=Paper=
{{Paper
|id=Vol-2938/paper-PROBLEMS-11
|storemode=property
|title=Value Creation from Data - Why is this a BPM Problem?
|pdfUrl=https://ceur-ws.org/Vol-2938/paper-PROBLEMS-11.pdf
|volume=Vol-2938
|authors=Shazia Sadiq,Ida Asadi Someh,Tianwa Chen,Marta Indulska
|dblpUrl=https://dblp.org/rec/conf/bpm/SadiqSCI21
}}
==Value Creation from Data - Why is this a BPM Problem?==
https://ceur-ws.org/Vol-2938/paper-PROBLEMS-11.pdf
Value Creation from Data – Why is this a BPM Problem?
Shazia Sadiq1, Ida Asadi Someh2, Tianwa Chen1, Marta Indulska2
1 School of Information Technology and Electrical Engineering,
The University of Queensland, Brisbane, Australia
shazia@itee.uq.edu.au, tianwa.chen@uq.edu.au
2 Business School, The University of Queensland, Brisbane, Australia
i.asadi@business.uq.edu.au, m.indulska@business.uq.edu.au
Abstract. The data deluge and associated technological proliferations have
significantly changed the landscape of how businesses are run. These changes, in
turn, necessitate profound changes in how business processes are managed. Yet,
as organisations aspire towards embracing data-driven approaches both
technically and culturally, the socio-technical barriers for value creation from
data are becoming increasingly evident. This paper highlights the important role
that BPM research and practice can play in lifting those barriers.
Keywords: Value Creation, Data-driven Organization, Technology Adoption,
Business Analytics, Data Quality
1 Why is value creation from data so hard?
The technical advancements in data science and machine learning, as well as the third
wave of AI [10], have raised expectations of business transformation. However, how
organisations exploit and adapt to these advancements remains an open question for
now. Extant research from Information Systems provides many insights in the context
of value creation from IT assets and capabilities [25], but value creation from data
challenges many of those findings. Furthermore, in the current characterisations of big
data, i.e., the so-called Vs,, a number of well-established data management practices
are no longer valid [38], leaving organisations to face the complex and unstable reality
of the data-driven promise. A number of aspects have contributed to these difficulties:
First, data re-purposing [41], has resulted in a distance between the design and use
intentions of the data, and is causing a fundamental shift in the way data is managed
and used. Traditional data modelling and design principles are thus challenged in the
context of data re-purposing and reuse. Yet, data re-purposing represents an
unprecedented opportunity for organisations to (re)create new value from existing data
assets, highlighting the central importance of effective data sharing [39] and use [8]
from the perspective of a socio-technical organisation.
Second, there is increasing evidence that data scientists spend over 80% of their time
tackling problems related to data access, linkage, and cleaning [7]. There is a plethora
of examples of situations where inadequate handling of data complexity has resulted in
Copyright © 2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
disastrous consequences. For example financial and reputational damage [15], and
issues of social justice and public harm, such as propagation of biases in data into
speech analyses [44], or data discrepancies and algorithmic assumptions that resulted
in discrimination [17]. At the very least, the under-estimation of data curation and
preparation needs results in time and cost over-runs in business analytics projects.
Third, data pipelines constructed by data engineers and scientists often lack
explainability due to the use of complex computational, statistical, and machine
learning techniques [34]. This causes, on the one hand, efficiency concerns due to poor
transferable and repeatability prospects, and, on the other hand, deterioration in the trust
of analytical results by business stakeholders and end-users. While literature on
explainability [23] is advancing, a practice of transparency is still not that prevalent in
the data science and analytics workforce [42]. These divides can create functional and
cultural friction between the teams, and a disconnect between how consumers think
their data should be handled and how it is actually treated [6]. While legislative and
regulatory frameworks are playing catch up, currently most are untested for adequacy,
and may simply be viewed as an increased compliance burden on organisations
(especially SMEs) thereby limiting the uptake of data-driven solutions and innovations.
