IT Support

Schön, Dietmar

doi:10.1007/978-3-658-41044-5_5

Dietmar Schön²

1065 Accesses

Abstract

In Chap. 5 (IT support), a brief overview of the history and the basic hardware components of planning and reporting solutions is given. The software solutions for the planning and reporting tasks are distinguished between special ERP systems, spreadsheet programs, special software programs (based on relational database technology) and data warehouse or business intelligence supported systems. For data warehouse or business intelligence supported systems, the focus of the investigation is on the OLAP data modeling, the OLAP storage concepts, the ETL processes, the different analysis tools, such as cockpit and dashboard solutions and portals. In addition, the latest developments in BI-supported controlling with the support of traditional and exploratory BI are shown, among others, big data technology, data discovery, data visualization, data mining, predictive analytics, artificial intelligence, chatbots, RPA, app technology, self service BI and cloud computing. Big data helps, among other things, with the processing speed (in-memory technology) of large heterogeneous data sets. Modern navigation interfaces can be created using the app technology with its tile types. Data discovery or visual discovery tools support big data analytics in terms of their forecasting and analysis capabilities. Especially data mining and predictive analytics receive a further boost for analysis and forecasting tasks, among others, through machine learning, deep learning and neural networks. Chatbots are computer-based dialogue systems that offer new possibilities for faster and more resource-efficient information provision for the users. With robotic process automation (RPA), there is another IT approach that can be used as an intermediate technology in controlling to replace tedious manual IT maintenance by software robots. Cloud computing is an interesting outsourcing option for companies, where IT services and resources can be used via external IT resources. Further innovations concern the topics of data quality and data modeling. The conclusion of this chapter is the topic of “mobile BI”, which is about the expansion of powerful mobile analysis and planning solutions with the help of tablets, phones and other mobile devices.

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Notes

1.
Cf. Mertens and Griese (1988, p. 5).
2.
Cf. Reichmann (2011, p. 18).
3.
Cf. Reichmann (2006, p. 662).
4.
Cf. Scheer (1990, p. 139).
5.
See Weber and Strüngmann (1997, pp. 30–36).
6.
Cf. Scheer (1990, pp. 37, 142 and 153).
7.
Cf. Hesseler and Görtz (2007, pp. 7 f. and 15 ff.).
8.
Cf. on the historical development of reporting and planning solutions, among others, Laudon and Laudon (1988), Gluchowski et al. (2008, p. 55 ff.), Oppelt (1995), Schinzer (1996), Mertens and Griese (2002), Oehler (2006) and Chamoni and Gluchowski (2004, p. 5 ff.).
9.
Cf. Wild (1981, p. 36 ff.) or Dahnken et al. (2003, p. 52 ff.).
10.
Cf. for further software selection criteria e.g. Becker et al. (2011, p. 16).
11.
The requirement criteria listed supplement the quality characteristics according to IS0 9126 (version until 2005) and the successor standard ISO/IEC 25000. However, they are not defined from a technical point of view (e.g. security and portability) or manufacturer’s point of view (e.g. maintenance and service). They rather serve the analysis and comparability of different software directions for planning and reporting solutions.
12.
Cf. Hesseler and Görtz (2007, p. 2 f.).
13.
Cf. Hesseler and Görtz (2007, pp. 17 f., 24).
14.
Cf. Hesseler (2009, p. 52).
15.
Cf. Buck-Emden (1995, p. 29).
16.
Cf. Bange (2013, pp. 134–135).
17.
Cf. Bange (2013, pp. 98–126).
18.
See Pütter (2011) for this. The study was conducted at the Chair of Business Informatics II of Prof. Dr. Peter Gluchowski in cooperation with the consulting firm Conunit (Frankfurt a. M.).
19.
Cf. Schön and Pook (2015, p. 13), Weber (2013, p. 219) and Schön et al. (2013, p. 258).
20.
Cf. Goecken (2006, pp. 11, 15 f.), Gluchowski et al. (2008, pp. 55–88), Chamoni et al. (2010, p. 6 ff.) and Bauer and Günzel (2013, p. 11).
21.
Cf. Behme (1996, p. 31) and Inmon, B.: Definition of a data warehouse. URL: www.billinmon.com (Accessed on 31.07.2002).
22.
