=Paper=
{{Paper
|id=Vol-3239/paper12
|storemode=property
|title=Towards managing analytics for incumbent banks: A maturity model
|pdfUrl=https://ceur-ws.org/Vol-3239/paper12.pdf
|volume=Vol-3239
|authors=Vilde Christiansen,Lester Allan Lasrado
|dblpUrl=https://dblp.org/rec/conf/stpis/ChristiansenL22
}}
==Towards managing analytics for incumbent banks: A maturity model==
https://ceur-ws.org/Vol-3239/paper12.pdf
Towards Managing Analytics for Incumbent Banks: A Maturity
Model
Vilde Christiansen and Lester Allan Lasrado
Kristiania University College, Kirkegata 24, 0153 Oslo, Norway
Abstract
Analytics improves organizational performance and becoming data-driven with analytics is
therefore a vision for many incumbent banks. However, successful deployment and
management of analytics in banks are often hindered by legacy systems, processes, and
organizational challenges associated with lack of a data-driven mindset and resistance to
change. Therefore, this research-in-progress paper presents a preliminary design of a
conceptual maturity model for managing analytics in the context of incumbent banks. By using
existing maturity model design and development guidelines, analytics-related literature from
the banking sector and related fields, and empirical evidence from one incumbent bank, this
research-in-progress paper presents a model with 4 dimensions, 13 sub-dimensions and 4
maturity levels. The research-in-progress paper also provides the basis for future development,
and validation.
Keywords 1
Analytics, Maturity Models, Stages of Growth, Business Models, Value Creation
1. Introduction
Banking and financial services organizations are dependent on analytics to perform core business
activities like calculating risk, control transactions and processing payment data as well as peripheral
business activities like processing consumer habits to create of personalized products and services [6;
51]. It is understood that the utilization of data analytics improves organizational performance and
competitive advantage [7; 11; 16; 17; 54; 58] and thus becoming data-driven with analytics is therefore
a vision for many of these incumbent banks. These Incumbent banks often have large and aging systems
consisting of various information technology (IT) and shadow analytics systems that have emerged in
business units. These have been built on top of each other over the course of many years without going
through the formal and controlled organizational IT structures [46]. Unlike digital companies (e.g.,
Amazon, Google) which are driven by data and analytics, these traditional banks are often challenged
by legacy technology and embedded organizational factors unsupportive of analytics; with complex
and large technological architectures, continuously growing pools of data and weak data governance
[19; 44], the generation of value from analytics can be difficult [33]. In addition to the technological
challenges, incumbent banks often face human (socio) challenges with lack of a data-driven mindset
and resistance to change listed as the main inhibitor of retrieving business value from analytics [44; 50].
It is understood that the bringing about this change can be time-taking and complex because of the
need of various stakeholders to establish a common language and interact with each other (e.g., IT,
analytics and business functions or units like operations, sales, marketing, etc.). Information systems
scholars [36; 56] argue that a maturity model is a useful organizational tool to guide such a change.
However, despite the vast amount of maturity models within analytics [17; 54], maturity models for
8th International Workshop on Socio-Technical Perspective in IS development, August 19-21, 2022 (STPIS’22)
EMAIL: vildee.christiansen@gmail.com ; lesterallan.lasrado@kristiania.no
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
121
managing analytics2 in the context of incumbent banks present a research gap, which is addressed in
this research-in-progress paper. The rest of this research-in-progress paper is structured as follows.
First, we briefly introduce the terminology associated with maturity models. Second, we present the
methodological background and choices. Third, we review the extant literature and present the first
iteration of the suggested maturity model. Lastly, we discuss the empirical evidence, present the second
iteration of the conceptual maturity model, and discuss future work.
2. Maturity Models
Maturity models and stage of growth models are organizational tools that facilitate internal and/or
external benchmarking while also showcasing future improvement and providing guidelines to help the
audience towards some desired outcomes [40]. The term “maturity” is defined as “the state of being
complete, perfect or ready” [40]. A maturity model usually consists of a sequence of maturity levels
[48], mostly four or six [30] and are often represented as fixed level models, continuous level models
or a matrix structure in form of focus area models [34; 45]. Each level expects a socio-technical entity
(i.e. people, process, technology, organisation) under maturation to fulfil certain requirements that
constitute that particular level [34]. Usually, this is determined by defining dimensions, benchmark
variables, capabilities or critical success factors and boundary conditions or dominant problems [35].
The dimensions as prescribed by the maturity model also mean better outcomes and thus higher business
benefits (value) as the organization progresses on the path to increased maturity. In general, maturity
assessment is understood as a measure to evaluate the capabilities of an organization [34; 48], with an
intention to provide a common vocabulary to facilitate discussion and thus a structure for prioritizing
actions [36], which is also the purpose of this paper.
Following the prior meta studies on maturity models by Mettler, Rohner, and Winter [41], J Becker,
Knackstedt, and Poeppelbuss [4] for this study we subscribe to the definitions and terminology proposed
by L. Lasrado, et al. [34]: “ (i) Maturity Level [Level1… Level n] are levels or stages the describe the
archetypal states of maturity of the entity with each level having a set of distinct characteristics [34; 42;
47]; (ii) Dimensions (Xmn, m factors and n levels): “Elements”, “Critical Success Factors”,
“Conditions”, “Factors”, and “Capabilities” are some of the other terms. Each dimension is divided into
sub-dimensions with specific characteristics at each level [34; 47]; (iii) Boundary Conditions [B1…
Bn]: Also termed “Triggers”, ”Dominant Problems” [56] and “Inhibitors”, “existential crisis” [13] are
specific conditions that the entity has to satisfy in order to progress from one level to another [35]”.
