Arch Bi Aims

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The Architecture of Business IntelligenceAdapted from the forthcoming bookCompeting on Analytics: The New Science of WinningHarvard Business School Press, March 2007

by Thomas H. Davenport and Jeanne G. Harrisprepared for Accenture Information Management Services

March 2007


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The Architecture of Business Intelligence

2 | Accenture Institute for High Performance Business

Many companies today are collecting and storing a mind-bogglingquantity of data. The numbers are hard to fathom: in just a few

years, the common terminology for data volumes has grown frommegabytes to gigabytes to terabytes (a trillion bytes). The sizeof some corporate databases is even approaching one petabyte(a quadrillion bytes). Just how much data is this? To put the 583 TBin Wal-Marts databases into perspective, for example, consider thatin 2006, U.S. Library of Congresss print collection was roughly 20TB. And gargantuan storage is not the only technological frontier:high-end 64-bit processors and specialty data appliances canquickly churn through virtually unfathomable amounts of data.

Building a robust analytical capabilityrequires much more than just collectingand storing data in large quantities.There are many moving pieces to put in

place, including software applications,technology, data, processes, metrics,incentives, skills, culture, and sponsorship.An important initial step for any orga-nization is to understand the elementsof a business intelligence architectureso that structure can be imposed onotherwise chaotic bytes of data.

However, while organizations havemore data than ever at their disposal,they rarely impose sufficient order onit and thus get limited value from all

that information. Further, many ITdepartments lack the capabilities to domore than support and maintain basictransactional and reporting capabilities.In short, while improvements in tech-nologys ability to store data have beenastonishing, most organizations struggleto manage, analyze and apply it.

Companies such as Netflix, Tesco, theglobal cement manufacturer CEMEX,Harrahs Entertainment and CapitalOne have only two things in common:

they compete on the basis of theiranalytical capabilities and they arehighly successful. By using businessintelligence to make better decisionsand to extract maximum value fromtheir business processes, these organi-zations are able to identify theirmost profitable customers, accelerateproduct innovation, optimize supplychains and pricing, and identify thetrue drivers of financial performance.


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for example, spent more than ten yearsand a billion dollars building, organiz-ing and protecting its data warehouses.

Complying with legal and regulatoryreporting requirements is anotheractivity that depends on a robust BIarchitecture. The Sarbanes-Oxley Act of 2002, for example, requires executives,auditors and other users of corporatedata to demonstrate that their decisionsare based on trustworthy, meaningful,authoritative and accurate data. It alsorequires them to attest that the dataprovides a clear picture of the business,major trends, risks and opportunities.

Responsibility for getting the data,technology and processes right is the

job of the IT architect. This executive(working closely with the CIO) mustdetermine how the components of theIT infrastructure as a whole (hardware,software and networks) will worktogether to provide the data, technologyand support needed by the business. Inlarge established organizations the ITinfrastructure can sometimes appear tohave been constructed in a series of weekend handyman jobs. It does the

job it was designed to do but is apt tocreate problems whenever it is appliedto another purpose.

Breaking the business intelligencearchitecture into its six elements canhelp IT executives leverage the analyticalpower of their IT investment.

1. Data management that defines howthe right data is acquired and managed.2. Transformation tools and processesthat describe how the data is extracted,cleaned, transmitted and loaded topopulate databases.3. Repositories that organize data andmetadata (information about the data)and store it for use.

What is business intelligence?

To make sure the IT environment fullyaddresses an organizations needs at

each stage of analytical competition,companies must incorporate analytics andother business intelligence technologiesinto their overall IT architecture.

Technologists use the term businessintelligence (often shortened as BI) toencompass analytics as well as theprocesses and technologies used forcollecting, managing, and reportingdecision-oriented data. The businessintelligence architecture (a subset of the overall IT architecture) is anumbrella term for an enterprise-wideset of systems, applications, and gover-nance processes that enable sophisticatedanalytics, by allowing data, content andanalyses to flow to those who needthem, when they need them.

Top management, functional heads,knowledge workers, and statisticiansall need information of these types atvarious times and in various forms. TheBI architecture must be able to quicklyprovide users with reliable, accurateinformation and help them makedecisions of widely varying complexity(See Top Ten Signs of EffectiveBusiness Intelligence). It also mustmake information available througha variety of distribution channels,including traditional reports, ad hocanalysis tools, corporate dashboards,spreadsheets, e-mail, and pager alerts.

