440 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 53, NO. 3, AUGUST 2006 Data Warehousing Process Maturity: An Exploratory Study of Factors Influencing User Perceptions Arun Sen, Atish P. Sinha, and K. (Ram)Ramamurthy Abstract—This paper explores the factors influencing percep- tions of data warehousing process maturity. Data warehousing, like software development, is a process, which can be expressed in terms of components such as artifacts and workflows. In software engi- neering, the Capability Maturity Model (CMM) was developed to define different levels of software process maturity. We draw upon the concepts underlying CMM to define different maturity levels for a data warehousing process (DWP). Based on the literature in software development and maturity, we identify a set of features for characterizing the levels of data warehousing process maturity and conduct an exploratory field study to empirically examine if those indeed are factors influencing perceptions of maturity. Our focus in this paper is on managerial perceptions of DWP. The results of this exploratory study indicate that several factors—data quality, align- ment of architecture, change management, organizational readiness, and data warehouse size—have an impact on DWP maturity, as per- ceived by IT professionals. From a practical standpoint, the results provide useful pointers, both managerial and technological, to or- ganizations aspiring to elevate their data warehousing processes to more mature levels. This paper also opens up several areas for future research, including instrument development for assessing DWP maturity. Index Terms—Capability maturity model, data warehouse, data warehousing process maturity, software development process, soft- ware engineering, workflow. I. INTRODUCTION T HE data warehousing (DW) market has experienced tremendous growth during the last few years. Data ware- houses have emerged as one of the most powerful decision support tools [2], [63]. Nearly 70% of the companies surveyed globally in a recent study are currently developing DW and business intelligence (BI) applications [27]. According to a forecast by the Gartner group, the DW market for BI applica- tions is expected to reach $29 billion by 2006. Behind the market success of DW technology, we see a grim picture; nearly one half of all DW initiatives end up as failures [24]. Although a majority of large US firms have adopted the technology, there have been a large number of failures [27], [38]. Among those firms, very few have been successful at achieving the ability to properly access, integrate, and analyze data across all of their most important channels [27]. Manuscript received March 1, 2005; revised August 1, 2005 and November 1, 2005. Review of this manuscript was arranged by Department Editor, R. Sabherwal. A. Sen is with the Department of Information & Operations Management, Mays Business School, Texas A&M University, College Station, TX 77843 USA (e-mail: asen@mays.tamu.edu). A. P. Sinha and K. (Ram) Ramamurthy are with the Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53201 USA (e-mail: sinha@uwm.edu; ramurthy@uwm.edu). Digital Object Identifier 10.1109/TEM.2006.877460 Data warehouse projects tend to be very expensive. Ac- cording to the Meta Group, an overall data warehouse annual budget can span from $2 million to $10 million with a support staff of 20 to 50 people [39]. The average first-year cost of a DW project is about $1.26 million and the average project effort is about 4.46 person-years. Given that a significant proportion of these projects end up as failures, it is imperative that the DW community devote more thought to understanding what afflicts DW implementation and management. The concept of software development maturity was created and formalized as a model called the Capability Maturity Model (CMM) almost twenty years ago by the Software Engineering Institute (SEI). The model was based on actual practices in soft- ware industry, and reflects the state of the art in software en- gineering, as well as the needs of personnel performing soft- ware process improvement and software process appraisals [58]. CMM has evolved over the years based on extensive feedback from industry and government, and continues to provide useful guidelines for software process management and improvement as the software field matures as an engineering discipline. The software industry has made great strides in using CMM over the last two decades. From just a couple of firms at CMM maturity level 5 (the highest level of the five levels) only 15 years ago, there are now over a hundred organizations at that level [61]. The success of these firms can be attributed to the fact that they have adhered to sound software engineering prin- ciples and practices. Compared to the general field of software engineering, data warehousing is a relatively new discipline. To better understand the problems afflicting DW implementations, the DW community should try to focus on data warehousing as a process, similar to what the software engineering field has done. In this paper, we draw upon concepts from CMM and argue the need for a CMM-like maturity model for data warehousing process (DWP). This argument is presented in Section II. In Sec- tion III we introduce the concept of DWP maturity. Using a set of model building steps, we define different levels of DWP ma- turity based on the standards and practices adopted in CMM, but with a focus on data warehousing needs and goals. A firm with a high level of DWP maturity will consistently follow a disci- plined process, conforming to the best engineering practices and standards. We believe that individuals in such a mature organ- ization would be better able to monitor, manage, and improve both the product and process of DW development. Section IV describes a set of features that could be used to characterize the maturity levels. Section V describes an exploratory field study we conducted to empirically examine if those features truly influence perceptions of DWP maturity. Given that currently there does not exist an instrument to 0018-9391/$20.00 © 2006 IEEE