3. Methodology
As discussed in section 2, information systems scholars [5; 18; 34; 41; 56] have prescribed
approaches, guidelines, and definitions to design and develop maturity models in a systematic manner.
For this study, we adopted the five step modelling process prescribed by Solli-Sæther and Gottschalk
[56], while also using some of the guidelines and definitions prescribed by Mettler [40]. At the time of
writing this research-in-progress paper, we are in the design phase i.e., developing a conceptual model
as shown in Figure 1.
Figure 1. Maturity model development based on Mettler [40] & Solli-Sæther and Gottschalk [56].
2
Within the scope of this current study, managing analytics is understood as “a set of activities and processes where data is analyzed, managed
and used and where statistical and quantitative analysis, explanatory and predictive models are applied in order to drive more effective and
fact-based decision-making that can enhance business value, performance, innovation, new product and services development, and transform
business processes”.
122
We have derived the suggested model through review of the literature and integrating ideas from
practice (the process is discussed in section 4). According to Solli-Sæther and Gottschalk [56], the next
step of deriving the conceptual model is a result of empirical testing wherein the descriptions of maturity
levels are developed in an iterative cycle and the dominant problems or boundary conditions are also
established [22; 34]. Solli-Sæther and Gottschalk [56] prescribe case studies for each of the maturity
levels and in this research-in-progress paper, we present one such case which is at maturity level 2 of
the suggested model. The selected case organization is an incumbent, leading Norwegian bank
functioning as a full provider of banking and financial services. A total of nine semi-structured
interviews were performed (see Table 1) at this case company, which were used to develop and enhance
the maturity model. Interviews were transcribed and categorized continuously through the process.
Transcription was conducted manually and simultaneously anonymized. In addition to these interviews,
documents about data governance, the data product concept and ambitions for technical platforms were
also examined3. Next, the data was categorized into main dimensions and sub-dimensions; while the
components of the interview guide made the foundation for the categorization, the data analysis also
resulted in the emergence of new sub-dimensions like data privacy and data & analytics presentation
(section 5).
Table 1. Interviews for Case 1.
Informant and Role Baking Entity Department Date Duration (min)
1 Section Leader Private Data Driven Sales 18.01.22 60
2 Lead Information Architect Private Digital Architecture 24.01.22 90
3 Data & Analytics Consultant Private Data Driven Sales 25.01.22 60
4 Lead Advanced Analytics Corporate Advanced Analytics 19.02.22 60
5 Privacy Steward Private Quality And Risk 21.02.22 40
6 Section Leader Wealth Mgmt. Customer Insight 22.02.22 60
7 Data Scientist Private Data Driven Sales 23.02.22 60
8 Section Leader Risk Risk Data 23.02.22 50
9 Department Manager Corporate Data Governance 02.03.22 45
4. Suggested Analytics Maturity Model
Following the guidelines prescribed by Solli-Sæther and Gottschalk [56], the suggested maturity
model was developed based on prior research (i.e., analytics maturity models, analytics in banking) and
practice (i.e., digital consultancy maturity models). Peer reviewed journals articles on analytics in
banking were fetched via Oria and Google Scholar using a combination of keywords in the titles:
"analytics" OR "data analytics" OR "data-driven" OR "data-driven decision-making" OR “decision
analytics” OR "decision support" OR "decision intelligence" OR "business analytics" OR "business
intelligence" OR “data management” OR “data governance” OR “dashboarding” OR “data
visualization” AND "bank" OR "banking" OR “financial services”. We were interested in articles that
discussed applications, capabilities and challenges related to managing analytics in the banking sector,
was relevant in today’s digital environment and hence set a timeframe of last 5 years.
The search process resulted in 341 articles, which after scanning titles and reading abstracts, resulted
in 16 selected articles [1; 2; 3; 14; 19; 21; 25; 26; 28; 29; 31; 38; 43; 44; 50; 53] that were read in full
and included in the review. Five more articles [10; 23; 27; 51; 52] were added because of backtracking
and snowballing. In addition to the domain specific articles, we also reviewed articles within analytics
(e.g., [24; 54; 55], [12; 20; 49],[8; 9; 15; 32; 39]). The process also resulted in us extracting 11
capabilities for analytics maturity into as shown in Table 2. These 11 capabilities (later referred as sub-
3
These documents became available during the interview process as one of the informants suggested them.
123
dimensions) were broadly grouped under four dimensions i.e., Technology & Analytical techniques,
Organization & Culture, People, and Data management respectively4.
Table 2. Categorization of capabilities (sub-dimensions) for analytics maturity.
