Building a Data Literate Business Workforce

Marketa Smolnikova

Department of Information Technologies, Prague University of Economics and Business,

W. Churchill Sq. 4, Prague, Czech Republic

Keywords: Data Literacy, Data Strategy, Competency Model, Strategic Management.

Abstract: Data literacy and problem-solving with data has been a hot topic in the academic, government as well as

business world for several years. However, the current research hasn’t provided companies and organizations

with a specific or easy-to-follow guide how to enhance data literacy of the most proliferated business roles.

This paper aims to map what different business roles require to do with data in their work tasks and to propose

a way how to improve data literacy of these roles by focusing on the right competences. To fulfil these aims,

authors analyzed job tasks of the most generic business roles to derive their objectives regarding the use of

data and gathered feedback from businesses via a preliminary web-based survey. Consequently, mapping of

the distinct data literacy competencies and priority work objectives of the selected job roles serves as a manual

where to focus training efforts to enhance company’s data literacy. This theoretical framework could be

further improved by a real-time automatic evaluator of the survey respondents’ inputs which would deliver

recommendations towards priority data literacy competencies customized to a respondent’s response.

1 INTRODUCTION

The use of data in business is motivated by numerous

incentives – to create new revenue streams or

generally stay competitive by improving the product

development, the customer service, the operational

excellence, or discovery of new markets (Lim et al.

2018, IE University 2019, Slansky 2019,

Balakrishnan et al. 2020). It must be said that

according to Ransbotham et al. (2016) realizing a

competitive advantage with data is becoming harder

and harder as many companies have been

successfully exploiting the data commodity and

staying ahead of the industry thanks to data requires

much more effort and investments than ever before.

To achieve the aforementioned goals, we

identified two general factors that companies must

deal with – the ability to extract value from data

(Accenture 2019, Balakrishnan et al. 2020, Engler

2020) and the strategic use of this data incorporated

in the company’s culture (Ross et al. 2013, Fergusson

2014, Accenture 2019). Beside technology and the

data itself, the organizational ability to derive value

from data requires appropriate workforce skills

(Gekara, Thanh Nguyen 2018) as well as set data

https://orcid.org/0000-0002-2631-920X

management rules. The second factor depends on

cultivating a data-driven culture (Fosso Wamba et al.

2020), having and implementing a clear strategy to

unlock the data potential (Dallemule, Davenport

2017, KPMG 2019) and the ability to translate

analytics into business outcomes (Kiron et al. 2015,

Ransbotham et al. 2016, Lin, Kunnathur 2019).

By decomposing these basic success factors, it

seems straightforward what to focus on even though

this general approach certainly needs to be adjusted

to different business fields. Nevertheless, the

implementation falls behind the theory and the

current research emphasizes these barriers to the data

use excellence – lacking the right skills at different

organizational levels (Ransbotham et al. 2016,

Bersin, Zao-Sanders 2020, WEF 2020), missing the

analytical culture in terms of values, approach or a

leadership support (Fergusson 2014, WEF 2020) and

lacking a solid data strategy and its implementation

(Dallemule, Davenport 2017, Accenture 2019).

Even though the research of the general necessary

data skills or rather data literacy, an ability to

understand and make use of data, has been on the rise

along with many commercial initiatives (Ridsdale

2015, Frank et al. 2016, Wolff et al. 2016,

Smolnikova, M.

Building a Data Literate Business Workforce.

DOI: 10.5220/0011587600003335

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS, pages 221-230

ISBN: 978-989-758-614-9; ISSN: 2184-3228

221

Bonikowska et al. 2019, QlikTech 2020, Data to the

People 2021, Jones 2021), it hasn’t been able to

define knowledge and skill necessities for different

business roles (Wolff et al. 2016, QlikTech 2020).

However, we believe this specification is

a steppingstone for the successful enhancement of

data literacy in companies as it would offer a concrete

manual for the management on which skills to focus

their training efforts. Therefore, this paper aims to

propose a way how to map what different business

roles require to do with data in their work tasks and

to improve data literacy of these roles by focusing on

the right competences.