Challenges aside, there is no doubt that data-driven organisations that overcome the
socio-technical barriers will emerge as winners in an increasingly competitive
environment across all major sectors. The question for BPM research, vendor and
practitioner communities is: what role can BPM play in helping organisations gain
value from their data assets? In the section below, we highlight some of these
opportunities.
2 Can BPM Help?
One central question for organisations that intend to pervasively use data for value
creation relates to structuring of the analytics teams in ways that can transform the
organisation into a data-driven entity [11]. However, this organisation-wide
transformation is challenging in practice due to existence of functional silos and
difficulties in creating proximity between analytics teams and business or domain
groups [36]. The traditional function-based approach is where a central unit is
established to serve multiple business units with their various analytics needs [16], and
this approach generally leads to a strained relationship between analytics-oriented and
business groups, because the former can rarely meet every demand of the latter [29].
Instead, data-driven work must be rooted in a pervasive enterprise-wide approach in
which analytics is woven into the fabric of the organisation. Such an approach demands
breaking down organisational (unit) boundaries to facilitate analytics teams and
business groups to develop a common language to work collaboratively, and iteratively,
and to integrate their knowledge into improved data-driven solutions, products and
processes. This approach ultimately results in close ties between analytics teams and
business groups, common language and motivation to interact, which brings about
organisation wide change. To enable this proximity, organisations typically go beyond
setting up centres of excellence that focus on enterprise analytics capabilities to build
cross-disciplinary analytics teams that embed themselves within process-oriented
groups.
BPM Opportunity: Process orientation has long advocated the need for
organisation-wide thinking and breaking down of functional silos, and has been shown
to be positively associated with firm performance [20]. How can process orientation
research help set up organisations for data-driven work?
Despite the importance of effective use, many firms struggle to achieve it.
Historically we know that, “...effective use is one of the greatest challenges for BI
[Business Intelligence] systems. … Despite increasing investments in BI systems,
many organisations are still unable to attain the desired success … due to
underutilization and ineffective use” [4]. One theory that explains how organisations
can use data and analytics systems more effectively is the Theory of Effective Use
(TEU) [8]. TEU suggests that effective use involves three dimensions: (a) transparent
interaction: seamlessly accessing the representations offered by a system, (b)
representational fidelity: obtaining more accurate representations from the system, and
(c) informed action: taking actions based on faithful representations.
We know that data can contain unexpected insights, and hence effective use of
analytics systems can help organisations reap unexpected gains. However, the speed
with which these insights can be effectively used is critical. Agility in value creation
from data can be challenged with the way in which analytical insights are delivered,
embedded and utilized within business. Organisational capability to support data-driven
process design and improvement becomes essential. IT business literature advocates
for a process perspective on value of IT resources and capabilities [25, 28].
Traditionally, analytics team outputs might be static reports or dashboards that exist
separate from business processes and might not meet the specific decision requirements
of managers and employees. However, data assets and capabilities generate business
value when they are embedded within organisational processes. The challenge is how
these operational and informational capabilities and experiences are blended into an
integrated application or platform that promotes user engagement and empowers users
with evidential decision making [40].
BPM Opportunity: Agile value creation from data requires new approaches for
process design and improvement, in which data-driven insights can be embedded into
processes in a timely and agile way that facilitate, extend and improve analytics-driven
user experiences. How can process design and adaptation research help facilitate agile
use of data-driven insights?
A notable process design choice related to how organisations integrate the insights
with existing processes or develop new tools and processes is between (1) the
augmentation of users’ capabilities with algorithmic insights and recommendations to
undertake evidential decision making and (2) the oversight mechanisms under which
algorithmic insights and recommendations could be monitored and contested. The
counter part is the design of fully automated decision-making, where algorithmic agents
decide and act independently. There is growing evidence that this can create tensions.
While algorithms can perform structured tasks and process massive datasets in real
time, humans usually fare better with less structured tasks, especially ones that require
creativity and interpretation [5]. Optimally, human-machine configurations should
leverage both agents’ strengths in a complementary manner. However, finding the right
balance between automation and human involvement is not easy and practical
guidelines are still emerging [30].
