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Journal of Computing and Information Technology - CIT 11, 2003, 3, 233-241 233

Controlling the Data Warehouse– a Balanced Scorecard Approach

Frank BensbergDepartment of Information Systems, University of Muenster, Germany

Data warehouse systems have become a basic techno-logical infrastructure in management decision making.Nevertheless, the overall utility of data warehousesremains unmeasured in most practical cases. As a conse-quence of this, IT-managers do not possess appropriatemeans to evaluate warehouse benefits in order to decideabout investments in warehousing technology.

This paper develops a controlling instrument for datawarehouse systems based on the Balanced Scorecard�BSC� approach. On the basis of the technologicalaspects of data warehouse systems, the BSC perspectivesare developed and populated with relevant objectives andmeasures for data warehouse success. These perspectivesare integrated into a consistent data warehouse scorecard.Finally, this instrument provides a holistic approach todrive the performance of data warehouse systems.

Keywords: data warehouse, balanced scorecard, control-ling, performance measurement, strategic management.

1. Introduction

In the last decade, the data warehouse concepthas experienced great acceptance. The primaryreason for building data warehouses is to im-prove information quality in order to achievespecific business objectives such as competitiveadvantage or enhancing decision making. Ac-cording to �18�, the annual expenses for a datawarehouse with 1 terabyte raw data sum up to5.3 million US-dollar, where costs for IT-staffand IT-services dominate. In most cases, thedata warehouse budget consumes a significantpart of the total IT-budet and therefore cost jus-tification is a conditio sine qua non.

Though many organizations have experiencedproblems with implementing data warehousesystems �12�, these are mostly not subject to de-tailed analysis of costs and benefits �3�. There-

fore, a dedicated controlling instrument is nec-essary which is able to track data warehousingsuccess and to steer investments in data ware-housing technology. Since data warehouse ben-efits are predominantly intangible and calcula-tion of the return on investment �ROI� of datawarehousing is in most cases infeasible �23�, itis necessary to evaluate data warehouse expen-ditures from both financial and non-financialviews. To achieve this objective, it seems rea-sonable to examine the balance scorecard con-cept for the domain of data warehousing.

From a research position, it is interesting to seethat the balanced scorecard approach has beenapplied to different areas of information tech-nology. In �5�, the development of a balancedIT scorecard for software producing businessunits is realized. Further scorecard-related pub-lications deal with performance measurementof ERP-software �e.g. �16� and �14�� or cre-ation of generic IT-scorecards which apply toan IT-department as a whole �e.g. �19�, �20��.However, no effort has been made in order toadapt the balance scorecard to the domain ofdata warehousing. This seems necessary sincethese systems are supremeley strategic and im-plementation failure rates are high ��23�,�2��.Consequently, data warehouse systems deservecloser attention by IT-controlling in order tosucessfully support business strategy.

To create an appropriate controlling instrumentfor data warehouses, this paper first discussesbasic technical and organizational characteris-tics of data warehouse systems. Afterwards,the balanced scorecard concept is introducedand applied to the domain of data warehousing.This is achieved by identifying relevant objec-tives and measures for each scorecard perspec-

234 Controlling the Data Warehouse – a Balanced Scorecard Approach

tive. Finally, the perspectives are merged intoa conceptual data warehouse scorecard and as-pects of integration into strategic managementare concluded.

2. The Data Warehouse Concept

2.1. Data Warehouse Architecture

According to INMON, a data warehouse is asubject-oriented, integrated, non-volatile, andtime-variant collection of data which serves asan infrastructure for management decisions �see�6�, p. 33�. This dispositive data collection isbased on operational data stores �e.g. enter-prise resource planning systems like SAP R�3�,but is arranged according to analytical interestsand is therefore not dependent on operationalbusiness processes. The high practical accep-tance of the data warehouse concept is a resultof the grown architecture of operational infor-mation systems. Since these systems are builtin order to achieve a high rate of transactions,generation of decisivemanagement informationimposes a number of technical and conceptualproblems �see �7�, p. 42–43�.