2 DEFINING DATA LITERACY

NEEDS FOR DIFFERENT

BUSINESS USERS

The journey of building a data literate workforce

starts with defining what data skills the employees

need to have to fulfil their work tasks. As QlikTech

in cooperation with Accenture suggest in their report

The Human Impact of Data Literacy (2020),

“different groups (of employees) will be accountable

for creating value with data in different ways”. This

calls for specification of generalized types of data

users which allows to group diverse occupations by

common characteristics in handling data and

consequently to strategically focus on upskilling of

these newly defined groups.

2.1 Data Roles in the Literature

We can find a few of such efforts in the recent

literature. In the pursuit of establishing a common

ground for data literacy teaching and learning Wolff

et al. (2016) identified four types of data literate

citizens – Readers who identify the need to consume

and interpret an increasing amount of data,

Communicators whose main mission is to gain insight

into data and help others understand these

interpretations, Makers (or Hobbyists) who acquire

ad-hoc advanced data skills according to their focus

of innovation in order to contribute to solving a real-

world problem and Scientists representing data

specialists whose occupation requires strong

technical skills related to handling data. However,

these roles were defined for the public in the context

of smart cities projects and don’t reflect the needs of

business data users whose motivation to work with

data or goals to achieve with data may differ from

these roles. Also, the roles as stated in Wolff et al.

(2016) may overlap in the business environment.

QlikTech and Accenture (2020), on the other

hand, differentiate four types of users typical for

companies – Business Users are identified as

consumers of simply presented information which is

necessary for their work, Analyst Users are more

inquiring and provide Business Users with deeper

explanations of “why”, Discovery Users focus on the

innovation enabled by data for which they need to

apply advanced technical skills and Data Scientists

are occupied with enhancing and developing data

models. These roles already cover the data task needs

we can typically encounter in the business

environment, and they may serve as a basic

categorization for the company’s data literacy

strategy as these roles won’t be so prone to significant

changes compared to constantly evolving data skills.

Nevertheless, their one-sentence-long specification

doesn’t offer to the management responsible for

company’s data strategy and workforce’s level of

skills sufficient instructions or a straightforward

manual for upskilling the employees.

As Bersin and Zao-Sanders (2020) pointed out:

“Too frequently, general-purpose corporate learning

systems start off claiming to do everything for

everyone and, lacking a tangible purpose, end up

doing little for anyone”. That is why we need to focus

on specific job-related skills. On that account, the aim

of this research is to define necessary data literacy

competences for business workforce at the level of

required knowledge and skills and to map these

competences to the most generic business roles which

would facilitate companies’ data strategy

development regarding employee training plans.

2.2 Data Literacy Needs in the Business

Environment

Our research focuses on data literacy of business

workforce – different levels of managers and generic

office job roles like marketing specialists, financial

accountants, or sales representatives. It arises from a

hypothesis that work tasks of different business roles

require different data literacy competencies. To fulfil

the research aims, the research is divided into two

steps. Firstly, we inquired into the needs of selected

business roles to deal with data in their work tasks via

a web-based survey. Secondly, we linked the

identified business needs with knowledge and skills

related to handling data – competencies in our data

literacy competency model.

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2.2.1 Data Literacy Objectives

We defined the work objectives of selected business

roles that are achieved by working with data and get

the feedback of a broad group of business users on

those instead. These objectives require a certain level

of data knowledge and skills to be successfully

achieved, thus we call them the data literacy

objectives.

Our goal was to examine the data literacy

objectives of the most generic business roles.

Therefore, we needed to identify the most often

represented business functions in companies, to select

the representative roles of these functional

departments and to derive the data literacy objectives

for the selected roles. The distinct job roles used in

our survey were derived from The Occupational

Information Network (O*NET) which serves as a

public database of almost 1,000 occupations that

cover the entire U.S. economy, and which is

developed under the sponsorship of the U.S.

Department of Labor/Employment and Training

Administration.

