Social Business Intelligence: a Literature Review and Research Agenda

Abstract

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects.

Full text

Draft, published as: Barbara Dinter and Anja Lorenz (2012). “Social Business Intelligence: a Literature
Review and Research Agenda”. In: Thirty Third International Conference on Information Systems
(ICIS 2012). Ed. by F. George Joey. Orlando, Florida: Association for Information Systems. isbn: 978-
0-615-71843-9. url: http://aisel.aisnet.org/icis2012/proceedings/ResearchInProgress/104/
(visited on 01/23/2013)
Research in Progress Paper
Social Business Intelligence:
a Literature Review and
Research Agenda
Barbara Dinter, Anja Lorenz∗
The domains of Business Intelligence (BI) and social media have mean-
while become significant research fields. While BI aims at supporting
an organization’s decisions by providing relevant analytical data, social
media is an emerging source of personal and individual knowledge, opin-
ion, and attitudes of stakeholders. For a while, a convergence of the two
domains can be observed in real-world implementations and research,
resulting in concepts like social BI. Many research questions still remain
open – or even worse – are not yet formulated. Therefore, the paper aims
at articulating a research agenda for social BI. By means of a literature
review we systematically explored previous work and developed a frame-
work. It contrasts social media characteristics with BI design areas and
is used to derive the social BI research agenda. Our results show that the
integration of social media (data) into a BI system has impact on almost
all BI design objects.
Keywords: Social business intelligence, social media, business intelli-
gence, social media analytics, business intelligence 2.0, literature review,
research agenda
∗Chemnitz University of Technology, Germany,
[barbara.dinter|anja.lorenz]@wirtschaft.tu-chemnitz.de
1

1 Introduction
Business intelligence (BI) solutions represent an essential and established compo-
nent in the enterprise application landscape. They supply the management and
further departments with decision-relevant information. BI hereby encompasses all
processes and systems that are dedicated to the systematic and purposeful analysis
of an organization and its competitive environment. Consequently, BI is of ongoing
high relevance for an organization (Arnott and Pervan 2008). Luftman and Ben-Zvi
(2010), for example, have identified BI as a key issue for CIO’s in several consecutive
studies.
Although not exhibiting such a long tradition as BI, social media is another topic
that attracts currently significant attention in both, research and practice. Initi-
ated by an investigation of use cases for social media in professional environments
(McAfee 2006), the term “Enterprise 2.0” and the subsequent application of social
media practices in information systems (IS) have been established as a promising
approach to increase employees’ effectiveness and satisfaction (cf. Cook 2008; Seo
and Rietsema 2010).
For a while, a certain convergence of both domains (BI and social media) can be
observed, resulting in concepts like social BI, social customer relationship (CRM),
or social media analytics. In the beginning pushed by vendors and market research
institutions, the scientific community increasingly pays attention to social BI, i. e.
the integration of social media data within BI environments. Social media applica-
tions are not restricted to marketing and CRM scenarios only, in which the potential
benefit of analyzing a customer’s voice is obvious. Customer insights, captured
and analyzed by means of BI, may also be used as input for product and service
innovation. Thus, social BI supports a broad range of processes in research and de-
velopment, sales, customer service, and operations, just to name a few (Bose 2011).
Although many authors mention rather specific research questions that can be
assigned to the social BI domain, there is – to the best of our knowledge – so far no
systematic and comprehensive research agenda for social BI available. This gap and
a still vague understanding of social BI in literature leads to the following research
question: What are the predominant research areas in the social BI domain?
The paper at hand aims at answering this question by deriving a research agenda
for social BI, based on the results of a literature review and guided by a framework
that investigates the impact of social media on BI design areas. Similar to the social
media phenomenon that can be attributed to several disciplines, social BI can (and
finally should) be investigated by multiple perspectives. We, however, focus in a first
step on the information systems (IS) point of view which should be complemented
in future work.
2

