Aligning BI with Corporate Strategy in SME

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Department of Informatics and Media

Spring Semester - 2020

Master’s Thesis

Management, Communication & IT

30.0 Credits

Aligning BI with Corporate Strategy in

SME

-

A case study based on the BISC Framework

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Acknowledgements

I would like to express my special thanks of gratitude to my supervisor, Leon Caesarius, for his able guidance and support in completing my master thesis. I would also like to extend my gratitude to the case company and respondents for their time, knowledge contribution and

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Abstract

According to research findings, SMEs are continuously faced with unexpected changes within their operating environment. The rapid development of technology master’s new competitors, new products and markets which creates a source of uncertainty for these organisations. These changes are for instance demonstrated through changes in customer demands, lower barriers and government regulations, offering both opportunities as threats. Considering that SMEs play a significant role for society and the worldwide economy, they genuinely need to strive for innovative and efficient solutions in their business. By focusing on smarter use of information through Business Intelligence, SMEs can stay competitive in such an environment. Nevertheless, while BI utilization for efficient decision-making has been highly attractive to larger companies for some time, this has not been a reality for SMEs. The reasons for this are several and

challenges vary. However, it is necessary to meet some basic conditions to effectively take advantage of BI, namely, to align BI with corporate strategies. This study applied the BISC framework on one strategic theme, the operations management, in an SME in order to identify gaps between BI and corporate strategy in their business performance management initiative. Gaps were identified by analysing current As-Is state of BI assets and the To-Be state. This thesis aims therefore to contribute in the understanding of problems and potentials regarding the process of aligning BI with corporate strategies in SMEs.

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1. Introduction

1.1 Background

Small and medium-sized enterprises (SMEs) play a major role in the economy of most countries and compound resources for economic development and economic stability (Olszak & Ziemba, 2008: Airaksinen et al., 2015). SMEs are defined by the European Commission (2020) as enterprises with less than 250 employees and with revenues less than 50 million Euros. More than 90 % of the enterprises in most economies belong to this group (Airaksinen et al., 2015). SMEs account for over 60% of GDP and over 70% of total employment in low-income countries, while they contribute to over 95% of total employment and about 70% of GDP in middle-income countries (Zafar & Mustafa, 2017). Since it is estimated that most SMEs support large

enterprises, provide specialty or outsourcing capabilities for larger companies (Jamieson et al., 2012), they also constitute important sources for global economic structures (Olszak & Ziemba, 2008). The EU acknowledges SMEs as a key factor in ensuring economic growth, job creation, innovation and social integration (Airaksinen et al., 2015). Rosu and Dragoi (2012) underline further the importance of SMEs by emphasizing their capabilities to stimulate the creation and development of a competitive culture based on high flexibility and productivity. This claim is evidentially demonstrated through large production of relevant technical innovations and market dynamism in the economy. SMEs are usually actively managed by their owners, their area of operations is typically local and funding for their growth hails from dependent internal sources of capital (Shaheb et al.,2017).

Acting as a SME-leader in today’s age and time is a challenging task. Such enterprises face a range of issues related to unexpected changes within the environment. The rapid development of modern economy through transition of technology master’s new competitors, new products and markets which creates a source of uncertainty for businesses (Schwenk, 2010). The development is for instance manifested in changes in customer demand, market liberalization, lower barriers and government regulations (Sharda et al., 2014), offering both opportunities and threats. SMEs, moreover, face significant constraints and struggles with resource issues in relation to larger companies. For that reason, SMEs have the need for constant monitoring of business and use of resources in everyday business, especially in the information management and decision-making segment (Chai et al., 2016). Grasping opportunities and encountering unexpected threats

uncovers a challenge for SMEs, therefore, reviewing and restructuring the business decision process becomes a priority. Under such conditions, SMEs genuinely need to strive for innovative and efficient solutions in their business. Focusing on the smarter and more efficient use of

information can create sustainable competitive advantage for these enterprises (Schwenk, 2010). The organisations can shift the basis of differentiation by creating the need for a better, faster and more efficient system of making decisions (Chai et al., 2016). SMEs with accurate and precise data sources based on carefully defined and selected performance measures that are altered and customized over time, can proactively affect internal and external changes. Current business climate has therefore enforced counter responses by organisations and brought computerized support, such as Business Intelligence (BI), into the limelight for many organisations (Sharda et al., 2014). The primary goal of BI is to enable the use of information, and an important aspect of BI projects is turning data into usable information (Ramakrishnan et al., 2012).

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position within the industry sector (Gilad & Gilad, 1988). BI allows the decision makers to adopt new reformatory actions, new management initiatives and new future strategies in a dynamic and competitive market (Sharda et al., 2014).

Enterprises are starting to understand the importance of enforcing achievements of the goals defined by their business strategies through business intelligence concept (Babu, 2012). While BI implementation is aimed at turning available data into information and delivering it to the

decision makers, business performance management (BPM) links this information to the business in order for the organisation to optimize business performance. By bridging the gap between the organisation's business objectives and technical capabilities to better access the information, enterprises can maintain flexibility and adaptability to internal and external challenges (Shi & Lu, 2010).

2. Problem Area

While BI utilization for efficient decision-making has been highly attractive to larger companies for some time, unfortunately, this has not been the case for most SMEs (Guarda et al., 2013; Oslzak & Ziemba, 2012). Even though SMEs constitute the backbone of most national

economies, existing research demonstrates that SMEs are lagging behind larger companies in this area (Llave, 2017). Large enterprises have been more likely to implement BI solutions since the application of those solutions used to be associated with intensive investments (Guarda et al., 2013; Oslzak & Ziemba, 2012). However, SMEs have recently started to notice and take

advantage of BI approaches that are suitable for their needs (Scholz et al., 2010). The growth of globalization, competition and increase of data amount to be processed has driven SMEs to purchase and implement BI tools (Olszak & Ziemba, 2012). Guarda et al. (2013) recognize the BI’s growing contribution to the business growing performance, particularly through improved decision support. Moreover, the SMEs usage levels are constantly increasing. For instance, approximately 85% of BI user firms are SMEs in southwest China (Zhi & Guixian, 2010). Nevertheless, different limitations, such as annual turnover, investment on information systems and number of employees are some factors to consider in different organizational entities of SMEs. Therefore, BI needs to suit SMEs organizational standards, as different businesses consist of different establishments and different views of BI performance (Guarda et al., 2013).