In order to keep data warehouse content up todate, it is necessary to establish a technologicalinfrastructure which extracts relevant data fromthe operational information systems and con-solidates these data within a well documenteddatabase system. Consequently, the data inthe data warehouse is made up of snapshots ofthe enterprise’s multiple operational databases.The resulting data warehouse architecture is de-picted in Fig. 1.

Fig. 1. The architecture of a data warehouse system.

This architecture consists of core componentsand processes which constitute a complex ana-lytical information system. The bottom layer ofthe warehouse is connected to the data stores ofoperational information systems �e.g. account-ing, sales, human ressources� in order to extractrelevant data which are pooled into a stagingarea. In addition to this, external data �e.g.from market research institutes or financial ser-vices� can be acquired to complete the dataneeded for management decisions. The stag-ing area serves as a temporary data store andallows the consolidation of data from heteroge-nous sources. A typical transformation task is toremove homonyms �e.g. using an identical labelfor different types of attributes� and synonyms�e.g. using two different labels for an attribute�.As a result, the integrated data is loaded intoa central data warehouse layer which is typi-cally normalized in order to avoid redundancies.These early tasks of the data warehouse processare executed by use of ETL-tools �extraction,transformation, loading�. These tools provideconnectivity to a broad set of different data stor-age formats �e.g. different database systems likeOracle, DB2 or SQL Server or different text fileformats�.

In order to turn warehouse data into decisiveinformation, it must be tailored to the needs ofthe end users located in different organizationalunits �e.g. functional departments�. Typically,the informational needs of the marketing de-partment differ from those of the accountingdepartment. As a consequence of this, spe-cific departmental views on the data have to becreated. These views, which are called datamarts, can be further customized in order tocomply with the informational needs of singleusers �e.g. a specific salesperson in a definedregion�.

To get information from these data marts, endusers are provided with a set of tools whichallow analytical processing. Most commonare report generation tools which support sim-ple aggregations �e.g. calculation of statisticalmeasures like mean values, etc.�. In order toprovide interactive analysis with user-definedviews, OLAP tools are frequently used. Whilereport generation tools and OLAP provide moreor less simple analytical operations, data min-ing tools permit the analysis of complex patterns��4�, p. 9�. For instance, data mining tools can

Controlling the Data Warehouse – a Balanced Scorecard Approach 235

reveal buying patterns of customers, which canbe used to optimize marketing campaigns.

2.2. Organizational Implications

High complexity of data warehouse systemsevolves from the fact that these systems haveto be tailored to the specific requirements of theorganization and cannot be set up out of the box.First, the data warehouse has to be built upon agiven data infrastructure which has grown his-torically in most organizations. This multitudeof heterogenous data sources requires a highnumber of interfaces which must be continu-ously managed in order to keep data warehousecontent up to date. Besides these technologi-cal dependencies, another source of complexityevolves from the end users of data warehouseservices and their different tasks. Managerialdecisions are carried out by numerous managersat very different hierarchical and departmen-tal levels. Consequently, informational needsof functional area management and executivemanagement differ significantly and lead to ahigh degree of specifity in information demand.Since an uncertain business environment withincreasingly intense competition also impliesdynamically evolving informational needs, datawarehouse systems have to be flexible in orderto comply with future requirements. Facing nu-merous technological and organizational depen-dencies, this flexibility is crucial for successfuldata warehouse evolution.

As the implementation and proliferation of adata warehouse is commonly a strategic goal, itis necessary to link data warehouses directly tobusiness strategy. A very popular instrument fortransforming strategy into action is the balancedscorecard, which has originally been introducedby KAPLAN and NORTON at the enterprise level�see �8�, �9�, �10�, �11��.