This task is often daunting: Amazon.com,

Business intelligence, analyticsand high performance


As part of Accentures research intohigh-performance businesses, we havefound that a growing number of com-panies have recognized the power of leveraging data-driven insights throughthe use of business intelligence. Someforward-thinking companies have gonea step further and are building theircompetitive strategies around analyticsthat is, the extensive use of data,statistical and quantitative analysis,explanatory and predictive models,

and fact-based management to drivedecisions and actions. Accentures highperformance business research founda powerful link between organizationswith pronounced analytical orientationsand market out-performance.

4. Applications and other softwaretools used for analysis.5. Presentation tools and applicationsthat address how information workers

and non-IT analysts will access, display,visualize and manipulate data.6. Operational processes that addresshow important administrative activitiessuch as security, error handling,auditability, archiving and privacyare resolved.

Well look at each element in turn,with particular attention to datamanagement since it drives all theother architectural decisions.


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Data management

The goal of a well-designed data man-agement strategy is to ensure that anorganization has the right informationand uses it appropriately. Large companiesinvest millions of dollars in systemsthat snatch data from every conceiv-able source. Systems for enterpriseresource planning, customer relation-ship management, and point-of-saletransactions, among others, ensure thatno transaction or exchange occurswithout leaving a mark. Many organi-zations also purchase externally gath-ered data from syndicated providerssuch as IRI and AC Nielsen in consumerproducts and IMS Health in pharma-ceuticals.

In this environment, data overload canbe a real problem for time-stressedexecutives. But the greatest datachallenge facing companies is dirty

data: information that is inconsistent,fragmented and out of context. Eventhe best companies often struggle toaddress their data issues. We foundthat companies that compete on ana-lytics devote extraordinary attention todata management processes and gover-nance. Capital One, for example, esti-mates that 25 percent of its IT organi-zation works on data issuesan unusu-ally high percentage compared withother firms.

Theres a significant payoff, however,awaiting companies that invest theeffort to master data management. Forexample, Continental Airlines integratesten TB (terabytes) of data from 25operational systems into its data ware-house. The data is used in analyticalapplications for both real-time alerts

and long-range strategic analysis. Alertsnotify customer agents of delays inincoming flights and identify incomingfrequent-flier customers who are

assigned alternative flights if they areunlikely to make their connections.Marketing analysts use other datacollected by the systems to studycustomer and pricing trends, andlogistical analysts plan the optimalpositioning of planes and crews. Thecompany estimates that it has savedmore than $250 million in the firstfive years of its data warehousingand business intelligence activitiesrepresenting an ROI of more than1,000 percent.

Top Ten Signs of Effective Business Intelligence

1. Managers and analysts have direct,nearly instantaneous access to data;

they never argue over whose numbersare accurate.2. Information workers spend theirtime analyzing data and under-standing its implications rather thancollecting and formatting data.3. Managers focus on improvingprocesses and business performance,not culling data from laptops, reports,and transaction systems.4. A hypothesis can be quicklyanalyzed and tested without a lotof manual behind-the-scenespreparation.5. Data is managed from an enterprise-wide perspective throughout its lifecycle, from its initial creation toarchival or destruction.

6. Rather than have data warehouseor business intelligence initiatives,

companies manage data as a strategiccorporate resource in all businessinitiatives.7. Both the supply and demand sides of the business rely on forecasts thatare aligned and have been developedusing a consistent set of data.8. High-volume, mission-criticaldecision-making processes are highlyautomated and integrated.9. Data is routinely and automaticallyshared between the company and itscustomers and suppliers.10. Reports and analyses seamlesslyintegrate and synthesize informationfrom many sources.


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To achieve the benefits of analyticalcompetition, IT and business expertsmust tackle their data issues byanswering the following five questions:

1. What data is needed to competeon analytics? The question behind thisquestion is, What data is most valuablefor competitive differentiation andbusiness performance?

Ensuring that analysts have access tothe right data can be difficult. Some-times a number is needed: the adventof credit scores made the mortgage-lending business more efficient byreplacing qualitative assessments of consumer creditworthiness with a single,comparative metric. But not everythingis reducible to a number. An employeesperformance rating does not give ascomplete a picture of his work over ayear as a managers written assessment.