2 Foundations
2.1 Social Media
With the success of platforms like Twitter, Facebook, or Wikipedia, the attribute
“social” has rapidly become a trend and has been (mis)used as a buzzword in many
cases (cf. Kietzmann et al. 2011). To overcome this situation, numerous efforts can
be found in IS literature aiming at establishing a common definition and categoriza-
tion scheme for social media that enables judgments on what belongs to this concept
(e. g. Boyd and Ellison 2007; Kaplan and Haenlein 2010; Kietzmann et al. 2011; Kim
et al. 2009; O’Reilly 2005; Parameswaran and A. B. Whinston 2007; Wigand, Wood,
and Mande 2010). However, still no final and clear understanding of social media
has emerged and definitions are overlapping with related terms such as social soft-
ware or Web 2.0. While the term Web 2.0 merely refers to an abstract concept, i. e.
the paradigm shift from a passive to an active and contributing way of internet us-
age (O’Reilly 2007), social media can be seen as the implementation of Web 2.0 by
a group of highly interactive “Internet-based applications that build on ideological
and technological foundations of the Web 2.0” (Kaplan and Haenlein 2010, p. 61).
Social media is used by “individuals and communities [to] share, cocreate, discuss,
and modify user-generated content” (Kietzmann et al. 2011, p. 241) within closer
and loosely joint communities, i. e. the social networks.
2.2 Social Business Intelligence
In anticipation of the literature review results (cf. next section) we could not find an
established definition of social BI in scientific literature. One of the reasons might lie
in the ongoing use of diverse related terms, such as “social media analytics”, “social
media intelligence”, “social intelligence”, and “business intelligence 2.0”. We follow
the understanding of Zeng et al. (2010, p. 15) who explicitly distinguish between so-
cial media analytics and social media intelligence and who define latter as follows:
“Social media intelligence aims to derive actionable information from social me-
dia in context rich application settings, develop corresponding decision-making or
decision-aiding frameworks, and provide architectural designs and solution frame-
works for existing and new applications (. . .).” However, in order to emphasize our
perspective of integrating social media data into a BI environment, we use the term
“social BI” for the remainder of the paper.
3 Literature review
3.1 Research Method
By conducting a literature review according to the well established methodology
by Webster and R. T. Watson (2002), we pursue two major objectives: (1) an explo-
3

ration of the research landscape of social BI and (2) the localization of the terra
incognita for further research. In order to conceptualize the topic and to identify
relevant search terms for literature selection, an explorative search with common
literature databases (Google Scholar, ScienceDirect, etc.) leaded us to a first collec-
tion of several social BI related terms, such as “business intelligence 2.0”, “social
intelligence”, “social media intelligence”, or “social media analytics”, and diverse
combinations of BI and social media terms (e. g. “social media” +“business in-
telligence”). Intentionally, we skipped the keyword “web analytics” as it refers in
most cases to the analysis of web data with the purpose of optimizing the web usage
which doesn’t comply with our understanding of social BI.
The keywords have been iteratively refined and extended during the literature
analysis process. We selected highly ranked and/or domain specific journals and
leading conferences of the last five years (2007–2012):
• Journals of the AIS Senior Scholars’ basket (Senior Scholar Consortium 2011),
i. e. European Journal of Information Systems (EJIS), Information Systems
Journal (ISJ), Information Systems Research (ISR), Journal of AIS (JAIS), Jour-
nal of MIS (JMIS), and MIS Quarterly (MISQ)
• BI and social media specific journals: Decision Support Systems (DSS), Inter-
national Journal of Business Intelligence Research (IJBIR), and Business Intel-
ligence Journal for the BI domain and suitable ACM and IEEE journals for the
social media domain
• Leading conferences: International Conference on Information Systems (ICIS),
Americas Conference on Information Systems (AMCIS), European Conference
on Information Systems (ECIS), Hawaii International Conference on System
Sciences (HICSS), Conference on Information Systems and Technology (CIST),
and Workshop on Information Technologies and Systems (WITS)
Whereas the basket and BI specific journals include a manageable amount of is-
sues and articles that enables a complete scan of titles and abstracts as suggested
by Webster and R. T. Watson (2002), we had to preselect conference papers by tracks
related to BI and social media. For ACM and IEEE journals, we conducted a key-
word search on the whole digital library as no journals focus in particular on the
social media domain. We scanned for the hits (resulting from keyword searches)
titles, abstracts, and keywords to assess the suitability of an article. Since we could
identify only few articles by this method, we subsequently conducted a keyword
search on literature databases (EBSCOhost, Scholar, ProQuest und ScienceDirect)
by using the aforementioned search terms. We completed the literature pool via a
backward search.
4