3. Balanced Scorecard as a StrategicControlling Instrument

The BSC has been developed in order to providea controlling instrument which does not merelyfocus on financial measures, but furthermoreconsiders non-financial measures. These mea-sures reflect relevant organizational objectives

which ensure strategy implementation. This ap-proach is based on the experience that a singularfocus on financial metrics reduces the quality ofstrategic decisions and therefore is not adequateto align business processes to strategy. TheBSC suggests to measure organizational perfor-mance in four key areas which are depicted inFig. 2.

Fig. 2. The basic model of the BSC.

Each perspective consists of strategic objectiveswhich are derived from business strategy andare linked to specific measures. In order to usethe BSC in business planning, the target val-ues for each measure have to be defined andconnected to corresponding actions which willensure achievement of the prospected value.

The financial perspective describes measureswhich are important to the shareholders of acorporation and reflect growth and profitabil-ity. Typical measures used for this perspectiveinclude sales, return on investment, and cashflow. They reflect strategic objectives like cor-porate survival or corporate success and dependon non-financial performance measures of otherperspectives.

The BSC suggests that organizations have toidentify the market segments that they plan tosupply. For each target segment measures haveto be defined which reflect the organizationalperformance from the market point of view. Ingeneral, measures like market share, customerretention, frequency of orders, and number ofnew customers are commonly used in order topopulate the customer perspective.

Consequently, the internal business processeshave to be aligned to support financial andcustomer-based objectives. This is achieved viathe internal business perspective which reflects


the performance of critical business processes.These processes, which have a significant im-pact on customer satisfaction or financial mea-sures, can relate to research and development�e.g. time to market of new technologies�, pro-duction and delivery �e.g. cycle time, productquality�, and service �e.g. response time�.

All described views of the BSC depend on theinnovation and learning perspective. This per-spective is based on the assumption that onlya learning and innovating organization is ableto survive intense competition. Therefore, thisperspective measures the innovative potential ofthe organizational infrastructure like employeesand information systems. Commonly usedmea-sures are employee satisfaction and informationcoverage ratio.

In order to implement a BSC for a given orga-nization, it is necessary to start scorecard de-sign at the strategic level of business. Conse-quently, the strategic business scorecard has tobe adapted in a top down mode for subordi-nated organizational units such as departments,groups, projects and individuals. Finally, theBSC has to be cascaded throughout the organi-zation to ensure goal alignment at every level.As a result of this, the BSC becomes a commu-nication tool revealing the strategy of the orga-nization via a set of well-defined objectives andmeasures. These relate to each other throughcause-effect-chains �e.g. employee satisfactioninfluences product quality�.

4. Development of a Data WarehouseBalanced Scorecard

4.1. Organizational Prerequisites

In order to develop a data warehouse balancescorecard, it is necessary to define the organiza-tional environment. The development and oper-ation of a data warehouse system is most com-monly realized by a corporation’s informationtechnology department �IT-department� whichalso is responsible for operational systems �e.g.ERP-systems� and other IT-resources. In or-der to guarantee an economic use of infor-mation technology, it is reasonable to run IT-departments as profit centers. This means thatthe IT-department offers products for internaland external customers. Internal customers

�e.g. functional departments� are entitled to de-cide freely if they buy IT-related services fromthe internal IT-department or from external sup-pliers. Of course, the IT-department has to de-fine transfer prices for each IT-related productoffered on the internal market.

Consequently, the operation of a datawarehousesystem leads to information products which areoffered to customers. There is a broad spec-trum of different services which can be offeredby an IT-department based on the warehousingplatform:

� reporting services �definition of reports,report creation and delivery�,

� OLAP-services �definition and delivery ofOLAP data marts and frontends to endusers�,

� data mining services �definition and exe-cution of data mining tasks, presentationand deployment of results�, and

� data quality management services.