The situation is complicated whenbusiness and IT people blame eachother when the wrong data is collectedor the right data is not available.Studies show that IT executives believebusiness managers do not understandwhat data they need and, conversely,that business managers believe IT exec-utives lack the business acumen tomake meaningful data available.While there is no easy solution tothis problem, the beginning of one isfor business leaders and IT managersto pledge to work together.

increasingly are embedding analyticalcapabilities into their enterprisesystems so that users can developsales forecasts and model alternative

solutions to business problems.

In addition to corporate systems, anorganizations personal computers andservers are loaded with data. Databases,spreadsheets, presentations and reportsare all sources of data. Sometimes thesesources are stored in a common knowl-edge management application, butthey are often not available across theentire organization.

For external information, managerscan purchase data from firms thatprovide financial and market informa-tion, consumer credit data and marketmeasurement. Governments at all levelsare some of the biggest informationproviders, and company Web sites areanother powerful resource. Data canalso come from the sources popularizedin the movies: e-mail, voice applications,images (maps and photos availablethrough the Internet) and biometrics(fingerprints and iris identification).The further the data type is fromstandard numbers and letters, however,the harder it is to integrate with otherdata and analyze.

The importance of collaborationbetween business and IT leaders can beseen in the complexity an insurer facesin defining health care customers. An

insurance company has many differentcustomers: corporations that contractfor policies on behalf of their employ-ees, individual subscribers, and mem-bers of the subscribers families. Eachindividual has a unique medical historyand current needs. Some may be cov-ered by other providers, including thegovernment. The insurance companyalso has relationships with serviceproviders such as hospitals, HMOs anddoctors, which come in their ownvarieties and are connected in manycombinations.

Companies must think through suchdifficult questions before they canmake full use of business intelligencecapabilities.

2. Where can this data be obtained?Data for business intelligence origi-nates from many places, but it must bemanaged through an enterprise-wideinfrastructure. Only by this meanswill it be streamlined, consistent andscalable. Having common applicationsand data across the enterprise is criticalbecause it helps yield a single versionof the truth.

To organize internal information, anorganizations enterprise systems area logical starting point. Enterprise sys-temsintegrated software applications

that automate, connect and manageinformation flows for business processessuch as order fulfillmentoften helpcompanies move along the path towardanalytical competition: they provideconsistent, accurate and timely datafor such tasks as financial reportingand supply chain optimization. Vendors


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if executives have to wade through dig-ital oceans of irrelevant data, they willgive up before they drown. For another,data hoarders quickly learn that they

cannot collect everything, and thateven if they try, the costs outweighthe benefits.

Second, companies should avoidcollecting data that is easy to capturebut not necessarily important. Many ITexecutives advocate a low-hanging-fruit approach because it relieves themof responsibility for determining whatinformation is valuable to the business.IT and business executives must be clearabout what drives value in an organiza-tion; this understanding will preventcompanies from collectingdata indiscriminately.

4. How can we make data more valuable? Quantity without quality isa recipe for failure. Executives areaware of the problem: in a survey of the challenges organizations face indeveloping a business intelligencecapability, data quality was second onlyto budget constraints. Organizationstend to store their data in hard-walled,functional silos. As a result, the data isgenerally a disorganized mess. For mostorganizations, differing definitions of key data elements such as customer orproduct add to the confusion. WhenCanadian Tire, for example, set out tocreate a structure for its data, it foundthat the companys data warehousecould yield as many as six different

numbers for inventory levels. Otherdata was not available at all, such ascomparison sales figures for certainproducts sold in its 450-plus storesthroughout Canada. Over several years,the company created a plan to collectnew data that fit the companysanalytical needs.



More data about the physical world,through sensor technology and radiofrequency identification (RFID) tags,is becoming available as well. For

example, a case of wine can bemonitored to see whether it is beingkept at the proper temperature.

