Data Quality Requirements of Collaborative Business Processes

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2012 45th Hawaii International Conference on System Sciences

Data Quality Requirements of Collaborative Business Processes Clarissa Falge University of St. Gallen [email protected]

Boris Otto University of St. Gallen [email protected]

Hubert Österle University of St. Gallen [email protected]

High-quality data is not just a competitive factor for individual companies but also an enabler for collaboration in business networks. The paper takes a cross-company perspective on data quality and identifies requirements collaborative business processes pose to data quality. A qualitative content analysis on Business Networking case studies is conducted. The results show which combinations of data classes (e.g. order data, forecast data) and quality dimensions (e.g. business rule conformity) are crucial for the different collaborative business processes in business networks. The paper interprets the results and closes with a discussion of current data quality trends for collaborative processes.

steps from supporting 1:1 BPs to 1:n BPs, and finally to facilitate m:n BPs [25]. Research in the area of information and data quality (DQ) has advanced significantly over the past twenty years. Until now, however, the scientific community has concentrated primarily on DQ as an enterprise internal factor, being a prerequisite for good business performance, compliance issues, increased customer focus, and other important aspects. As networkability has become a critical success factor in the past years, the view on information and DQ management needs to be extended to a cross-enterprise perspective. According to Kagermann et al., “master data can bring a position of power even within one company; between different companies, it can bring a strong competitive advantage” [25].

1. Introduction

1.2. Research question and approach

Abstract

1.1. Motivation and problem statement To be ready for 2020 a recent Capgemini study concludes that companies need to significantly increase their degree of collaboration as well as their networking capability. Given all the limitations of a consulting company’s research study, networkability seems to be a sustained trend. The main difference to Capgemini’s earlier value chain studies is that the urgency to act has risen immensely [7]. Integrated in a partner network offering a complementary portfolio of products and services, collaboration allows to concentrate on one’s core competencies [45]. Networkability allows enterprises to meet customer demands in real-time, develop tailor-made solutions, and offer these solutions costefficiently in close collaboration with partners of the value creation chain. Modern business models aim at realizing the vision of media disruption free business process management (BPM) when working and communicating with customers and suppliers. To be able to do so, information and data must be of high quality. The term networkability refers to the ability of any number (m) of suppliers to speak the “same language” with any number (n) of customers at the interfaces between business processes (BPs) and systems. The challenge to a company’s networkability is to take the 978-0-7695-4525-7/12 $26.00 © 2012 IEEE DOI 10.1109/HICSS.2012.8

The paper addresses the research question as to what requirements collaborative BPs pose to the DQ of an enterprise. In this respect, the paper examines collaborative BPs, which exchange data between enterprises and which is where information is created and modified. To do so, a qualitative content analysis was applied to examine case studies on Business Networking, in order to be able to put up a category system based on a combination of data classes and DQ dimensions [34]. The analysis presented aims to contribute both to the state of the art in practice and to the scientific body of knowledge. Regarding the former, the paper illuminates the role of data quality management (DQM) when it comes to realizing Business Networking and collaborative BPs. The results of the research are supposed to support DQM experts in focusing on those data classes which are the main enablers to collaborative BPs. The scientific contribution lies in offering a Business Networking perspective on DQM. Interestingly, already back in 1998 Wang et al. [62] pointed out that “in an increasingly networked world, information of varying quality is being aggregated for business use”, and that enterprises should “view, manage, and deliver information as a product”. As [44] shows, sound evidence has not been provided yet as to what requirements on DQ need to be derived from different collaborative BPs. 4347 4316

The paper is organized as follows: Section 2 presents the background of the research conducted, namely BPM and collaborative BPs on the one hand and DQ on the other hand. After that the research approach is outlined and the results from the qualitative content analysis are presented. Based on that, the paper analyzes requirements posed on DQ by different collaborative BPs.

Fleisch and Österle [18]. The defining characteristic of a collaborative BP is that two or more autonomous organizations jointly execute a process with the purpose of creating a certain output [8, 28]. Beginning with, and based on, the activities of the customer, the architecture of an enterprise operating in the information age [41] defines six types of generic collaborative BPs, which connect the activities of the enterprise and the activities of the customer [40]: • Customer learns about the possibilities which might solve their problems by using the information offered by the partners (Content & Community). • Customer specifies their requirements and participates in the creation of the design of the solution (Product Lifecycle). • Customer buys products and services (Commerce). • Customer wants to use these products and services at the right time in the right place (Supply Chain). • Customer uses support for maintaining the solution acquired (Maintenance & Repair). • Customer pays for the benefits received (Finance).