Cf. Mucksch and Behme (2000, p. 11 f.) and Hahne (2005, p. 8).
23.
Cf. the explanations on the terms OLAP and OLTP in Sect. 5.5.4.
24.
Cf. Bauer and Günzel (2013, p. 6). Kapp and Kusterer refer the data collection in the data warehouse only to strategically relevant information (Knapp and Kusterer 1996, p. 219 ff.).
25.
Cf. Schinzer et al. (2000, p. 15).
26.
Alternatively, one can also find 5-level representations of the architecture of data warehouse systems, which separately show the data sources, the ETL process, the data management, the data provision for the evaluations via the OLAP server or the OLAP engine, and the presentation level. Cf. Goecken (2006, p. 27). However, since the data sources themselves do not belong to the data warehouse, but only the data connection, and the OLAP engine is a technical system component of the data distribution, the representation with 3 levels is preferred here.
27.
Cf. Sinz and Ulbrich vom Ende (2010, p. 190 f.).
28.
Cf. Hahne (2016, p. 150 ff.).
29.
Cf. Heuer et al. (2001, p. 469).
30.
Cf. Jordan and Schnider (2011, p. 7).
31.
Cf. Manhart (2011b).
32.
Cf. Kemper et al. (2010, p. 23) as well as Sinz and Ulbrich vom Ende (2010, p. 188).
33.
Cf. Martin and von Maur (1997, p. 105).
34.
Cf. Vaduva and Vetterli (2001, p. 273).
35.
See the explanations of the terms OLAP and OLTP in Sect. 5.5.4.
36.
See Farkisch (2011, p. 27).
37.
See Navrade (2008, p. 20).
38.
Operational data stores (ODS) are often used to map the requirements of a real-time data warehouse and an active data warehouse (see below), as here operational and constantly updated data are used for business process control.
39.
Cf. Kemper et al. (2010, pp. 12, 141 ff.).
40.
Cf. Goecken (2006, p. 26 ff.).
41.
Extended data warehouse definition of the author based on Mucksch and Behme (2000, p. 6) and Gabriel et al. (2000, p. 76).
42.
Cf. Kemper et al. (2010, pp. 92–96).
43.
Alternatively, there are also virtual data connections without intermediate storage, which access the data of the source systems directly. This approach is less common in practice. With the use of in-memory technology (see Sect. 5.8.2.1), some software vendors increasingly use the direct virtual data access to primary sources without building an intermediate data storage.
44.
Cf. Kemper et al. (2010, pp. 26–38).
45.
Cf. Oehler (2000, p. 21 f.).
46.
Besides the real physical storage in target databases, other forms of virtual storage are also possible, in which only the data structures but not the data contents are stored in the data warehouse system, but these access the source system directly when requested.
47.
Cf. Müller and Keller (2015, pp. 394–395).
48.
See Apel et al. (2009, p. 67).
49.
Cf. IBM Consulting Services (2003, p. 32).
50.
Cf. IBM Consulting Services (2003, p. 32).
51.
Cf. IBM Consulting Services (2003, pp. 154–155).
52.
Cf. Ruprecht (2003, p. 126).
53.
Cf. Bauer and Günzel (2013, p. 44).
54.
Cf. Chamoni et al. (2010, p. 164).
55.
Cf. Chamoni (1997, p. 294) and Codd et al. (1993).
56.
Cf. e.g. Düsing and Heidsieck (2009, p. 108) and Oehler (2000).
57.
Cf. Pends and Creeth (1995).
58.
See Humm and Wietek (2005, p. 5).
59.
Cf. Caesar and Friebel (2011, p. 548).
60.
Cf. Mohr (2006, p. 93 ff.). For example, SAP AG supplements its star schema for SAP BW with the points listed.
61.
Cf. Bauer and Günzel (2013, p. 204 f.).
62.
Cf. Azevedo et al. (2005, p. 46).
63.
Cf. Azevedo et al. (2005, p. 52 f.).
64.
Cf. Behme et al. (2000, p. 229).
65.
Cf. Elmasri and Navathe (2007, p. 37 f.).
66.
Cf. Kemper et al. (2010, p. 97).
67.
Cf. Oehler (2006, p. 93).
68.
See also the explanations of the historical development of Management Support Systems (MSS) in Sect. 5.2.
69.
See also the survey results of the study by Schön (2011, p. 31).