2007 2008 2009 2012 2010 2011
2
10
Journal articles (AIS basket)
Journal articles (domain specific)
Conference papers
Other
Figure 1: Literature Findings by Publication Type and Year
3.2 Analysis Results
The literature review resulted in 76 adequate articles for social BI. Not surprisingly,
due to the rather young research topic the majority has been published since 2010
(see Figure 1). Also, most articles appeared in conference proceedings and domain
specific journals, only a very few in the more generic journals of the AIS Senior
Scholars’ basket. The same is true for other domain independent IS journals – many
contributions on social media in general are published, however little papers can
be assigned to social BI. We consider the wider range of topics in those journals,
the stronger focus on theory, and longer publication processes as reasons for the
underrepresentation within our literature pool.
Overall, we identified less articles than expected that address explicitly social BI.
The majority focuses on aspects which can be summarized by the concept of “so-
cial media analytics”, i. e. applying analysis techniques to social media data (e. g.
Ebermann, Stanoevska-Slabeva, and Wozniak 2011; Gray, Parise, and Iyer 2011;
Heidemann, Klier, and Probst 2010; Lin and Goh 2011; Xu, Li, et al. 2011, as we
can only mention some examples here). Most authors describe a setting without a
BI system (and thus they do not fit into our understanding of social BI) and investi-
gate certain techniques, such as text mining or sentiment analysis. Examples can be
found in Sommer et al. (2011) or Xu, Liao, et al. (2009). Thereby, solutions for CRM
scenarios seem to be dominant, such as user profiling (Tang, Wang, and Liu 2011),
opinion mining (Venkatesh et al. 2003), or social recommendations (Arazy, Kumar,
and Shapira 2010). Some contributions analyze the impact of social media on deci-
sion support systems and processes (Heidemann, Klier, and Probst 2010; Power and
Phillips-Wren 2011).
Papers, dedicated to social BI, present an overview or a framework (e. g. Böhringer
et al. 2010; Hiltbrand 2010; Zeng et al. 2010) or discuss the application areas in
general (e. g. Bartoo 2012; Bonchi et al. 2011) or social CRM in particular (e. g.
5

Greenberg 2010; Reinhold and Alt 2011; Seebach, Pahlke, and Beck 2011; Stodder
2012). Others deal with specific aspects like a methodology for BI process improve-
ments considering social networks information (Wasmann and Spruit 2012), data
modeling aspects (e. g. Nebot and Berlanga 2010; Rosemann et al. 2012) or techni-
cal architecture. As examples for the latter aspect, Reinhold and Alt (2011) suggest
a framework of an integrated social CRM system and Rui and A. Whinston (2011)
propose a framework for a BI system based on real-time information extracted from
social broadcasting streams. Repeatedly, journal editors and authors who discuss
perspectives and trends in BI research highlight the potential, importance, and need
of social BI research and practical solutions (H. Chen 2010; Laplante 2008; Mao,
Tuzhilin, and Gratch 2011; Zeng et al. 2010; Zhang, Guo, and Yu 2011, e. g.).
4 Framework for a Social BI Research Agenda
In order to guide the derivation of a social BI research agenda systematically and
comprehensively, we developed a framework. It also assures a clear and transparent
research methodology. The research question in mind (cf. introduction) we seek for
all BI design questions that are impacted if the BI system integrates social media
data. To get a clearer understanding of this “impact” we first derived social media
characteristics that capture the differences to traditional, transactional data (which
usually serve as data sources for BI systems). In a second step we compiled and
systemized the main BI design decisions in terms of design areas. Combining both
perspectives leads to a framework that is used in the next section to articulate the
research agenda.
4.1 Social Media Characteristics
Table 1 shows the characteristics of social media relevant for the social BI discus-
sion (right column). To identify these characteristics, we selected seminal journal
articles with attention to a definition, understanding, and categorization of social
media for IS research. Elaborating the differences between Web 1.0 and Web 2.0
and the impact of this shift resulted in eight characteristics of social media data
that we consider as relevant if that data is used in other domains. We took previous
work into account which was however too generic for our purpose, i.e. the later
application in the BI domain (e. g. Schlagwein, Schoder, and Fischbach 2011, who
investigate general social IS).
6