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by improving its business processes and transforming decision-making processes to be more data-driven’’ (Sharda et al., 2014:16).

Despite the importance of BI as the main tool for achieving performance management (Aho, 2009; Frolick & Aryachandra, 2006; Heesen, 2012; Tonchia & Quagini, 2010), research

underscores that in most cases, strategic planning and performance management projects are done independently from BI projects (Williams, 2008). Moreover, research regarding BI Maturity Models conclude that BI is only achieved through its combination with performance management frameworks such as the Balance Scorecard (BSC) and its alignment with organizational strategies (Eckerson, 2004; Rajteric, 2010; Wixom & Watson, 2010). Therefore, more than often, the factors for non-proliferation of BI technology among SMEs include the nonexistence of a coherent definition of performance, the mismatch between business needs and BI functionalities and lack of alignment between performance measures and business strategy (Arnott et al., 2017).

In order to fill this research gap, authors Atieh Dokhanchi and Eslam Nazemi, proposed a holistic and integrated framework for aligning BI initiatives with corporate strategy, called BISC. The framework borrows the approach of the Enterprise Architecture frameworks and its structured logical thinking about the organisation to achieve its purpose. By combining the structure of the Balanced Scorecard and Business Intelligence, the authors aspire for the framework to be helpful for organisations in time of deploying Strategic BI. This study has therefore chosen to focus on the alignment challenge between BI and corporate strategy in SMEs. Confronting this challenge in practical sense may contribute to the body of knowledge on how SMEs can align these two parts to take advantage of their information in more intelligent ways and drive business performance by generating value at operational, managerial and strategic levels.

3. Research Aim and Question

The research on BI in SMEs is limited because most of these systems are implemented in larger enterprises, and previous empirical research has mostly been conducted in that context. Research on challenges regarding BI implementation in SMEs is still limited but increasing. Very few researchers have however addressed the issue on the alignment difficulties between BI initiatives and corporate strategies in SMEs. As of this, a research gap was found. In order to fill the gap a research aim has been identified, which is supported by a main research question.

This study aims therefore to apply the BISC framework in an SME setting in order to contribute with practical findings on how this alignment can be achieved. The study aspires to gain valuable understanding into the factors that can influence successful BI implementation in SMEs and facilitate the progression of BI and performance management research on SMEs.

This study addresses the main question,

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4. Literature Review

Case studies are bound to theories and to understand the present knowledge within the research area of SMEs and BI, SMEs and business performance management, a literature review was conducted. The author’s intention with this section is to deliver an overview to the reader where different characteristics are shown as challenges and the research gap presented. This chapter provides therefore an overview of research on the chosen topic for this study and the central theories the research framework relies on.

4.1 Business Intelligence

The term Business Intelligence (BI) reached popularity in the 1990’s in business and IT communities (Chen et al., 2012) and has since been evolving. BI is defined by Wixom and Watson (2010:13) as a “broad category of technologies, applications, and processes for

gathering, storing, accessing, and analysing data to help its users make better decisions”. There

is no consensus in literature on a single definition of BI and despite this fact some definitions have been used more often than others. Davenport and Harris (2017:12) define BI as something that “incorporates the collection, management and reporting of decision-oriented data as well as

the analytical technologies and computing approaches that are performed on that data”. This

definition encapsulates the broad and actual process of collecting data that may support BI initiatives in businesses, ranging from dedicated and highly advanced BI systems, via Enterprise Resource Planning (ERP) systems, to spreadsheet tools used for analytical purposes. The

definitions in research regarding BI are many, but the key component consists of providing correct data on a need-to-use basis accompanied and accomplished by various technologies to support information-based decision making.

Given the broad definition of the term, one can expect that the different definitions share some commonalities regarding the role of BI. As for instance, BI should highlight the importance of supporting achieving business goals (Wells, 2008) and business processes (Davenport & Harris, 2007). BI should also support decision making (Davenport, 2010; Wixom & Watson, 2010). Sharda et al. (2014:44) encapsulates the role of BI by stating its course which is a

“transformation of data to information, then decisions, and finally to actions”. Moreover, BI emphasizes analysis of large volumes of data about the organisation and its operations. Analysis ranges from simple reporting to slice-and-dice, drill down, answering ad hoc queries, real-time analysis, and forecasting (Negash et al., 2008). BI systems may therefore differ in complexity and functionality or in the scope, as characterized by the authors. Different scope levels have

therefore different effects on the overall business (Wider & Ossimiyz, 2015).

BI systems are accompanied by functional IT infrastructures, which include data warehouses and data marts, as well as extract, transform, and load (ETL) tools (Ong et al., 2011). The process and BI solution usually involve a data warehouse (DW). Data, internal as well as external, are

extracted from various source systems into the DW at a certain interval or at specific times (Davenport & Harris, 2007). When the DW has been populated with data, managers use various tools to analyse the information stored in the DW on a need-to-use basis. Data warehouses and databases are usually both relational data systems, but their purposes differ. A data warehouse is built to store large quantities of historical data and enable fast analysis of data and complex queries across all the data. Data warehouses usually also involve Online Analytical Processing (OLAP). OLAP allows users to analyse information from multiple database systems

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different points of view. A database is built to store current transactions and allows fast access to specific transactions for ongoing business processes, known as Online Transaction Processing (OLTP) (Sharda et al., 2014). However, since analysts oftentimes need to aggregate, join and group data, such operations in relational databases are resource intense. With the help of OLAP, making faster analysis becomes possible due to databases being divided into one or more cubes and through pre-calculated and pre-aggregates data (Sharda et al., 2014).

Another approach to organise divergent data into a standard structure source, in an efficient way, is to use the Common Data Model (CDM). CDM is a shared data model that serves as a place to keep all common data to be shared between applications and data sources. CDM provides a shared data language for business and analytical applications to use. The metadata of the CDM facilities data and its meaning to be shared across applications and business processes such as Microsoft BI, PowerApps and Dynamics365. In addition to the metadata system, the CDM includes a set of standardized, extensible data schemas that Microsoft and its partners have published. The purpose with these predefined schemas is to represent commonly used concepts and activities and to simplify the creation, aggregation and analysis of data. This in turn enables users to create reports that pull common data elements quickly and cleanly (Microsoft, 2020).