Cu ppli
An
ltu
Da ourc
aly
Sy
re
Da neag
Da
ta
ste
Da
tic
A
Da rs
&
ta
ta
An izat
Go
Le
al
m
ta
ta
O
S
Li
Te
s&
St
aly
ad
ve
Qu
ca
rg
or
rn
ch
tio
e
sts
an
ali
To
ag
an
ni
ns
in
ty
e
e
qu
ols
ce
g
ion
es
Authors
Ali et al. [2] W W W W
Al-Nattar and Alazzavi [1] W
Clarke [10] W W W W W
Cosic, Shanks and Maynard [12] W W W W W W W
Dash and Das [14] W W
Davenport and Harris [16] W W W W W W W W W
Delgosha, Hajiheydari and Fahimi [19] W W W W W
Deloitte [20] W W W W W
Dicuonzo et al. [21] W W W
Forrester [24] W W W W W
Hajiheydari et al. [25] W W W W W W W
Hung, He and Shen [28] W
Joshi, Pratik and Podila [29] W W W W
Karkošková [31] W W W W
Lacković, Kovšca and Vincek [23] W W W W W
Law and Chung [38] W
Owusu [43] W
Pillay and van der Merwe [44] W W W W W W
Rezaie, Mirabedini and Abtahi [50] W W W W W W W
Sadok, Sakka and El Maknouzi [51] W W W W
Scherbaum, Novotny and Vayda [52] W W W W
Schmidt, Drews and Schirmer [53] W W W
Count 11 14 13 9 6 6 8 7 6 4 9
W Size indicates the level of Importance or mention in the article
Technology & analytical techniques characterizes the adoption and application of technology,
infrastructure, tools, and techniques that support analytics in the organization. Lately, financial
decision-making has become highly dependent upon sophisticated analytical tools; fraud and risk
analysis is ranked as the most important applications of analytics in banking [19; 25]. Banks are also
dependent on analytics to know their customers (KYC) for anti-money laundering (AML) regulations
and risk management [21; 26] and utilizing analytics here is a matter of survival for banks because of
laws and regulations. On the other hand customer analytics, which involves utilizing data for customer
acquisition, satisfaction and retention, was ranked the second most important and was associated with
a higher analytical maturity [19]. The third most important application was considered operational
4
Sub-dimensions Systems & Tools, Analytical Techniques and Applications are grouped as Technology & Analytical techniques. Analysts
and Leaders are grouped as People. Culture & Organization is left as is and rest of the 5 sub-dimensions are grouped under Data Management.
124
analytics, involving utilization of data to renew or innovate business models, products, and services,
and streamline existing ways of working which strengthened employee learning, which enhanced
innovation, NPD and internal processes [19; 43]. In addition to application of analytics, the literature
lists security, flexibility, integration, accessibility, user friendliness and dependency on legacy systems
as technological requirements needed to leverage value from analytics. Compatibility and integration
problems concern insufficient data sharing across business units due to information silos [19; 25; 44;
50], with time and money invested in legacy systems that are incompatible with new analytics
technologies is acting as a strong inhibitor or dominant problem towards higher analytical maturity.
Organization & culture includes organizational norms, values, and formalized departments or
functional units to systematically work with analytics as the lack of a data-driven approach is a main
inhibitor of realizing business value from analytics [19; 25; 44]. Top management support,
empowerment of end users, analytics promotion towards the entire organization and all stakeholder
groups [2; 19; 25; 44] and an agile culture [1] were all seen as enabler of data driven culture.
People includes professionals i.e., employees and consultants utilizing analytics in their job function
and leadership support for analytical competence [15; 16]. Good recruitment processes and allocation
of resources for analytics training is considered crucial to sustain skills and competence by several
studies. The main organizational challenges are seen as lack of skilled professionals, especially on
machine learning, and lack of TMS due to low managerial analytics competence, risk taking, and
implementation costs [2; 25; 44; 50].
Data management includes data infrastructure, data sourcing, quality, warehousing, accessibility,
and data governance [15; 57]. Data quality and quality of working flows (e.g., ETL) is critical during
early stages of implementation [25]. Data sourcing involves both internal and external searches [19],
with data lineage heighted as an important factor. To strengthen data lineage, master data should be
managed in a way that ensures traceability and distributed discovery through the value chain [57] with
a standard data catalogues including business glossary, information about data ownership, and technical
descriptions being implemented across the enterprise. Data management dimension also includes data
governance [31] which is as a framework of control mechanisms such as processes, policies,
organizational structures and roles that ensures the allocation of decision rights and responsibilities for
governing data and analytics in an organization [31; 57]. Higher levels of maturity prescribes a right
balance between centralized and decentralized data governance [29] to ensure control, flexibility,
productivity, data quality and transparency with strategic and operational data committees with well-
defined data roles such as CDO, data stewards, and quality managers [31].
This process of content analysis of the sub-dimensions also resulted in us extracting four maturity
levels and the characteristics of the four levels are as described in Table 2.
Table 2. Suggested Analytics Maturity Levels.
Level Name Characteristics of the Level
1 Analytical Analytical processes are ad-hoc, unstable, inconsistent, and ungoverned.
Beginner Analytics is only utilized for necessary banking operations. Analytical
investments are incompatible with existing infrastructure. The organization
is silo-organized, has poor data quality and technical debt which needs to
be addressed before anything else. Lack of analytical talents and leadership
visions, competence, support, and engagement in analytics.
2 Analytical Architecture and processes are developed and prepared to scale analytics.
Developer The organization is implementing some data management and governance
and working on improving data quality. Analytics is promoted and beginning
to attract interest enterprise-wide. Management is beginning to understand
the importance of analytics, has some analytical competence and are willing
to allocate some resources.