2. Background 2.1. Business Process Management The foundation of BPM as a systematic, customer focused managerial approach was established by the practices of Kaizen [33], Business Process Reengineering [12, 22, 23], Total Quality Management [46] and Process Innovation [10]. BPs can be defined as a sequence of activities transforming certain inputs into an output that is of value to the customer. Among the key characteristics of BPs are results orientation, customer focus and cross-functional coordination, as well as the use of information technology [4, 23, 42, 60]. BPs are frequently called an organization’s strategic assets [54]. The definitions of BPM range from IT focused views (e.g. technologies for BP automation) to BPM as a holistic management practice, as described by the Australian Community of Practice. It defines BPM as “a structured, coherent and consistent way of understanding, documenting, modeling, analyzing, simulating, executing and continuously changing end-to-end business processes and all involved resources in light of their contribution to business success.” The six core factors of BPM are strategic alignment, governance, methods, IT, people, and culture [6].

2.3. Data quality Information systems provide data in a certain business context. When data is being used by human beings, it turns into information, and information finally turns into knowledge by being interpreted and linked for a given purpose [11]. Regardless of such clear theoretical differentiation between data and information, practitioners use the term “data” in a broader sense. The analysis to be presented in this paper does not so much aim at a theoretical differentiation of certain terms, but rather focuses on the practical use of data in BPs.

2.2. Business Networking and collaborative business processes

Table 1. Data quality dimensions [43] DQ dimension accuracy

Enterprises operating in a digitized economy take customers’ BPs as the starting point for organizing in value creation networks. Such networks aim at supporting each individual customer process as comprehensively as possible in a collaborative effort [41]. Business Networking has its roots in the 1990s, when the term was initially used in the IS community. Business Networking is defined as the organization and management of IT supported business relationships with internal and external business partners [41]. Research has taken a variety of perspectives on Business Networking. Examples are the transaction cost theoretical perspective [65], a coordination theoretical [31] and organizational theoretical perspective [1, 38]. Apart from that, much attention has been paid to the BP perspective on Business Networking introduced by

completeness timeliness consistency

relevancy accessibility

Definition The extent to which data correctly represents an action or a real-world object. The extent to which values are present in a data collection. The extent to which data represents the real world at a given point in time. The extent to which data in one database corresponds to data in a redundant or distributed database. The extent to which data is applicable and helpful for the task at hand. The extent to which data is available at a given point in time.

DQ has been a research topic in numerous studies [17, 30, 47, 50, 61, 63], the outcome of which mainly were lists and categories of DQ dimensions. Many of these studies have in common that DQ is defined by the degree of benefit (or value) perceived by a user

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case study had been analyzed and coded, no other case studies were added to the case base. This procedure is in compliance with the recommendations by Yin [66].

using certain data in a certain context ('fitness for use') [17, 39, 51]. Table 1 lists the definitions of the quality dimensions used in [43] as the major dimensions proposed by [17, 50, 63]. Some contributions exist which focus on DQ in BPs [32]. However, a comprehensive DQ perspective on BPs has not been presented so far.

Table 2. Case studies analyzed Supply Chain

BP

3. Research design In order to find out what requirements collaborative BPs pose on DQ, twelve case studies were analyzed. These case studies, which comprise enterprises from different industries and of different size, cover four of the six types of generic collaborative BPs listed in Section 2.2 [40], namely Supply Chain, Commerce, Maintenance & Repair, and Finance. The topics of the case studies are presented in Table 2, and the respective company details are given in Table 3. To analyze the requirements, a qualitative content analysis (according to [34]) was conducted. The case studies [56, 66] belong to a set of twenty case studies, which were conducted in a research program on Business Networking at the University of St. Gallen and which are documented in [52]. These case studies form the foundation for a comprehensive framework of Business Networking. Therefore, they can be regarded as covering the various topical facets of Business Networking and collaborative BPs to a significant extent. All cases were conducted according to ProMet BECS (Project Method Business Engineering Case Studies), which is a standardized research method stipulating a specific, uniform content structure [53]. The case studies were analyzed following the rules of a coding tree, which has evolved by inductive category development during the qualitative content analysis [34, 35]. The categories determining DQ requirements were formed by combination of data classes and DQ dimensions. Text passages were assigned to categories, which needed to be carefully founded and revised within the process of analysis. For the purpose of generalization the content base was extended step by step, adding one case study after another from the selfcontained collection of twenty Business Networking case studies. As a strategy for systematic comparison of cases, theoretical sampling was applied. In the framework of grounded theory, theoretical sampling facilitates inductive theory development during the process of data collection. This is an iterative process that comes to an end when sufficient evidence has been found (i.e. saturation) [21]. As far as the research presented in this paper is concerned, the coding process produced similar results (i.e. literal replication) after the case base comprised twelve case studies. So, after the twelfth