70.
Cf. Turban et al. (2004, p. 103). See also the explanations in Sect. 5.2.
71.
Cf. Dahnken et al. (2004, p. 55 ff.).
72.
Cf. Meier et al. (2003, p. 90 ff.).
73.
Cf. help texts of the SAP portal (2010).
74.
Cf. help texts of the SAP portal (2009).
75.
Cf. help texts of the SAP portal (2009), URL: http://help.sap.com/saphelp_sem60/helpdata/de/05/242537cedf2056e10000009b38f936/frameset.htm (accessed on 09.02.09).
76.
Cf. Egger et al. (2005, p. 163 ff.).
77.
Cf. Knöll et al. (2006, pp. 212–215).
78.
Cf. Egger et al. (2009, p. 101 f.).
79.
Cf. Gluchowski (2010, p. 278).
80.
Cf. Search Business Analytics (2016).
81.
Cf. Lixenfeld (2015, p. 24).
82.
Cf. Forrester Research (2016). Forrester Research is a US-based market research company for the information technology sector.
83.
Cf. Gluchowski (2010, p. 278).
84.
Cf. among others Kemper et al. (2010, pp. 148–153).
85.
Cf. on MQE among others Manhart (2011a).
86.
Cf. Pastwa (2010, p. 11 f.).
87.
Cf. Manhart (2011a), Krudewig (2012, p. 29) and Feindt (2014, p. 53 ff.).
88.
For the technical design and application possibilities of mobile devices, see also Sect. 5.10.3.
89.
Cf. Winterstein and Leitner (1998, p. 34), Kemper et al. (2010, p. 10) and Chamoni and Gluchowski (2004, p. 119).
90.
Cf. among others the definitions of Schrödel, King and the authors mentioned in the further course of this chapter: Schrödl (2009, p. 9) and King (2014, p. 37).
91.
Cf. Hanning (2008, p. 77).
92.
Cf. Taschner (2013, pp. 9–11).
93.
Cf. Mertens (2002, p. 4).
94.
See Behme and Mucksch (1997, p. 15).
95.
Cf. Bange et al. (2009, p. 7).
96.
Cf. Jetter (2004, p. 33).
97.
Cf. Gleich (2001). Alternatively to the term Business Performance Management, the term Corporate Performance Management (CPM) is also used.
98.
Cf. Engels (2015, p. 15).
99.
Cf. Horváth (2008, p. 125) and Reichmann (2006, p. 13).
100.
Business intelligence definition by Prof. Dr. Dietmar Schön in the field of controlling at the University of Applied Sciences and Arts Dortmund, July 2017.
101.
Cf. Schrödl (2009, p. 13 f.).
102.
Cf. Bange et al. (2013, p. 9 ff.).
103.
Cf. Kemper et al. (2010, p. 9).
104.
On the term AI, see Ertel (2016, p. 1ff.) and Lubos (2020, pp. 45–49). On AI in accounting and controlling, compare e.g. Wullenkord (2018, pp. 113–129).
105.
Cf. University of Helsinki and Reaktor (2020).
106.
Cf. Frochte (2019, p. 13) and Portal (2020, p. 69).
107.
Cf. Wuttke (2020).
108.
Cf. Rogers (1967, p. 2) and Leiserson et al. (2010, p. 5 f.).
109.
Cf. Ertel (2016, p. 194).
110.
Cf. Wuttke (2020).
111.
Cf. among others Schrödl (2009, p. 26 f.), Gluchowski et al. (2008, p. 196 ff.), Chamoni et al. (2010, pp. 329–356), Cleve and Lämmel (2014, pp. 57–192), Alpar and Niedereichholz (2000, p. 11), Küsters (2001, pp. 95–130), Gabriel et al. (2009, pp. 144–276), Runkler (2010, p. 96), Petersohn (2005, pp. 73–255), University of Helsinki and Reaktor (2020) and Ertel (2016, p. 194).
112.
Cf. Hammann and Erichson (2006, p. 322 ff.).
113.
Cf. Scheuer (2020, p. 20 f.), Berry et al. (2018, p. 4), Buxmann and Schmidt (2019, p. 10 f.) and Folkers (2019, p. 3 ff.).
114.
Cf. Wuttke (2020).
115.
Cf. University of Helsinki and Reaktor (2020).