3 Analytically Analytical architecture, processes, data management, governance and
Established more advanced techniques are established. Data quality is improved,
measured and transparent. Data-driven culture is extensively promoted.
125
Organization and management are in support of analytics and has
established enhanced analytical competence.
4 Analytical Analytical architecture and processes are optimized, governed, and
Differentiator continuously improved. Data quality is optimized and continuously
monitored and analyzed. Analytics is considered the key source of
competitive advantage as it has full enterprise-wide engagement and
support. The organization possesses great analytical talents. Managerial
competence is high and risk-taking is encouraged. Innovation is driven by
analytics. The organization continuously adapts to market changes and
delivers value back to the customer via real-time analytics. Analytical
activities are unique and generate strong revenue growth.
5. Future Agenda - Towards a Conceptual Analytics Maturity Model
As discussed in section 3, the conceptual model should empirically test the characteristics of each
level, significant differences between levels and the boundary conditions using case studies [56]. We
applied the suggested model on an incumbent Norwegian bank (section 3) and assessed it to be at
maturity level 2 (i.e., analytical developer) as shown in Table 3a, 3b, 3c and 3d5. During this assessment
conducted via semi-structured interviews, we updated the characteristics of the maturity levels, tested
the relevance of the dimensions, sub-dimensions, and boundary conditions. The data analysis resulted
in addition of two sub-dimensions i.e., Data & Analytics Presentation and Data Privacy. Data &
Analytics Presentation involves dashboarding, presentation, reporting and visualizing data towards
decision-makers, management, and end-users. The bank has one primary tool for reporting data and
was discussed by the interviewees as a significant factor towards maturity. However, low maturity in
data quality and lineage affects the success of data presentation in terms of dashboard reusability.
Data privacy became a bigger talking point throughout the interview process and was continuously
brought up as an obstacle for managing analytics. In 2018, the bank was forced to address data privacy
to a greater extent due to the ‘General Data Protection Regulation’ (GDPR). The empirical findings
show that data privacy is functioning as a very important aspect of data management in Norwegian
banking, which in many cases functions as an obstacle of analytics applications. Moreover, the duty of
confidentiality reduces the ability to share data between entities which doesn’t advocate for producing
less copies of the same data. This sub-dimension also produces a tradeoff between increased control
over data lineage which strengthens data governance. Data privacy was therefore considered an
additional sub-dimension of data management as shown in Table 3b.
Maturity in terms of Analytical Systems & Tools are assessed as between stage one and two.
Multiple legacy systems with many integrations, usability and compatibility challenges are seen in stage
one. Moreover, lack of operational central IT unit provokes shadow IT. We observed that analytical
developers are attempting to address these challenges while new analytics infrastructure is established
in the next stage. Results show that analytical differentiators possess an automated, cloud-based storage
and distribution platform, with agile ETL and data management solutions realising business value. The
assessment shows that the Analytical Tools & Techniques sub-dimension align with the DELTA model
presented by [15]. Analytical differentiators are standardizing and monitoring the business value of
their techniques and tools.
Maturity in terms of data quality was assessed as stage one. Analytical beginners are characterized
by possessing adequate data quality on operational applications (e.g.., transactions, updating balance
on accounts, credit risk) and low data quality resulting in gut feeling decisions on analytical applications
(e.g., CRM, sales, pricing, management reporting, compliance, defining goals). The developing stage
involves optimizing quality on critical operational data before moving ahead. To increase maturity in
this sub-dimension, companies need to establish a common understanding of the importance of data
quality improvement as it is commonly down prioritized because of short-term projects producing
5
Maturity levels (of the case i.e., incumbent Norwegian bank) of each sub-dimension is shaded. At place, two levels are shaded, when the
transition is taking place and some of the obstacles or boundary conditions are not fully overcome.
126
higher ROI. Findings also show low Data Lineage maturity where data management tools are employed
but not utilized leading to numerous undocumented and uncontrolled data copies that decreases integrity
and credibility. The journey towards stage four in this sub-dimension is difficult for incumbent banks.
Realistically, analytical differentiators therefore manage to trace above 80 % of their data.
Our case was assessed as stage one with data storage as well. Several central data warehouses are
seen to be the cause for low maturity in quality and lineage with batch processing, high operational
costs, long lead-time by central IT units listed as major challenges. While data Lake and data mesh were
discussed as possible solutions, the interviewees acknowledge that there is no right answer as to how
data storage should be designed. Analytical differentiators are characterized by possessing a
functioning, tailor-made storage solution that facilitates real-time analytics, increased user friendliness
and accessibility regardless of what type of architecture or combination of architectures that they use.
The case is assessed as stage two regarding Data Governance because most units are not ready for
implementation of established frameworks and roles. Moreover, a balance between centralization and
decentralization is suggested for analytical differentiators as our findings indicate that a combination
between a central data governance function and a federated model can ensure control, productivity, and
transparency of data quality. Discovering new data sources was seen as an important dimension.
However, informants did not bring much intel on data sourcing other than privacy and licensing being
obstacles for continuous discovery, which is to be performed at the highest maturity level.
The organization is currently suffering because of historical technical and data competence
outsourcing, with lack of skilled professionals being a major obstacle. Data engineers are observed to
be lacking and analytical competence is missing in the business and product environments. The bank is
assessed as between stage two and three as some routines for building competence and training via
onboarding is well established. Leadership is assessed at stage two as several business units experience
lack of TMS towards data management and governance.