Company L'Oréal RAG Coal Röhm Schiesser

Fi- Maintenance nance & Repair

Commerce

Lindt & Sprüngli Olin pharma mall Roche Vitamins Xiameter (a Dow Corning brand) ABB Turbo

Case study topic VMI between L’Oréal and drugstores SCM for imported coal Extended VMI with BASF Coatings operating a consignment depot Outsourcing of procurement logistics Outsourcing of fulfillment in the B2C business B2B solutions for electronic ordering and information exchange E-commerce platform for pharmaceutical corporations B2B internet shop for sale of vitamins E-commerce solution covering business customers’ ordering and information BPs Portal solution for service management of turbo chargers

AMAG

Reorganization of spare parts business

Aventis

Implementation of a tool based capital expenditure process

Table 3. Company details of the case studies USA

ABB Turbo AMAG Aventis Lindt & Sprüngli L'Oréal Roche Vitamins Röhm Schiesser RAG Coal pharma mall Xiameter Olin

Europe

Number of case studies per criteria Industry Automotive 1 + Energy & Mining 1 + Chemical 3 + + + Consumer products 3 + + + Engineering 1 + Pharmaceutical 3 + + + Sales in Swiss Franc (2003) Sales < 100 million 1 + 100 million -1billion 2 + + 1-5 billion 4 + + + + 5-10 billion 3 + + + Sales > 10 billion 2 + +

For developing a category system based on a combination of data classes and DQ dimensions, the following procedure was chosen (following [13, 34, 37]): 1. Determination of categories, starting with frequently cited data classes [14, 29, 55, 64], such as suppliers, customers, products, employees, assets, and contracts, and the DQ dimensions according to [43] (see Table 1). 2. Coding of text modules using the categories at hand. 3. Revision of coding scheme and, if deemed necessary, extension and adaptation of categories. 4. Re-coding of text modules.

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5. Clustering of categories. 6. Coding by means of combination of category groups “data class” and “DQ dimension”. 7. Re-clustering and, if need be, reentering the process at Step 4. 8. Interpretation of results. Unlike in the first phase of grounded theory (the open coding), the process here starts with a set of basic categories. Grounded theory and open coding is always appropriate if there is no groundwork available regarding the phenomenon to be examined. Since this is not the case here, the authors start the coding process with existing concepts. For extension and modification purposes, axial and selective coding is used [57, 58]. Reproducibility of results is a central requirement of qualitative content analysis. Its results must be evaluated against the criterion of reliability. With regard to the research presented in this paper, high reliability can be ascertained if there is a high level of congruence of the codings made by various persons (intercoder reliability). A high level of reliability indicates that the classification results produced in the analysis are accurate and dependable. By calculating reliability metrics, hence, researchers aim to counteract the criticism about the coding process offering too much leeway for individual interpretation. Reproducibility was made possible by detailed documentation of the coding scheme, comprising category names, category definitions, and examples. The case studies were imported to MAXQDA, which is a software for computer based analysis of qualitative data allowing to calculate a coefficient for intercoder reliability. For calculation of reliability, two case studies were double-coded, before MAXQDA calculated the percentage of congruent codings. Congruence of codings can be ascertained if the coded text passages have an identical overlap of at least 20 percent. Keywords, or the direct reference to them, provide legitimacy to code a passage. The relevance of the sentences around a keyword, explaining or complementing it, is often subordinated to a certain level of subjectivity, which explains the chosen 20 percent for the overlap. In order to calculate a coefficient for intercoder reliability, the codings of both coders are compared. The coefficient for intercoder reliability is 75 percent on average across all codings. Generally, coefficients higher than 0.7 are considered acceptable or even good [3] (in standardized surveys for retests, for example, the average reliability coefficient is around 70% as well [27], p.175).