Web 1.0 Web 2.0 Ref. Impact Characte-
ristic
Relatively sta-
ble data
High dynamics in
data updates and
volumes
1, 5,
6, 7
High data update
rates
Rapidly growing
data volume
Highly dy-
namic data
High data
volume
Standard struc-
ture in central
databases
Individual structured
data in decentralized
uniquely collected
databases or user
generated content
1, 4,
5, 6,
7
No standard data
structure, individ-
ual APIs
Unstructured or
semi structured
data
Semi or
unstruc-
tured data
Manually ente-
red meta data
Meta data automat-
ically added or sup-
ported by easy enter-
ing syntax
3, 6
Increasing sup-
port of meta data
by rich media
content
Extensive
meta data
Clear data in-
tent
Highly interpretative
on context 1, 3,
4, 5,
6, 7
No predefined
meaning of data Unknown
data qual-
ity
Standardized
QA procedures
Non- redundant
data sets
Unstructured
peer feedback
Redundancy by dis-
tribution and sharing
Hardly assessable
data quality and
relevance
Local clients
Big enterprises
as proprietary
data providers
Web as a platform
Medium sized data
providers, user built
data mashups
1, 4,
5, 6,
7
Multiple platforms
as data sources Wisdom of
the crowds
Institutional
content
User generated con-
tent
Contribution and
distribution of
user knowledge,
collaborative filte-
ring
7

Web 1.0 Web 2.0 Ref. Impact Char.
Small crowds,
relatively sta-
tic, little in-
formation on
connections
User Network
determined by
hierarchies
Massively connec-
ted, architecture
of participation, no
strict boundaries,
contact information
Bottom up network
governance, fluent
reputation
1, 3,
4, 5,
6, 7,
8
Dynamic user net-
works with highly
transient mem-
bers, information
on personal net-
works accessible
No hierarchically
fixed user position
and reputation
Easy ac-
cess to
user net-
work infor-
mation
Official and
authorized data
providing and
usage
Use of corpo-
rate or licensed
data
Personally identi-
fiable information
published on several
levels of privacy
Hardly traceable da-
ta origin, requested
copyright for plat-
forms on shared data
3
No general per-
mission to use so-
cial media data for
further analyses
Complex ques-
tions of authorship
and ownership
Unclear
legal situa-
tion
Table 1: Derivation of Social Media Characteristics
Ref.: 1. Ali-Hassan and Nevo (2009) 2. Bartoo (2012) 3. Kietzmann et al. (2011) 4. Kim
et al. (2009) 5. O’Reilly (2007) 6. Parameswaran and A. B. Whinston (2007) 7. Schlagwein,
Schoder, and Fischbach (2011) 8. Smith (2006)
Besides well known facts like growing data volumes because of frequent updates,
attention is required when reusing social media data in other domains. In such cases
data quality cannot be assured as user generated content does not pass any instance
of institutional quality control. Web 2.0 is also characterized by extensive meta data
that are automatically captured e. g. keywords are provided by hashtags or the user
location can be derived by GPS information of mobile devices. Finally, the usage
of social media data is characterized by a complex legal situation: Copyrights and
rights of publicity are easily violated, in particular in domains such as BI. Also,
the use and analysis of social media (data) is not limited to one country; therefore
different and maybe conflicting legal situations have to be taken into account.
4.2 Business Intelligence Design Areas
Although the BI domain is addressed in countless research contributions, so far no
established design framework exists which comprises all relevant design question
8

for building, using, and maintaining a BI system. Given that limitation, we have cho-
sen the work system (WS) methodology by Alter (2008) as a domain independent ap-
proach to cover all IS design areas. While the understanding of a WS encompasses
a broader view, an IS can be regarded as a special case of WS, constituted by nine
elements. We adapt these elements to our context by rearranging, merging, and
detailing them, resulting in the following BI design areas:
Users & customers: The first building block includes all user and customer re-
lated design questions, regarding e. g. user profiles, user training concepts,
and the communication and interaction with customers.
Products & services: This design area describes which (and how) products and
services, such as reports, dashboards, analytical applications, and alerting ser-
vices are provided by the BI system.
Processes: BI processes support the gathering, storing, accessing, and analyzing
of business relevant information and can be considered as further BI design
objects.
Data: In light of the main purpose of a BI system (to provide analytical information)
many design questions have to be addressed when building such an IS. Conse-
quently, we break down this work system element further by combining it with
the data management framework, developed by the Data Management Associ-
ation (DAMA International 2008). This framework suggests ten data manage-
ment functions, from which we select four as suitable in our context: a) data
architecture and development (which among others includes data analysis and
modeling), b) data security management, c) meta data management and d) data
quality management.
Information & communication technology (ICT): Slightly different to Alter (2008),
we summarize in this topic all “technical” design questions, many of them
about hardware and software.
Techniques: This element includes all methods and practices used in the BI sys-
tem, such as ETL (stands for Extraction, Transformation, Loading) procedures
or modeling techniques for slowly changing dimensions. Notably analysis tech-
niques are relevant BI design objects.
Governance: The building block covers the organizational structures for BI (e. g.
represented by a BI competence center) with roles and responsibilities, prin-
ciples and guidelines for BI, and further aspects of an “environment” (as the
element has been noted by Alter (ibid.) originally), in particular the regulations
that apply to an organization.
9