116.
Cf. University of Helsinki and Reaktor (2020).
117.
Cf. Ertel (2016, p. 291).
118.
Cf. Mertens and Barbian (2019, p. 11).
119.
Cf. Alexander et al. (2018, pp. 11–19).
120.
Cf. Winter (2018, p. 66).
121.
Cf. Gabriel (2010) http://www.oldenbourg.de:8080/wi-enzyklopaedie/lexikon/ (Accessed on 15.01.2011).
122.
Cf. Shortliffe (1976): Computer-Based Medical Consultations: MYCIN. Elsevier, New York 1976.
123.
Cf. Sperner (2020).
124.
Cf. Gentsch (2018, pp. 32–34).
125.
Cf. Sperner (2020).
126.
Cf. Bissantz et al. (2001, pp. 130–131) or Determann and Rey (1999, p. 143).
127.
Cf. Kononenko and Kukar (2007).
128.
Cf. Shearer (2020, pp. 13–22).
129.
Cf. e.g. Siegel (2013).
130.
See Petersohn (2005, pp. 10–11) and Cleve and Lämmel (2014, p. 38).
131.
See Feindt and Grüßling (2014, p. 181 f.).
132.
See Felden (2010, pp. 307–328).
133.
Cf. Burow et al. (2014, pp. 13–20), Bitkom (2014, pp. 21–24, 45–47) and Schubert (2013).
134.
Cf. Gehra (2005, p. 22 f.), Krystek and Moldenhauer (2007, p. 124) and Hammer (1998, p. 252 ff.).
135.
Cf. University of Helsinki and Reaktor (2020).
136.
Cf. Baars (2016, p. 175).
137.
Cf. among others Schrödl (2009, p. 26 f.), Gluchowski et al. (2008, p. 196 ff.), Chamoni et al. (2010, pp. 329–356), Cleve and Lämmel (2014, pp. 57–192), Alpar and Niedereichholz (2000, p. 11),Küsters (2001, pp. 95–130), Gabriel et al. (2009, pp. 144–276), Runkler (2010, p. 96) and Petersohn (2005, pp. 73–255).
138.
Cf. Schrödl (2009, p. 28 f.).
139.
Cf. Weigend (2017, p. 16).
140.
Cf. Ruf and Schwab (2016, pp. 495–501) and BARC (2017).
141.
Taken from: Freiknecht (2014, p. 345).
142.
For example QlikTech (2016) and Jedox (2016).
143.
Cf. R (2016).
144.
Cf. Wuttke (2020).
145.
Cf. Richter (2003, pp. 407–430).
146.
Cf. Mehler and Wolf (2005, p. 2).
147.
Cf. Hotho et al. (2005, pp. 19–62).
148.
Cf. Mertens (2002, pp. 17–19), URL: http://www.wi1-mertens.wiso.uni-erlangen.de/veroeffentlichungen/download/Business_Intelligence-ein_Ueberblick_Arbeitspapier_der_Universitaet_Erlangen-Nuernberg.zip, (accessed on 23.07.2011).
149.
Cf. Behme and Mucksch (1997, p. 150) and Schinzer et al. (1999, pp. 284, 314 f.).
150.
See Leßweng (2004, p. 43).
151.
Cf. Leßweng (2004, pp. 41–49).
152.
Really Simple Syndications (RSS) is a family of formats for the simple and structured publication of changes on internet pages.
153.
Cf. Barton et al. (2018, p. 116).
154.
Cf. Langmann (2019b).
155.
Cf. Dinnessen and Halfmann (2018).
156.
Cf. Smeets et al. (2019, p. 8).
157.
Cf. Smeets et al. (2019, p. 10).
158.
Cf. Martens (2019).
159.
Cf. Tripathi (2018, p. 12).
160.
See Smeets et al. (2019, p. 9).
161.
See Sellmair et al. (2019).
162.
See Smeets et al. (2019, p. 9).
163.
See Langmann and Turi (2020, p. 12).
164.
Cf. Barton et al. (2018, p. 120).
165.
Cf. ibid.
166.
Cf. Allweyer (2016, p. 35).
167.
Cf. among others Alexander et al. (2018, pp. 11–19) from Bearingpoint and Deloitte (2019).
168.
Cf. Obermaier (2019, p. 692).
169.