Whereas, process mining has proven a valuable approach in providing insights into
the actual business process from organisational and case perspectives, the bulk of the
advancements, understandably, are on the support of BPM [33], where process mining
can enable evidence-based BPM [2], be used as a tool for Delta analysis and
conformance testing [1] or to detect discrepancies, improve process, and provide better
support (e.g. in the (re)design and diagnosis phases) for BPM life-cycle [26]. However,
the rich body of knowledge on process mining tools and techniques can be translated
into several novel domains including those that support value creation from data. For
example, process mining can also reveal how people and/or procedures actual work [1]
and provide understandable models that enables experts to understand the actual
workflow and to detect specific user behaviours patterns [13]. The literature highlights
the value of using process mining to understand human behaviors [33]. For example,
model understanding has explored using process mining on eye tracking data (i.e., one
of the physiological variables used as a technique to reflect the changes in cognition
[27]) to find reading patterns in hybrid processes of DCR-HR [3], sensemaking
behaviors in dual artefacts of business processes and rules [9], on domain and code
understanding tasks from the developers’ interactions [24], as well as on discovering
data workers interaction behaviors and strategies in finding data quality issues data
curation work [18].
BPM Opportunity: Process mining offers a rich set of methods and tools that can
be used to understand human behaviour processes as well as process that have a mix of
automated and human tasks. How can process mining help achieve the right balance
between automation and human involvement in data driven processes?
Related to this opportunity is another that stems from the concept of reference
models. Reference models are blueprints of best practice with the aim of reusability.
They were popularised in the early 90s (see e.g. [35] and, since then, have been applied
in a broad range of contexts [14]. The use of reference models has been associated with
several benefits, including process improvement outcomes and risk reduction [32]. All
data work, from data curation through to the development of AI models, is in itself a
process. There is evidence that suggests that data curation processes are currently done
in an ad-hoc manner [18, 19] and that process mining is helping to uncover a closer to
optimal approach [18]. High profile failures in developing AI solutions, contrasted
against notable successes [43], also suggest that there are best practices to be learned
from.
BPM Opportunity: Process reference models offer a blueprint for best practice and
enable organisations to improve their process performance. How can process reference
models be used to capture and share best practice approaches to value creation from
data?
3 Is the problem worth solving?
Evidence based decision making is not a new concept and has been the flagship
approach for many sectors such as clinical research [12], policy reform [46] and
financial markets [22]. However, we highlighted above the characteristics and
challenges in evidential decision making with big data and complex black-box
algorithms. In fact, advancements in machine learning (ML) and artificial intelligence
(AI) are being valued at contributing up to US$15.7 trillion [31] to the global economy
by 2030. AI is enabled by data [45] and the need for robust mechanisms for ‘generating,
sharing and using data in a way that is accessible, secure and trusted’ is clear. Indeed,
data gone wrong is acknowledged as the biggest risk factor for AI and other emerging
technologies [21]. Unless organisations can see business value in data-driven work, the
opportunity for responsible [37] and agile value creation from data will not materialise.
The authors of this paper posit that BPM research and practice holds a significant
amount of knowledge capital that can be harnessed to contribute to the problem of value
creation from data. We call the BPM community to assemble behind this exciting and
interesting challenge of our times.
Acknowledgments. This work is supported by the ARC Industry Training Centre for
Information Resilience – CIRES - IC200100022.
References
1. Aalst, W.M.P. van der: Business alignment: using process mining as a tool for Delta
analysis and conformance testing. Requir. Eng. 10, 3, 198–211 (2005).
2. Aalst, W.M.P. van der: Process Mining: Overview and Opportunities. ACM Trans.
Manag. Inf. Syst. 3, 2, 1–17 (2012).
3. Abbad Andaloussi, A. et al.: Exploring the Understandability of a Hybrid Process
Design Artifact Based on DCR Graphs. In: Reinhartz-Berger, I. et al. (eds.) Enterprise,
Business-Process and Information Systems Modeling. pp. 69–84 Springer International
Publishing, Cham (2019).