Users & customers
Products & services
Processes
Data architecture/
development
Data security
management
Meta data
management
Data quality
management
ICT
Techniques
Governance
Strategy
Data
Highly dynamic data      
High data volumes   
Semi or unstructured data    
Extensive meta data     
Unknown data quality      
Wisdom of the crowds       
User network information       
Unclear legal situation        
Coverage by literature    
Table 2: Framework for the Social BI Research Agenda
Strategy: Finally, the BI strategy as a concept to systematically pursue long range,
enterprise wide, aggregate goals in sync with business and IT strategy (cf.
Dinter and Winter 2009), completes the relevant BI design objects.
5 Direction for Future Research on Social BI
The two dimensions (social media characteristics and BI design areas) serve now
as the framework for articulating a social BI agenda. Table 2 combines both per-
spectives in a matrix. Each cell includes the information to which extent a certain
social media characteristic (in that row) has impact on a BI design area (in that
column). In particular, impact means in this context that modified or new artefacts
(methods, models, etc.) are needed considering the social media (data) properties.
A filled square stands for significant impact, an empty square for some impact and
no square for no impact. The last row consolidates our insights from the literature
review and shows how comprehensive each BI design area is already addressed by
previous social BI literature. Comparing the impact of social media characteristics
on BI design areas with this coverage supports the identification of current research
gaps.
10

5.1 Discussion and identification of research topics
Due to space limitations we cannot explain and discuss each single cell in detail.
Following, we discuss shortly our insights for every BI design area and highlight
some promising research topics.
Users & customers: Adding social media data to the pool of available data for
analysis purposes can attract new BI users within an organization. The em-
phasis on social interaction among users (cf. J. Chen et al. 2009) or revised
training concepts (enabling users to work with social media (data)) are further
examples for new requirements in this design area. If the (social) BI system
allows and encourages the interaction with customers (in social networks, for
example) also additional support is necessary.
Products & services: With the availability of social media data and “the wisdom of
the crowds” new or extended BI products and services can be offered. In this
context interesting research questions are how to include social media data
and analysis results (such as network structures, sentiment analysis results,
etc.) in BI products and how products can be designed that combine “tradi-
tional” BI data with social media data. Previous work (cf. section 3) already
suggests many usage scenarios and illustrates in some cases how (internal) BI
products can constitute the basis for (external) product and service offerings
to the customer (e. g. Bonchi et al. 2011; Stodder 2012). Potential limitations
regarding data quality or data security might also require a redesign of prod-
ucts and/or services (and of service level agreements respectively).
Processes: Some BI processes should be adapted if social media data is integrated.
The research need is rather low here – in contrast to the case, when an orga-
nization uses the BI system in order to interact via social media channels with
customers. Then new processes are required and stimulate further research.
Data: Almost all social media characteristics have impact on the functions of data
architecture management and of data development. There is a broad range
of BI design questions that have to be addressed differently if not only tra-
ditional transactional data, but also social media data is processed. This is
true for data integration, for data modelling (both, relational and multidimen-
sional), and for further functions. We illustrate the impact by the example
of information requirements engineering: Established methodologies will be
applicable only to a certain extent if social media data is included. How can
(business) users articulate their need for information and have an understand-
ing of future use cases if they have a rather vague or no knowhow of external
social media data? How can the information need be mapped with available
information (which can – cf. the characteristic “highly dynamic data”– change
11

