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Company managers competences adjustment within the
frames of business digital transformation
Lyudmila Gadasina
Department of Economics,
Saint-Petersburg State University,
7-9, University Embankment, St. Petersburg,
Russian Federation, 199034
E-mail: l.gadasina@spbu.ru
Victoriya Ivanova
Department of Economics,
Saint-Petersburg State University,
7-9, University Embankment, St. Petersburg,
Russian Federation, 199034
E-mail: v.ivanova@spbu.ru
Tatiana Lezina
Department of Economics,
Saint-Petersburg State University,
7-9, University Embankment, St. Petersburg,
Russian Federation, 199034
E-mail: t.lezina@spbu.ru
Structured Abstract
Within the frames of digital business transformation the data are one of
the basic company assets. Many companies adopt cumbersome expensive
software to provide analytical reports, revealing the causes of success and
failure. The observed gap between business expectations and the obtained
results is not associated with software quality, but with data management.
Based on the analysis of world practices in data management and Russian
professional standards the principle of forming required competences in
data management for companies’s top-management is proposed. The
article proposes the competency model for company’s management in the
sphere of data management.
Purpose – The purpose of this work is to create a competency model of
different levels of executives in the sphere of data management within the
frames of global digital transformation. This model will give a chance to
formulate additional list of managers’ competencies involved in the
process of data management.
Design/methodology/approach – The comparative analysis of global
practices in data management and Russian professional standards; case
research of Russian company managers’ attitude to competencies in data
management.
Originality/value – Digital medium formation is an integral element of
enterprise integration into the broad scale global digital transformation
processes. These changes include, in particular, cross-industry digital
transformation, development of new industries as well as “joint” economy
formation. This, in turn, requires all company management levels to
acquire new professional competences corresponding to the requirements
of digital economy. Since the data are the main part of digital economy,
the data management organization that goes out of limits of mere creation
and introduction of the corporate information system is required. In the
article new principles of formating company management competences
concerning data management are suggested.
Practical implications – The presented competency model will let
correctly formulate and structure the requirements for employees in the
field of data management. The model can be adapted for companies of
different sizes, different organizational structures and scope of activities.
Keywords – Digital economy, Data management competency model,
Business Roles
Paper type – Academic Research Paper with Practical Issue
1 Introduction
Digital transformation is the transition of companies to new conditions
of the current business existence within the frames of rapid growth of
informational technologies. All the elements of the company architecture
such as business aims, business models and business processes are subject
to changes. According to Gartner reports digital business is based on
development of Internet of Everything (IoE) and includes people,
processes, information and things. In spite of the fact that modern digital
companies are arranged on the basis of business models that differ from
each other, all of them are based on data. Digital transformation presumes
application of all available data sources, access to analytics as well as
accumulation of huge amount of data and the ability of using them in one’s
own interests. As the result, digital company ecosystems that include, in
particular, external and internal information the company is interested in
are formed.
Data appear fantastically rapidly, while the cost of using them
decreases: cloud companies collect and process informational flows and
offer cheap comfortable access to the data concerned. The main challenge
for companies nowadays is to learn to acquire valuable knowledge and
choose information for company development. Plus to the above said, the
business model should be built on the basis of the data that can become
available tomorrow rather than those available today.
Still, business changes as well as digital transformation are not the
sphere of responsibility of IT department. These changes are initiated by
the company top management while they and their improvement depend
on all levels of management as well as employees involved. This, in its
turn, requires new professional competences connected with data – the
main domain of digital economy – from various levels of company
management. The basic necessity in this case is data management that goes
far beyond the task of creation and development of corporate informational
system. As the result, the success of modern company depends on
management competence levels in the sphere of data management. This
point becomes the most acute one within the discussion concerning the
liquidation of the role of IT director and its devolution to the top-manager
of the.
According to many foreign authors, the leading companies know that
problems of data management are the result of principal problems of
organizing business, such as weaknesses of business strategy, for instance
(Lee, Y., 2014; Mecca, M., 2015). Some investigations acknowledge that
data management is the organizational issue that demands close
cooperation of IT experts between managers (Dahlberg, T., 2015; Otto, B.,
2011; Ритзерт, Р., 2011). This is so according to the research report (Data
Governance, 2013) of the survey of 18 financial institutions: 44% of the
institutions surveyed believe that data governance issue should be
discussed on the top-management level.
The assessment methodology of organization readiness for introducing
data management system is the major point for discussion. (Data
Management Maturity Model V1.0, (2014); Otto, B., 2011). A number of
articles are devoted to the requirements for employees in the sphere of data
management. (Aiken, P., 2014; Lee, Y., 2014; Great expectations, 2015).