and DQ dimensions as sub-categories. Excerpts of the coding system are displayed in Table 4, with data classes written in bold and DQ dimensions written in normal letters. The number in brackets following a DQ dimension depicts the number of codings. Table 4. Excerpt of the coding system Codesystem [1123] Customer accuracy [42] completeness [39] consistency [29] temporal validity [4] timeliness [40] Product

…

Codesystem [1123] (continued) Order accuracy [77] believability [1] business rule conformity [33] completeness [56] consistency [25] data security [7] data standard conformity [23] multi-language availability [6] temporal validity [3] timeliness [78] …

A DQ dimension was coded only if it was relevant to one of the data classes occurring in the case studies. Therefore, the newly emerged DQ dimension “believability” (see Table 5 for definition), for example, is listed only under the categories “Finance data” and “Order”. Whereas the DQ dimensions “relevancy” and “accessibility” from Table 1 could be neglected in the case studies, new DQ dimensions emerged during the inductive coding cycles (see Table 5 for definitions). Table 6 depicts a selection of examples of codings representing a requirement on DQ. Table 5. Newly emerged DQ dimensions (see Table 1 for the other DQ dimensions) DQ dimension temporal validity data standard conformity data security business rule conformity believability multi-language availability

Definition The extent to which correct historic data is necessary and the temporal validity of data stored. The extent to which data is defined by a common data standard. The extent to which data is sensitive. The extent to which data is defined by business rules (e.g. e-ordering portal or currency conversion). The extent to which data is plausible and realistic. The extent to which data is stored in different languages.

Table 6. Selection of coding examples Text passage From the service station spare parts orders via replenishment by the VW spare parts warehouse in Kassel (Germany) and by other suppliers, to Swiss customs, inventory levels in the central and regional warehouses, and the delivery to the service stations, the entire process utilizes the same database in real time. The data exchanged in IDOC format contain order number, amount, material number, and price, among other items. Multiplied by the current 52 different types, generations, and sizes, the possible product configurations reach around 182 million.

4. Results presentation and analysis The outcome of the qualitative content analysis is a coding system comprised of data classes as categories

Data class/DQ dim. Spare part, Order, Stock information/timeliness, accuracy, completeness, consistency Order/data standard conformity Product configuration/accuracy, completeness

In the case study texts, which all had a similar structure according to the standardized ProMet BECS

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standardized Supply Chain BPs correct data needs to be available for all partners in real time. As for the data classes “Order”, “Product”, “Stock information”, and “Transportation”, not just the DQ dimensions mentioned before are important, but also “data standard conformity” in order to be able to realize m:n interoperability. The case studies stress that data standards need to be agreed on at an early stage of a project. Moreover, master data mapping is mentioned in the case studies as a first step in order to be able to agree on a common data standard. This is why passages on master data mapping were coded with “Product\data standard conformity”. In the Schiesser case study, “Order” and “Transportation” data were converted to XML by SAP R/3 using iDocs, before being sent over the Internet. In the Röhm case study, EDIFACT (Electronic Data Interchange for Administration, Commerce and Transport) was used. Especially for product data captured by different communication channels, also the DQ dimension “consistency” is very important. An important aspect of the data classes “Order” or “Transportation”, for example, is their process status attributes. They need to contain accurate, timely and business rule compliant data in order to enable collaborative Supply Chain BPs. The Schiesser case study on outsourcing of procurement logistics focused on an SCM cockpit, allowing the partners to call up all purchase and transport orders including their current status in real time. Transporting the goods to the production sites in different countries including customs clearance is a major part of the procurement process and needs to be reflected in the status attributes. In the case of RAG Coal International, management of the supply chain for imported coal from overseas required exchange of transport information between all parties involved (m:n networkability). RAG Coal International has improved the BP quality in its supply chain by collecting all information as soon as it is entered in the central “Coal Supply Chain” database and making it available to all parties involved in a way that suits the needs of the addressee. “Business rule conformity” of “transportation” data is vital here. In order to avoid errors during data entry, plausibility checks were implemented (e.g. whether the cargo fits to the selected ship). Furthermore, “data security” becomes relevant if some of the network partners are direct competitors. Every party (e.g. ship owners, power plants, harbors) receives a different view on the data, depending on their role in the supply chain. In the Schiesser case, “Vendor” data, such as production volume, delivery scope, or quality, were coded eight times in the context of the procurement process for the selection of suppliers. Schiesser’s central procurement department has negotiated outline agreements with suppliers. In general, a manageable

