An Application of the Analytic Hierarchy Process to the Evaluation of

Companies’ Data Maturity

Simone Malacaria

, Andrea De Mauro

, Marco Greco

and Michele Grimaldi

Department of Enterprise Engineering, University of Rome “Tor Vergata”, Rome, Italy

Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy

Keywords Big Data, Data Analytics, Analytic Hierarchy Process, Assessment System.

Abstract: The study reports the data maturity evaluation on a sample of Italian firms of different sectors and sizes,

retrieved through an online assessment made by 261 professionals and entrepreneurs operating in the data

domain. The paper's objective is to derive the relative importance of the critical factors to impact successful

big data initiatives, according to organization reality and manager perspective. The questionnaire was

distributed among IT professionals and decision-makers in Italy using the LinkedIn platform. The assessment

was divided into two sections: the 1st one contained the assessment of 8 critical success factors for big data,

whereas the 2nd section assessed weights based on an application of the analytic hierarchy process. The result

of this process is a scoring system that includes the characteristics a company "must-have" to become data-

oriented and make data-driven decisions. The application of the weights allows giving more importance to

the domains that managers think are more important in a data-driven company. Respondents agreed to the

importance of integrated architecture, data-friendly corporate culture, and integrated organization domains.

Once the results consider the weights from the AHP, data friendliness becomes the most sought-after

characteristic. The findings provide direction for further development of the assessment system.

1 INTRODUCTION

Data science is the set of statistical techniques and

methods necessary for the extraction, analysis, and

interpretation of data. In the era of "Big Data" where

a huge amount of information is available to

companies, data-driven choices are essential for

defining a company's medium and long-term strategy

and can turn into a huge competitive advantage

success (Grover et al., 2018; Kubina et al., 2015). The

major internet and manufacturing companies like

Google, Facebook, and Apple hire the best data

science talents to work in their vast data science

departments. Being a successful company today

means making data-driven decisions (Ghasemaghaei,

2019; Wamba et al., 2017). Companies that have

overlooked the potential of data science have

observed their competitors seize market share and

enlarge their customer base over the past years.

Pioneers like Facebook, Amazon, and Google instead



https://orcid.org/0000-0003-0736-3464

https://orcid.org/0000-0001-9050-5018

https://orcid.org/0000-0002-6935-7775

https://orcid.org/0000-0002-5837-0616

developed dominant market positions. Nowadays,

basically, companies of all sizes are investing heavily

in data and AI initiatives to narrow the gap with the

tech giants(Davenport & Bean, 2019). Although the

value that data analytics brings to companies has been

recognized (Grover et al., 2018; Günther et al., 2017;

Mikalef et al., 2019), there is still confusion on how

to properly integrate big data initiatives within the

organization for long-term planning (McShea et al.,

2016; White, 2019). This is today the main reason for

the actual failures of more than half of big data

programs worldwide. Being a data mature

organization means being able to spot new data-

driven opportunities in advance while they are still

invisible to the competitors, using analytic insights to

deliver business outcomes.

In this study, we analyze the data maturity of a

representative sample of Italian companies of

different sectors and sizes. To score what the ripeness

level of the enterprises is, we relied on an eight-

Malacaria, S., De Mauro, A., Greco, M. and Grimaldi, M.

An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity.

DOI: 10.5220/0011088000003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 50-61

ISBN: 978-989-758-569-2; ISSN: 2184-4992

dimensional assessment system derived from the

literature (De Mauro et al., 2021) - the CBDAS -

consensual with the existing big data maturity

models. The CBDAS applies the analytic hierarchy

process to assign weights to the critical success

factors for big data initiatives. We analyze how

respondents (Senior Manager and IT Decision-

makers) agreed or disagreed with questions that

underlined the importance of each success factor

proposed. As a result, the paper derives insights on

the importance that the managers give on data-driven

choices and on the validity of the CBDAS to apply to

companies of different sizes and industry sectors.