When observing the components of BI, it becomes clear that BI has roots in many areas and technologies. It is therefore important to direct attention to the notion that BI is not a product, technology or methodology but a combination of these aspects (Davenport & Harris, 2007). The combination of these key aspects is meant to leverage key information to key business processes and assure accurate decision-making in order to achieve improved business performance. The main role of BI is therefore to leverage information advantage in terms of business information and analysis within the context of key business processes (Williams & Williams, 2006).

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Figure 1. Example of a BI-model in a decision-making process (Olszak & Ziemba, 2007:137)

For the organisation to effectively evaluate their success at reaching targets, they may use measurable values. These values demonstrate how the organisation is achieving key business objectives and organisations can follow this progress using KPIs. The data needed to calculate the KPIs is extracted from a back-end solution (Sharda et al., 2014). KPIs can be monitored and represented by using BI techniques such as dashboards and scoreboards, in combination with the BSC.

Lastly, besides not having a common agreed definition of BI, there is a discourse in the research on whether business analytics (BA) is a subdivision of BI (Davenport & Harris, 2007) or as described by Laursen and Thorlund (2010) an advanced discipline of the BI concept. The definition can be declared as closely related to the definitions of BI. In this thesis, analytics are viewed as an integrated part of BI and can be categorized in three segments:

● Descriptive or reporting analytics, refers to current happenings in the organisation and

understanding some underlying trends and causes of such occurrences. This stage usually involves consolidation of data sources and availability of all relevant data in a form that enables reporting and analysis. From this data infrastructure, organisations can develop appropriate reports, queries and trends using various reporting tools and similar. A significant technology that has become a key player in this segment is visualisation (Sharda et al., 2014). Information from BI systems have historically been demonstrated through heavy reports excluding storytelling for the end-user and indirectly creating inefficient business performance outcomes. Such problems can be solved by visualising the information in the form of tables, line charts or similar (Presthus & Canales, 2015; Acosta et al., 2015). Presenting information in this manner enables the end-user to analyse and monitor business processes.

● Predictive analytics involves determining what is likely to happen in the future using analysis based on statistical techniques as well as other recently developed techniques. The aim of these techniques is to be able to predict different customer outcomes.

Furthermore, several techniques are used in developing predictive analytical applications, including various classification algorithms and clustering (Sharda et al., 2014).

● Prescriptive Analytics, the third and last category of analytics. This category aims to

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for organisations. The aim here is to provide a decision or recommendation for a specific action. These recommendations can be presented to a decision maker in a report or directly applied in an automated decision rules system (Sharda et al., 2014).

4.1.1 Information based on levels and roles of decision making

With the increase of external competitive pressure along with the pressure to manage daily operations (Sharda et al.,2014), business decisions need to be made on three levels: strategic, tactical and operational. Information therefore needs to be specified, scheduled, in real time and detailed but also broad (University of Porto, 2018).

Strategic decisions are made on longer time horizons. These are mostly unstructured decisions that seek general information with broad scope, interactive in real or near real-time and internal and external. However, these decisions are made with greater uncertainty and therefore also greater alteration on current business models (Ramakrishman et al., 2012). On a tactical level, semi-structured decisions are made with a need for specific, focused and internal information that is interactive in real time. Lastly, operational decisions involve short-term day-to day activities. These structured decisions are normally taken by operational management or individuals and teams. With the help of available and correct information at the right time and to relevant users, organisations can make conscious, fact-based decisions. For this to be possible, information that is specified, scheduled, detailed and internal is required (Pranjic, 2018: 605-606). BI delivers on the needs of all three levels of decision-making in a structured decision process (Popovic et al., 2010). By facilitating past performance to be compared against targets, new managerial

objectives for strategic decision-making can be set (Pirttimäki, 2007). Pirttimäki (2007) highlights further the importance of relating the BI needs to specific business objectives and to the overall corporate strategy. The need for this alignment might seem obvious as the value of delivered information is determined by improved business decisions and more systematic information usage (van Roekel et al., 2009).

Information can therefore be based on levels and roles of decision-making in an organisation. To illustrate this, Van Roekel et al. (2009), describe four levels of information services within an organisation that recount the information needs of each organisational level:

● Information services for operational workers: Covers everyday operations and process data usually in a structured form to enable efficient, fast and reliable transaction

processing.

● Information services for operational management: Covers monitoring and managing the primary business processes and up-to-date reporting of results. Data is structured and usually segmented by organisation function, business process or product type. ● Information services for tactical management: Covers trends by indicating and

comparing the results across processes, product groups and departments.

● Information services for strategic management: Covers the development of business models based on past and future market development and internal capabilities.

Information requirements are different during different phases of the strategic planning cycle.

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such as financial information or customer records (Pirttimäki, 2007). External information covers information that is gathered from outside the enterprise in different forms such as, publications and other external sources. Moreover, internal information tends to be structured, focused and jointly aligned to operational information services and thus also easily processed and analysed via BI tools. In contrast, external information tends to emerge from diverse sources in an

unstructured form making the information processing and use of traditional BI tools challenging (Kaario & Peltola, 2008).

Most of the information presented in operational enterprise applications concerns current state of business (Popovic et al., 2010). To transform this operational information into strategic

information, timely and forward-looking information becomes valuable (Hovi et al., 2009). Nonetheless, Pirttimäki (2007) explains that due to greater impact of strategic decisions on an enterprise, the quality requirements of strategic information and its sources are higher than in operational decisions. Processing, obtaining and disseminating necessary information for securing efficiency in reaching business goals are one of the most critical manager's tasks. Quality information dependency is especially crucial in the stage of data collection since it is the starting point of the decision-making process. If data and information are corrupted, then the whole decision-making process will be infected and based on false and incorrect inputs (Pirttimäki, 2007).