method [53], the following sections and contents, respectively, were analyzed and coded: the abstract (giving an overview of the case), paragraphs on the initial situation (outlining the challenges and the problem to be solved), paragraphs presenting the solutions developed and implemented to solve the problem, and the summary and outlook section. The coding “Order” contains pricing information, which is connected with “Billing”. “Invoice” was coded as “Order” or “Billing”, depending on the context. In all cases in which the issue of data exchange was coded with “data standard conformity”, “consistency” would have been a possible coding as well, as in all case studies no consistency could be ensured prior to the realization of a common data standard. Table 7 shows the coding results, separated into data classes and DQ dimensions across the generic types of collaborative BPs, namely Supply Chain, Commerce, Maintenance & Repair, and Finance. In total, 1123 codings have evolved from the analysis. The data classes "Billing", "Customer", "Order", "Product", "Stock information", "Transportation" listed in Table 7 occur in all collaborative BPs except for the Finance type. All case studies aim at process automation, which is why the DQ dimensions “timeliness”, “accuracy” and “completeness” by far hold the top three ranks. Table 7. Codings for all collaborative BPs Data class Asset Billing Customer Documentation Employee Finance data Forecast data Material Order Product Product configuration Service Spare part Stock information Transportation Vendor Sum

Total 13 56 154 11 2 45 56 12 309 96 25 7 44 179 106 8 1123

DQ dim ension Total 301 accuracy 6 believability 44 business rule conformity 208 completeness 109 consistency 23 data security 85 data standard conformity 7 multi-language availability 26 temporal validity 314 timeliness Sum 1123

For a more differentiated view on the DQ requirements the results for each type of collaborative process are presented in the following. Tables 8 to 11 depict the number of codings for the combinations of data classes and DQ dimensions. Supply chain collaboration is the first logical step toward supply chain optimization. Regarding collaborative BPs of the Supply Chain type, four case studies were analyzed, whose content is presented in Table 2. The results of this analysis are shown in Table 8. All data classes show the high relevance of the DQ dimensions “accuracy”, “completeness”, and “timeliness”, as in highly automated and

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number of suppliers is beneficial for DQ, as lesser data mappings have to be maintained. A vendor managed inventory (VMI) always requires up-to-date product data, coded as “Product\timeliness”. The data class “Forecast data” contains codings for planning the amount or number of products to be delivered to the consignment inventory of Röhm or to the drugstores L’Oreal collaborates with. L’Oreal intends to realize Collaborative Planning Forecasting and Replenishment (CPFR) [19] with its business partners and therefore has a strong focus on “forecast”, “product sales”, “stock information”, and “data standard conformity”. The case studies also contain a few customer and product codings, with statements in the outlook sections of the texts saying that SCM should be extended to the customer by means of system consolidation. As far as data management is concerned, the enterprises in the case studies either use sophisticated tools for collaboration or, like Schiesser, have fully outsourced data management to an external application service provider.