4. Ain, N. et al.: Two decades of research on business intelligence system adoption,
utilization and success – A systematic literature review. Decis. Support Syst. 125, 113113
(2019).
5. Asatiani, A. et al.: Implementation of automation as distributed cognition in knowledge
work organizations: Six recommendations for managers. In: 40th International Conference on
Information Systems, ICIS 2019. Association for Information Systems (2020).
6. Australian Competition and Consumer Commission: Digital platforms inquiry - final
report, https://www.accc.gov.au/publications/digital-platforms-inquiry-final-report, last
accessed 2021/05/19.
7. Belkin, R., Patil, D.: Everything We Wish We’d Known About Building Data Products
| First Round Review, https://review.firstround.com/everything-we-wish-wed-known-about-
building-data-products, last accessed 2021/05/19.
8. Burton-Jones, A., Grange, C.: From use to effective use: A representation theory
perspective. Inf. Syst. Res. 24, 3, 632–658 (2012).
9. Chen, T. et al.: Sensemaking in Dual Artefact Tasks – The Case of Business Process
Models and Business Rules. In: Dobbie, G. et al. (eds.) Conceptual Modeling. pp. 105–118
Springer International Publishing, Cham (2020).
10. Daugherty, P.R., Wilson, H.J.: Human + Machine: Reimagining Work in the Age of
AI. Harvard Business Press (2018).
11. Davenport, T.H., Bean, R.: Big Companies Are Embracing Analytics, But Most Still
Don’t Have a Data-Driven Culture, https://hbr.org/2018/02/big-companies-are-embracing-
analytics-but-most-still-dont-have-a-data-driven-culture, (2018).
12. Engebretsen, E. et al.: Unpacking the process of interpretation in evidence‐based
decision making. J. Eval. Clin. Pract. 21, 3, 529–531 (2015).
13. Fernández-Llatas, C. et al.: Process Mining for Individualized Behavior Modeling
Using Wireless Tracking in Nursing Homes. Sensors. 13, 11, 15434–15451 (2013).
14. Fettke, P. et al.: Business process reference models: survey and classification. In:
Business Process Management Workshops. pp. 469–483 Springer (2006).
15. Gillespie, N. et al.: Achieving Trustworthy AI: A Model for Trustworthy Artificial
Intelligence. The University of Queensland and KPMG, Australia (2020).
16. Gregory, R.W. et al.: IT consumerization and the transformation of IT governance. 39.
17. Hajian, S. et al.: Algorithmic bias: From discrimination discovery to fairness-aware
data mining. Presented at the ACM SIGKDD ICKDDM.
18. Han, L. et al.: On Understanding Data Worker Interaction Behaviors | Proceedings of
the 43rd International ACM SIGIR Conference on Research and Development in Information
Retrieval.
19. Hey, T., Trefethen, A.: The Data Deluge: An e-Science Perspective. In: Berman, F. et
al. (eds.) Wiley Series in Communications Networking & Distributed Systems. pp. 809–824
John Wiley & Sons, Ltd, Chichester, UK (2003).
20. Kohlbacher, M., Reijers, H.A.: The effects of process‐oriented organizational design
on firm performance. Bus. Process Manag. J. 19, 2, 245–262 (2013).
21. Korolov, M.: AI’s biggest risk factor: Data gone wrong,
https://www.idginsiderpro.com/article/3254693/ais-biggest-risk-factor-data-gone-
wrong.html, last accessed 2021/05/19.
22. Leuz, C.: Evidence-based policymaking: promise, challenges and opportunities for
accounting and financial markets research. Account. Bus. Res. 48, 5, 582–608 (2018).
23. Li, X.H. et al.: A Survey of Data-driven and Knowledge-aware eXplainable AI. IEEE
Trans. Knowl. Data Eng. 1–1 (2020).
24. Loannou, C. et al.: Mining reading patterns from eye-tracking data: method and
demonstration. Softw. Syst. Model. 19, 345–369 (2020).