Cf. Peper (2018, p. 27 ff.).
170.
Cf. Langmann (2019b).
171.
Cf. Barton et al. (2018, p. 117).
172.
Cf. Friedl (2019, p. 35).
173.
An avatar is an artificial person or graphic character that is assigned to a web user in the virtual world, for example in a computer game or as here in a chat bot.
174.
Cf. Schonschek and Haas (2020, p. 1 ff.).
175.
Cf Hundertmark (2020, p. 1 f.).
176.
Cf. Mori et al. (2017, p. 395 et seq.).
177.
Cf. McTear et al. (2016, p. 125 et seq.).
178.
Cf. Kumar and Tiwari (2017, p. 60).
179.
Cf. Stephan (2020, p. 1 f.).
180.
Cf. Friedl (2019, p. 35 f.).
181.
Cf. Friedl (2019, p. 35 ff.).
182.
Cf. Sauer and Sturm (2019, p. 35).
183.
Own compilation: Due to the many row and column information, the table was divided for better readability.
184.
Cf. Gentsch, Peter (2019): Artificial Intelligence for Sales, Marketing and Service, 2nd ed., Springer Gabler Verlag. p. 71.
185.
See Friedl (2019, p. 36).
186.
Cf. Gleich and Tschandl (2018, p. 189).
187.
Cf. Oehler (2020, p. 23 ff.).
188.
Cf. Oehler (2020, p. 27).
189.
Cf. Spitzner and Schneider (2015, p. 5).
190.
Cf. Oehler (2020, p. 28).
191.
Cf. Oehler (2020, p. 30).
192.
Cf. Davenport and Kirby (2016, p. 21 ff.).
193.
Cf. Geißner and Wolfrum (2015, p. 243).
194.
Cf. Oehler (2020, p. 27).
195.
Bliznak, Karol (2020, pp. 160–167).
196.
Bliznak, Karol (2020, p. 156).
197.
Cf. Werner, Roland (2021): PwC: Reporting 5.0. URL: https://www.pwc.de/de/im-fokus/finance-transformation/future-ofsteering/reporting-5-0-ki-revolutioniert-das-reporting.html from 20.03.2021.
198.
Bliznak, Karol (2020, pp. 158–159).
199.
Cf. Schneider Steffen (2021) Reporting Pulse Check.—What topics move the experts from Controlling & Finance? URL: https://www.haufe.de/controlling/controllerpraxis/reportingpulse-check-das-bewegt-finanzexperten_112_471720.html from 24.03.2021.
200.
126 Cf. Schmitz, Robert (2018). BI Scout: BI Trends 2019: Integrating Artificial Intelligence in Analytics Scenarios. URL: https://www.bi-scout.com/bi-trends-2019-integration-vonkuenstlicher-intelligenz-in-analytics-szenarien from 21.03.2021.
201.
Cf. Werner Roland (2018) PwC Germany: Reporting 5.0. URL: https://www.pwc.de/de/pressemitteilungen/2018/pwclauncht-reporting-5-0.html from 18.03.2021.
202.
Cf. Pariser (2011) Eli Pariser: The Filter Bubble: What the Internet Is Hiding from You. Penguin Press, New York, 2011.
203.
Cf. BitKom (2017): Understanding artificial intelligence as automation of decision making. URL: https://www.bitkom.org/sites/default/files/file/import/Bitkom-Leitfaden-KI-verstehen-als-Automation-des-Entscheidens-2-Mai-2017.pdf from 24.03.2021.
204.
Cf. Davenport and Kirby (2016, p. 21 ff.).
205.
Cf. Seufert (2014, p. 25).
206.
Cf. IDC (2011).
207.
Cf. Gesellschaft für Informatik et al. (2013).
208.
Reinsel et al. (2018, p. 3).
209.
Similar examples can be found in different sources, among others Dorschel (2015, p. 109) and Bitkom (2013).
210.
Cf. e.g. BARC (2014, pp. 23–24) and Dorschel et al. (2015, p. 2) as well as Institute for Business Intelligence (2013).
211.
Cf. Finlay (2014, p. 13).
212.
Cf. IBM Institute for Business Value and Säid Business School (2012).
213.
Cf. Gartner (2015) and Brücher (2013, p. 41 ff.).
214.
Cf. Schroeck et al. (2015, p. 3 f.).
