Data Gathering and Analysis

Data gathering and analysis are connected, as analyses are based on specific data types and specific CI issues. Data gathering includes the assessment of the usefulness of the data for analysis (DeVault 2011) and this is linked to CI scanning. Information systems are platforms to support CI scanning by structuring the gathered data (Turban, Aronson, and Liang 2005). Qiu (2008) found that the attitude of managers to solving problems (process, practice, decision making) impacts CI scanning or gathering, and CI scanning impacts competitive advantage. The scanning cycle consists of some interrelationship between managerial business motivation, intelligence scanning and sense-making.

Keszey (2011) explored how managers can better understand CI information, noting how data quality from trusted sources and studies, aiming at specific CI problems lead to better CI learning. In contrast, poor data quality (Rohr 2011) can lead to CI analysis bias. Tanev and Balletti (2008) found that information needs are related to variation in innovation performance across different firms (ranging from supplier technology-dominated companies; large-scale producers; specialized suppliers; and science-based companies). They found that competitor and industry information was least considered and applied but highly relevant for firm performance, which this PhD study sees as highly relevant as well. This importance of relevant contextual information was highlighted in other studies, for instance Molnar and Strelka 2012; Rohrbeck 2010;

Lavbic, Vasilecas and Rupnik 2010; when investigating collaboration in a network of analysts, showed how analysis results varied according to how well relevant focus and CI issues had been identified prior to data gathering.

Analysis is central to CI activity; Draghici (2011) used the term CI as denoting ‘analysis of competition’ and Fleisher and Bensoussan (2003; 2007) viewed analysis as the core activity of CI. In considering analysis, Businessdictionary (2010b) defines it at two levels:

“1. A systematic examination and evaluation of data or information, by breaking it into its component parts to uncover their interrelationships. Opposite of synthesis.

2. An examination of data and facts to uncover and understand cause-effect relationships, thus providing basis for problem solving and decision-making.”

The first definition emphasises data evaluation as the basis for analyses (which may be quantitative or qualitative, primary or secondary). The second definition emphasises the purpose of analysis as basis for impact (either cause-effect relationship or problem-solving impact). Specific analyses are a way to generate specific outcomes, because they call for specific data and apply specific procedures. Therefore, alternative analysis procedures can lead to different results. Amongst others, Grout (2007) identified a lack of standardisation in data gathering and evaluation, noting what he called ‘outcome-based detection’, which aims to identify mistakes ‘outcome-based on the expected outcome or known error patterns. Zheng, Fader and Padmanabhan (2012) identified key measures of competitor’s customer activity, and a way on how to analyse these by showing a specific analysis application. They emphasised that relaxing assumptions leads to alternative, and more detailed models, which may reduce bias by controlling results from different viewpoints. Analyses can be understood as sub-processes interlinked with the main CI process. An analysis process explains interrelationships between data sets. The results serve to suggest a specific decision alternative. Specific software tools are designed to identify priorities, if various alternatives are suggested (Mindtools 2010).

3.3.3! Communication of Data Analysis Outcomes and Link to Decision-Making This section discusses first decisions and support, second communication. Staskeviciute and Ciutiene (2008) identified process and product intelligence when stating:

“The product of organizational intelligence is decision, characterized by qualitative features and effective and well – timed implementation of decisions. … While implementing organizational intelligence, … it is necessary to decide, which kind of organizational intelligence is needed – process or product. The organizational product intelligence is centred on the use of internal and external knowledge in a decision making process. The organizational process intelligence is oriented to the development of

organizational processes according to the plan in order to create surplus.”

When viewing the CI process as composed of process and product elements, as noted in Section 3.2, the CI process (process intelligence) may be seen as the basis of ongoing CI activity, whereas communicated CI analysis results lead to decisions (product

intelligence). Anica-Popa and Cucui (2009) proposed a framework for decision support based on data mining, at the core of which are three tiers, namely data, logic and decision. The CI process works similarly by focussing on data gathering and analysis to suggest decisions. Panian (2009) stated that firms operate under changing conditions (customers, competitors, partners, market forces, regulatory forces) and proposed that real-time decisioning (RTD) helps to identify process steps and adds analytic insights.

