Repercussions of Poor Data Quality (DQ) on Business Process Improvement (BPI) Projects

Authors: Krishna Valluru

DOI: https://doi.org/10.37082/IJIRMPS.v9.i2.232903

Short DOI: https://doi.org/hbnb2d

Country: United States

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Abstract: Business environment of the modern day is at the mercy of accurate and reliable data for informed decision making and continuous business process improvement (BPI). Business Process Improvement (BPI) projects rely significantly on high quality data to identify, assess, and improve processes that add value to the organization as a whole. Redman (2016) states that IBM’s estimate of the yearly cost of poor data quality is $3.1 trillion in 2016 in the US alone [1]. Poor data quality poses significant challenges and severely undermines these projects, leading to inappropriate and inaccurate data-driven decisions, establishing futile strategies that barely come to fruition, unexploited time, money, and resources, and attracting anti-trust from key stakeholders. When it comes to BPI projects, if organizations are unable to put their money where their mouth is, their credibility and reputation is at stake which eventually leads to negative impact, plummeted monetary gains and loss of sustenance for organization’s goals. This paper explores the multifaceted ramifications of poor data quality on BPI projects and highlights the importance of data quality management (DQM) for the success of these initiatives.

Keywords: Data Quality (DQ), Business Process Improvement (BPI), Operational Excellence (OpEx), Lean Six Sigma, DMAIC.

Paper Id: 232903

Published On: 2021-04-05

Published In: Volume 9, Issue 2, March-April 2021

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