Afterwards, each job task of the listed O*NET

occupations was analyzed in the optics whether the

task was realized based on working with data and we

could mark it as a data task. By further analysis of the

marked data tasks of managerial occupations, we

arrived at the conclusion that the data literacy

objectives (the work objectives which are achieved

with these data tasks) are the same for executive and

mid-level management and the business function they

represent didn’t play a significant role in specifying

these objectives. Nevertheless, we expected that these

two levels of managers could assign different priority

to the same objectives. These conclusions then led to

the establishment of an executive management role

(C-suite level managers and directors) and a mid-

level management role and a set of managerial data

literacy objectives which are presented in Table 1.

Table 1: Managerial Data Literacy Objectives.

Managerial Data Literacy Objective Full Description

To ensure collection of the right data To ensure collection of relevant data (internal and also external if

applicable) to support solving a specific business problem

To have available relevant data To have available relevant data (internal and also external if

applicable) to resolve a certain business problem/to make qualified

decision in that matte

To be able to trust the company's data To be confident about the evidence the company's data is telling

To identify problems, causes or opportunities To identify a change in data while monitoring reports and dashboards

on day-to-day

asis

To derive actionable insights To discover the pattern in data or a relationship between variables that

you didn’t previously know existed. To be actionable, these findings

need to provide insight into the "why" of the finding.

To monitor company data and evaluate

information

To monitor company data for a selected domain and evaluate

information from data analysis in a given context

To inform decisions and to be able to direct To use data and insights to make qualified decisions and to manage

the assigned department or unit

To be able to plan To plan company's/department's future needs as well as revenues

To be able to forecast or predict future

develo

ment

To predict expectable outcomes and events that can ifluence

com

's strate

To control accuracy of the company's data and

reports

To ensure data reliability by giving feedback to report builders and

database administrators on company's data accuracy

To ensure compliance with regulatory

requirements

To ensure that the company's sensitive digital assets are guarded to

meet legally-mandated minimum standards or to ensure reporting

legally require

data to the designate

authorit

To back ideas and decisions with evidence To support ideas or decisions with evidence when presenting at

meetings, consulting or negotiating with others

To convicingly and compellingly present

information from data

To win others over for your business objectives by compelling data

storytelling based on evidence (no faked or distorted data)

To keep a competitive advantage in a data-centric

business environment

To keep a sustainable competitive advantage in a data-centric

business environment by identifying and implementing emerging data

analytics scenarios to delive

usiness innovation

Building a Data Literate Business Workforce

223

When analyzing the data tasks of operational level

occupations, we identified two groups of data users –

while the occupation like Accountant is supposed to

focus on the correct data entry, the compliance with

regulatory requirements or analyzing discrepancies in

data, Market Analysts can be also responsible for

gathering external data, cleaning and transforming

data and preparing complex reports and dashboards.

It is obvious that these roles will require different data

literacy skills to achieve their work objectives. But for

simplicity of the survey and the opportunity to

identify more advanced analytical roles in the current

business environment despite the O*NET database

not proposing so at the moment, we summarized all

possible data literacy objectives of operational roles

inTable 2.

2.2.2 The Survey

The collection of a preliminary survey responses was

underway in November and December 2021. The

survey took a form of a web-based questionnaire

accompanied by a dedicated webpage with detailed

description of the survey and the evaluated objectives

for managerial and operational roles. The

respondent’s pool was generated primarily from

alumni and corporate partners of our university.

As we recognize that different business roles can have

different objectives and different priorities regarding

data analysis aims, we asked the survey respondents

to confirm or disprove the data literacy objectives

listed for their business role and to assign priority to

each of them on the 5-point Likert scale from Very

Low to Very High. The operational level of R&D

occupations as well as academic occupations were not

subjects of the survey for its specificity regarding the

role of data in their work tasks. We believe that these

roles deserve their own data literacy research.

2.2.3 Data Literacy Competency Model

Data literacy encloses all the competencies that are

required for working with data. It has two sides of a

story – a knowledge part (what is needed to know)

and a skills part (what is needed to be able to do). In

our perspective, data literacy is “an ability to

understand data and to make use of data” with

emphasis on the context it is used within (Smolníková

Table 2: Operational Data Literacy Objectives.

