An Integrative Framework for Strategic Intelligence

An Integrative Framework for Strategic Intelligence

Mark Xu (University of Portsmouth, UK) and G. Roland Kaye (University of East Anglia, UK)
DOI: 10.4018/jsita.2010100101
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This paper defines strategic intelligence as a distinct organisational resource that differs from competitive intelligence (CI) and business intelligence (BI). A literature review unfolds a number of deficiencies in intelligence practice and systems. The findings, based on a case study, suggest that there is a lack of systematic scanning, analysing and support for strategic intelligence. An integrative framework is proposed to guide and improve strategic intelligence activity, which comprises of Key External Intelligence (KEI) and internal Key Performance Indicators (KPI). Implementation issues are discussed. The paper develops novel insight into strategic intelligence, and the proposed solution has implications on enhancing managers’ and an organisations’ sensibility and capability by detecting and responding to emerging strategic signals.
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Gaining information and knowledge to develop foresight about future opportunities and threats and taking appropriate action promptly become a core competency of wining organisations. Companies will differentiate themselves based on what they know. Foreknowledge (intelligence) developed from information is a distinctive, difficult to imitate and non-substitutable corporate resource (Calof, 2008), which could lead to sustainable competitive advantage. The business environment becomes more turbulent, and competition becomes ever fiercer that demand executives’ attention to the emerging strategic and risk signals. However, the increasing demand for strategic information has not been satisfied by the explosive growth in data available and multiple data sources. Senior managers are often overwhelmed with the glut of data instead of significant, meaningful, and actionable information – the distinct features of strategic intelligence.

The term “Strategic Intelligence” (SI) has been increasingly used by researchers and practitioners (Liebowitz, 2006; Lonnqvist & Pirttimaki, 2006; April & Bessa, 2006; Xu & Kaye, 2007), although its meaning has not been generally agreed. SI is often used interchangeably with other terms, typically, Competitive Intelligence (CI), Business Intelligence (BI), and domain specific intelligence – e.g. customer intelligence, competitor intelligence, technical intelligence, market intelligence. Liebowitz (2006) considers strategic intelligence as the convergence and synergies of knowledge management (KM), business intelligence and competitive intelligence. Calof (2008) suggests that intelligence is derived from a systematic and ethical process that involves planning, collection, analysis, communication and management, the output will help a company sustain distinct competitive advantages by developing actionable insights about the environment (customers, competitors, regulators, technology ….). Strategic intelligence used in this paper means strategically significant information provided to senior managers that is scanned, filtered, analysed, digested, and is meaningful and actionable to support strategic decision making, to update executive’s knowledge and to challenge their long-held view points and assumptions. In a simple term, Strategic Intelligence focuses on obtaining strategically significant information from a company’s internal and external environment and makes it available to senior managers. Strategic intelligence is related to, but different from competitive intelligence and business intelligence. CI, from a narrow point of view, refers to competitor’s intelligence (Wright et al. 2002), whereas commonly accepted broad definition of competitive intelligence (Dishman & Calof, 2008) includes not only competitor intelligence, but also intelligence collected on customers, suppliers, technology, environments, or potential business relationships. As so defined, CI in general neglects internal data processing for developing strategic insight of the business operation. Business Intelligence (BI), as defined by IBM means using data assets to make better business decisions through access, analysis, and uncovering new opportunities. The process of BI, according to Watson and Wixom (2007) includes two primary activities – getting data in and getting data out. The former refers to data warehousing that involves moving data from a set of source systems into an integrated data warehouse. The later is commonly referred to BI, which consists of business users and applications accessing synthesised data from the data warehouse to perform enterprise reporting and predictive analytics. Most BI systems are using scorecards and dashboards to present/report synthesised key performance indicators and patterns to senior management (Simons, 2008). In practice, BI has been hyped and extended to cover a wide range of information and applications. In European literature, the term BI is considered a broad umbrella concept for CI and other intelligence (Lonnqvist & Pirttimaki, 2006). Bose (2008) suggests that CI is sometime confused with BI. The difference is that BI is internal intelligence about and within one’s own company, whereas CI is external intelligence about the firm’s competitors. Abdullaev and Seok Ko (2007) echo their support to this view by stating that most of the BI systems tend to be tailored upon the organisations internal requirements. Although BI combines the company’s data into one point, it does not deal with external issues like customer behaviour and market situations.

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