THE ROLE OF BIG DATA STEWARDSHIP AND ANALYTICS AS ENABLERS OF CORPORATE PERFORMANCE MANAGEMENT

MEDEIROS, MAURICIUS M.; MAÇADA, ANTONIO C. G.; HOPPEN, NORBERTO

doi:10.1590/1678-6971/eRAMD210063

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Special Issue • RAM, Rev. Adm. Mackenzie 22 (06) • Nov-Dec 2021 • https://doi.org/10.1590/1678-6971/eRAMD210063 copy

THE ROLE OF BIG DATA STEWARDSHIP AND ANALYTICS AS ENABLERS OF CORPORATE PERFORMANCE MANAGEMENT

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Purpose:

Digital transformation and big data (BD) have generated a real revolution in data-driven management. While BD improves corporate performance management (CPM), this also implies increasing exposure to risks at various BD’s life cycle stages. As regulatory requirements and the need for database analysis in various business areas increase, the organization must establish definitions, policies, and processes to ensure data quality in order to protect and leverage its data to obtain a competitive advantage. Therefore, understanding data stewardship (DS) and business analytics (BA) is essential for business management. The purpose of this study is to analyze the role of BD, DS, and BA as enablers of CPM.

Originality/value:

We contribute to the theory by conceptualizing, validating, and discussing the DS construct and by highlighting its role together with BA in the relationship between BD and CPM. The evidence in this study indicates that, in practice, DS and BA are critical paths for organizations to obtain better control over the effects that BD can have on business performance management.

Design/methodology/approach:

A survey was conducted with 312 managers who use big data analytics (BDA) in Brazilian organizations. The data were analyzed through structural equation and mediation tests.

Findings:

The findings suggest that DS and BA, both alone and jointly, can transmit the BD effect to CPM. However, a better level of model adjustment is obtained when there is a serialized multi-mediation in this relationship, being DS an antecedent to BA.

KEYWORDS:
Big data; Business analytics; Data governance; Data stewardship; Corporate performance management

Total informants/organizations (n = 312)
Age (years)	(%)	Professional experience (years)	(%)	Frequency of BDA use	(%)
≤ 25	8	≤ 2	3	1 - Rarely	1
26 ≤ x ≤ 35	44	2 ≤ x ≤5	11	2	6
36 ≤ x ≤ 45	32	6 ≤ x ≤ 10	31	3	19
46 ≤ x ≤ 55	12	11 ≤ x ≤ 15	25	4	26
x > 55	4	x >15	30	5 - Very often	48
Company size	(%)	Economic sector	(%)	Geographic region	(%)
Small	24	Services	61	North	1
Medium	31	Industry	14	Northeast	6
Large	45	Trade	6	Midwest	8
		Government	12	Southeast	39
		Others	7	South	46

Variable¹ 1 Big data (BD); business analytics (BA); data stewardship (DS); corporate performance management (CPM).	Indicators² 2 Cronbach's Alpha (α); composite reliability (CR); average variance extracted (AVE).			HTMT Criterion
	α	CR	AVE	BA	BD	CPM	DS
BA	.875	.914	.728
BD	.811	.876	.638	.518
CPM	.910	.930	.690	.578	.332
DS	.863	.897	.593	.639	.540	.561

Hypothesis	Path	Coefficient	T-statistics	p-values	Effect size (f²)	Cohen's f² analysis	Empirical evidence
H1	BD -> CPM	-.006	0.009	.927 ns	.000	Not relevant	Not supported
H2	BD -> DS	.456	9.707	.000^* ***** p < 0.001, ns - not significant.	.263	Medium	Supported
H3	BD -> BA	.229	4.097	.000^* ***** p < 0.001, ns - not significant.	.064	Small	Supported
H4	DS -> BA	.458	9.062	.000^* ***** p < 0.001, ns - not significant.	.258	Medium	Supported
H5	DS -> CPM	.309	5.284	.000^* ***** p < 0.001, ns - not significant.	.090	Small	Supported
H6	BA -> CPM	.346	5.131	.000^* ***** p < 0.001, ns - not significant.	.115	Small	Supported

Model	Hypothesis	SRMR	Direct effect	Indirect effect	Total effect	Mediation type
Simple mediations	H7a: BD -> DS -> CPM	.066	.073 ns	.215^* ***** p < 0.001, ns - not significant.	.288^* ***** p < 0.001, ns - not significant.	Complete
	H7b: BD -> DS -> BA	.072	.229^* ***** p < 0.001, ns - not significant.	.209^* ***** p < 0.001, ns - not significant.	.438^* ***** p < 0.001, ns - not significant.	Partial
	H8a: BD -> BA -> CPM	.054	.074 ns	.213^* ***** p < 0.001, ns - not significant.	.287^* ***** p < 0.001, ns - not significant.	Complete
	H8b: DS -> BA -> CPM	.062	.307^* ***** p < 0.001, ns - not significant.	.194^* ***** p < 0.001, ns - not significant.	.501^* ***** p < 0.001, ns - not significant.	Partial
Multiple type A	H7a, H8a: BD -> \|DS+BA\| -> CPM	.106	-.007 ns	.294^* ***** p < 0.001, ns - not significant.	.287^* ***** p < 0.001, ns - not significant.	Complete
Multiple type B	H7b, H8b: BD -> DS->BA -> CPM	.059	-.006 ns	.293^* ***** p < 0.001, ns - not significant.	.287^* ***** p < 0.001, ns - not significant.	Complete