4.1.2 BI strategy

BI and performance management systems, such as the BSC, eliminates information overload by making sense of the massive amount of information available to the organisation. For the companies to fully utilize the internal and external information, they need to establish a vision about the strategic use of information and plan to implement this vision (Boyer et al., 2010). Despite the promises of BI technologies, many companies still don’t possess a business

alignment strategy that allows them to plan their strategies via data-driven metrics and BI. The effort of alignment is unfortunately not given enough attention as a part of the basic success of the corporate strategy (Boyer et al., 2010). A business therefore needs a systematic effort of planning in order to not only act reactively. With an established strategy, a business can prepare for forthcoming changes in its business environment and hence minimize the costly and

disruptive effects of improvised actions. By doing so, organisations can focus on being proactive instead of reactive (Kaario & Peltola, 2008).

Mintzberg (1994) suggests that analysis and planning should be complementary and not

determining. Changes in business strategy create information needs which can lead to changes in information management and in IT infrastructure. This implies that there should be a match between the information requirements of the business strategy and the information processing capacity of the IT. Information requirements are translated into a BI strategy, which is then aligned with the more extensive IT strategy (Bergeron et al., 2004). Therefore, BI strategy should bring together the business aims of an organisation and create an understanding about the

information needed to support those aims, without unrecognizing the technology to provide the information (Davenport, 2006).

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solution that helps to solve the immediate goals of that entity. Such a solution may be expressed in a reporting or analytical tool that is easy to implement or through a customized legacy to meet the specific requirements of the independent decision-making area. Applying BI in a sequential technology perspective can create reporting systems on a number of data sets. However, with a solid business alignment strategy, a link can be created between strategic and operational objectives (Boyer et al., 2010). Boyer et al. (2010) argue that business impact is best achieved when the use of BI and performance management spans across departments and silos to provide a coherent distribution of information and a collaborative team approach to organizationally

achieving goals. A top down approach enables strategy to become linked directly to the operations of the organisation. Further benefits arise through goals being measurable using agreed-upon metrics. The strategy can moreover move the organisation from tactical use of information to a focused organisation that uses the information strategically in everyday processes (Boyer et al.,2010). The key to executing the strategy is to communicate the strategy across the organisation and act on new directions identified by the strategy. The actions can be initiated by using tactical tools and measurement implemented within key decision areas to provide visibility and ability to anticipate and drive future outcomes (Boyer et al., 2010). The business goals will contribute to defining how to get there and produce better collaboration across teams to work together to achieve those results. Without the connection to strategy, independent silos at operation level or within cultures tend to be created naturally which eventually will counteract the process of aligning overall information needs with business needs.

4.2 Performance Management Systems - The Balanced Scorecard

Performance Management Systems (PMS) are characterized by Fried (2010) as strategic expert systems which organizations can observe and measure their intangible performance elements in the form of qualitative and quantitative statements. According to Gimbert et al. (2010) PMS consists of a limited and precise set of measures both financial and non-financial that supports the decision-making process of an organisation. Until the 80s, PMS had their focus on cost control without paying much attention to quality, flexibility and delivery focus. This traditional model proved to be weak when organisations started to change their strategic focus which initiated new, non-traditional models (Ghalayini & Noble, 1996). Based on non-traditional PMS, which is the current recommended model, organisations may be guided by aspects that make the process more productive and effective.

There are plenty of PMS found in the literature, none of them stated as an absolute solution for all organisations to use. Each framework possesses specific features and focus, creating advantages and disadvantages depending on the purpose of use. See table 1 for examples of different PMS and their key attributes. For example, SMART framework is beneficial when integrating

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Table 1. Different performance management systems

Performance management systems Key attributes

SMART - Performance Pyramid (C Ross & Linch, 1990)

● Performance measures of internal efficiency and external effectiveness ● Performance measures deployed from

organization's strategy

● Strong links between corporate objectives and operational performance indicator Balanced Scorecard (Kaplan & Norton,

1992, 1996)

● Performance measures grouped into four perspectives

● Cause and effect relation between perspectives reflecting the strategy ● Focus on present and future

generations Performance Measurement Matrix (Keegan

et al., 1989)

● Performance measures internal and external, financial and nonfinancial ● Performance measures linked to

strategy Result and Determinants Framework

(Fitzgerald et al., 1991)

● Focus on service business ● Specific measurement model for

time-based competition ¨ ● Lagging and leading factor to

measure the performance Performance Prism (Neely & Adams, 2000) ● Performance measurement of

stakeholder’s satisfaction ● Strategy communication

● Approach for the management of business processes

The BSC is a method or framework to measure and govern. On top of being an effective

communication tool of key strategies, the BSC provides clear connections between strategies, the business processes executed by strategies, and the KPIs that measure the business performance (Phadtare, 2010:72-73). To improve, organisations must first control and measurement is the first step in this process (Petkoska et al., 2019). Organizations are therefore encouraged to measure, in addition to financial outputs, those factors which influence the financial outputs. The underlying rationale is that organizations cannot directly influence financial outcomes, and the use of financial measures alone to inform the strategic control of the firm is an unwise decision. Organizations should therefore expand their measurement initiatives and measure those areas where direct management intervention is possible. In practice, early scorecards achieved this balance by encouraging managers to select measures from three additional categories or

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● The financial perspective addresses the question of how shareholders view the firm and which financial goals are desired from the shareholder's perspective. These goals depend on the company’s stage in the business cycle.

● The customer perspective addresses the question of how the firm is viewed by its customers and how well the firm is serving its targeted customers in order to meet the financial objectives. Generally, customers view the firm in terms of time, quality, performance, and cost.

● The internal perspective addresses the internal processes in an organization, what are the critical processes, what are the objectives and targets needed to be defined from the customer as well as the shareholders point of view.