(Xiameter case). Business rules can include the definition of the order volume as “logical and efficient shipping units”, (e.g. pallet, truck, or tanker, depending on the product) or rules for late payment etc. Compliance with such business rules requires high-quality data and was coded as “Order\business rule conformity”. Data standard conformity has to be ensured for Product and Stock information if the e-ordering process should be automated. In the Roche Vitamins case, the Business2Business (B2B) web shop also offers a direct connection using the CIDX (Chemical Industry Data Exchange) standard. For a collaborative process of the Commerce type comprising an eordering platform, “timeliness”, “accuracy”, and “completeness” of “Order”, “Customer”, and “Billing” data is essential (see Table 9). Since in the cases notifications containing order status information are sent to the customer, workflow status information of the data class “Order” also has to be up-to-date, accurate and complete. In the Xiameter case, for example, the company sends an order acknowledgement notification to the customer, before it sends a shipping notice with a guaranteed shipping date at a later point in time. If the company fails to meet the shipping date, it has to grant a discount on the next order. As far as “Transportation data” is concerned, “timeliness” needs to be ensured, as the customer wants to monitor the transport status online and in real time. Especially if a service provider is used for tracking of the transport status, the company has to put its focus on data consistency. Also, “consistency” must be ensured for “Customer”, “Order”, and “Product” data. “Consistency” is particularly important if the customer not only uses an e-ordering platform but also other communication channels (e.g. the phone). Furthermore, the history of “Customer” and “Billing” data is critical for the excellence of the collaborative BP and for customer retention. This is reflected by the coding combinations of these data classes with the DQ dimension “temporal validity”. Regarding collaborative BPs of the Maintenance & Repair type, the data classes “Spare part”, “Stock information”, “Product configuration”, “Order”, and “Customer” are of high importance (see Table 10). The case studies analyzed for Maintenance & Repair refer to the topic of reorganizing the spare parts business as well as to the topic of introducing a portal solution for supporting a service and sales process. Challenges from a data perspective mainly result from complex configurations of the machines and equipment to be maintained and repaired, a high number of variants, and short production cycles. For example, the number of possible product configurations of an ABB turbocharger for heavy Diesel engines amounts to approx. 182 million. On average, only five turbochargers are built identically worldwide.

Billing 10 Customer 1 Forecast data 19 Material 5 Order 18 Product 17 Stock information 21 Transportation 27 Vendor 3 Sum 121

0 0 0 0 4 0 0 8 0 12

8 0 1 1 12 2 2 1 15 4 13 6 18 10 22 6 2 0 93 30

0 0 0 0 4 0 0 0 0 4

0 0 2 0 6 16 25 4 0 53

0 0 0 0 1 0 0 0 0 1

Sum

timeliness

data standard conformity multi-language availability temporal validity

data security

consistency

completeness

accuracy

Data class

business rule conformity

Table 8. Coded combinations of data classes and DQ dimensions for Supply Chain BPs

0 9 27 0 1 4 0 10 45 0 4 12 1 15 68 0 18 70 0 41 115 0 25 92 0 3 8 1 126 441

Billing 7 Customer 35 Order 54 Product 4 Stock information 6 Transportation 3 Sum 109

0 0 1 0 0 0 1

0 4 0 0 33 26 29 38 18 0 2 2 0 1 4 0 1 0 29 79 50

0 0 3 0 0 0 3

0 0 15 7 5 1 28

0 0 4 0 0 0 4

Sum

timeliness

temporal validity

data standard conformity multi-language availability

data security

consistency

completeness

business rule conformity

believability

Data class

accuracy

Table 9. Coded combinations of data classes and DQ dimensions for Commerce BPs

0 6 17 3 32 129 1 53 216 0 1 16 0 7 23 0 6 11 4 105 412

Regarding collaborative BPs of the Commerce type, the respective case studies focused on the introduction of electronic ordering solutions. Their customers are willing to change their BPs, in order to, for example, meet the companies’ business rules in return for significant cost savings on commodity products

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• All case studies aim at process automation, which makes “timeliness”, “accuracy” and “completeness” the top three DQ dimensions. • Supply Chain BP: “Stock information”, “Transportation”, “Product”, “Order” and “Forecast data” were coded most frequently. Data standards are especially needed for “stock information” and “product” data. • Commerce BP: “Order” and “Customer” are the most important data classes. As they are often used in multiple communication channels, the dimension “consistency” is of particular importance. An essential DQ aspect of the data classes “Order” or “Transportation” is their process status attributes. • Maintenance & Repair BP: The data classes “Spare part”, “Stock information” and “Product configuration” and the DQ dimension “temporal validity” are key. • Finance BP: For “Finance data” the DQ dimensions “accuracy” and “data security” are of highest priority.