2 BIG DATA MATURITY

MODELS

In the digital era, data analytics becomes a central

point of achieving corporate objectives (Khanra et al.,

2020). The ability of a company to take advantage of

the usage of big data (degree of corporate data

maturity) determines the degree of success or failure

of a data-driven initiative (Constantiou & Kallinikos,

2015; Santos-Neto & Costa, 2019; Sharma et al.,

2014).

The big data maturity models represent robust

frameworks that support the evaluation of old and

new big data initiatives among specific aspects or

domains to rule whether they can generate new

knowledge for a company (Grover et al., 2018;

Olszak & Mach-Król, 2018; Santos-Neto & Costa,

2019; van Hillegersberg J., 2019).

By leveraging a maturity model, data maturity can

be evaluated at the sub-domain level when it refers to

micro-level factors such as routines and

organizational requirements, at the domain level

when it refers to the macro-level factors to assess the

needed conditions to reach maturity stages. While

macrolevels generally assess strategic factors of big

data initiatives' success, microlevels make clear the

actions to be taken to guide maturity within

organizations (Comuzzi & Patel, 2016; Halper &

Krishnan, 2014; Nott, C. and Betteridge, 2014).

The aspects investigated through the maturity

models can be many, such as IT management,

business intelligence ecosystem, and data warehouse

adoption, among others. In general, big data maturity

models give the company the maximum value when

used to analyze how business processes and strategies

integrate with big data initiatives, providing

management with the needed information to support

strategic and operational decisions (Al-Sai et al.,

2019).

Data models help to outline the optimal choices

for a path of improvement of the business

management system. The absence of specific

procedures regarding the assessment and operation of

maturity models may represent a limitation for the use

of the model as an organizational and diagnostic-

prescriptive management system.

So far, only a few of the big data maturity models

present in the literature contain details on the

development, validation, and evaluation processes of

the model itself, constituting a limit to the validity and

usefulness of many proposals (Pöppelbuß &

Röglinger, 2011; Santos-Neto & Costa, 2019).

We rely on the Consensual Big Data Assessment

System (CBDAS) proposal (De Mauro et al., 2021),

which starts from a holistic and conceptual

integration of existing models. It encompasses the

key elements of success that are coherent with big

data's essential components and consensual with the

most prominent existing models. The CBDAS offers

a robust conceptual model complemented by a

practical assessment and recommendation system to

grant usefulness and applicability for industries.

3 METHODOLOGY

3.1 Sample Collection

In this work, we submitted a Likert-scale (1-strongly

disagree to 5-strongly agree) questionnaire to 261

Italian companies' employees, where the participants

were asked to answer questions that measure a

company data maturity. The participants were mostly

company managers and IT experts. We used the

LinkedIn platform to draw a representative sample of

professionals worldwide to conduct the online

assessment. Although the LinkedIn community is not

encompassing the population of industry

representatives exhaustively, it might be considered

suitable for targeting professionals in scope. The

process of sample selection leverage publicly

available information about the respondents provided

by LinkedIn users, which increases the credibility of

the sample and permits control over its composition.

The inclusion criteria were related to (a) the

seniority of the respondents (Senior Managers and

Directors), (b) the position covered in their

organization (IT Director, IT Responsible, IT

Specialist, Senior Data Specialist, Senior Data

Scientist, Senior Business Analyst, IT Consultant),

An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity

of Data Analytics, Big Data, and IT Management.

With the inclusion criteria identified, we had a

potential audience of more than 320.000 unique

respondents, targeted with LinkedIn campaigns

launched from October to December 2021. Exclusion

criteria have been applied to filter (a) uncomplete

assessments and (b) companies operating outside

Italy since the study focus on the Italian territory.

3.2 Assessment Structure

The CBDAS assessment was structured in two parts:

the first part is composed of 40 questions divided into

8 domains and allows the evaluation of data maturity

on critical success factors for big data initiatives; the

second part is made of 15 questions that focus on the

pairwise comparison of data maturity characteristics

of the company, which represent a multifactorial

combination of the 8 critical success factors.

Table 1: Assessment submitted to the participants.

Domain Questions

DATA STRATEGY

)

The com

has a solid data anal

sis strate

)

The com

uses data anal

sis to make strate

ic decisions.