As iteration 2 (conceptual model) is still work-in-progress, future work would involve to further
refine this preliminary conceptual model (Table 3), verify the levels and dimensions further through
more case studies by employing interviews and focus group discussions. To empirically test the
maturity levels, we follow the steps prescribed in maturity model literature [22; 37; 56], wherein the
preliminary conceptual model (Table 3) would be presented to the stakeholders in a bank and ask them
to indicate which level they most closely see themselves. The preliminary version of the conceptual
model would then be used to conduct self-assessment in these banks, which will also help in scoping
the maturity model further, validating it and providing a foundation for the development of a maturity
assessment tool, that can be used as a step towards managing analytics in banks. In the future, we
envision this conceptual maturity model can also be modified to analyze a banks readiness and when
used by multiple banks, benchmarking tool can also be created. Furthermore, in our future publications
we plan to formalize the learnings from the different cases, share the assessments and prescribe
strategies to successfully navigate the different levels.
127
Table 3a. Conceptual Analytics Maturity Levels - Technology & Analytical techniques.
Analytical beginner Analytical developer Analytically established Analytical differentiator
Analytical Analytical investments are There is an attempt to integrate Standardized IT infrastructure Technological architecture and
Systems & Tools incompatible with existing IT existing systems/infrastructure and SLAs are established for system qualities optimized. Well
infrastructure. Central IT unit and thus increased readiness real time analytics. Information established decentralized self-
does not facilitate analytics and for analytics projects. siloes and shadow IT is either service data & analytics
leads to shadow IT within nonexistent or their presence is platform. Cloud-based scalable
business units. well documented and storage and distribution
monitored. platform supports real time,
heavy-duty analytics. IT
investments generates business
value. There is increased
standardization of processes
without hindering innovation.
Analytical Ad-hoc descriptive techniques. Some predictive statistical and Use of predictive and Standardized and optimized use
Techniques forecasting techniques applied. prescriptive techniques in a of advanced analytics with
systematic manner. monitoring of business value.
Data & Analytics Reporting tools implemented, Reporting tools are modified or Reporting tools are well Reporting tools & processes are
Presentation but poor data quality leads to switched out to enhance agility. established. Most dashboards optimized. Data is integrated
inefficient reporting. No Developing new reporting are considered data products and there is a sense of single
reporting on data quality. routines to improve quality, and reused. End user capability source of truth. Reporting on
Dashboards developed in siloes reusing of dashboards and is enhanced, and common data quality is discussed through
are difficult to reuse. improved end-user capabilities. vocabulary is developed. monitoring of KPI’s.
Applications Analytics only applied to core In addition to operational Exploring analytics for business Operational and analytical data
operational activities and activities, applications are using model innovation, NPD, is optimized. Differentiating
banking processes like analytical data (e.g., CRM, price operational excellence, and innovations and NPD is
risk/transaction data/AML to optimization). Privacy and compliance. delivering value back to the
keep operations going. confidentiality are considered customer through Fintech-like
as obstacles. services. Innovation is driven by
analytics across the enterprise.
128
Table 3b. Conceptual Analytics Maturity Levels - Data management.
Analytical beginner Analytical developer Analytically established Analytical differentiator
Data Quality Most managerial decisions are Optimizing quality of critical Data quality is monitored and Data quality is fully optimized.
on gut feeling. While quality is data is a priority and the tracked. The business value There are well established
adequate on operational or organization starts exploring to realized from use analytics is processes for monitoring,
transactional data, the data define and improve quality on measured. Data quality and tracking, and assessing data
associated with KPI’s or needed analytical data. The business credibility is more transparent. quality. The metrics are
for analytics is not credible. value of working with data A clear understanding on data discussed in well-established
Improving data quality and quality is communicated across quality established across the governance forums and is part of
credibility is not a priority. business units. enterprise. the higher management
discussions.
Data Lineage Data management tools are not There is a drive to develop data Standardized data and master Data management and master
employed across the management plans and data management processes data management processes are
enterprise. Undocumented, processes. Business units begin established. Majority of the optimized and performed across
uncontrolled data workflows. to use data management tools analysts employ data the enterprise. Data and meta-
Low data integrity and and interact using common management tools and follows data are well documented
credibility i.e., multiple copies vocabulary. However, data and established protocols. There is leading to minimal copies of the
and versions of the same data. meta-data documentation is a unified data catalogue and same version. Majority (>80%)
No single source of truth. not standardized. business glossary. of the data sources can be
tracked.
Data Storing Centralized Datawarehouse Developing centrally managed New architecture is established Scalable, distributed, and
handling structured data and data storage to handle (e.g., Data Lake) and modified decentralized data architecture
only understood by some unstructured, semi-structured to handle real-time processing. aligning with business needs
specialists in the enterprise. and structured data (e.g., Data The centralized IT creates a (e.g., Data Mesh). Balance
Unmonitored, high costs and Lake). Still batch processing, more agile solution with between centralized and
heavy batch processes. lack of control and user increased accessibility for decentralized architecture is
Centralized IT unable to deliver friendliness. Centralized IT analysts. There are projects optimized. Data architecture is
on time, thus leading to unable to deliver on time, thus supported to decentralize tailor-made for analytics with
analysts establishing storage leading to analysts establishing ownership and move towards data quality and data life cycle
solutions in silos. storage solutions in silos. federated governance. processes fully optimized.