4 1 3 4 4 0 6 5 2 2 2 1 1 0 0 4 2 1 5 3 3 3 0 1 5 3 2 0 0 0 13 9 2 12 5 7 1 0 0 60 34 22

0 0 0 2 0 0 0 2 0 2 0 0 0 6

0 0 0 0 0 0 2 0 0 0 2 0 0 4

0 0 0 0 0 0 1 0 0 0 0 0 0 1

3 0 1 0 0 0 1 1 9 2 0 0 0 17

Sum

timeliness

multi-language availability temporal validity

data standard conformity

data security

consistency

accuracy

Data class Asset Billing Customer Documentation Employee Forecast data Order Product Product Service Spare part Stock information Transportation Sum

completeness

Table 10. Coded combinations of data classes and DQ dim. for Maintenance & Repair BPs

2 13 4 12 7 21 3 10 1 2 3 10 10 25 3 10 6 25 3 7 18 44 17 41 2 3 79 223

1 9 1 11

0 5 0 5

0 3 0 3

0 2 0 2

0 0 7 10 0 0 7 10

0 1 0 1

0 4 0 4

5. Discussion Sum

timeliness

temporal validity

multi-language availability

data security

consistency

completeness

business rule conformity

accuracy

Data class Documentation Finance data Forecast data Sum

believability

Table 11. Coded combinations of data classes and DQ dimensions for Finance BPs

The analysis shows which data classes and which DQ dimensions are central for different collaborative BPs. In order to be able to effectively collaborate on the basis of a common business agreement, partners must agree on common data standards and a common system as well as on SLAs for DQ. Also, it must be specified at the beginning who provides what resources, and whether data management should be outsourced to an external provider. Although the analysis has used case studies dating back to the year 2003, the collaborative BPs described are still highly relevant. Yet, new technologies supporting enterprise collaboration have emerged meanwhile (e.g. social networking), from which additional requirements on DQ with regard to different data classes can be derived. However, these new developments have not been examined in case studies yet, satisfying the criteria of scientific rigor [15]. For instance, the few results of a database search for social networking case studies on Google Scholar, EBSCO and ProQuest either did not address collaborative BPs or did not describe the new solution extensively enough so that data classes and DQ dimensions could be coded. In order not to disregard this important development, though, the paper briefly discusses recent developments regarding DQ requirements of collaborative BPs. The increasing popularity of social networking has led to a growing involvement of consumers in Business Networking activities. Best Buy [16], Starbucks [20], Dell [5], USSA [59], Orange and Cisco [49] are examples of companies that use social media to better understand, interact with, and serve their customers. The impact of the social media trend on the six types of collaborative BPs (defined in section 2.2)

0 1 4 45 0 1 4 47

The codings in Table 10 show that in order to meet this challenge up-to-date, complete and accurate “Spare part”, “Stock information”, “Product configuration”, and “Order” data is required. Data standard conformity is the prerequisite for managing the above mentioned complexity. Interestingly, the history of “Product configuration” data is particularly important for Maintenance & Repair BPs. So the reason for the codings “Product configuration\temporal validity” is that in the individual service stations the maintenance record including earlier specification changes are needed to carry out maintenance and repair work. Keeping a data history is required also for being able to optimize the service cycle by forecasts. Regarding collaborative BPs of the Finance type, “data security”, “accuracy”, and “consistency” hold the top three ranks in the list of DQ dimensions (see Table 11). Especially “data security” is very important in order to protect sensitive data against unauthorized access. “Consistency” is beneficial for the DQ dimension “believability”. “Timeliness” does not have a top priority here, as the case analyzed was about data updated quarterly only. However, “timeliness” might be of high relevance in cases in which data needs to be made available in real time (like on stock markets, for example). In the following, the key findings of the analysis are summarized for each collaborative BP:

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ploying underage workers, management needs to be able to gain insight into and track the actions of their suppliers, their suppliers’ suppliers, and so on. New deployment models, such as Software as a Service (SaaS) and cloud computing, are gaining momentum [26], influencing Supply Chain BPs in terms of adding increased importance to the DQ dimensions “data security” and “believability”. Regarding collaborative BPs of the Maintenance & Repair type, historization of service data is of particular importance in order to be able to optimize the service cycle by means of forecasts. This fact can also be seen in the current trend of predictive maintenance [24]. As far as collaborative BPs of the Commerce type are concerned, the case studies used for the analysis show very typical aspects, except for the fact that most of the respective platforms are not multilingual. Multilanguage availability of product data is hard to realize in the face of fast time-to-market. Customer addresses may be verified by purchasing data from business partners, as long as the data refers to enterprises, not to end consumers. Business partner data providers are companies like Avox, Bureau van Dijk, and D&B, or specialists for data from emerging markets, such as e.g. Sinotrust for the Chinese market. As far as limitations of the research presented in this paper are concerned, it should be mentioned that the coded texts slightly varied in length, which has an effect on the number of codings in general. As categories were clustered, they were kept generic enough so that the coding of further case studies would not have a significant effect on the results (except for Finance). The generic nature of the categories was chosen in order to enable other researchers to validate the results by coding additional, new case studies. Requirements on DQ with regard to collaborative BPs of the Finance type have only been examined for producing industries so far, not for banks, as this would have exceeded the scope of the paper. For the same reason, the two other types of generic collaborative BPs (Content & Community and Product Lifecycle) were left out of the qualitative content analysis.