If the IT-department is run as a profit center,these information products are subject to thetransfer price regime of the organization. Con-sequently, end users have to pay these trans-fer prices for usage of data warehouse ser-vices. These prerequisites form the organiza-tional framework for further data warehouseBSC development.

4.2. The Financial Perspective

The financial perspective of the data warehouseBSC has to reflect the contribution of a datawarehouse to the profitability of the IT-depart-ment. Relevant measures are the sales gener-ated by data warehouse-related end user ser-vices provided to internal customers. Sinceprofit center organizations are also entitled toserve external customers, it is necessary to dif-ferentiate between internal and external sales.External sales could be generated by providinghigh quality address data for mailing purposesor consulting services for external data ware-housing projects. Nevertheless, it should be as-sured that the ratio of internal to external salesis well-balanced in order to provide an incen-tive for warehouse management to serve both


groups of customers. This can be measured viathe sales mix as a ratio of internal to externalsales.

In addition to sales, the costs of the ware-house system are also relevant for the finan-cial perspective. A basic phenomenon of datawarehousing is that predominantly fixed costsare allocated. Therefore, it is impossible totrace down data warehousing costs to originat-ing costing units.

Since a significant portion of IT-related costsare commonly hidden costs, it seems reasonableto adopt a total cost of ownership approach inorder to assess the effectiveness of an organiza-tion’s IT expenditures �17�. According to this, itis necessary to differentiate between direct andindirect costs. Direct costs are budgeted costsfor hard- and software, operation, and adminis-tration. According to �18�, the following typesof direct costs have to be considered for datawarehouse systems:

� data warehouse platform,

� ETL-platform,

� database and miscellaneous software,

� IT-staff and services, and

� support and maintenance.

In contrast to this, indirect costs are non-budgetedcosts which are caused by inefficient systemoperations or usage. Indirect costs may springfromunplanned datawarehouse downtimewhichimpedes managerial decisions to be taken and

Fig. 3. The financial perspective.

therefore decreases productivity ofmanagementprocesses. Moreover, another driver of indirectcosts are inefficient end users’ operations. Forinstance, it is not efficient that end users carryout tasks that should be done by dedicated ITstaff.

In order to complete the financial perspective,it is necessary to integrate all components ofthis sales and cost framework into the financialperspective as shown in Fig. 3.

4.3. The Customer Perspective

In order to define the customer perspective ofthe data warehouse BSC, the internal and exter-nal customer�s� of the data warehouse-relatedservices have to determined first. As far asend user-oriented analytical services like report-ing, OLAP and data mining are concerned, pre-dominantly managers at different departmentallevels are the primary segment of relevant in-ternal customers �e.g. executive managers andfunctional area managers�. For this segment, acommon strategic objective is to become pre-ferred provider of managerial information. Inorder to evaluate the achievement of this ob-jective, coverage of the data warehouse-relatedinformation products has to bemeasured. Possi-ble measures for this information coverage arethe percentage of business decisions coveredor the percentage of managerial positions sup-plied with data warehouse-related informationproducts. In addition to this, the percentage ofoperational systems whose data are processedby the data warehouse may be another relevantmeasure.

Additionally, it seems necessary to measureend user satisfaction with the data warehouse--related information products. This can be eval-uated via surveys permitting the calculation ofa customer satisfaction index, and behavioraldata �e.g. log files� which reveal patterns of in-formation usage �e.g. frequency and durationof use�. The resulting customer perspective forthis segment is shown in Fig. 4.

Internal customers for data warehouse-relatedservices do not only exist at the manageriallevel, but at the operational level too. Since op-erational information systems frequently suffer


Fig. 4. The customer perspective.

frompoor data quality �13� and source data qual-ity problems generally become evident once thedata are loaded into the warehouse �15�, the ser-vice of a data warehouse environment is to pro-vide data quality management services. Forinstance, during the ETL-process of data ware-housing duplicates in operational data storescan be identified and properly removed. Thesecleaned data can be provided to the operationallevel in order to reduce data quality problems.For this segment, the strategic objective of a datawarehouse is to become preferred provider ofdata quality management services. The internalmarket share of this service could be measuredas percentage of operational systems suppliedwith cleaned data. In the long run, this propor-tion could decrease over time if data warehouseservices persistently enhance the data quality ofoperational systems.