)

Data anal

sis is not an im

ortant

art of the com

's transformation strate

4) The Corporate Data Strategy has been documented, approved, and communicated by Top

Mana

ement to the entire or

anization.

)

Leadershi

romotes the use of data anal

tics throu

hout the com

6) There is a list of key analytical projects or analysis priorities whose progress is regularly

tracked.

7) The legal procedures on data usage and management are documented and communicated to

the entire or

anization.

)

There are re

ular audit

rocesses on data usa

e and mana

ement within the or

anization

DATA-PROCESS

INTEGRATION

9) Business processes are guided by numerical evidence, which directly impacts the way the

com

erates.

10) The Key Performance Indicators related to data processes are stored and could be

anal

zed in real-time.

11) The organization uses automated analyses (e.g., systems that suggest in-depth analysis or

build models, alert systems based on control levels, reports that automated data processing

and output delivery).

12) Your company has organizations (internal or external) that focus on data engineering,

software development, data quality to ensure proper support to analytical processes.

13) Managers and process owners know what data are available in the company to support

their business decisions.

TECH

INFRASTRUCTURE

14) The data infrastructure is adequate to the size of the organization, and the organization is

using the following types of data management technology where needed: Cloud Systems, Big

Data Architectures.

15) The organization is able to monitor more data pipelines. Therefore, the organization is

able to manage multiple analytical projects in parallel.

16) The organization has designed its data architecture to integrate multiple sources and

facilitate data access and anal

sis.

17) The computational power and the size of the available memory are adapted to the current

information in

ections.

)

stems com

with hi

h-securit

standards and are sub

ect to

eriodic intrusion tests.

19) How many of the following technologies use your organization to analyze your data?

(Spreadsheets, reports, dashboard, predictive analysis/machine learning, deep learning, and

other aspects of the AI).

20) Only a few managers in the company have access to the analysis results.

INTEGRATED

ARCHITECTURE

21) The organization collects and manages structured (i.e., sales data in tabular format) and

unstructured

(

i.e., Video and Audio

)

data t

es for its anal

sis activities.

22) Employees can access data as needed, including structured and unstructured data, through

a well-defined

overnance

rocess.

)

The data formats are standardized and documented.

)

There is a sin

le data model to which all the business units refer.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

Table 1: Assessment submitted to the participants (cont.).

Domain Questions

DATA

INTERFACE

25) Analysis solutions are designed to provide the best user interface to the right person (for

example, corporate analysts, business users, data scientists, data engineers, et al.).

26) Employees do not receive guides on how to access the data.

27) It is easy to get data in a format not covered by existing interfaces; the technical support

needed is minimal, and the request is standardized.

28) Corporate data are accessible through a business intelligence interface that allows users to

combine different data sources, create graphs and tables with a high degree of customization,

and allows users to share the most interestin

views with other stakeholders.

29) Data Scientists and Business Analysts are able to connect any application developed with

the latest data available at the needed level of detail.

ANALYTICAL

SKILLS

30) The knowledge of data science techniques is widespread in the organization, even outside

the business units dedicated to data anal

sis.

31) The analysts use tested and documented tools and methodologies to respond to the business

uestions of the or

anization, which re

uires anal

tical su

ort.

)

There is a career model for Business Anal

sts and Data Scientists.

)

The com

has a clear recruitment strate

for data

rofessionals.

34) There is a broad and modular program for analytical skills development open to all

ees and modulated accordin

to career as

irations and

ersonal interests.

)

The or

anization invests in the trainin

of data anal

sts.

INTEGRATED

ORGANISATION

36) Business Analysts and Data Scientists operate in an integrated manner with the rest of the

organization.

37) Analysts are in contact with corporate opportunities and challenges; they can directly

impact decisions and influence the corporate strategy.

38) Analysts' priorities are defined according to urgencies and are not linked to the company's

opportunities.

DATA FRIENDLY

APPROACH

39) The entire organization is pervaded by widespread knowledge and live interest in data, and

the role of analytics is recognized in guiding the company to success.

40) Top Managers are consistently leveraging the recommendations generated by data and

algorithms.

