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The lack of direction can make it challenging to start the data mining process and identify cost-
saving opportunities. As an analyst faced with the challenge of finding cost savings through the
analysis of large amounts of data with little direction, there are several steps that I would take to
approach this task effectively and efficiently. As an analyst, I would begin by understanding the
business objectives of this exercise. Targeting cost savings is a broad statement, and by exploring
the request deeper through interviews with various stakeholders, the analyst can better
understand the organizational goals and what "cost savings" means to those stakeholders.
Next, I would thoroughly examine the available data to understand better its sources, its
relevance to the organization's goals, and any existing patterns or trends that may be present.
More so, does the data seem complete? What is the quality of the data? Are there apparent
anomalies that warrant questioning its source and validity? By using exploratory data analysis,
we can better understand the data by systematically examining the data characteristics to identify
patterns, relationships, and anomalies that may lead to cost-saving opportunities (Thanos et al.,
2023).
Several methodologies provide a standardized approach to data mining, including the Cross
Industry Standard Process for Data Mining (CRISP-DM) model and Microsoft's Team Data
Science Process (TDSP) framework. CRISP-DM is a process framework for designing, creating,
building, testing, and deploying machine learning solutions (Schröe et al., 2021). Microsoft's
TDSP framework emphasizes a team approach, distributing the functional roles to more team
members, making it a more conducive approach for big data (Saltz, 2022). Both start with
understanding the business objectives, planning, and data familiarity before data preparation,
modeling, evaluation, and deployment. The models deviate slightly in later stages, but the
general methodology and approach are the same, with similar objectives.
References
Saltz, J. (2022). Nine Questions to Evaluate a Data Science Team's Process: Exploring a Big
Data Science Team Process Evaluation Framework Via a Delphi Study.
2022 IEEE International
Conference on Big Data (Big Data), Big Data (Big Data), 2022 IEEE International Conference
On
, pp. 2667–2672.
https://doi-org.lopes.idm.oclc.org/10.1109/BigData55660.2022.10020499
Schröer, C., Kruse, F., & Gómez, J. M. (2021). A Systematic Literature Review on Applying
CRISP-DM Process Model.
Procedia Computer Science, 181
, 526–
534.
https://doi.org/10.1016/j.procs.2021.01.199
Thanos, C., Meghini, C., Bartalesi, V., & Coro, G. (2023). An exploratory approach to data-
driven knowledge creation.
Journal of Big Data, 10
(1).
https://doi.org/10.1186/s40537-023-
00702-x
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