Operational Data Literacy Objective Example

To record, store and maintain data Using accounting software to record, store, and maintain

data about receivables and liabilities

To ensure correct data entry Checking figures, correct customer codes or invoice codes

while entering or reviewing financial data

To collect data for analysis Gathering data on competitors or conducting research on

consumer opinions

To clean and transform data Cleaning and transforming collected data into a target

structure (e.g. for a specific visualization method or for

appropriate reporting data model)

To comply with regulatory requirements Maintaining and submitting financial data to authorities in

compliance with regulations

To monitor and verify discrepancies in data Monitoring status of loans and accounts to ensure that

payments are up to date

To analyze and evaluate data Examining all relevant information to assess validity of

customer complaints and determine possible causes OR

evaluating employee selection techniques by reviewing

data of supervisors' satisfaction with the hired candidates

To prepare reports and dashboards Compiling budget data and documents and preparing

summary reports in spreadsheets or visualizing data with

tables and charts in data visualization tools

To propose ideas and give recommendations based on

evidence

Conferring with management to develop or implement

personnel policies based on data about employee

fluctuation

To inform decisions Determine depreciation rates to apply to capitalized items

or redesigning the movement of goods to maximize value

and minimize costs based on logistics data

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et al. 2021). We focus on defining necessary data

literacy competencies for business workforce –

managers at any level and generic business roles like

accountants, marketing specialists or HR specialists.

Our model is a result of a synthesis of several

existing models of data literacy competencies (Prado,

Marzal 2013, Ridsdale et al. 2015, Grillenberger,

Romeike 2018) which were evaluated in the context

of usefulness for our target audience. We identified

five categories of competencies in which knowledge

areas (A and B sections) represent ability to

understand basic concepts necessary for data analysis

and skills areas (C, D and E sections) cover ability to

apply those concepts in real-case data scenarios.

• A. Data Concepts, Ethics, and Protection

• B. Analytical Principles and Methods

• C. Data Collection and Preparation

• D. Data Analysis and Evaluation

• E. Data Interpretation, Communication and

Decision-Making

We also assumed two levels of mastery within these

competencies. First level should preferentially cover

data analytics consumer needs, while the second

level comprises of the first level competencies and

adds up especially data preparation skills to prepare a

person to become a data analytics power user.

3 RESULTS

By the end of November 2021, we acquired 53

responses to the survey out of which 32 respondents

were operational level employees and 21 respondents

were managers at different levels (1 CEO, 1 self-

employed owner, 4 CXOs, 6 directors or equivalent,

9 managers or equivalent). More than half of the

respondents was from companies with more than

1000 employees (60 %). Respondents from small

companies (up to 49 employees) represented only 11

%. Despite of the pervasive number of operational

level employees among respondents, more than a half

of respondents (53 %) has more than 5 years’

experience at their position.

From the perspective of respondents’ industry

background, these fields were the most pervasive:

Information Technology (17), Manufacturing (11),

Retail and Wholesale Trade (6), Banking and

Financial Services (4) and Education (4). As we

intentionally also collected responses from data

specialists to compare whether they tend to be biased

and simply indicate every data literacy objective as

highly important and to use their requirements of

Business Intelligence/Data Analysts as the opposite

pole to the researched (more administrative)

positions, Data Analytics department (10) is along

with Marketing & Sales department (10) the most

represented. They are followed by other IT

departments (9), Human Resources (6), Operations &

Production (4) and R&D (3). 75 % of respondents’

companies operate in the Czech Republic, 11 % in

Germany and 8 % in the USA. Visualized results for

the further analysis are available online (see

References – Smolníková 2022).

3.1 Managerial Data Literacy

Objectives

The managerial data literacy objectives were

evaluated only by respondents at managerial

positions. The objectives’ priority was assessed for

2 levels - chief executives & directors and mid-level

management. The results for the first group are

visualized in Figure 1, for the second in Figure 2.