129
Data Governance Ad-hoc, inconsistent or non- Central data governance Data governance roles, Enterprise-wide data strategy
existing data strategy, roles, structures are not operational. responsibilities, ownership, aligned. Data governance is
responsibilities, and ownership. Some data governances (e.g., data sharing routines, federated and the balance
Lack of understanding why data stewards) are being informally workflows, vocabulary, and between centralized and
governance is needed. allocated within business units, frameworks are formally decentralized decision-making
but centralized data ownership established both centrally and policies optimized. Continuously
or responsibility is not within business units improving data governance is
established. Existing data (decentralized). Variation in prioritized and part of the KPIs
owners and stewards have too maturity is seen across units, across the enterprise.
much to maintain and track. with initiatives to formalize
Some top management support across the enterprise.
and budget for data governance
initiatives is allocated.
Data Privacy Privacy on operational data Development of privacy roles, Privacy processes are Privacy processes optimized,
adequate. Lack of knowledge routines for legal assessment, established. Protocol list distributed, and anchored in
on GDPR and confidentiality for treatment protocols, deletion tracking is not standardized, business units and NPDs.
analytical purposes. rules, access control for and responsibility is not Optimized processes to solve
operational and analytical distributed towards business trade-offs between compliance
purposes is initiated. There is units. Trade-offs between risk and data opportunities.
discussion on compliance, compliance risk and data
however. poor data lineage opportunities. NPDs not always
results in privacy concerns. considering privacy issues.
Data Sourcing Analysts are only utilizing Analysts are working with Internal sources optimized and Use of internal and external data
necessary internal data and not internal data discovery. some external data discovered. is optimized. Processes and
working with data discovery. Licensing and privacy may be workflows for data sharing
obstacles when sharing sources across business units and teams
across entities. are well defined. Analysts and
Stewards are continuously
working with data discovery
across the enterprise.
130
Table 3c. Conceptual Analytics Maturity Levels - Organization & Culture
Analytical beginner Analytical developer Analytically established Analytical differentiator
Organization and Weak understanding about the Promotion of analytics has Analytics is promoted heavily Data-driven culture (i.e., agile
Culture importance of analytics received some interest across across the enterprise and thinking, data-driven-
throughout the organization. the enterprise. People question business units have access to innovation, trial-error culture)
No promotion of data-driven analytics even if some errors analytics support. End-users are is promoted. Data and analytics
culture. People are sceptic in occur and justify gut feeling empowered. An analytics are the top agenda across the
trusting analytics and often use over data driven decision understanding is established. enterprise. Collaboration
gut feeling for decision making. making. Analytics is promoted Analytics initiatives are across units optimized.
Lack of data collaboration by some but lack engagement encouraged from analysts and
across business units. among end users is seen. Data engaged end users. Enhanced
not considered in NPD data collaboration is seen
processes. across units. Data concerns are
part of NPD.
Table 3d. Conceptual Analytics Maturity Levels - People.
Analysts Recruitment of analytical Analytical competence is built Enhanced recruitment and Excellent recruitment process.
competence is insufficient. Not up (e.g., external consultants). training processes are The best analytical talents are
attracting the best talents. Data scientists must perform established. Analytics training attracted. Rich training of
Weak analytical competence data pre-processing due to lack is established as part of employees according to
among employees. of data engineers. Need for onboarding processes. changing business needs.
more “light” analytics Attracting data engineers Analytics is standardized part of
competence in business units. ensuring. Data professionals onboarding process across the
more like software developers. enterprise.
Leadership Low analytics competence Leaders understand the Analytics competence is Leaders prioritize analytics and
among leaders. Leaders not importance of analytics, established among leaders. are open to discover new
supporting analytics and not developing some competence Leaders allocate significant analytics opportunities that
allocating financial resources. and are allocating some financial resources, and could lead to change in
Leaders not willing to take risks resources. Analytics is analytics is part of the vision. business models.
with analytics. becoming the main agenda, but
strategic roles like CDO are
appointed.
131
References
[1] Al-Nattar, B. A., & Alazzawi, A. (2020). "Data Analytics of Strategic Agility and Competitiveness
in Operation Performance: A case of Banking Sector in Saudi Arabia"." 2020 International
Conference on Decision Aid Sciences and Application (DASA)", IEEE: 293-298.
[2] Ali, Q., Yaacob, H., Parveen, S., & Zaini, Z. (2021). "Big data and predictive analytics to optimise
social and environmental performance of Islamic banks"." Environment Systems and Decisions".
41: 616-632.
[3] Andriosopoulos, D., Doumpos, M., Pardalos, P. M., & Zopounidis, C. (2019). "Computational
approaches and data analytics in financial services: A literature review"." Journal of the Operational
Research Society". 70: 1581-1599.
[4] Becker, J., Knackstedt, R., & Poeppelbuss, J. (2011). "Developing Maturity Models for IT
Management – A Procedure Model and its Application"." Business & Information Systems
Engineering". 1: 213-222.
[5] Becker, J., Knackstedt, R., & Poeppelbuss, J. (2011). "Developing Maturity Models for IT
Management – A Procedure Model and its Application", Business & Information Systems
Engineering. 1: 213-222.