We leveraged the Analytic Hierarchy Process

(AHP) (Thomas L. Saaty, 1990) to obtain appropriate

weights for the questionnaire answers. The result of

this process is a scoring system that evaluates the data

maturity of a company and includes the principal

characteristics that a company must have to become

data-oriented and make data-driven strategic

decisions. Moreover, this assessment gives more

importance to the domains that managers think are

more important in a data-driven company.

3.3 The Analytic Hierarchy Process

The AHP process is a quantitative method for making

decisions based on the relative importance that people

arbitrarily assign to certain factors. This process

requires answering pairwise comparison questions

structured in the following way:

(A) is X times more important than (B)

(A) is as important as (B)

(B) is X times more important than A

Weights for the single criteria are then computed in

the following way.

𝑉







𝑥



∗𝑥



∗𝑥





Which is the criterion 1 geometric mean. Then each

geometric criterion mean is divided by the sum of all

criteria:

𝑊





𝑉



𝑉



𝑉



𝑉



The 15 questions in the second section of the CBDAS

require the respondent to choose between a pairwise

comparison of data maturity characteristics and how

much it counts on a specific aspect versus one other

to improve big data management, according to the

organization's reality.

In our specific case, the AHP process was used to

evaluate the following company characteristics,

derived from the conceptual CBDAS:

An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity

1. The proliferation of a data culture across the

entire organization.

2. Availability of IT services.

3. Managers' support in data-driven projects.

4. Care of analytical talents within the company.

5. Satisfaction of technological needs.

6. Business sponsorship to facilitate data-driven

decision-making.

The respondents were allowed to rank one of the

options from equally important to 3 times more

important, according to the respondent's perspective

on its organization reality. It is crucial to figure out

that not all the domains could always be relevant to a

particular context (Walls & Barnard, 2019). For the

same reason, certain factors may be more important

than others in specific sectors. To respond and collect

all those situations, we included the respondents'

possibility to assign different weights to each

organizational need in this section of the assessment.

The resulting AHP matrix is shown in Table 2.

Table 2: AHP Matrix results based on the interview of 261 Italian managers and entrepreneurs.

Proliferation of

a data culture

across the

entire

organization

Availability

of IT

services

Managers'

support in

data-driven

projects

Care of

analytical

talents

within the

company

Satisfaction

technological

needs

Business

sponsorship

to facilitate

data-driven

decision

makin

Proliferation of a data

culture across the

entire or

anization

1.0000 1.6061 0.9394 1.0000 1.6902 0.5926

Availability of IT

services

0.6226 1.0000 0.5000 0.5000 1.2677 0.5505

Managers' support in

data-driven projects

1.0645 2.0000 1.0000 0.8451 0.8923 0.8165

Care of analytical

talents within the

compan

1.0000 2.0000 1.1833 1.0000 0.9646 0.6246

Satisfaction of

technological needs

0.5916 0.7888 1.1208 1.0366 1.0000 0.6768

Business sponsorship

to facilitate data-

driven decision

makin

1.6875 1.8165 1.2247 1.6011 1.4776 1.0000

The associated weights are depicted in Figure 1:

Figure 1: AHP weights for each of the dimensions chosen in this study. According to more than 261 managers interviewed,

the key factors for a company's data maturity are that the business facilitates data-driven decisions and the proliferation of

data culture across the entire organization.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

4 ASSESSMENT RESULTS

The questionnaire results are synthesized in Figure 2,

where the respondent proportion for each question is

represented as vertical bars of distinct colours. As

shown in the bar chart, more than half of the

respondents agreed or strongly agreed to the

importance of "Integrated architecture", "Data

Friendly" and "Integrated organization" domains.

This can also be seen in Figure 3, where the

unweighted score shows how the domain in which the

interviewed agreed more are the same mentioned

before. The situation changes dramatically if one

considers not only how strongly each person agreed

to a certain question but, most importantly, how much

importance relative to the other domains each person

would give (i.e., by applying the AHP weights to the

unweighted scores). This can be seen in Figure 4.