The survey results in Figure 1 suggest that the chief

executives’ and directors’ highest priority is to reach

these work objectives with data:

• To inform decisions and to be able to direct

(80 % responses of Very high priority)

• To be able to trust the company’s data

(70 % responses of Very high priority)

• To be able to forecast or predict future

development (70 % responses of Very high

priority)

• To have available relevant data

(65 % responses of Very high priority).

The top priority objectives for mid-level

management (Figure 2) are less definite compared to

executive level objectives, however, we can still

identify several high priority objectives:

• To have available relevant data (65 %

responses of Very high priority)

• To be able to trust the company’s data (50 %

responses of Very high priority)

• To identify problems, causes and

opportunities (50 % responses of Very high

priority)

• To inform decisions and to be able to direct

(65 % responses of High priority)

• To ensure collection of the right data (50 %

responses of High priority)

Besides the top priorities, we can see tendencies

regarding certain objectives – while objectives To

ensure collection of the right data, To monitor the

company’s data and evaluate information and To

control accuracy of the company’s data and reports

Building a Data Literate Business Workforce

225

Figure 1: Priority data literacy objectives for CXOs and directors.

Figure 2: Priority data literacy objectives for mid-level management.

are more important for mid-level managers,

objectives like To create actionable insights, To

convincingly and compellingly present information

from data and To keep a competitive advantage in a

data centric business environment play a more

important role in executive management.

Regarding the influence of data analytics background

on managerial objectives, there are not enough

respondents at managerial position in the results to

make any statistical inferences.

3.2 Operational Data Literacy

Objectives

In contrast to the managerial objectives, the

operational data literacy objectives could be

evaluated by both the operational level employees

and managers at any level. Nevertheless, the

employees were in the evaluation far more

numerous. Given the representation of data

specialists in the sample, the highest number of

evaluations was received by the role of Business

Intelligence/Data Analysts (9). It is followed by

Marketing Specialists/Marketing Analysts and Sales

Representatives which have the same number of

evaluations (7) as they both represent the same

department (Marketing & Sales) and thus got

assessed together. Project Management Specialists

with 7 evaluations are also among top assessed as

they were chosen to be an operational level role for

several departments (Operations & Production and

R&D).

The Business Intelligence/Data Analysts role

represents the antipole of most of the examined

business roles, in other words the highest bar of data

analytical skillset to reach, and therefore it will serve

as a reference. As the results which were generated

by respondents at similar position show, the Data

Analyst’s job is focused on collecting data (89 % of

respondents marked High or Very high priority),

cleaning and transforming data, analyzing and

evaluating data and preparing reports from data (for

all these objectives 78 % respondents marked High

and Very high priority).

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When comparing the results of the Data Analyst

role with other roles’ results, Marketing

Specialists/Marketing Analysts seem to have the most

similar profile. They also tend to have the highest

proportion of “High” and “Very high” responses for

objectives like To collect data for analysis (86 % of

responses), To clean and transform data (71 %), To

analyze and evaluate data and To prepare reports

and dashboards (both 86 %). However, they

significantly outweigh Data Analysts in designated IT

department in proposing ideas and giving

recommendations based on evidence which situates

100 % responses in High and Very high priority.

These results may point out that there are more

analytical positions within a company that require

appropriate training in working with data. What is

more, marketing specialists/analysts as subject matter

experts are more likely to deliver new ideas rather

than just prepare reports for others to consume.

We can recognize a similar trend for a role of HR

Specialists too. The survey results evince even higher

requirement of data collection by HR Specialists – 46

% respondents perceive it as a Very high priority

compared to 29 % in case of Marketing

Specialists/Analysts. Cleaning and transforming data

remain one of the priorities (69 % of responses within

High and Very high priority) along with analyzing

and evaluating data (also 69 %) and preparing reports

and dashboards (77 %). However, the results also

show the heighten importance of ensuring correct

data entry (84 % of responses within High and Very

high priority) and the very high importance of data for

informing decisions (92 % within High and Very high

priority compared to 43 % within the same scope for

Marketing Specialists/Analysts). On the other hand, it

is not surprising that complying with legal

requirements is much more important for HR

Specialists (70 % with High and Very high values)

compared to Marketing Specialists/Analysts (30 %).