seems to be particularly high for the Commerce BP, but also for the Content & Community BP and the Product Lifecycle BP. Swiss retailer Migros, for example, took up the results of a voting project among consumers on its online community platform “Migipedia” and, as a consequence, introduced a new packaging for one of its ice tea products [36]. Moreover, Connor [9] examines the implications of user generated content for travel businesses, focusing in particular on hotel reviews on TripAdvisor.com, the largest online network of travel consumers. Taking a DQ perspective on the case study, it can be derived that the central data classes are product (hotel) and consumer data. Particularly valuable is the combination of these two data classes in order to see, for example, the rating of a hotel in relation to reviewers’ profile data (age, gender etc.). “Business rule conformity” of the ratings has to be ensured, because the reviews have to conform to content guidelines. “Temporal validity” needs to be supported, so that the users can trace the development of the ratings over time. The US health club Equinox’s iPhone app, for example, enables members to track their fitness goals, book time and space in fitness classes, and extend invitations for trial memberships [2]. In this case the process status attributes of the customer data class need to contain timely data, which complies with the coding results of the Commerce BP. Furthermore, “temporal validity” and “consistency” of the data is important in order to be able to present the customer’s training progress over time. The same DQ requirements can be derived from a case study about the Nike+ platform [48], a collaboration between Nike and Apple, consisting of an Apple iPod music player, a wireless device to connect the music player to running shoes, a pair of Nike shoes with a special pocket to accept the wireless device, and membership in the iTunes and Nike+ online communities. Adidas has taken up a similar concept with its android app micoach. The challenge to enrich an organization’s customer data with profile data from social media applications is likely to change data management practices in the future. Regarding collaborative BPs of the Supply Chain type, the use of low-cost RFIDs as an effective anticounterfeiting technology is currently being examined, with the aim to make counterfeiting financially unattractive instead of treating the symptoms of the problem only. The new "corporate social responsibility" (CSR) movement inside 21st century organizations adds another layer of complexity to collaborative Supply Chain BPs, posing further requirements on DQ. In order to prove that a company has lowered its carbon emissions, does not dump hazardous materials into rivers, and does not buy materials from suppliers em-

6. Summary and outlook DQ is a critical issue for effective collaborative BPs. Managing DQ is essential to any Business Networking scenario. The paper identifies and explains the requirements collaborative BPs in Business Networking pose on the quality of a company’s data resources, and it creates a basis for further research. The paper shows which combinations of data classes and DQ dimensions are crucial for the different collaborative BPs in business networks. The results can be used by BP analysts as a “checklist” in order to be able to iden-

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tify needs for DQM initiatives more efficiently and structure the BP analysis. The discussed DQ requirements help prioritize data classes for a DQ oriented collaborative BP design, taking into consideration the data lifecycle. When partners in a business network want to define a DQ SLA for a collaborative BP, they need to define which DQ dimensions of certain data classes should be measured and managed. Further research on the topic should focus on enterprises characterized by a high level of networkability, examining their strategy regarding data exchange in a networked business scenario. Furthermore, research should analyze limitations and boundaries of existing data governance models and propose methods and guidelines for data governance in networked business scenarios. Another topic for research should be collective outsourcing of data management to external service providers, such as providers of business partner data or providers of Business Networking solutions. Research on information and data quality management should significantly advance the body of knowledge with regard to DQ in collaborative BPs of the Commerce type, which is rapidly changing under the influence of social networking and increased customer orientation. In general, the relevance of the DQ issue in Business Networking will continue to increase in the wake of social networking and novel technological solutions, such as smart metering or apps, raising new research questions.

7. Acknowledgements The work presented in the paper at hand was supported by the contribution of the Competence Center Corporate Data Quality (CC CDQ) which is part of the research program Business Engineering (BE HSG) of the University of St. Gallen.

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