● The learning and growth perspective address the view of the learning, innovation and the improvement trend that should be focused by the organization in order to meet its

objectives. The focus is placed on the intangible assets building the core of the company. Within each of these perspectives, what to measure and how to measure objects should be defined. What to measure is represented in the definition of the strategic objectives under each perspective, therefore, how to measure is represented in the key performance indicators. Each KPI is represented by a set of properties or values that are essential to configure the performance measurement results such as the base value and target value. Base value is considered the current value for the objective achievement in a company or organisation. Target value is the value for the measure lying under an objective and the closed-loop control is where actual performance is monitored and measured. The measured value is compared to a reference value and based on the difference between the two corrective interventions are made as required (Kaplan & Norton, 1999). The prime metrics should be selected based on general availability of the data to support them. Data can be deployed and selected from the company’s various systems. Having predefined metrics that can be used to translate vision enables management to more rapidly focus on specific business elements necessary to achieve and improve the strategy. By taking action, targets for all metrics can be achieved. Breaking down top-level objectives into smaller concrete targets will contribute to reaching the benchmark values aspired by the enterprise, therefore the strategy should be reflected in every measure of the BSC (Huang et al., 2017). The BSC can therefore be considered as a framework that allows an organization to transform its vision and corporate strategy into a tangible set of performance measures (Amal, 2015). Every measure must be part of a chain of events that results in a goal that reflects the strategy (Kaplan & Norton, 1999). The strategy map plays a major role in this process.

The strategy map as a tool, demonstrates how objectives in the different perspectives of the BSC are interlinked and combined to describe the strategy. The strategy map aims to give a

visualization of the cause-and-effect linkages between the different strategic key measures in the BSC. The BSC translates the strategy map objectives into measures and targets. There are several processes in an organization that create value in some way. All processes should be managed well, but a few processes should receive special attention and focus (Kaplan & Norton, 2004a:47). The strategy map serves to identify those few critical processes that have most strategic importance to value creation. To balance the value creation over the long and short-term, processes should be identified in all (four) perspectives (Kald & Nilsson, 2000).

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Figure.2 BSC (Amal, 2015:3)

4.2.1 Performance measurement and KPIs

The performance of an organization is ultimately how it is achieving its goals. The performance measurement gives a well-defined image of organisations progress in doing so. Additionally, it offers a learning culture by involving employees in decision-making based on data and helps in discovering opportunities to better develop the business processes (Wolk et al., 2009).

The ability to receive insight and visibility into the information that matters is indeed one of the strong enablers of overall success. Greater insight enables new opportunities, processes

improvements and more informed decisions while reducing operating costs. Nevertheless, the first step in delivering such insights through tools BI is to ensure that the organisation measures what matters. This may be accomplished by defining the metrics that need to be measured, aligned to corporate priorities and lastly to understand the users and their intention regarding this information (Boyer et al., 2010).

Measurement is usually accomplished by using KPI measures, which are used to quantify management. They basically reflect on how an organization is doing in a specific aspect of its performance (Cekerevac, 2013). In other words, KPI is information collected at regular intervals that track performance of an enterprise at any level or activity. KPIs provides focus for strategic and operational improvement, creates an analytical basis for decision making and helps to focus attention on what matters the most. As Peter Drucker famously said, “What gets measured gets

done.” (Bauer, 2004). One typical characteristic of performance measurement is that the

measurable objects are predefined. Managing with the use of KPIs includes setting targets which equals the desired level of performance and tracking progress against that target.

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improve leading indicators that will later drive lagging benefits (Petkoska et al., 2019). From another aspect, it is also suggested that companies base KPIs on two levels as follows: strategic KPIs; and operational KPIs. Strategic KPIs should address the measurements required at a high level and take a top down approach. These KPIs don’t require constant monitoring nor

measuring, they are simple indicators on progress or trends towards stated goals. As your corporate strategy shouldn’t change too often nor should the set of KPIs the enterprise use to measure progress toward the stated destination. Conversely, operational KPIs should be

approached from the ground up. At this level, KPI measures seek to get closer to instantaneous measurement. This enables access to insights on an hourly, weekly or monthly basis in order to identify areas of improvement (Marr, 2019). Marr (2019) explains that strategic and operational KPIs are equally important but in different means and for different purposes. To obtain the full effect of KPIs, strategic and operational KPIs must be aligned so that every stakeholder in the business can witness the connection between actions and the enterprise accomplishments. Unfortunately, more than often, a disconnect between indicators on the strategic level and the operational level seem to exist. Therefore, in order for the enterprise to reach success, it is necessary to develop and link strategic objectives to the operational objectives.

In order to identify the right KPIs for any business it is important to be clear about the objectives and strategic directions (Kaplan & Norton, 1993). Where many KPIs are available, a decision – making problem appears. The amount of KPIs to choose from may create difficulties for the company to select the right KPI. For that reason, it is important to predefine the main business processes and from there on identify the right KPIs for measuring those processes. As a further guidance, Eckerson (2009) defines successful characteristics of effective KPIs based on

following parameters:

● Sparse: The fewer KPIs the better; ● Drillable: Users can drill into detail;

● Simple: Users understand the KPI. Clearly indicate what action is required by staff; ● Actionable: Users know how to affect outcomes and have a significant impact; ● Owned: KPIs have an owner. Are acted on by the CEO and management team; ● Referenced: Users can view origins and context;

● Correlated: KPIs drive desired outcomes. They encourage appropriate action by having been tested to ensure they have a positive impact on performance. Poorly designed measures need to be eliminated due to the risk of dysfunctional behaviour;

● Balanced: KPIs consist of both financial and non-financial metrics; ● Aligned: KPIs don't undermine each other;

● Validated: Workers can't circumvent the KPIs; ● Regulated: Are measured frequently;

● Distributed: Are measures that tie responsibility.

When identifying and selecting effective KPIs, the enterprises can also follow the SMART criteria which was proposed by Doran (1981). It is based on five specific characteristics: specific,

measurable, achievable, realistic and time bound.

4.3 SMEs and BI

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several ways; normally several ways; normally, SMEs have limited internal information technology (IT) resources and competencies available, and they are dependent upon external expertise when starting new IT projects, such as acquiring and implementing new BI applications (Larson & Chang, 2016). Garenco et al. (2005) describe the main differences between large and small to medium sizes enterprises with three aspects: uncertainty, innovation and evolution. The most important characteristics which differ SMEs from larger companies include resource and knowledge limitations, lack of money, reliance on a small number of customers, and need for multi-skilled employees (Dyczkowski et al., 2014). This indicates that equivalent BI is not suitable for SMEs as used in large industries and therefore needs to be adapted to the specific organisation.