frequently, thus availability cannot be guaranteed over a period of time)? Fi-
nally, the challenge to identify appropriate data sources and legal and quality
aspects need to be addressed. Sketching these few questions already empha-
sizes the urgent need for contributions by the scientific community.
Some social media characteristics require also adaptions for the remaining
data management functions (data security, meta data and data quality man-
agement). Interestingly, two properties of social media can have opposite
effects on data quality. While some Web 2.0 properties can result in low or
unknown data quality, the so-called “wisdom of the crowds” can contribute to
high quality data. Wikipedia represents a convincing example for the setting
that user generated content, the sharing, and the mutual control can result in
increasing quality of that data (Giles 2005).
We found in our literature review only some previous work about “social me-
dia data management”. Bonchi et al. (2011), Rui and A. Whinston (2011), and
Stodder (2012) discuss various aspects of data acquisition, processing, and
integration for social BI and can serve as an appropriate starting point for
further research in this topic. In addition, Nebot and Berlanga (2010) and
Rosemann et al. (2012) focus on data modeling.
Information & communication technology (ICT) In particular, the high data vol-
umes and frequent update rates of social media data have impact on the ICT.
Surprisingly, there are very few scientific contributions available that provide
adequate support, e. g. for a (technical) reference architecture or for data in-
tegration. Unstructured data also requires specific software (and potentially
hardware) for data processing. The currently very popular concept of “big
data” should offer support for this BI design area.
Techniques: Similar social media characteristics (high data volume and unstruc-
tured data, but also new content provided by social media) result in a con-
siderable research demand for analysis techniques. In contrast to ICT, these
research gaps are broadly covered in many publications (cf. section 3). How-
ever, further techniques, such as for ETL, are not addressed so far.
Governance: Integrating social media data in a BI system might demand for new
roles and responsibilities. The impact on the definition and control of princi-
ples and guidelines becomes obvious in the context of data quality and meta
data management. The most interesting and demanding research need might,
however, arise in different legal requirements when data is used from or dis-
tributed to social media, even more a challenge in face of the international
context of social media.
Strategy: The BI strategy aims at supporting the business strategy optimally. Us-
ing the capabilities of social media (data) offers many means to contribute to
12

an organization’s business goals, such as customer satisfaction. As a strategy
process also covers the control of strategic activities, an interesting research
question would be, to which extent social BI contributes to the organizational
performance. For example, the considerations in Larson and R. Watson (2011)
are not yet BI specific and might be transferred to the social BI context. Finally,
also rather technical oriented strategic decisions can be affected by social me-
dia properties (high data volume, etc.).
Table 1 illustrates that (1) all BI design areas are affected by social media and
that (2) previous research does by far not address all open research questions since
it focuses mainly on selected topics. Both findings emphasize the need for a social
BI agenda as sketched in the paper at hand. Having the restriction in mind that
not all research questions can be discussed in detail here, we would like to call the
researchers’ attention to two topics as a potential starting point:
• What are adequate products and services for social BI?
In our opinion addressing this research question has two benefits. It elabo-
rates the added value for organizations when including social media data in
BI systems (and therefore in decisions) and supports the feasibility (and prof-
itability) assessment. Also, it can be used to guide further research, as IS
research in general and in particular for social BI should be mainly driven by
business requirements.
• How should information requirements engineering be designed that deals with
social media data?
This research question needs to follow the aforementioned one. It also sup-
ports organizations shifting from previous, rather on internal and historical
data based analysis to decisions based on a comprehensive and very actual
data including valuable customer information and outside-looking-in view of
an organization’s brands, products, services, and competitors (Stodder 2012).
As already mentioned, social BI relies as a data based decision support technique
heavily on its main asset – the data. Consequently, data management practices need
to be adapted and extended accordingly and can be regarded as a precondition for
organizations to take the integration of social media data in BI solutions as given in
future.
6 Conclusions
The ongoing high relevance of BI and social media and an increasing demand in
practice to integrate both domains motivate the articulation of a social BI research
agenda. We derived the corresponding research areas by means of a literature
13

review and by using a framework that allows the systematic and comprehensive
consideration of all relevant research questions for social BI.
In future research we plan to overcome limitations of this paper by broadening the
literature review and by evaluating the research agenda with focus groups (practi-
tioners, vendors, etc). Besides a further detailing of the research agenda, we plan
to sketch a research landscape that extends the chosen IS perspective and investi-
gates the interplay with related research domains, as social BI research calls for a
highly integrated multidisciplinary approach (cf. Zeng et al. 2010).
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