215.
Cf. Finlay (2014, p. 13) and Gesellschaft für Informatik et al. (2013).
216.
Cf. TECChannel (2014).
217.
Cf. Finlay (2014, p. 13).
218.
Cf. Sack (2013).
219.
Cf. Finlay (2014, p. 13).
220.
Cf. IBM Institute for Business Value and Säid Business School (2012, p. 4).
221.
Cf. Freiknecht (2014, p. 13).
222.
Cf. Kreutzer and Sirrenberg (2019, pp. 78–80).
223.
Cf. e.g. Gluchowski and Chamoni (2016, p. 189).
224.
Cf. Brenckmann and Pöhling (2012).
225.
Cf. Gentsch (2018, pp. 21–23) and Bauer (2020).
226.
Cf. Schmitz (2015, p. 236).
227.
Cf. Bitkom (2014, pp. 21–24, 45–47).
228.
Cf. Walker-Morgan (2010).
229.
Cf. Sack (2013).
230.
Fasel and Meier (2016, p. 6 f.).
231.
NoSQL Databases (http://nosql-database.org/. Accessed on 15.12.2014).
232.
Cf. Warner (2007, pp. 480–485).
233.
Cf. Edlich et al. (2010, pp. 31–33).
234.
Fasel and Meier (2016, p. 12).
235.
Fasel and Meier (2016, p. 124).
236.
Cf. Freiknecht (2014, p. 20).
237.
Cf. Luber and Litzel (2017).
238.
Cf. Rouse (2014).
239.
Cf. Freiknecht (2014, p. 20) and Bitkom (2014, p. 39).
240.
Cf. Big Data Blog (2015).
241.
Cf. Kaufmann (2014, p. 369).
242.
Cf. Freiknecht (2014, p. 20).
243.
Data Academy and Davenport (2008).
244.
Cf. Wartala (2012, pp. 180–183).
245.
Cf. Müller (2014, p. 450) and Alexander and Grosser (2017).
246.
Cf. Berg and Silvia (2013, p. 41).
247.
Cf. Berg and Silvia (2013, p. 41).
248.
Cf. Alexander and Grosser (2017) and Intelligence.de (2017).
249.
Cf. Intelligence.de (2017).
250.
Cf. BARC (2014, pp. 23–24).
251.
Cf. Baumöl and Berlitz (2014, p. 169).
252.
For example, SAP BW on Hana at the shoe company Reno, Schäfer (2014).
253.
Cf. Welker (2015).
254.
Frietsch (2016, p. 169 f.).
255.
Frietsch (2016, p. 171) and Seiter (2017, p. 83).
256.
Cf. Ballhorn (2017).
257.
The differentiated presentation of the levels Staging, Cleansing, Core DWH and Data Marts was omitted here. See Fig. 5.13.
258.
Cf. Ballhorn (2017).
259.
Cf. Gluchowski (2016, p. 277).
260.
Cf. Chamoni and Gluchowski (2017, p. 9) and Felden (2017, pp. 1–8).
261.
Cf. Hortonworks (2013, p. 4).
262.
Cf. März and Warren (2015, p. 18 ff.).
263.
Cf. e.g. Inform (2017).
264.
Cf. Gartner (2016).
265.
Cf. Chamoni and Gluchowski (2017, p. 9).
266.
Cf. Langmann (2019a, pp. 5–8).
267.
Cf. Chamoni and Gluchowski (2017, p. 8 ff.).
268.
Cf. e.g. Bissantz et al. (2000, pp. 377–407).
269.
Some authors even use both terms and the acronym BIA for Business Intelligence & Analytics. This mixture shows in my opinion how vague the terms are used. See Ereth and Kemper (2016, pp. 458–464) and Chen et al. (2012, pp. 1165–1188).
270.
See Lanquillon and Mallow (2015, p. 55).
271.
Following the argumentation of Felden (2017, pp. 1–8).
272.
See Möller et al. (2016, pp. 509–518).
273.
Cf. Kemper et al. (2010, p. 9).
274.
Cf. Luber and Litzel (2019).
275.
See also Luber and Litzel (2019), Krishnan (2013, pp. 191–195) and Iffert and Bange (2018).
276.
See also Luber and Litzel (2019) and Iffert and Bange (2018).
277.
