Dynamic SME
Agenda: 1. Definition 2. Advantages 3. Implementation 4. Recommendations 5. Attachments 6. Bibliography 1 2 3 6 8 11
1. Definition
In today´s business environment, the ability to create new knowledge and un-derstand the market forces has become a crucial factor for the maintenance of market share and profitability.1 This requires the ability to permanently retrieve, store, analyze and communicate data. The amount of data enterprises daily have to cope with has, however, constantly increased in the last years. Espe-cially the internet has contributed to a never ending flow of data input, that has made it difficult for the management to capture the information relevant for proper decision making. As a result enterprises have developed a business in-telligence (BI) KMT, which facilitates the retrieval of information out of data.2 There are many different definitions for BI. While some differentiate between BI as the management for internal and CI for external data,3 others define it “as the ability to extract actionable insight from the internal and external data available to an organization, for the purpose of supporting decision making and improving corporate performance.” (see illustration 3)4 This thesis will fol-low the second definition and thus refers to BI as the integration of strategies, processes and techniques to generate actionable intelligence of dispersed and inhomogeneous data of an enterprise, the market or competitors.5 BI can be hence understood as a supply chain process for data. It sources data from the organizational functions of an enterprise and the exterior, i.e. competitor or market data and converts it into actionable intelligence (see illustration 4). As a result BI helps to organize oceans of scattered data and extract all the relevant information needed by the management to take good business decisions.6 With the increasingly turbulent economic environment, that demands enter-prises to remain constantly updated on market movements, the role of BI in the business world has changed over the last years. From a basic subordinated tool
1 Cf. Kudyba, S./ Hoptroff, R. (2001), p. 5. 2 Cf. Hannig, U. (2002), p. 3.
3 Cf. Steyl, J. (2009): URL: see list of references. 4 Canes, M. (2009): URL: see list of references.
5 Cf. Institute for Business Intelligence (w/o Y.), URL: see list of references. 6 Cf. Harper, D. (w/o. Y.), URL: see list of references.
for the analysis of data, BI has emerged as complete new management ap-proach which is imperative in the decision making process.7
2. Advantages
As many SMEs are merely owner-managed, decisions are mostly taken based on static reports, simple spreadsheets or instinct. While this routine is practica-ble if an SME is small and easily manageapractica-ble, the method soon reaches its limit with quick market changes or intensifying competition, which demand decision making based on relevant and current information.8
The BI tool facilitating the extraction of information from data and providing intelligence needed for strategic decision making, serves this need.9 Through gaining information i.e. of a new product or service of a competitor, it enables enterprises to track the
move-ments and changing strategies of the competition. Based on this, an enterprise is able to identify gaps in the market, which it can use to fill customer needs.10 By gaining relevant information about the competitor, BI furthermore allows an enterprise to determine its
weaknesses and thus provides insight about the competitive position in the market environment.11 As a result, BI provides an SME with a good overview
Illustration 1: Business intelligence leads to the discovery of new opportunities
Source: Original scene from the video.
7 Cf. Olszak, C.M./ Ziemba, E. (2012), p. 130. 8 Cf. Canes, M. (2009): URL: see list of references. 9 Cf. Guarda, T. et al. (2013), p. 187f.
10 Cf. Frost, S. (w/o Y.), URL: see list of references. 11 Cf. Bose, R. (2007), p. 511f.
over threats and opportunities in the market, based on which it can derive measurements for the improvement of its operations (see illustration 1).12
Another advantage of BI is the ability to forecast trends.13 Analytical techniques of BI identify relationships or unusual patterns in the obtained data and are hence able to visualize information that has not been recognized before.14 By the discovery of these anomalies, an enterprise becomes aware of emerging trends and is able to adjust its strategy accordingly. The ability to discover pat-terns and relationships data also holds advantages for the customer service. Through the analysis of i.e. sales data, BI assesses the demography as well as habits of the customers and determines correlating products or services. As a result, BI offers valuable opportunities for up-selling15 and cross-selling prod-ucts16. This allows an enterprise to streamline its marketing efforts, resulting in an improved customer experience and better use of resources.17
3. Implementation
Researchers have proposed many different approaches for the design of a BI process. This thesis will focus on the most commonly applied structure, which consists of five distinct phases (see illustration 2).18
The process starts with the planning phase, in which the objectives for the process are set.19 At this point it is important that the decision makers, for whose demand the process is ultimately created, communicate their intelli-gence needs, as these establish the foundation for the later phases. Based on
12 Cf. Vedder, R.G. et al. (1999), p. 110.
13 Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177. 14 Cf. Guarda, T. et al. (2013), p. 188.
15up-selling is a sales strategy in which the seller encourages a customer to purchase more ex-pensive items or upgrades for a higher sales profit
16cross-selling is the encouragement of an existing customer to purchase an additional product 17 Cf. Williams, S./ Williams, N. (2007), p. 162.
18 Cf. McGonagle, J.J. (2007), p. 71.
these needs, key intelligence topics (KITs) can be created, which point out the topics of greatest interest for the achievement of the strategic goals of the company.20
Once the KITs are defined, the process continues with the collection phase, in which the data has to be sourced and preprocessed. Here it is first of all impor-tant to match the available data sources with the KITs formulated in the plan-ning phase.21 This compliance with the intelligence needs is necessary to en-sure that a correct decision can be taken based on the resourced data. Before collecting the data, the source should thus furthermore always be checked for
its suitability.22 For the collection itself, SMEs have a large variety of potential sources at their disposal. They can i.e. source the data directly from interviews, focus groups or executives. The most recommendable source, is probably, however, the internet, as it has made research, i.e. on competitors, easy and affordable. Here the SME has the option to connect with experts, customers and suppliers or gather data from webpages, i.e. of competitors. While these
Illustration 2: The business intelligence process
Source: Own illustration based on Bose, R. (2007), p. 513; Guarda, T. et al. (w/o Y.), p. 187f.