These results may suggest that HR Specialists also

require deeper analytical and data preparation skills,

nevertheless, based on the priorities the training

should focus more on data quality or decision-making

based on data compared to Marketing

Specialists/Analysts.

Based on the results of this limited survey, we can

already say that the reality of data user types in

companies is more complicated than in QlikTech and

Accenture’s report (2020). Combining their user

types and our results for example HR Specialists, we

might say that this role blends in a Business User with

an Analyst User and possibly a Discovery User as

well. If HR Specialists worked with outcomes of

advanced data models, we could even name them

Data Scientists according to that vague, one-sentence-

long definition of the user type. Our preliminary

results therefore suggest that to cater different needs

of examined roles, the recommendations regarding

data literacy skills need to be more specific.

3.3 Linking Data Literacy

Competencies to Business Roles’

Needs

In order to fulfil the paper’s aim of providing

organizations with an easy-to-follow manual

uncovering required data literacy competencies for

different job roles, the second part of our research

deals with mapping the identified business needs in

the form of our survey results with concrete data

literacy competencies.

Firstly, we mapped all data literacy objectives

with adequate data literacy competencies. As most of

the skill competencies inevitably apply concepts from

the theoretical competencies and logically assume the

usage of these knowledge competencies, we listed

only the most critical skills in the main mapping table.

Let‘s propose an example of the Marketing

Specialists/Analysts role. The survey results revealed

that the top priority objectives are:

• To collect data for analysis

• To clean and transform data

• To analyze and evaluate data

• To prepare reports and dashboards

• To propose ideas and give recommendations

based on evidence

Therefore, to make Marketing

Specialists/Analysts the most effective in their

priority work tasks, their managers should focus on

cultivating the data literacy competencies that support

these activities (Table

3).

We can continue in the same manner for other

examined business roles. However, the training plans

need to be aligned with the context of the company

and its technological possibilities. Even though the

aim of this research is to offer more targeted approach

to the data skills training, we can’t avoid some sort of

results generalization to accommodate the needs of

the most companies. The generalization is for

example already contained in the effort to define data

literacy skills for the most common job roles in the

business environment and also in the fact that any

employee of a given department can evaluate a

typical job role of his department regardless of his/her

own position. On that account, before applying any of

suggestions, it is necessary to assess the company’s

strategy and orientation and reflect on its business

Building a Data Literate Business Workforce

227

Table 3: Marketing Specialists/Analysts Required Data Literacy Skills.

Objective Operational level objectives Competency Data Literacy Competency

3 To collect data for anal

sis C1.1 Abilit

to define business re

uirements of data

O_3 To collect data for analysis C1.2

Ability to identify relevant data sources for a given

roble

O_3 To collect data for analysis C2.1

Ability to access data, to extract and to store data in

a required structure

3 To collect data for anal

sis E1.5 Abilit

to adhere to data ethics and le

al limitations

O_4 To clean and transform data C2.2

Ability to recognize data quality issues and to apply

asic methods to clean data

O_4 To clean and transform data C2.3 Ability to transform data into the target structure

4 To clean and transform data C2.4 Abilit

to use and creat com

lex metadata

O_7 To analyze and evaluate data D1.1 Ability to apply basic analytical methods

O_7 To analyze and evaluate data D1.3 Ability to read basic graphs and tables

7 To anal

ze and evaluate data D1.4 Abilit

to a

anal

tical methods used in business

O_7 To analyze and evaluate data D2.1

Ability to apply knowledge of basic statistical methods

used in data analysis

O_7 To analyze and evaluate data D2.4 Ability to read more complex visualizations

7 To anal

ze and evaluate data E1.3 Abilit

to derive actionable insi

hts

O_8 To prepare reports and dashboards D1.1 Ability to apply basic analytical methods

O_8 To prepare reports and dashboards D1.2 Ability to apply elementary visualization methods