25. Melville, N. et al.: Review: Information technology and organizational performance:
An integrative model of IT business value. MIS Q. 28, 2, 283–322 (2004).
26. Netjes, M. et al.: Supporting the full BPM life-cycle using process mining and
intelligent redesign. Contemp. Issues Database Des. Inf. Syst. Dev. 100–132 (2007).
27. Paas, F. et al.: Cognitive Load Measurement as a Means to Advance Cognitive Load
Theory. Educ. Psychol. 38, 1, 63–71 (2003). https://doi.org/10.1207/S15326985EP3801_8.
28. Pang, M.-S. et al.: IT Resources, Organizational Capabilities, and Value Creation in
Public-Sector Organizations: A Public-Value Management Perspective. J. Inf. Technol. 29, 3,
187–205 (2014).
29. Peppard, J.: Rethinking the concept of the IS organization. Inf. Syst. J. 28, 1, 76–103
(2018).
30. Rai, A. et al.: Editor’s comments: next-generation digital platforms: toward human-AI
hybrids. MIS Q. 43, 1, iii–x (2019).
31. Rao, A.S., Verweij, G.: Sizing the prize: what’s the real value of AI for your business
and how can you capitalise? PricewaterhouseCoopers Australia (2017).
32. Rosemann, M., van der Aalst, W.M.: A configurable reference modelling language.
Inf. Syst. 32, 1, 1–23 (2007).
33. Rubin, V.A. et al.: Process mining can be applied to software too! In: Proceedings of
the 8th ACM/IEEE International Symposium on Empirical Software Engineering and
Measurement - ESEM ’14. pp. 1–8 ACM Press, Torino, Italy (2014).
34. Samek, W. et al.: Explainable artificial intelligence: Understanding, visualizing and
interpreting deep learning models. 1, 10 (2017).
35. Scheer, A.-W.: Business Process Engineering: Reference Models for Industrial
Enterprises. Springer-Verlag, Berlin Heidelberg (1994).
36. Someh, I. et al.: Reconceptualizing synergy to explain the value of business analytics
systems. J. Inf. Technol. 34, 4, 371–391 (2019).
37. Stoyanovich, J. et al.: Responsible data management. Proc. VLDB Endow. 13, 12,
3474–3488 (2020).
38. Surbakti, F.P.S. et al.: Factors influencing effective use of big data: A research
framework. Inf. Manage. 57, 1, 103146 (2020).
39. Wixom, B.H. et al.: Data Sharing 2.0: New Data Sharing, New Value Creation,
https://cisr.mit.edu/publication/2020_1001_DataSharing_WixomSebastianGregory, last
accessed 2021/05/18.
40. Wixom, B.H., Ross, J.W.: How to Monetize Your Data. MIT Sloan Manag. Rev. 58,
3, (2017).
41. Zhang, R. et al.: Discovering Data Quality Problems. Bus. Inf. Syst. Eng. 61, 5, 575–
593 (2019).
42. Beijing Consensus on Artificial Intelligence and Education - UNESCO Digital Library,
https://unesdoc.unesco.org/ark:/48223/pf0000368303, last accessed 2021/05/19.
43. Delivering AI programs in the public sector: guidelines for government leaders,
https://www.sap.com/documents/2020/11/48c79bd1-b97d-0010-87a3-c30de2ffd8ff.html,
last accessed 2021/05/19.
44. TayTweets: Microsoft AI bot manipulated into being extreme racist upon release -
ABC News, https://www.abc.net.au/news/2016-03-25/microsoft-created-ai-bot-becomes-
racist/7276266, last accessed 2021/05/19.
45. The effective and ethical development of artificial intelligence: An opportunity to
improve our wellbeing, https://acola.org/hs4-artificial-intelligence-australia/, last accessed
2021/05/19.
46. Tools for Institutional, Political, and Social Analysis of Policy Reform: A Sourcebook
for Development Practitioners. World Bank Publications (2007).