In Figure 4, one can see that once one considers

the weights from the AHP, data friendliness in the

organization becomes the most sought-after

characteristic. Followed by "Integrated Organization"

and "Analytical Skills."

Figure 2: Relative percentage of answers for each domain.

Figure 3: Unweighted final scores. This graph compares the sum of the Likert scores given by each of the 261 people

interviewed.



An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity

Figure 4: Final scores weighted using the AHP process. The results now look quite different since the domain "data-friendly"

was considered the most important (higher weights) in the AHP process.

4.1 Correlation among Parameters

As one may expect, there may be correlations among

domains due to their nature or to the similarity of

questions. To investigate that, we calculated a

correlation coefficient between every domain listed in

Table 1. To do that, we used a Spearman correlation

coefficient (Spearman rank correlation) (Spearman,

1904) that has the advantage of not being limited to

continuous numerical variables but can also be

applied to discrete ordinal variables. Moreover, this

method of calculation can spot strictly non-linear

correlations and can assess how much two variables

are correlated by a monotonic function (Zar, 1972).

For linear relationships, the two methods give similar

answers. The value of Spearman's R is always

between -1 (indicating a perfect negative correlation)

and +1 (indicating a perfect positive correlation).

Weak correlations have R values 0

𝑅

0.2,

moderate correlations 0.2 

𝑅

0.6 and strong

correlations 0.6 

𝑅

 1 We have created a

correlation Matrix using the software R 4.1.2 and the

command rcorr. The results are shown below in

Figure 5.

It was to be expected that only positive

correlations had to be found since all questions in the

Likert scale go in the same direction. The strongest

correlations happen to be between data process

integration and data-friendly approach, where a

R=0.78 indicates a strong correlation. It also appears

a strong correlation between the domain integrated

architecture and data interface with a Spearman's

R=0.72.

Figure 5: Spearman correlation matrix among domains

scores.

4.2 Stratification By Company Sector

and Size

A series of demographic questions were asked to the

participants when the assessment was submitted to

them. We collected information about the

characteristics of the company to which they

belonged. The questions were focused on the

company size and the sector in which it operates. This

allowed us to stratify for such parameters and search

for statistically significant differences.

The results of this stratification are shown in

Figure 6 and Figure 7.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

Figure 6: AHP-weighted average score by industry size. The results look very similar among different company sizes.

Table 3: ANOVA output. The p-value << 0.05 indicates that we can reject with a high degree of confidence the hypothesis

that the average scores of companies by DOMAIN are the same.

ANOVA

SUMMARY

Groups Count Sum Average Variance

DATA STRATEGY 3 1.726453388 0.575484463 0.000372

DATA-PROCESS

INTEGRATION

3 1.767461022 0.589153674 0.000201

TECH

INFRASTRUCTURE

3 1.389559865 0.463186622 0.003198

INTEGRATED

ARCHITECTURE

3 1.310043357 0.436681119 0.000883

DATA INTERFACE 3 1.030418065 0.343472688 0.001125

ANALYTICAL

SKILLS

3 1.852298318 0.617432773 0.000828

INTEGRATED

ORGANISATION

3 2.077575264 0.692525088 0.001193

DATA FRIENDLY

APPROACH

3 2.597837495 0.865945832 0.008814

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 0.557008477 7 0.07957264 38.31854 7.88E-09 2.657197

Within Groups 0.033225746 16 0.002076609

Total 0.590234223 23



The stratification by company size shows that the

only domain where there could verify differences

among the different-sized company is the data-

friendly approach. However, such differences have to

be ascertained by means of appropriate statistical

tools. We performed an ANOVA (ANalysis Of

VAriance) test (Fisher, 1946) to search for statistical

differences among groups. ANOVA tests the null

hypothesis that the averages of the groups belong to

the same distribution by testing the variance between

and within groups. We first tested for significant

differences among domains, the results of which are

shown in Table 3.

Since the p-value is p << 0.05, we can reject the

hypothesis that the different domains have equal

means (i.e., there are significant differences).