All the authors acknowledge that data management is NOT an entry level
profession. It is rather a profession that requires having immense
experience and mature judgment.
At present, it has become clear that stakeholders as well as company
top managers should possess relevant competencies. Still, the company
employees who do not work in IT departments but have access to data and
deal with them as well as take managerial decision at various levels are
seldom required to have competencies connected with data management.
The result is disbalance of working processes with data: employees have
their own idea about the value and correctness at every level i.e. they have
different points of view on the process of company data management.
Nowadays Russian Federation is in the process of legal
acknowledgement of professional standards. As the rule they describe the
professional functions required, as well as knowledge and skills of the
specialists working at special positions. The structure of professional
standards presupposes the necessity of the educational levels they cannot
do without. In reality, this means the determination of professional vertical
lines. The article (Гадасина, Л.В., 2017) analyses Russian and Western
approaches to formating data management competencies. This analysis
shows that nowadays data management functions are shifted mainly to IT
roles rather than business roles. The world experience shows that company
business managers are “legislators” while IT specialists are executives.
Company success requires rational approach to data management that
presupposes policy development in the given sphere, in determining the
roles in the data management process, as well as provision of process
transparency and curtailment of operational disagreements.
Object of investigation are managers’ competencies in various Russian
companies.
Subject of investigation is managers' competency model assessment in
the sphere of data management. The competency model was discussed
within the in-depth interview of the respondents among which were
leaders of various departments of Saint Petersburg companies. According
to the results of the case research the competency model was improved.
2 The comparative analysis of global practices in data management
and Russian professional standards
In the sphere of data management DMBOK – Data Management Body
of Knowledge (Mosley, M., 2010) – is the code of knowledge for any
company in its professional sphere. It contains the description of
approaches to plans, policies, projects, processes, practices and procedures
that controls, protects delivers and enhances the value of data and
information assets. DMBOK identifies more than 20 roles of the
participants of data management process. Role functions let formulate
competencies as well as to determine knowledge, skills and labor functions
for the roles mentioned above.
The owners of the managerial process according to DMBOK should be
representatives of the company top management. Consequently, labor
functions of managers and top managers (not IT specialists) should include
the functions connected with data management. DMBOK distinguishes 4
types of data management individual business roles:
• Business data steward;
• Coordinating data steward;
• Executive data steward;
• Data stewardship facilitator.
As we see it, no one should absolutize the availability of all the
business roles mentioned. Let us discuss in more details the generalized
role of managers involved in company's data management. The authors
have pointed out 10 competency groups, relevant to DMBOK business
processes in the sphere of data management.
Data Governance: understanding of methods of strategic enterprise data
needs determination; understanding of data management projects; ability
to participate in data management project implementation; ability to
estimate data asset value and associated costs.
Data Architecture Management: understanding of the company's
business processes and ability to formalize processes for determining the
necessary data structures; understanding the basics of the organization of
operational and analytical data and the ability to form a list of the
company’s informational needs; understanding of the methods of forming
the classifiers of the company.
Data Development: understanding the data representation, storage and
transformation possibility.
Data Operations Management: understanding the final results of
database operation in the operational and analytical activities of the
company; determination of the level of data representation for different
groups of users; identifying needs for improving content and presentation.
Data Security Management: ability to define the company’s security
policies and ability to apply them to the conceptual requirements of data
security.
Reference and Master Data Management: ability to define business
objects from the master data and company’s reference data, as well as the
business rules for harmonization and using the master data.
Data Warehouse and Business Intelligence Management: determination
of the company's needs for analytical data; understanding the methods and
technologies of data research; ability to formulate the task of data needs
for analysis.
Document and Content Management: ability to define the rules of
workflow in the company, ability to work out the scheme of document
circulation.
Meta-data Management: understanding of meta-data requirements and
maintain metadata standards definition of characteristics; business objects,
business rules for processing data related to objects.
Data Quality Management: understanding of concepts of data quality;
ability to profile, analyze, and assess data quality; ability to define the data
duality business rules; ability to define data quality metrics; organization
of a data quality assessment system.
This kind of interpretation of the spheres of data management sounds
strange for most Russian companies.
Professional standards are the main documental requirements for the
specialists in Russian Federation.
The authors of the article carried out the comparative analysis between
the described above competency groups in the sphere of data management
and the ones required in the set professional standards for all levels of
managers. The professional standards for company leaders of various
levels were analyzed:
• The company leader (this standard is being under consideration up
to now) (Профессиональный стандарт Руководитель организации).