Define necessary data Collect data, data pre-processing, data analysis Data analysis, Project results Databases Internal Data Sources External Data Sources Decision Making Re-initiation of Process Data Distribution Feedback
Planning Collection Analysis Dissemination Feedback
20 Cf. Krizan, L. (1999), URL: see list of references. 21 Cf. Bose, R. (2007), p. 513.
are all primary resources23, an SME can furthermore tap secondary resources24. For this purpose search engines and online subscription databases have be-come popular tools to collect data from commercial news organizations or news filtering services.25 Lastly the collected data needs to be refined and structured for further analysis.26
The next phase, the analysis phase, is the core stage and most critical part of the BI process. It differs from the collection process in the way, that its purpose is not to collect a set of data from diverse sources (i.e. internet, internal or ex-ternal databases), but to illustrate the significance of a predefined data set.27 To do so the refined data collected before is systematically examined, analyzed and validated.28 The main activity consists of mining the data29 to identify pat-terns and relationships for the extraction of actionable intelligence.30 SMEs of-ten encounter difficulties performing this task, as they are not able to afford the complex BI infrastructures of larger companies (see illustration 5). Nowadays, however, there are many possible alternatives. Open source software for in-stance is often available online at no cost and thus offers the SMEs an opportu-nity to start mining data without committing to a large investment.31
Once the intelligence has successfully been extracted, it needs to be dissemi-nated. In this stage the extracted intelligence is reported back to management through meetings, reports or dashboards, providing insight on the KITs. For this purpose it is essential that the report is visualized an easy-to-understand format so possible misunderstandings can be avoided.32 Provided with the intelli-gence on the KITs, the decision makers are then able to take action. Besides for
23Primary sources are first hand sources that provide direct evidence of a topic.
24Secondary sources interpret or analyze primary sources and are thus one step remote from the event.
25 Cf. Botha, A.P. (2007), p. 53.
26 Cf. Barnard, S. (w/o Y.), URL: see list of references. 27 Cf. Krizan, L. (1999), URL: see list of references. 28 Cf. Sangar, A.B./ Iahad, N.B.A. (2013), p. 177.
29data mining is a set of activities to identify hidden relationships in data. 30 Cf. Miller, S.H. (2001), URL: see list of references.
31 Cf. Chen, X. et al. (2007), p. 4.
decision making, the intelligence may also serve for further analyses such as competitor profiling, scenario planning or scenario analyses.
The process is concluded with the feedback of the executives. It includes an assessment of the quality and accuracy of the intelligence as well as guidance for the analyst of how the process can be improved in the future.33 A BI process thus has to be understood as iterative in the sense that it permanently repeats upon completion and is always subject to improvement.34
4. Recommendations
As many enterprises are opposed to change or new technology, the support from top management is a critical factor for the success of BI. A dedicated management which endorses the BI process ensures financial resources and effective project management.35 It is thus the responsibility for those involved in an intelligence program to convince the senior executives of its usefulness. They should be encouraged to use the system actively instead of contemplat-ing it just as another reactive management resource.36 Besides its endorsement for BI as a system, it is important that the top management considers the intel-ligence program an iterative process rather than a one time project.37 This con-tinuity is essential, as strategic planning requires long-term intelligence and a continuous basis of information.
Closely connected to continuity are also other critical success factors. A culture of trust and cross-organizational collaboration, i.e. is vital for the effective knowledge exchange and thus critical to transform knowledge of an individual into organizational knowledge. Especially in SMEs, that rely mostly on tacit knowledge, knowledge workers can only be replaced to a certain extent. A
col-33 Cf. Bose, R. (2007), p. 514.
34 Cf. William, Y./ Koronios, A. (2010), p. 23ff. 35 Cf. William, Y./ Koronios, A. (2010), p. 23ff.
36 Cf. Global Intelligence Alliance (2004), URL: see list of references. 37 Cf. William, Y./ Koronios, A. (2010), p. 23ff.
laborative culture facilitates a healthy flow of information within the organiza-tion, which is needed to make the collected and analyzed information about customers, competition, market conditions, vendors, partners, products and employees available at all levels.38 A properly functioning BI process in this sense also requires the utilization of tools and applications. They not only facili-tate the collaboration by providing means to communicate the intelligence within the organization, but furthermore encourage BI users to produce con-tent themselves. In the end, it should, however, always be clear to an enterprise that tools merely support the managing knowledge process and that BI can consequently only be successful, if the knowledge and skills of the people are used effectively.39
38 Cf. Atre, S. (2003), URL: see list of references.
5. Attachments
Illustration 3: Definition of Business Intelligence
Source: Own illustration.
Data Processing
Data Sourcing Data Visualization
Data Intelligence
Data Integration from Departments and External Sources Intelligent Data Analysis Data Dissemination Internal Data Sources External Data Sources
Source: Own illustration.
Illustration 4: The business intelligence value chain
Increasing potential to support business decisions Data Knowledge Insight Action Information
Source: Own illustration based on Hannig, U. (2002), p. 6.
Illustration 5: The architecture of business intelligence
Internal Data Sources
(ERP Systems) External Data Sources(Internet)
Standardized
Reporting Ad-hoc Re-porting MiningData
Meta-Data Bank ETL-Tools Extract Transfer - Convert - Filter - Aggregate Load OLAP Tools Archive Data Warehouse Data Mart
6. Bibliography
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Botha, A.P. (2007): Knowledge - living and working with it, Cape Town, South Africa.
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