For very different data warehouse-related ser-vices external customers may also exist. Forexample, cleaned address data can be extractedfrom the data warehouse and optimized for dif-ferent marketing purposes. These addressescould be provided to affiliated or cooperatingcompanies. A strategic objective for this seg-ment could be to become preferred mailing ad-dress supplier. In order to measure the achieve-ment of this objective, the number of addressessold can be quantified.

4.4. The Internal Business Perspective

The internal business perspective focuses on theinternal conditions for satisfying the customersupplied with the data warehouse’s services.

Since the internal processes of a data warehouseprimarily serve to provide appropriate informa-tion for managerial decisions, measures are nec-essary which reflect information quality. In lit-erature, there aremany approacheswhich can beapplied to information quality �22�. Accordingto �1� �p. 142�, information quality characteris-tics belong to two categories: inherent and prag-matic information quality characteristics. Theformer are independent of the processes thatuse the data for specific business purposes andindicate static quality characteristics:

� Completeness of values. This character-istic is measured as the degree to whichvalues are present in the data warehouseand are not missing.

� Accuracy to reality or a surrogate source.This criterion reflects the degree to whicha data value in the data warehouse con-forms to reality or an original source ofdata �like a document or a form�.

� Accessibility characterizes the ability toaccess the information within a data ware-house when it is required. Consequently,this criterion does reflect if the data ware-house does contain corresponding infor-mation.

Pragmatic information quality characteristicsdescribe the appropriateness of information forspecial business tasks:

� Relevance. This criterion describes thenecessity of information for business de-cisions. It can be measured empiricallyby observing end user behavior in relationto the data. If data objects are not usedat all, they may be irrelevant for businessdecisions.

� Timeliness of information. In order to en-sure a responsive supply of information,it is necessary to deliver it in time. Thiscriterion could be measured as the percent-age of information retrieval processes per-formed within the desired time frame.

� Interpretability. Information can be effi-ciently used for decision processes only if


it is easy to interpret, e.g. by use of vi-sualization techniques. Consequently, thiscriterion can be subjectiveley measured asthe degree to which information is directlyusable for decision purposes.

In literature, a vast multitude of different in-formation quality criteria are proposed ��22�,�21��. In practical application domains theseshould be selected according to situational re-quirements in order to meet the specific infor-mational needs of the management. Due to therelevance of information quality for warehousesuccess, it could be reasonable to integrate thesemetrics into a dedicated information quality per-spective.

In addition to these dedicated information qual-ity criteria, general availability of data ware-house services influences end user acceptanceand indirect TCO caused by downtime. As aconsequence of this, general performance mea-sures such as average system availability, aver-age downtime, and average response time, playan important role. The resulting internal busi-ness perspective is shown in Fig. 5.

Fig. 5. The internal business perspective.

4.5. The Innovation and LearningPerspective

This perspective has to reflect the flexibility tomeet actual and future requirements. Technicalflexibility stems from software and hardwareplatforms used for data warehouse implementa-tion. A key factor of future flexibility is vendorreliability. This can be measured by the num-ber of stable releases per year which can pro-ductively be used without major changes. In

addition, the compliance to relevant standards,conventions or legal regulations is another ma-jor driver for warehouse adaptability. This is-sue can be measured inversely by the numberof standards the data warehouse system fails tocomply to �see �21�, p. 2.24�. Another relevantfactor for technical flexibility is interoperabilityof data warehousing components. This measurequantifies the number of information systemsthe data warehouse is able to interact with �see�21�, p. 2.24�.