Gudfinnsson et al. (2019) examined small manufacturing companies in Sweden and found that their motivation to introduce BI was low. The research results found by Gudfinnsson et al. (2019) is supported by a survey made by McCabe in 2012. The survey discovered that 33% of medium size companies adapted BI solutions and a further 28% planned to use BI. However, amongst smaller organisations, only 16% were using BI and 16% planned to use BI. Nonetheless, the implementation and application of BI systems are not an exclusive right and opportunity only for large companies. As expressed by Tatic et al. (2018), larger organisations may possess more resources than SMEs, but the implementation and application process tend to be more complex in large enterprises compared to SMEs. The cause for this is found in the amount of data,

infrastructure and organisational factors. By emphasizing the usage of BI in the decision-making process, SMEs can take advantage of their smaller size. Of course, BI needs to be modified depending on functions, needs and levels.

There are many examples of unsuccessful implementation of BI, directly affecting financial and all other operations. For instance, about 60% to 70% of BI applications fail due to the technology, organizational, cultural and infrastructure issues (Clavier et al., 2012). The issue of impaired expertise and technical know-how to select and adopt a suitable BI solution still poses a huge challenge for most SMEs. Yet, implementing BI does not only entail the purchase of a

combination of software and hardware; rather, it is a complex undertaking requiring appropriate infrastructure and resources over a lengthy period including alignment between business, strategy and IT (Yeoh & Koronios, 2010).

The incomplete understanding regarding the beneficial aspects of BI in combination with so often restrained budgets can make SMEs reluctant to invest in implementation of any BI solution (Gudfinnsson & Strand, 2017). Cost of deployment for the cause of using data in business continues to be a limiting factor for many organisations, including the need to integrate various BI components pushes further the overall implementation cost of BI solutions (Thompson, 2009). Nevertheless, recent technological developments have contributed to outsourcing the cost

intensive integration of advanced data warehouses and delivery of analytical applications through cloud computing, Software as a Service, as well as open source BI. Nowadays, suppliers have developed BI components that could be easily integrated which massively reduces the

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Further on, the challenge of integrating BI components with a company’s existing systems and business goals remains a difficult problem to solve for many SMEs. Research underscores that a feasible BI strategy is missing in many SMEs which tends to create challenges when striving to ensure a successful implementation. Cases have previously been reported on large investments in various BI initiatives over lengthier periods, underscoring little or no benefits for the

organizations implementing them due to this challenge (Hidayanto et al., 2012). Additional research evidence on the alignment problem was demonstrated by Olszak and Ziemba (2012). The authors highlighted different business challenges in SMEs in Poland, one of them being the lack of alignment between BI systems and key business processes. Scholz et al. (2010)

proclaimed the alignment challenge between business and BI as a key challenge for SMEs when implementing BI. Gudfinnsson and Strand (2017) found evidence supporting the problem of relevant KPIs in SMEs, which in turn creates challenges when connecting the performance measurement with BI tools. Several BI initiators in different organisations fail to recognize what metrics to measure or what information is available to them. Misalignment between business and BI in SMEs was also present in a study conducted by Dzafic & Tatic (2018) in Bosnia and Herzegovina. According to research results, there was a certain discrepancy between indicators available to managers and employees, creating confusion about the factual situation in the company. A study conducted by IBM in September 2010 found that only 22% of organisations are successful in linking strategy to execution with BI and performance management. The fact that only one in five companies are successful in linking these two sides, creates great

opportunities for these businesses. As expressed by Boyer (2010), such organisations can reap the benefits of overall performance improvements, find new opportunities, gain competitive

advantage and create business impact that is aligned with their goals. With the increased ability to make sense, anticipate and shape outcomes through technologies, these organisations can

increase efficiencies and effectiveness throughout the key areas of the organization.

Successful BI implementation requires making a strategic decision and clearly defining the implementation steps as a part of the BI strategy. Implementation of BI solution has several basic preconditions that relate to the existing information systems of SMEs or the construction of adequate databases as well as DW. This means, SMEs should have adequate information systems, regulated business systems, and satisfying databases based on which it is possible to generate proper KPIs, data and information (Dzafic & Tatic, 2018).

4.4 SMEs and Performance Management Systems

To bring about a competitive advantage in the dynamic environment, small to medium sized companies must think of new ways to improve their businesses. They all try to succeed in the market space and achieve competitive advantage. SMEs can clearly obtain value from PMS, but knowledge and awareness of PMS among SMEs are low (Marr et al., 2004). Evidence found in research demonstrates a much lower usage level of BSC among SMEs than among larger firms (CIMA, 2009). A study by Rigby (2001) found that 44 percent of organisations in North America and 35 percent of large US firms use the BSC. Though found to be popular in large organisations, literature reporting on the uses and limitations of the BSC in SMEs is rare.

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Another study conducted by Tennant and Tanoren (2005) found similar results. Three studies conducted in Portugal by Machado (2013) found that most firms in the country lacked knowledge about BSC and only a small portion of the investigated enterprises were using it. In Norway, evidence shared by Pedersen and Bødtger (2014) found that about 40 percent of firms claimed to have heard of the BSC, however, only 7 % were using it.

Reasons for non-adoption of the BSC vary and there are few studies investigating this

phenomenon. However, Giannopoulos (2013) could conclude that one of the main causes is the perceived thought of BSC as not applicable for small companies. Measures used in SME tend to be more focused on operational and financial performance and lack measures dealing with other areas (Hudson et al., 1999). Research suggests that there are several pitfalls and barriers

associated with BSC implementation in SMEs. One of them being social issues (Fernandes et al., 2006) and resource limitations, as well as training and development of employees in relation to the BSC (Fernandes et al., 2006: Hudson et al., 2001). Furthermore, lack of time is a common problem in the implementation phase of the BSC (Tenhunen et al., 2001), along with incomplete corporate structure compared to larger companies. Hudson et al. (2001) noted that the BSC was difficult to apply for SMEs due to its high level of diligence. Similar statement was made by Russo and Martin (2005). The authors point to the BSC requirements on formality and

complexity as a cause for mismatch. Lastly, Rompho (2011) suggests a different angle and argues that the problem exists in the operation environment. SMEs generally don’t operate in stable business environments which causes frequent changes in strategies.