8 To

are re

orts and dashboards D1.4 Abilit

to a

anal

tical methods used in business

8 To

are re

orts and dashboards D2.1

Ability to apply knowledge of basic statistical methods

used in data anal

sis

O_8 To prepare reports and dashboards D2.2 Ability to create a simple dimensional model

8 To

are re

orts and dashboards D2.3 Abilit

to a

advanced visualization methods

O_9

To propose ideas and give

recommendations based on

evidence E1.1

Ability to adapt the communication of data to the

usiness problem and the expected audience

O_9

To propose ideas and give

recommendations based on

evidence E1.2

Ability to clearly and coherently present arguments

and analytical outcomes

O_9

To propose ideas and give

recommendations based on

evidence E1.3 Ability to derive actionable insights

needs – for example whether a highly data literate

marketing analyst is one of the means for staying

competitive or by which technology the company can

afford to accompany and support the application of a

newly acquired data analytical skills of its employees.

4 CONCLUSIONS

The aim of this research was to map what different

business roles require to do with data in their work

tasks and to propose a way how to improve data

literacy of these roles by focusing on the right

competences. We approached the research intent by

analysing work tasks of selected business roles from

which we derived occupational objectives that are

achieved by working with data – the data literacy

objectives. We identified groups of objectives

common for all managers and for selected operational

level roles. Nonetheless, we assumed that achieving

these objectives can have different priority for

different roles.

To find out, which objectives are important for

which roles or positions, we launched a preliminary

web-based survey among alumni and partners of

Prague University of Economics and Business in

November and December of 2021. The preliminary

results of 53 respondents mostly confirmed assumed

trends and findings which we could use in the further

research. For example, the highest priority of data

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literacy objectives of chief executives and directors

were informing decisions and ability to direct, ability

to trust the company’s data and ability to forecast or

predict future development. The first two objectives

were also important for mid-level management,

however, with a bit lower priority. On the other hand,

the mid-level management is much more responsible

for ensuring correct data collection. Even though

revealing these priorities is not surprising, the survey

results still serve as a validation of the data skills

necessity and verify the recent business needs.

Despite the limited pool of respondents, the

survey results also confirmed that examined business

roles prioritize different work objectives which leads

to different requirements of skills for working with

data. By assessing results for operational roles, we

could identify roles like Marketing

Specialists/Analysts or HR Specialists whose

distribution of priority among the data literacy

objectives highly resemble occupational

requirements of Business Intelligence/Data Analysts.

However, we could still recognize differences – for

example proposing new ideas and recommendations

based on data is more important for Marketing

Specialists/Analysts than Data Analysts.

In addition, as the survey results supports the fact

that different job roles use data differently (they

prioritize different skills to achieve their goals), there

are different levels of data literacy necessary within a

company. For example, while Marketing

Specialists/Analysts are supposed to rely on data

preparation quite heavily, Sales Representatives role

lays out the priority more evenly among more priority

values. We could than assume that Sales

Representatives require data preparation skills, but

most likely on different level of mastery than

Marketing Specialists/Analysts which opens door to

different levels of data literacy in the company.

Based on our previous research of data literacy

competencies and the survey results, we could then

map the data literacy objectives with specific data

literacy competencies to validate the developed data

literacy competency model and verify its

completeness. What is more, the discovered different

priority of data literacy objectives for selected job

roles allowed us to propose how to make data literacy

trainings job-position-specific and therefore more

effective. Even though it requires a certain level of

generalization, it is the most particular guideline for

different job positions available. It must be said that

it shouldn’t be applied without taking the company’s

business as well as information strategy into account.

We would also like to contribute to the data

literacy enhancement in companies by allowing

business users to measure their current level of data

literacy. As what can’t be measured, can’t be

improved, the measurement tool would naturally

accompany the survey tool. The second one helps to

state the employees’ needs to focus on, while the

assessment tool would allow them to measure

whether the efforts are sufficient. In addition, the data

literacy measurement of trained employees could

help the company management to track how the

investment in training is paying off.

ACKNOWLEDGEMENTS

This research was supported by Prague University of

Economics and Business (IGA project) under Grant

[F4/61/2021].

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