Towards Managing Analytics for Incumbent Banks: A Maturity Model

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: [e-mail] ; [e-mail] ️© 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) 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-Saether 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. 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”.

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-Saether 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-Saether 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).

4. Suggested Analytics Maturity Model

Date 18.01.22 24.01.22 25.01.22 19.02.22 21.02.22 22.02.22 23.02.22 23.02.22 02.03.22

Duration (min) 60 90 60 60 40 60 60 50 45

Following the guidelines prescribed by Solli-Saether 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 sub3 These documents became available during the interview process as one of the informants suggested them. dimensions) were broadly grouped under four dimensions i.e., Technology & Analytical techniques, Organization & Culture, People, and Data management respectively4.

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. 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 welldefined 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.

Characteristics of the Level

Analytical processes are ad-hoc, unstable, inconsistent, and ungoverned.

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.

Architecture and processes are developed and prepared to scale analytics.

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.

Analytical architecture, processes, data management, governance and more advanced techniques are established. Data quality is improved, measured and transparent. Data-driven culture is extensively promoted.

Analytical

Differentiator

Organization and management are in support of analytics and has

established enhanced analytical competence.

Analytical architecture and processes are optimized, governed, and 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. 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. 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 selfdoes 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. 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 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 metaLow 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.

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. 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.

Weak understanding about the Promotion of analytics has Analytics is promoted heavily

importance of analytics received some interest across across the enterprise and throughout the organization. the enterprise. People question business units have access to

No promotion of data-driven analytics even if some errors analytics support. End-users are

culture. People are sceptic in occur and justify gut feeling empowered. An analytics trusting analytics and often use over data driven decision understanding is established. gut feeling for decision making. making. Analytics is promoted Analytics initiatives are

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. Data-driven culture (i.e., agile thinking, data-driveninnovation, trial-error culture) is promoted. Data and analytics are the top agenda across the enterprise. Collaboration across units optimized.

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.

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