In contempt of above research evidence, the BSC is believed to be as beneficial for SMEs as it is to large organisations (Andersen et al., 2001; Kaplan & Norton, 2004) and successful

implementation is as possible as for larger companies (Kaplan & Norton, 1996: 369). Although some existing literature concludes the absence of PMS in SMEs, there is empirical evidence showcasing the opposite (Hvolby & Thorstensen, 2000; Tenhunen et al., 2001; Fernandes et al., 2006). The concept of BSC might be viewed as successful in several small to medium sized organisations, with employees ranging from about a dozen up to a couple of hundred (Kaplan & Norton, 2001: 369).

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4.4.1 SMEs and Key Performance Indicators

At the heart of the BSC are key performance indicators. In order to introduce and implement PMS successfully in SMEs, the research suggests that only the most critical performance indicators be selected and utilised because of constrained resources (Hvolby & Thorstensen, 2000). As corporate strategy shouldn’t change too often nor should the set of key indicators the enterprise use to measure progress toward the stated destination. Conversely, operational KPIs should be approached from the ground up. At this level, KPIs measure seek to get closer to instantaneous measurement enabling access to insights on an hourly, weekly or monthly basis in order to identify areas of improvement (Marr, 2019). It is relatively uncomplicated to make a difference between those two aspects in large companies but more difficult in SMEs. The main reason for this is the impairment of clear strategic goals in SMEs. They usually lack a concrete mission and vision and are not so often strategically oriented (Hidde & Masurel, 2000).

Moreover, in instances where strategic management is in actual practice, research indicates that it is informal, unstructured and irregular. It is often supported by insufficient and ineffective

information, usually obtained through informal sources and reactive rather than proactive (Flavel, 1991).

Van der Stede et al. (2006) claim that SMEs with extended measures, especially covering non-financial measures possess better overall performance. Further evidence from Stede et al. (2006) suggest that non-financial performance measures are better than financial measures in assisting organisations when implementing and managing new initiatives. Moreover, the non-financial measures adopted most frequently by SMEs were found in the customer perspective.

There are different ways to measure the success of the SMEs and there are many indicators that can be used to present it in a PMS. Measurement is important whereby it highlights various business processes and identifies existing problems. In present society, KPIs are fundamental for planning and controlling. By fusing information, insights and transparency can be created and as a result, the decision-making process can be aided. It is only when SMEs consider both financial and non-financial KPIs that a holistically view of the information can be obtained (Nastasiea & Mironeasa, 2015).

5. Theoretical Framework – BISC

This section covers the chosen theoretical framework for this study, BISC, and its theoretical foundation. The framework is used to analyse the empirical findings in a later section of the thesis.

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Figure 3. BISC Framework

Various organisations can apply the BISC framework to create and align their BI resources and infrastructure to execute their strategies and manage performance. The BISC framework can therefore be applied when planning for transitions from the As-Is to the To- Be state (Dokhanchi & Nazemi, 2015).

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5.1 BISC Framework: Level 1

Figure 4. BISC Framework: Level 1

As illustrated in figure 4, organisations can apply the framework for all identified strategic themes at the same time in parallel and every theme can be considered as the smallest strategic granularity. However, in some cases, depending on the size of the organisation and the strategy map, organisations can apply bigger granularities too. For instance, an organisation could possess many strategic themes such as customer service, innovation or operational management

(Dokhanchi & Eslam Nazemi, 2015). Different components of this framework are:

Vision: Vision statement describes what the company wants to be in the future.

Mission: A mission statement describes the present of the organization and its philosophy of

existence leading to its future. Based on the mission statement, organizations can define their strategies in BISC.

Corporate Strategies: Inputs of this framework and assumed that they are already written down

and based on created strategic themes. Strategic themes are as expressed by Kaplan and Norton (2003: 49) “strategic themes allow organisations to focus actions and to provide a structure for

accountability. Strategic themes are the building blocks around which the execution of strategy occurs”.

Business Drivers: Business drivers typically derive from changes in strategies, business and the external environment.

Technology Drivers: Technology drivers typically derive from changes in technology

infrastructure and data.

As – Is State: Dokhanchi and Nazemi (2015) explains that based on the defined vision, mission,

corporate strategies and the chosen strategic theme(s), organisations need to describe their so-called As-Is state according to different perspectives of the BISC.

To – Be State: Based on documented As-Is state for the chosen strategic theme(s) and different

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Transitional Processes: The next step after the definition of As-Is and To-Be, organisations can

plan and prioritize different initiatives and transitional processes.

Standards: For the transition to take place, organisations need to define some standards. These

standards could be technology related or based on agreements such as naming rules for different data fields, etc.

5.2 BISC Framework: Level 2

In figure 5, the second level of BISC is showcased. The second level consists of 8 perspectives (shown in light colours) and 3 subsidiary parts (shown in dark colours). The subsidiary parts;

security management, change management and project management are perspectives that the

organisation need to pay careful attention to and manage in the transition phase. However, as mentioned in the earlier section, this study will exclude the transition phase and only focus on applying and highlighting the 8 perspectives presented below. As demonstrated in figure 5,

Strategic theme(s), Strategy Maps and Balance Scorecard(s) and corresponding business

processes are placed on top of the framework. All other supporting BI related perspectives are held at the bottom: Decisions and Requirements Descriptions (Why?), Data (What?), System

(How?), Technology (Where?), People (Who?), Time (When?). All perspectives are described in

detail (Dokhanchi & Nazemi, 2015):

Figure 5. BISC Framework: Level 2

Perspective 1. Strategy Maps and Balanced Scorecard(s): Based on corporate strategies and the

chosen strategic theme(s) (Kaplan & Norton, 2008), the objectives of the four perspectives of BSC and their cause and effect relationships are defined, and strategy maps created. Then, for each objective, measures and KPIs and their corresponding initiatives are defined (Kaplan & Norton 2008; Williams 2004).

Perspective 2. Process: This perspective regards the corresponding business processes of the

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Perspective 3. Decisions and Requirements Descriptions (Why?): Besides having strategic

objectives and KPIs of different levels of the organisation stated, organisations need to know the detail requirements of users in all levels of the organisation in order to extract data from

transactional systems and use in decision-making (Dokhanchi & Nazemi, 2015).