The organizational flexibility of a data ware-house is predominantly driven by the qualifica-tion of employees. This particularly refers to thetechnical data warehousing staff who must haveadequate business knowledge in order to com-municate and to understand end users informa-tional needs �23�. As a measure of qualification,experience with similar projects can be takeninto account. In addition, warehousing staff hasto keep pace with technical development. Thiscan be measured by the number of training daysper employee. In order to strengthen end users’involvement, adequate training has to be pro-vided, metered as number of training days perend user. Of course, this measure influences in-direct TCO in terms of inefficient self-supportor peer-to-peer-support. According to WATSONand HALEY, it is of vital importance to pro-vide appropriate meta data, such that end usersare enabled to search and identify relevant data��23�, p. 36�. Consequently, it is necessary tomeasure the degree to which data warehouseprocesses and entities are documented.

The resulting innovation and learning perspec-tive is depicted in Fig. 6.

Fig. 6. The innovation and learning perspective.


5. Conclusion

This paper proposed a balanced scorecard frame-work which can be used by IT-departments toplan and control data warehouse systems in aprofit center organization. Each perspective ofthe balanced scorecard was supplied with ad-equate objectives and measures, such that theresulting framework represents a holistic ap-proach. The data warehouse BSC can be usedas a strategic IT-management tool in order tocheck performance based on the objectives thathave been defined in advance. All perspectivesof the resulting scorecard are depicted in Fig.7. This figure also reveals cause-effect-chainswhich exist between major measures.

Fig. 7. The data warehouse BSC.

In practice, the proposed framework has to beassociated with corporate strategy. If a cor-porate scorecard already exists, the data ware-house scorecard has to conform to corporateobjectives. In particular, it has to be deter-mined if there are corporate objectives the datawarehouse can directly influence. If there isa strategic objective like “Increase competitiveadvantage by using information technology tofocus on premium customers”, the data ware-house BSC has to translate this directive intolocal objectives. For instance, a resulting ob-jective for the customer perspective could be:“Integrate and supply value-based customer in-formation for sales & marketing activities”.

Besides, for a data warehouse BSC it is neces-sary to conform to other scorecards developedfor the IT-department. In �20�, a scorecard cas-cade is proposed which derives a strategic IT-BSC from the corporate BSC. Furthermore, thestrategic IT-BSC is divided into a development

BSC and a operational BSC. For a data ware-house system this approach does not seem to bereasonable. This is because data warehousingsystems do not conform to traditional devel-opment models of software engineering whichstrictly differentiate between development andoperation. Consequently, it seems more suc-cessful to link the data warehouse BSC directlyto the strategic IT-BSC.

According to data warehouse research litera-ture, most of the measures presented in this pa-per represent critical success factors �see �3�,�15�, �2�, �23�, �21�� that have been identifi-ed in practical success and failure studies. There-fore, this framework can be used as conceptualblueprint for the development of customizeddata warehouse BSCs in practical applicationcontexts. These will play an important rolein further research evaluating the effectivenessof the BSC approach to drive data warehouseperformance.

6. Acknowledgements

The author would like to thank Mrs. ChristinaReichel at Mummert ConsultingAG for insight-ful discussions on data warehouse engineering.My thanks also go to Mr. Martin Weich andMr. Volker Manthey at Horvath & Partners fortheir helpful experiences on practical balancedscorecard management.

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Received: June, 2003Accepted: September, 2003

Contact address:

Frank BensbergDepartment of Information Systems

University of MuensterLeonardo-Campus 3D-48149 Muenster

GermanyPhone: �49-�0�251-83.38028

Fax: �49-�0�251-83.38029e-mail: bensberg�uni�muenster�de

FRANK BENSBERG studied economics at the University of Muenster.Afterwards, he acquired a doctoral degree for his thesis on Web LogMining. Since 2000, he is working as scientific assistant at the Uni-versity of Muenster. His main research interests are controlling anddecision support systems, data mining and e-learning.

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