Panian (2009) identified six steps of the RTD process: performance goals, connection of systems and customer processes, monitoring processes, learning about customers and processes, evaluating impact on actions, and refining processes. RTD has some relevance for the CI process, as it focuses not just on task processes but also includes system characteristics, such as overall firm processes, the business context and linkages between analysis processes and decision-making. Panian (2009) argued that RTD was more dynamic than business intelligence, as it is looks forward from planning and analysis to action, while business intelligence is concerned with analysis of past data.

Communication concerns knowledge exchange and creation (Businessdictionary 2015).

De Backer and Gurven (2006) identified that individual learning in firms can be improved through effective communication. Specifically, they found that ad hoc communication and exchange of personal experience in teams can enhance learning.

Sutanto, Tan, Battistini and Phang (2011) found that mediating is superior to directing in teams that are highly dynamic and manage complex tasks, which explains the

importance of communication for CI teams. Tsitoura and Stephens (2012) expressed the importance of communication when identifying causes of CI failures, noting three areas of failures, data gathering and identification, analysis, and communication with senior management.

Capatina and Bleoju (2012) identified that knowledge transfer mechanisms are related to a firm’s culture and leadership. They emphasised that organisational communication needs some structures to ensure successful knowledge transfer for strategic alignment.

This PhD study argues that both standardised and ad hoc approaches (De Backer and Gurven 2006; Tsitoura and Stephens 2012) are necessary for communication. Urgent and unforeseen matters could possibly be communicated in a different way than regular issues (Almarshad 2013; Gilad 2004).

Many past studies have commented on the communication of data outputs – the

dissemination of CI information (Fuld 2004; Gilad 2006; Leavitt, Prescott, Lemons and Hasanali 2004). Tsitoura and Stephens (2012) noted the importance of communicating intelligence results and making accurate use of results – with negative consequences from ignoring this. They identify a need to communicate results in understandable ways to management, not just to master reporting and dissemination.

Weick (1995) has written extensively on organisational sense-making; while Kouji, Shunichi and Akihiko (2010) noted in their research the importance of story telling as means of converting data from multiple sources into more effective knowledge sharing practices. Sense-making and story telling, among other, alternative elements can be regarded as key contributors to CI intelligence. Miller (2008) found that some areas of competitive understanding are often driven by implicit knowledge:

“The high level of allocative efficiency in experimental markets is driven largely by the

‘intelligence’ implicit in the rules of the market.”

By investigating robots programmed to do (market) auctions, he argued that the rule ‘do no harm’ triggered no auction. Thus an implicit rule applies to ‘do harm’ when

participating in an auction – this rule applies for competing firms when providing new services or products, or when a telecom competitor wins an auction for partnering with an MVNO (Mobile Virtual Network Operator), or (invited) frequency licence auctions.

Also linked to communication and decision-making, is the team management approach.

The way teams and managers operate in relation to CI activities, shows both explicit and implicit characteristics. Woolley (2011) suggested a model for strategic team

orientation, noting how effective strategic orientation is aware of the strengths of

opponents and how to react. The presence of strategic orientation is often based on implicit assumptions that can influence the attitude of CI analysts and managers towards internal or external knowledge practice, towards team communication (whether

reporting or story-telling) and towards how CI evaluation links to decision-making and performance of the firm.

This section has identified commonly agreed CI tasks or activities from past research – the sequence of tasks, moving from planning and gaining focus on relevant CI issues to data gathering and analysis and finally to communication of CI analysis outputs that link to decision-making is one way of identifying the basic blocks that constitute a CI process across firms. A preliminary representation of these elements is shown in Figure 3.1 below.

Figure 3.1: Overview of Key Elements of CI Management

Schlick, S. (2015) this thesis, adapted from works of Prescott 2003; Blanchard 2001;

Haberfellner, Nagel, Becker, Büchel and von Massow 2002; and Spickers 2004

The next section now takes further what is often regarded as core CI content – the actual forms of and scope of CI analysis.

3.4! Knowledge Management (KM), CI Quality and Effectiveness

!

Section 3.4.1 and 3.4.2 offer an overview of knowledge management and identify how KM is related to CI when examining internal firm processes (Ghanny and Mamlouk 2012; Mathi 2004) and considering the strength of knowledge sharing and knowledge dissemination. This links to CI quality, where the effectiveness of knowledge sharing in CI implementation needs to be considered. The effectiveness and sophistication of CI implementation is addressed in Section 3.4.3, where the ability to gather relevant

knowledge at a strategic level in CI Management and CI Content is outlined.