Cf. Berg and Silvia (2013, pp. 33–35).
278.
Koglin (2016, p. 61 ff.).
279.
Cf. Merz et al. (2015, pp. 153 ff. and 277).
280.
Cf. Haupt (2011).
281.
Cf. Merz et al. (2015, p. 259 ff.).
282.
Cf. @tfxz-Blog (2014).
283.
Cf. SAP SE (2013), Merkt et al. (2015) and Kessler et al. (2014, pp. 31–37).
284.
Extended from Iffert (2017).
285.
See BARC (2017) and BARC GUIDE (2020, p. 11).
286.
Cf. Zarinac (2016, p. 140 f.).
287.
Cf. Buschbacher et al. (2014, p. 90).
288.
Cf. Giegerich (2014, p. 321 f.).
289.
Cf. Bitkom (2013, p. 24 ff.).
290.
Cf. Mell and Grance (2011, pp. 2–3) and Duisberg (2011, p. 49).
291.
Cf. Birk and Wegener (2010, p. 642).
292.
Cf. Bollmann and Zeppenfeld (2010, p. 4).
293.
Cf. Bollmann and Zeppenfeld (2010, pp. 87–111).
294.
Ultrabook is a registered trademark of Intel.
295.
NN (2020, p. 9).
296.
Cf. Bollmann and Zeppenfeld (2010, pp. 87–111).
297.
Cf. Lossau (2018, pp. 1–5).
298.
Cf. Keist et al. (2016, pp. 109–113). The difference is similar to the one in Sects. 5.10.5.1 and 5.10.5.2 for mobile business applications.
299.
Cf. Mathew (2015, p. 1 ff.) and Engelbrecht and Wegelin (2015, p. 25).
300.
Cf. Krüger (2015, p. 204 ff.).
301.
See the following source for this: SAP Fiori (2017b).
302.
Cf. Bensberg (2008, p. 72).
303.
Cf. Bensberg (2008, pp. 75–79).
304.
Cf. Fuchß (2009, pp. 137–151).
305.
Cf. Schill and Springer (2007, pp. 265–271).
306.
Cf. SAP AG (2016, p. 5).
307.
Cf. Bensberg (2008, p. 76).
308.
Cf. Schill and Springer (2007, pp. 274–280).
309.
Cf. Donie and Raeburn (2015).
310.
Cf. Kersten and Klett (2012, p. 103 ff.).
311.
Cf. Hansel (2015).
312.
Cf. Bensberg (2008, p. 77).
313.
Cf. Louis and Müller (2013, p. 23).
314.
Cf. Beckert et al. (2012, p. 139 f.) and Homann et al. (2013, p. 52 f.).
315.
Cf. Homann et al. (2013, p. 53 f.).
316.
Cf. Kemper et al. (2010, p. 251).
317.
Cf. Dresner Advisory Services LLC: Mobile Business Intelligence Market Study, 2010 and 2011. URL: http://www.microstrategy.com/mobile/mobile-bi-landscape-dresner.pdf (accessed on 25.07.2011) and URL: http://www.informationbuilders.com/pdf/press/dresner_mobile_bi_2011.pdf [accessed on 25.07.2011].
318.
Cf. BARC (2017). Trend Monitor 2017 http://barc.de/trend-monitor. Accessed on 29.07.2017.
319.
Cf. Jung (2011, pp. 207–209).
320.
Cf. Bollmann and Zeppenfeld (2010, p. 42).
321.
Cf. Bensberg (2008, p. 76).
322.
Cf. Lopez (2009, p. 2).
323.
Cf. Bollmann and Zeppenfeld (2010, pp. 127–130).
324.
Cf. Kersten and Klett (2012, p. 103 ff.).
325.
Cf. Bensberg (2008, p. 77).
326.
Cf. Dresner Advisory Services LLC (2011, p. 23).
327.
Cf. Bensberg (2008, pp. 72–79).

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Fachhochschule Dortmund, University of Applied Sciences and Arts, Dortmund, Germany
Dietmar Schön

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Schön, D. (2023). IT Support. In: Planning and Reporting in BI-supported Controlling. Springer, Wiesbaden. https://doi.org/10.1007/978-3-658-41044-5_5

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DOI: https://doi.org/10.1007/978-3-658-41044-5_5
Published: 25 July 2023
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