• The specialist in risk management (Профессиональный стандарт
Специалист по управлению рисками, 2015).
• HR specialist (Профессиональный стандарт Специалист по
управлению персоналом, 2015).
• Statistics specialist (Профессиональный стандарт Статистик,
2015).
• Finance consultant (Профессиональный стандарт Специалист
по финансовому консультированию, 2015).
• Product quality specialist (Профессиональный стандарт
Специалист по качеству продукции, 2014).
• Company management specialist (Профессиональный стандарт
Специалист по организационному обеспечению управления
организацией, 2015).
The analysis was carried out according to qualification levels 6 and 7,
that is, according to the levels determining the labor functions as well as
competencies of the company leaders.
It should be mentioned that we failed to find the complete semantic and
syntactical correspondence of competencies, so the analysis is based on the
authors’ expert opinion.
Below are given the results of the comparison: competence groups in
the field of data management (according to DMBOK) and competences
established in professional standards:
• in the sphere of data governance professional standards indicate
competencies related to the overall strategy as well as policies and
procedures for their implementation, with data management not being
mentioned. Practically all professional standards reflect the competences
related to project management. Only one of them (Профессиональный
стандарт Руководитель организации) defines such competence as
"project realization" connected with “ implementation of optimization
tools necessary for information processing."
• in the sphere of data architecture management: a number of
standards contain the competencies related to the description and
formalization of the company business processes as well as consulting
"hardware-information support" projects. As absolutely necessary
knowledge the standards describing business processes are given which in
its turn presupposes the ability to describe the information flows. Deep
attention in several professional standards is given to the competencies
having to do with creating the company information and reference fund,
evaluating information resources and defining requirements for
information structure. Practically all standards include requirements
related to the development and improvement of information
standardization and classification system
• in the sphere of information procurement skills in effective
information processing are required. Virtually all standards indicate
demand for knowledge and skills of collecting and processing different
indicators’ data, still understanding of contemporary technologies of data
processing is not mentioned.
• in the sphere of operational data management only in the draft
standard for managers, the requirement for the ability "of determining the
needs of managers and specialists in information" is specified as well as
the implementation of actions for data bank creation.
• Practically all standards contain the competencies directed at
ensuring organization’s information security.
• In the sphere of Reference and Master Data Management, the
relevant competences in a number of standards are defined in special
terms. For example, for all categories of employees understanding the
reference, methodological and other information is necessary, as well as
the procedure of the formation and maintenance of reference data.
• Key competence of the group Data Warehouse and Business
Intelligence Management is "understanding of quantitative and qualitative
data analysis methods". In the draft standard (Профессиональный
стандарт Руководитель организации) the competences (given in
DMBOK) are mentioned practically in whole.
• In the group of competencies Document and Content Management
practically all standards contain the necessity of understanding the
workflow scheme, knowledge and skills of its analysis and optimization.
• In the competence group Data Quality Management, all standards
emphasize the necessity of knowledge of quality standards and the skills of
implementing quality control system. The standard (Профессиональный
стандарт Руководитель организации) does not expect that the manager
is supposed to be able to make data quality assessment himself but should
attract experts for making quality information assessment.
The authors have made their conclusions on the analysis results:
• The descriptions of competences connected with information given in
Russian professional standards are too vague and prevent from
understanding whether they have to do with the field of data management
or not. As the result, this kind of requirements are not compulsory for
managers concerned.
• Competencies in Russian professional standards are ascribed to
different labor functions. This prevents them from being correlated with
business roles.
3 Case study research
In order to verify the formulated results the case research, according to
the approach suggested by Paré's (Paré, G., 2004) research framework,
was conducted. Case research is useful (Paré, G., 2004; Yin, R.K., 2003)
when a phenomenon is broad and complex, the existing body of
knowledge is insufficient to permit the posing of causal questions, a
holistic, in-depth investigation is needed, and a phenomenon cannot be
studied outside the context in which it occurs.
Table 1. Structure of case study
Steps
Activities
Characteristics of the conducted research
project
1. Design of
Definition of
Q1: To identify the type of participation in
the case
study
research
questions
data management processes of the various
management groups of the company.
Q2: Which competencies in data
management are most important for the
company management?
Prior
theorizing
Comparing the body of knowledge
DMBOK with Russian professional
standards within the frames of digital
business transformation
A priori
constructs
Assigning competencies from DMBOK
Unit of
analysis
Data management competencies
Selection of
cases
Multiple-case design, selection of four
cases.