[6] Borgogno, O., & Colangelo, G. (2020). "The data sharing paradox: BigTechs in finance"." European
Competition Journal". 16: 492-511.
[7] Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). "Strength in numbers: How does data-driven
decisionmaking affect firm performance?"." Available at SSRN 1819486".
[8] Carvalho, J. V., Rocha, Á., Vasconcelos, J., & Abreu, A. (2019). "A health data analytics maturity
model for hospitals information systems"." International Journal of Information Management". 46:
278-285.
[9] Chen, L., & Nath, R. (2018). "Business analytics maturity of firms: an examination of the relationships
between managerial perception of IT, business analytics maturity and success"." Information
Systems Management". 35: 62-77.
[10] Clarke, N. (2019). "How to ensure provision of accurate data to enhance decision-making"." Journal
of Securities Operations & Custody". 11: 112-127.
[11] Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). "Using business analytics to enhance
dynamic capabilities in operations research: A case analysis and research agenda"." European
Journal of Operational Research". 281: 656-672.
[12] Cosic, R., Shanks, G., Maynard, S., & Lamp, J. (2012). "Towards a business analytics capability
maturity model"." Proceedings of the 23rd Australasian Conference on Information Systems 2012,
ACIS, [Geelong, Vic.], pp. 1-11.".
[13] Damsgaard, J., & Scheepers, R. (1999). "Managing the crises in intranet implementation: a stage
model"." Information Systems Journal", Blackwell Science Ltd. 10: 131-149.
[14] Dash, M. D., Kajal (2017). "Customer Attrition Analytics in Banking"." International Journal of
Business Analytics & Intelligence". 5: 7-14.
[15] Davenport, T. (2018). "DELTA Plus Model & five stages of analytics maturity: A primer"."
International Institute for Analytics".
[16] Davenport, T., H, & Harris, J., G. (2007). "Competing on Analytics: The New Science of Winning",
Harvard Business Press.
[17] Davenport, T. H. (2018). "From analytics to artificial intelligence"." Journal of Business Analytics".
1: 73-80.
[18] De Bruin, T., Freeze, R., Kaulkarni, U., Rosemann, M., Campbell, B., Underwood, J., & Bunker, D.
(2005). "Understanding the Main Phases of Developing a Maturity Assessment Model"."
Australasian Chapter of the Association for Information Systems".
[19] Delgosha, M. S., Hajiheydari, N., & Fahimi, S. M. (2020). "Elucidation of big data analytics in
banking: a four-stage Delphi study"." Journal of Enterprise Information Management".
[20] DELTA-Model-Accenture (2013). "The Accenture Netherlands Analytics DELTA Survey 2013
http://www.accenture.com/nl-en/Pages/insight-netherlands-analytics-delta-survey-2013.aspx
Retrieved on 20th October 2014".
132
[21] Dicuonzo, G., Galeone, G., Zappimbulso, E., & Dell'Atti, V. (2019). "Risk management 4.0: The
role of big data analytics in the bank sector"." International Journal of Economics and Financial
Issues". 9: 40.
[22] Duane, A., & OReilly, P. (2012). "A Conceptual Stages of Growth Model for Managing an
Organization's Social Media Business Profile (SMBP)"." International Conference on Information
Systems (ICIS) 2012 Proceedings".
[23] Dvorski Lacković, I., Kovšca, V., & Lacković Vincek, Z. (2020). "A Review of Selected Aspects of
Big Data Usage in Banks’ Risk Management"." Journal of Information and Organizational
Sciences". 44: 317-330.
[24] Gill, M., & VanBoskirk, S. (2016). "The digital maturity model 4.0"." Benchmarks: digital
transformation playbook".
[25] Hajiheydari, N., Delgosha, M. S., Wang, Y., & Olya, H. (2021). "Exploring the paths to big data
analytics implementation success in banking and financial service: an integrated approach"."
Industrial Management & Data Systems".
[26] Hassani, H., Huang, X., & Silva, E. (2018). "Banking with blockchain-ed big data"." Journal of
Management Analytics". 5: 256-275.
[27] Holter Antonsen, H., & Madsen, D. Ø. (2021). "Developing a Maturity Model for the Compliance
Function of Investment Firms: A Preliminary Case Study from Norway"." Administrative Sciences".
11: 109.
[28] Hung, J.-L., He, W., & Shen, J. (2020). "Big data analytics for supply chain relationship in
banking"." Industrial Marketing Management". 86: 144-153.
[29] Joshi, D., Pratik, S., & Podila, M. (2021). "Data Governance in Data Mesh Infrastructures: the Saxo
Bank Case Study", EasyChair.
[30] Karkkainen, H., Jussila, J., & Lyytikkä, J. (2011). "Towards Maturity Modeling Approach for Social
Media Adoption in Innovation"." 4th ISPIM Innovation Symposium, Wellington, New Zealand -
ISBN 978-952-265-167-9.".
[31] Karkošková, S. (2022). "Data Governance Model To Enhance Data Quality In Financial
Institutions"." Information Systems Management": 1-21.
[32] Król, K., & Zdonek, D. (2020). "Analytics maturity models: An overview"." Information". 11: 142.