Perspective 4. Data (What?): This perspective exists in response to the requirements that are

gathered in perspective 3 but also from KPIs in perspective 1. Data includes both primitive data and derived data which is the output of analysis on primitive data (Inmon, 2005). In the lower operational level of this perspective, primitive data and unstructured data from internal and external environments are found in transactional systems, databases and spreadsheets. At

strategic levels, derived data from data warehouses, what if analysis, statistical models, and more can be found (Dokhanchi & Nazemi, 2015).

Perspective 5. System (How?): In this perspective, organisations need to acknowledge or plan all

the systems used in the organisation. This may include transactional systems and operational, tactical and strategic dashboards, analytical applications, reporting applications and other strategic management applications (Dokhanchi & Nazemi, 2015).

Perspective 6. Technology (Where?): This perspective is focused on defining and documenting

different BI architecture, tools and network infrastructure from a technology outlook.

Organisations also need to define the organisational departments that are involved in the chosen strategic theme (Dokhanchi & Nazemi, 2015).

Perspective 7. People (Who?): This perspective requires organisations to consider all

stakeholders related to the organisation, internal as external, in all organisational levels (Dokhanchi & Nazemi, 2015).

Perspective 8. Time (When?): In this perspective, organisations need to document the timings for

different activities during the implementation of strategic BI for the chosen strategic theme. Operational KPIs should be documented along with operational and tactical review meetings. Furthermore, organisations should document updated time for strategic KPIs and set time for strategy review meetings and test and adapt meetings (Kaplan & Norton, 2008).

6. Method

This section features the methods used in this study, where a case study research was conducted. The Case company is described along with data collection consisting of interviews, documents, artefacts and observations. Theimpact of the COVID-19 outbreak on the thesis is mentioned and a critical assessment about the research quality such as the reliability, validity and objectivity of the research ends the section.

6.1 Philosophical Considerations

This master thesis revolved around observing real-life scenarios, following actors in the process of strategic reformulation and business performance management initiative. Such kind of

research has rarely been documented and explored earlier in the context of BI implementation in SMEs with a focus on the alignment process between corporate strategy and BI.

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participants is important to understand the research aim, prior opinions and judgements also play a significant role. The author's worldview and view on how knowledge is constructed are

reflected within the constructivism paradigm. The paradigm reflects on how phenomena are understood through the meanings that people assign to the researcher (Yin, 2018). Constructivism deals with the development of subjective meanings and understandings of one’s personal

experiences concerning topics based on their social and historical background.

This thesis takes its ground in relativist ontology, meaning that reality is constructed within the human mind such as reality being relative according to how individuals experience it at any given place and time (Given, 2008:751). By interpreting the relationship between a subject and an object one can explore the idea of epistemology and how it shapes and influences research

design. Different ontologies and epistemologies that a researcher adopts require different kinds of methodology (Levers, 2013). For example, objectivist epistemology assumes that reality exists independently of the individual mind. This research approach is useful in providing reliability and external validity when results are applicable to other contexts. However, a qualitative case study assumes subjectivist epistemology. The in-depth case study conducted in this thesis can therefore be taken as an example of the practical application of subjective view of epistemology. In this scenario, the researcher had to interpret the reality subjectively.

6.2 The Abductive Research Approach

This study is conducted with abductive reasoning, also referred to as abductive approach. The approach uses both deductive and inductive approaches and, thus, constantly moving from the empirical to theoretical dimensions of analysis. Duboi and Gadde (2002) found the logic of this approach as useful as it does not follow a pure deduction nor a pure induction. This approach often results from an unexpected observation that calls for explaining an anomaly that cannot be explained using an established theory. As in this research, the phenomena being researched has rarely been studied before and therefore lacks theories in research. The deductive research scans theory and derives logical conclusions from theory and presents them in the form of hypotheses. However, abductive reasoning emphasizes the search for suitable theories to an empirical observation, also called “theory matching” (Duboi & Gadde 2002). As implied, data is collected simultaneously to theory building (Taylor et al., 2002), or as Duboi and Gadde (2002) describes it as a “back and forth” motion between theory and empirical study.

6.3 Case Study Design

A research design should include defining the study’s questions, propositions and case, which eventually lead research design into identifying the data that are to be collected. The last two components, that is, defining the logic linking the data to the propositions and the criteria for interpreting the findings will eventually lead the design into anticipating the case study analysis, suggesting what is to be done after the data have been collected (Yin, 2018).

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research aim was made. In the process of data collection, theoretical frameworks were being searched by the author as the basis for data collection and analysis at a later stage. In this iterative process, chosen frameworks were rejected as new ones came forward. The final selection fell on the BISC framework, established by authors Dokhanchi and Nazemi (2015) and described in a later section of this thesis.

Figure 6. Research design - the iterative process

For the purpose of the research aim, this thesis uses embedded-single case design (Yin, 2018). As Yin (2018) explains, in embedded-single case designs, there are more than one sub-unit of

analysis. In this study, the case company constitutes the main unit of analysis, while IT and business management constitute the subunits of analysis (Yin, 2018).

6.4 Case Study

Since the research aim is to gain a more in-depth understanding of how SMEs can identify information gaps in their business performance management and take advantage of BI by aligning BI with corporate strategy, close collaboration with companies is necessary. One approach to reach a deeper understanding is through a case study.

Case study can be defined as “an empirical inquiry that investigates a contemporary phenomenon

in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident’’ (Yin, 2009:13). What this definition usefully captures is that

case studies are intended to provide a level of understanding regarding complex and

particularistic natures of distinct phenomena. Yin (2018) explains that such study cannot be replicated since it is spatially and temporally bounded, it is simply particularistic in nature. The method’s unique characteristic and power is its ability to deal with many different types of empirical material, such as documents, artefacts, interviews and observations, without the possess of any special procedure for the collection or analysis of the data. (Merriam & Nilsson, 1994). There are several advantages in using case studies. The examination of the data is most often conducted within the context of its use (Yin, 2018), that is, within the situation in which the activity takes place. Unlike studies based on generalisation, where a phenomenon is deliberately isolated from its context, case studies aid to explore or describe data in real-life environments and additionally assist in explaining the complexities of real-life situations. However, the examination of data within the context of use counteracts the generalisation of study results (Yin, 2018). The method is therefore often criticised for its lack of rigour and the tendency for a researcher to have a biased interpretation of the data. Grounds for establishing reliability and generality are