Case study
protocol
Description of the research project,
definition of objectives and scope,
interview guidelines, and topics and
structure of the case study reports
2. Conduct
of the case
study
Data
collection
methods
Deep interviews, questionnaires, DMBOK,
professional standards.
Data
triangulation
Multiple sources of information were
consolidated and results were compared to
increase validity.
Theoretical
saturation
Key respondents were interviewed and
documents collected until the theoretical
saturation was reached.
3. Analysis
of the case
study
evidence
Early steps
in data
analysis
Deep interviews were transcribed and
analyzed with open coding techniques.
Information was structured and recorded in
a project database.
Within-case
analysis
Explanation-building was used as
verification of conclusions based on the
results of comparison of professional
standards with DMBOK.
Cross-case
analysis
Differences of chosen companies were
analyzed to illustrate difference in
demands in various competencies in
different companies.
4. Writing
up the case
report
Case study
reports
Reports were written according to the
standardized guideline and reviewed by
interviewees in a feedback loop until final
results were approved by all the
participants.
Characteristics of companies involved in Case study research are presented
in the table 2.
Table 2. Characteristics of the four companies which are analyzed in this
research paper
Case
A
B
C
D
Industry
Production and
retail
Constructin
g
State sector
Government
sector
Production
Number
of
employees
>20000
>20000
>5000
>20000
Interview
partners
Leading
Specialist,
Project
Manager
Financial
director
Leading
Specialist
Head of
department
Analysis of the in-depth interviews results allowed to formulate the
following answers to the questions.
Q1: To identify the type of participation in the data management
processes of the various management groups of the company.
The respondents were asked to make their choice from the given type
of participation categories: Responsible, Consultative, Informed,
Approver, Executor. The research showed that
• Type of top management participation is mainly “Informed”, in rare
cases, “Consultative”
• Type of participation of heads of departments: “Responsible”,
“Consultative”.
• Type of participation of the IT department “Approver” and
“Executor’.
Q2: Which competencies in the data management are most important
for the company top management?
The survey made it possible to clarify competence model (Table 3) for
managers of the different levels involved in data management within the
global digital transformation of business.
Table 3. The final model of competences.
Manager /
Director
IT
departmen
t
Heads of
departmen
ts
Data Governance
Understanding of methods of
Strategy Enterprise Data Needs
determination
+
+
+
Ability to Estimate Data Asset
Value and Associated Costs
+
+
Data Architecture Management
Understanding of Enterprise
Business-Processes
+
+
Ability to formalize business
processes to identify sources and
consumers of data (including
external data)
+
+
Ability to form a list of the
company’s information needs
+
+
Understanding the basics of data
organization
+
+
Data Development
Understanding the methods of
forming the company’s
classifiers
+
+
Understanding the data
representation, storage and
transformation capabilities
+
Data Operations Management
Understanding the final results
of database operation in the
+
+
+
operational and analytical
activities of the company
Determination of the level of
data representation for different
groups of users
+
Identifying needs for improving
content and presentation
+
+
Data Security Management
Understanding the company’s
security policies
+
+
+
Ability to apply company’s
security policies to the
conceptual requirements of data
security
+
+
+
Reference and Master Data Management
Ability to define business
objects reflected in Master data
and company’s reference data
+
Ability to define the business
rules for harmonization and
usage reference data
+
+
Data Warehouse and Business Intelligence Management
Ability to determinate company
needs in analytical data
+
+
Understanding the methods and
technologies of data research
+
+
Document and Content Management
Ability to define the rules and
scheme of workflow
+
+
Data Quality Management
Understanding the concepts of
data quality
+
+
Ability to develop the data
quality business rules (from the
management point of view)
+
+
Organization of data quality
assessment system
+
+
4 Conclusions
The study shows that managers of different levels are aware of the
necessity of professional data management and the need for the companies
to develop competences in this sphere. The presented competency model
will let correctly formulate and structure the requirements for employees in
the field of data management. The model can be adapted for companies of
different sizes, different organizational structures and scope of activities.
In any case, the assessment of the level of professionalism of managers,
working within the frames of digital transformation of business, should
include assessment of competencies in the field of data management. The
digital economy requires adaptation of Russian professional standards.
The following questions require further discussion:
1. Is it necessary to strengthen the training of managers in the field of
data management?
2. How the presented model can be adapted for small and medium-
sized companies?
3. How to correlate the competence model in data management with the
company's maturity level presented by Gartner?
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