[33] Lange, H. E., Drews, P., & Höft, M. (2021). "Realization of Data-Driven Business Models in
Incumbent Companies: An Exploratory Study Based on the Resource-Based View".
[34] Lasrado, L., Vatrapu, R., & Andersen, K. (2016). "A Set Theoretical Approach to Maturity Models:
Guidelines and Demonstration"." International Conference on Information Systems (ICIS)", AIS
Electronic Library (AISeL).
[35] Lasrado, L., Vatrapu, R., & Normann, K. (2016). "A Methodological Demonstration of Set-
Theoretical Approach to Social Media Maturity Models Using Necessary Condition Analysis"."
Proceedings of the 20th Pacific Asia Conference on Information Systems".
[36] Lasrado, L. A., Vatrapu, R., & Andersen, K. N. (2015). "Maturity Models Development in IS
Research: A Literature Review"." IRIS Selected Papers of the Information Systems Research
Seminar in Scandinavia 2015.Paper 6".
[37] Lasrado, L. A., Vatrapu, R., & Mukkamala, R. R. (2017). "Whose Maturity Is It Anyway? The
Influence Of Different Quantitative Methods On The Design And Assessment Of Maturity
Models"." In Proceedings of the 25th European Conference on Information Systems (ECIS)".
Guimarães, Portugal.
[38] Law, K., & Chung, F.-L. (2020). "Knowledge-driven decision analytics for commercial banking"."
Journal of Management Analytics". 7: 209-230.
[39] Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). "Prescriptive analytics:
Literature review and research challenges"." International Journal of Information Management". 50:
57-70.
[40] Mettler, T. (2009). "A design science research perspective on maturity models in information
systems"." Universität St. Gallen, St. Gallen, Switzerland, Technical Report BE IWI/HNE/03".
[41] Mettler, T., Rohner, P., & Winter, R. (2010). "Towards a Classification of Maturity Models in
Information Systems"." Management of the Interconnected World", Physica-Verlag HD: 333-340.
[42] Nolan, R. L., & Gibson, C. F. (1974). "Managing the Four Stages of EDP Growth"." Harvard
Business Review January–February 1974".
133
[43] Owusu, A. (2017). "Business intelligence systems and bank performance in Ghana: The balanced
scorecard approach"." Cogent Business & Management", Cogent OA. 4: 1364056.
[44] Pillay, K., & Van der Merwe, A. (2021). "Big Data Driven Decision Making Model: A case of the
South African banking sector"." South African Computer Journal". 33: 55-71.
[45] Pöppelbuß, J., & Röglinger, M. (2011). "What makes a useful maturity model? a framework of
general design principles for maturity models and its demonstration in business process
management"." ECIS 2011 Proceedings.Paper 28".
[46] Potančok, M., Karkošková, S., & Novotný, O. (2021). "Shadow analytics"." 29th Interdisciplinary
Information Management Talks-Pandemics: Impacts, Strategies and Responses, IDIMT 2021": 119-
124.
[47] Raber, D., Winter, R., & Wortmann, F. (2012). "Using Quantitative Analyses to Construct a
Capability Maturity Model for Business Intelligence"." 45th Hawaii International Conference In
System Science", IEEE Computer Society: 4219-4228.
[48] Raber, D., Winter, R., & Wortmann, F. (2012). "Using Quantitative Analyses to Construct a
Capability Maturity Model for Business Intelligence"." In System Science (HICSS), 45th Hawaii
International Conference", IEEE Computer Society: 4219-4228.
[49] Raber, D., Wortmann, F., & Winter, R. (2013). "Towards The Measurement Of Business Intelligence
Maturity"." ECIS 2013 Completed Research. Paper 95.".
[50] Rezaie, S., Mirabedini, S. J., & Abtahi, A. (2017). "Identifying key effective factors on the
implementation process of business intelligence in the banking industry of Iran"." Journal of
intelligence studies in business". 7.
[51] Sadok, H., Sakka, F., & El Maknouzi, M. E. H. (2022). "Artificial intelligence and bank credit
analysis: A review"." Cogent Economics & Finance". 10: 2023262.
[52] Scherbaum, J., Novotny, M., & Vayda, O. (2018). "Spline: Spark lineage, not only for the banking
industry"." 2018 IEEE International Conference on Big Data and Smart Computing (BigComp)",
IEEE: 495-498.
[53] Schmidt, J., Drews, P., & Schirmer, I. (2018). "Charting the emerging financial services ecosystem
of Fintechs and banks: Six types of data-driven business models in the Fintech sector".
[54] Seddon, P. B., Constantinidis, D., & Dod, H. (2012). "How Does Business Analytics Contribute to
Business Value?"." ICIS 2012 Proceedings".
[55] Shanks, G., Bekmamedova, N., & Willcocks, L. (2012). "Business Analytics: Enabling Strategic
Alignment and Organisational Transformation"." ECIS 2012 Proceedings. Paper 18".
[56] Solli-Sæther, H., & Gottschalk, P. (2010). "The modeling process for stage models"." Journal of
Organizational Computing and Electronic Commerce", Taylor & Francis. 20: 279–293.
[57] Spruit, M., & Pietzka, K. (2015). "MD3M: The master data management maturity model"."
Computers in Human Behavior". 51: 1068-1076.
[58] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). "Big data
analytics and firm performance: Effects of dynamic capabilities"." Journal of Business Research".
70: 356-365.
134