An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity

Regarding the scores of companies of different sizes,

the results were opposite and are summarized in table

Since the p-value >> 0.05, we observe no

statistically significant differences among different

company sizes in this case. This can be interpreted as

companies of varied sizes having the same data-

maturity aspirations and ambitions. The stratification

for the company sector also shows similar results.

Figure 7 and Table 5 show no statistically significant

differences in data needs and maturity scores among

companies operating in different sectors.

Table 4: ANOVA output. The p-value >> 0.05 indicates that we cannot reject the null hypothesis that the average AHP-

weighted scores of companies by SIZE are the same.

ANOVA (company size)

SUMMARY

Groups Count Sum Average Variance

Small 0-50 employees 8 4.433638116 0.554204765 0.023713

Medium 50-500

employees

8 4.47730905 0.559663631 0.027267

Large > 500 employees 8 4.840699608 0.605087451 0.031556

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 0.012485788 2 0.006242894 0.226917 0.798916 3.4668

Within Groups 0.577748435 21 0.02751183

Total 0.590234223 23

Figure 7: AHP-weighted average score by industry sector. The results look similar among different company sizes.

Table 5: ANOVA output. The p-value > 0.05 indicates that we cannot reject the null hypothesis that the average AHP-

weighted scores of company sector are the same.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

ANOVA (company sector)

SUMMARY

Groups Count Sum Average Variance

Consulting 8 4.445729 0.555716 0.030538

Consumer goods 8 5.172092 0.646512 0.035667

Education 8 4.705451 0.588181 0.030794

Manufacturing 8 3.972509 0.496564 0.015973

Services 8 4.37681 0.547101 0.040978

Transformation 8 5.208651 0.651081 0.022189

Other 8 6.123635 0.765454 0.054318

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 0.379039697 6 0.063173 1.918852 0.096414 2.290432

Within Groups 1.613199204 49 0.032922

Total 1.992238901 55

5 DISCUSSIONS AND

CONCLUSIONS

We have created an AHP based evaluation system for

estimating companies' data maturity and the

importance that their managers assign to data-driven

choices. Our results suggest that the data-maturity

estimator that is considered as most important by the

interviewed managers was "Data friendliness",

followed by "Integrated Organization" and

"Analytical Skills". Moreover, we have found

evidence that the relevance of the 8 critical success

factors included in CBDAS is statistically

independent of the size of the company and the sector

in which it is operating, making the assessment of

general applicability for a broad range of business

organizations. Our findings suggest that, when

companies look for new opportunities to use

analytics, the presence of data-driven culture is of

primary importance for making data initiatives able to

generate business value (McAfee & Brynjolfsson,

2012; Vidgen et al., 2017). We believe that managers'

support rule should be promoting a broad sense of

data ownership by all employees and a solid

connection between data professionals and business

functions (Bahjat et al., 2014; Comuzzi & Patel,

2016). This enables data experts to directly impact

business decisions and influence business strategy.

By having top managers seeking advice from data

analysts, organizations recognize and accept the

central role of data in decision-making, business

transformation, and innovation. Our research also

highlighted how the characteristics identified by

managers as relevant (i.e., corporate culture) do not

correspond linearly to those with a higher degree of

maturity. This mismatch between managers'

perceptions and the implementation of concrete

actions suggests the usefulness of a system of

recommendations for bridging the existing maturity

gap in higher priority areas.

The current study is affected by some known

limitations that provide opportunities for future

research. Firstly, the limited sample size requires the

assessment to be tested with a broader audience

involving a larger number of enterprises respondents

to confirm preliminary insights obtained from the

current analysis. Secondly, the scope of the

interviewed audience was limited to Italy, causing its

findings to be prone to specific local dynamics.

Thirdly, more robust qualitative research is needed to

assess the sufficiency of the critical success factors

included in the assessment model that was used in this

study. A future direction of the study would be to

create a specific model for different company

contexts capable of thoroughly evaluating how every

aspect of data management change according to the

complexity of the organizational network (Daryani &

Amini, 2016; Gökalp et al., 2021). This will allow

increasing the practical applicability of the rule-based

recommendations, obtaining specific indications to

An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity

be implemented in the process of improving business

choices.

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