Construct (references)	Item	Load	Item description
Big data	BD-01	.756	In the organization's business environment.... .... handles a large volume of data.
Big data	BD-02	.820	... data is generated (collected, created) at high speed.
Alharthi et al. (2017)Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons , 60(3), 285-292. and (Chen et al. (2014)Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209..	BD-03	.835	... it deals with a wide variety (types and formats) of data.
	BD-04	.782	... deals with great variability of data.
Business analytics	BA-01	.799	Business data are .... ... analyzed using descriptive techniques (categorization, consolidation, and classification) to convert them into useful information for managers.
Delen and Zolbanin (2018)Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90, 186-195. and Duan et al. (2020)Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686..	BA-02	.847	... explored to find out the causes of a specific problem.
	BA-03	.863	... to identify behaviors and predict trends.
	BA-04	.900	... to identify the best alternatives and optimize objectives.
Data stewardship	DS-01	.725	In the organization it is adopted... ... governance policies and data management formally established.
Fleckenstein and Fellows (2018)Fleckenstein, M., & Fellows, L. (2018). Implementing a data strategy. In M. Fleckenstein & L. Fellows, Modern data strategy (pp. 35-54). Cham: Springer ., Lillie and Eybers (2018)Lillie, T., & Eybers, S. (2018, August). Identifying the constructs and agile capabilities of data governance and data management: A review of the literature. In K. Krauss, Turpin, M. Naude F. (eds.), Locally Relevant ICT Research. IDIA 2018. Communications in Computer and Information Science (pp. 313-326). Cham: Springer., Khatri and Brown (2010)Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152., Koltay (2016)Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal, 42(4), 303-312., Plotkin (2020)Plotkin, D. (2020). Data stewardship: An actionable guide to effective data management and data governance. London, UK: Elsevier., Rosenbaum (2010)Rosenbaum, S. (2010). Data governance and stewardship: Designing data stewardship entities and advancing data access. Health Services Research, 45(5), 1442-1455., Surbakti et al. (2020)Surbakti, F. P. S., Wang, W., Indulska, M., & Sadiq, S. (2020). Factors influencing effective use of big data: A research framework. Information & Management , 57(1), 103-146., and Tallon et al. (2013)Tallon, P. P., Ramirez, R. V., & Short, J. E. (2013). The information artifact in IT governance: Toward a theory of information governance. Journal of Management Information Systems , 30(3), 141-178..	DS-02	.790	... a data architecture that makes it possible to identify what data is collected and in which processes it is used.
	DS-03	.775	... an organizational structure or management committee responsible for data quality and guidance to BDA users.
Data stewardship	DS-04	.721	... BDA in an accessible corporate environment, in a shared way among business areas.
Fleckenstein and Fellows (2018)Fleckenstein, M., & Fellows, L. (2018). Implementing a data strategy. In M. Fleckenstein & L. Fellows, Modern data strategy (pp. 35-54). Cham: Springer ., Lillie and Eybers (2018)Lillie, T., & Eybers, S. (2018, August). Identifying the constructs and agile capabilities of data governance and data management: A review of the literature. In K. Krauss, Turpin, M. Naude F. (eds.), Locally Relevant ICT Research. IDIA 2018. Communications in Computer and Information Science (pp. 313-326). Cham: Springer., Khatri and Brown (2010)Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152., Koltay (2016)Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal, 42(4), 303-312., Plotkin (2020)Plotkin, D. (2020). Data stewardship: An actionable guide to effective data management and data governance. London, UK: Elsevier., Rosenbaum (2010)Rosenbaum, S. (2010). Data governance and stewardship: Designing data stewardship entities and advancing data access. Health Services Research, 45(5), 1442-1455., Surbakti et al. (2020)Surbakti, F. P. S., Wang, W., Indulska, M., & Sadiq, S. (2020). Factors influencing effective use of big data: A research framework. Information & Management , 57(1), 103-146., and Tallon et al. (2013)Tallon, P. P., Ramirez, R. V., & Short, J. E. (2013). The information artifact in IT governance: Toward a theory of information governance. Journal of Management Information Systems , 30(3), 141-178..	DS-05	.799	... practices to ensure the protection of the data used in BA in the management or operation of the business.
	DS-06	.807	... data sharing to reduce “islands” or “silos” of data in business areas.
Corporate performance management			Big data analytics...
	CPM-01	.843	... assists in the formulation and planning of the business strategy.
	CPM-02	.792	... assists in the control and evaluation of business performance.
Acito and Khatri (2014)Acito, F., & Khatri, V. (2014). Business analytics: Why now and what next? Business Horizons, 57(5), 565-570., Chen et al. (2012)Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188., Mikalef et al. (2019)Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research , 98, 261-276., Richards et al. (2019)Richards, G., Yeoh, W., Chong, A. Y. L., & Popovič, A. (2019). Business intelligence effectiveness and corporate performance management: An empirical analysis. Journal of Computer Information Systems, 59(2), 188-196., and Weeserik and Spruit (2018)Weeserik, B. P., & Spruit, M. (2018). Improving operational risk management using business performance management technologies. Sustainability, 10(3), 640-659..	CPM-03	.844	... contributes to add economic and financial value to the business.
	CPM-04	.842	... helps optimize the efficiency and effectiveness of internal business processes.
	CPM-05	.818	... favors the management of relationships with customers, suppliers, and stakeholders.
	CPM-06	.845	... favors the development and learning of other organizational capabilities.

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[1] Correspondence concerning this article should be addressed to Mauricius M. Medeiros, Rua Washington Luiz, 855, Centro Histórico, Porto Alegre, Rio Grande do Sul, Brazil, CEP 90010-460. E-mail: mauricius.medeiros@gmail.com