there are only

channels for information transmission and reception,

and no channels for information feedback, this

communication process is incomplete and its

information communication is insufficient.

Nowadays, in the process of channel integration,

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special attention should be paid to the correction and

supplementation of feedback channels.

Figure 7: Equal communication model.

4.3 Improve Managers'

Communication Skills in the

Context of Big Data

First, improve managers' skills in listening

effectively. In the process of communication,

managers should first concentrate and not be

absent-minded; Second, don't be preconceived,

interrupt the other person, or show impatience. This

will make the other party reluctant to continue the

communication; Finally, be empathetic, appreciate

the other person's emotional changes and unspoken

meanings, and understand the spirit. Second,

improve non-verbal communication skills. When

you are listening to the messenger, the messenger

will judge whether you are listening carefully and

truly understanding by observing your expression.

So, making eye contact with the messenger allows

you to focus, reduces the likelihood of distraction,

and can encourage the messenger. In addition,

effective listeners will express the information they

hear in non-verbal ways, such as approving nods,

puzzled head shaking, appropriate facial expressions

(such as smiling), etc., which indicate to the

messenger that you are listening carefully and

whether you understand, which is conducive to

improving communication. Third, pay attention to

emotional communication. In actual work,

communication skills are usually manifested in

whether managers can accurately describe the

current situation and prospects of enterprise

development, and let employees understand the

current situation and development strategy of the

enterprise through certain channels. The recognition

of employees is the greatest support for managers

and the most direct goal of manager communication.

The humanistic management of modern enterprises

requires managers not only to be good at correctly

using oral expression and language and writing

ability, clear expression, clear organization, concise

language and strong pertinence; Be good at listening

and paying attention, and respect the views of others;

Improve bad communication habits, properly use

expressions, movements and other non-verbal

communication methods, but also require managers

to be good at emotional communication with

employees, integrate emotions into the whole

process of management, so that employees realize

their value to the development of the enterprise, so

as to stimulate lasting work enthusiasm.

4.4 Set Up A Scientific Big Data

Organization

In the context of big data, organizational setup: On

the one hand, it is necessary to consider the excessive

organizational hierarchy caused by the organizational

scale effect, resulting in easy loss and distortion of

information, as well as the alienation effect caused

by the organization being too large, and efforts

should be made to reduce the organizational level

vertically to reduce communication links, keep

information smooth, and reduce interference, delay

and distortion. At the same time, we will broaden the

channels of information communication and promote

the exchange of information through multi-channel

communication. Horizontal exchanges and

cooperation should be strengthened between

horizontal departments, and the links between

departments and between personnel should be

strengthened; On the other hand, special

communication bodies should be set up to facilitate

communication. Although the informal

communication mechanism has a certain degree of

efficiency, it will inevitably be influenced and

constrained by the personal preferences of managers,

and greatly affect the effectiveness of communication.

Therefore, it is necessary to establish special

communication institutions to promote

communication, and to truly achieve effective

communication by institutionalizing communication.

4.5 Improve the Communication and

Feedback Mechanism in the

Context of Big Data

Communication without feedback is not a complete

communication, and complete communication must

have a perfect feedback mechanism. Otherwise, the

effectiveness of communication will be greatly

reduced. The establishment of the feedback

mechanism should first start with the sender of the

information, and the sender should obtain feedback

information by asking questions and encouraging

the recipient to give positive feedback after

B

C

D

E

A

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transmitting the information. In addition, the sender

should also carefully observe the reaction or action

of the other party to obtain feedback indirectly.

Because feedback can be intentional or unintentional.

As the recipient of information, it is actually in the

subject position in the communication feedback. In

the context of big data, the information transmitter

should actively accept the feedback from the

recipient, so that organizational communication

becomes a two-way communication in the true

sense.

4.6 Learn to Think Differently Based

On Big Data

In the context of big data, the most important

communication strategy is empathy. People within

an organization often have different backgrounds,

values, and conflicts of all kinds. People will also

process and choose information for the following

reasons, one is that the information is not harmful or

good to me, and the other is to transform it into what

I like to hear. Therefore, the internal information

changes its taste after layer by layer transmission,

which is the obstacle and risk of internal

communication within the organization. The

solution is to think from the other party's point of

view, that is, "empathy", to take the initiative to

think about the other party's thoughts, opinions,

emotions, attitudes, that is, how he thinks about this

problem. We must let empathy permeate the specific

strategy of communication, that is, transposition in

the organization of information; transposition

yourself in your own self-positioning; Consider how

to take care of each other's emotions. (

Zhao,2022)

5 CONCLUSIONS

At present, the downstream application fields of

China's big data industry are very extensive,

involving almost all fields of our daily life.

According to the data, the top three applications of

China's big data industry are finance, medical health

and government affairs, accounting for 30%, 14%

and 13% respectively (Figure 8).With the rapid

development of information technology, we have

entered the era of information explosion

characterized by "big data, Internet of Things, cloud

computing and mobile Internet". In business,

economics and beyond, decision-making is

increasingly based on data and analysis rather than

experience and intuition. As a prerequisite for

operational decision-making, big data has become

the basis for influencing the government and

enterprises to make major decisions such as

investment analysis, credit financing, enterprise

operation and management, and corporate strategic

planning, and playing an increasingly important role

in promoting high-quality economic and social

development. In the future, business intelligent

decision-making, building a smart government,

building a smart city, and developing a smart society

will all be related to big data, and management

(communication) talents who understand big data

and have big data thinking will be the bridge

connecting business and technology, and will be the

intelligent hub of data-driven enterprises.

Figure 8: Big data application statistics in 2020.

In the era of big data, technology continues to

iteratively upgrade, market competition is becoming

increasingly fierce, cooperation and communication

with all parties has become a regular activity of

enterprises, the success of cooperation depends on

the effectiveness of communication, rather than

simply relying on the application of big data

technology, therefore, communication should

become one of the basic skills that managers must

master. Based on the background of big data, the

strategy of communication subject positioning

should take into account elements such as integrity

ethics and active listening, although the

technological development of big data has a great

impact, but sincerity and credibility are the most

effective constructive communication weapons,

integrity is the character of leaders, integrity is the

source of leadership. In the context of big data,

active listening means recognizing the importance of

two-way communication, listening from the position

of affirming the other party, overcoming a priori

consciousness and mental model, and giving the

other party a timely and appropriate response. In this

way, our communication and management will reap

joy.

30%

27%

14%

13%

8%

Finance Other Healthcare

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ACKNOWLEDGEMENT

This article is a paper on the effectiveness of

management communication in the context of big

data. I would like to thank all the companies we

visited for the valuable experience they shared. At

the same time, thanks to the valuable research

results of the reference author of this article.

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