It can be difficult and expensive tocapture some highly valuable data. (Insome cases, it might even be illegalfor example, sensitive customerinformation or competitor intelligenceabout new product plans or pricingstrategies.) Analytical competitorsadopt innovative approaches to gainpermission to collect the data theyneed. Progressive Insurances TripSenseprogram offers discounts to customerswho agree to install a device that col-lects data about their driving behavior.Former CEO Peter Lewis sees thiscapability as the key to more accuratepricing and capturing the most valu-able customers: Its about being ableto charge them for whatever happensinstead of what they [customers] say ishappening. So what will happen? Wellget all the people who hardly everdrive, and our competitors will getstuck with the higher risks.

3. How much data is needed? Inaddition to gathering the right data,companies need to collect a lot of itin order to distill trends and predictbehavior.

Two pitfalls must be balanced against

this need for large quantities of data.First, companies have to resist thetemptation to collect all the datathey can just in case. For one thing,

Several characteristics increase thevalue of data:

It is correct. While some analyses can

get by with ballpark figures and othersneed precision to several decimalpoints, all must be informed by datathat passes the credibility tests of thepeople reviewing it.

It is complete. The definition of com-plete will vary according to whether acompany is selling cement, credit cardsor season tickets, but completenesswill always be closely tied to anorganizations distinctive capabilitytheintegrated business processes and capa-bilities that together serve customers inways that are differentiated from com-petitors and that createan organizations formula forbusiness success.

It is current. Again, the definition of current may vary; for some businessproblems, such as a major medicalemergency, data must be availableinstantly to deploy ambulances andemergency personnel (also known aszero latency); for most other businessdecisions, such as a budget forecast, it

just needs to be updated periodicallydaily, weekly or monthly.

It is consistent. In order to help deci-sion makers end arguments over whosedata is correct, standardization andcommon definitions must be applied toit. Eliminating redundant data reduces

the chances of using inconsistent orexpired data.


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It is in context. When data is enrichedwith metadata (usually defined asstructured data about data), itsmeaning and how it should be used

become clear.

It is controlled. In order to comply withbusiness, legal and regulatory require-ments for safety, security, privacyand auditability, it must be strictlyoverseen.

5. What rules and processes areneeded to manage the data from itsacquisition through its retirement?Each stage of the data managementlife cycle presents distinctive technicaland management challenges.

Data acquisition. Creating or acquiringdata is the first step. For internalinformation, IT managers should workclosely with business process leaders.The goals include determining whatdata is needed and how to best inte-grate IT systems with business processesto capture good data.

Data cleansing. Between 25 and 30percent of a BI initiative typically goestoward initial data cleansing: detectingand removing data that is out of date,incorrect, incomplete or redundant. ITsrole is to establish methods and systemsto collect, organize, process and maintaininformation, but data cleansing isthe responsibility of everyone whogenerates or uses data.

Data organization and storage. Oncedata has been acquired and cleansed,processes to systematically extract,integrate and synthesize it must be

established. The data must then beput into the right repository andformat so that it is ready to use.

Data maintenance. After a repositoryis created and populated with data,managers must decide how and whenthe data will be updated. They mustcreate procedures to ensure data privacy,security and integrity (protection fromcorruption or loss via human error,software virus or hardware crash). Andpolicies and processes must also bedeveloped to determine when and howdata that is no longer needed will besaved, archived or retired. Some compa-nies have estimated that they spend$500,000 on maintenance for every $1million spent on developing new busi-ness intelligence technical capabilities.

Once an organization has addresseddata management issues, it must deter-mine the technologies and processesneeded to capture, transform and loaddata into a data warehouse.

Transformation toolsand processes

For data to become usable by managers,it must first go through a processknown in IT-speak as ETL, for extract,transform and load. While extractingdata from its source and loading it intoa repository are fairly straightforwardtasks, cleansing and transformingdata are not.

The first step is to clean and validatedata using business rules and datacleansing tools such as Trillium. (Forexample, a simple rule might be to havea full nine-digit ZIP code for all U.S.addresses.) Transformation procedures

then define the business logic thatmaps data from its source to its desti-nation. Both business and ITmanagers must expend significant

effort in order to transform data intousable information. While automatedtools from vendors such as Informatica,Ab Initio and Ascential can ease thisprocess, considerable manual effort isstill required. Informaticas CEO, SohaibAbbasi, estimates that for every dollarspent on integration technology,around seven to eight dollars is spenton labor [for manual data coding].

Transformation also entails standardizingdata definitions to make certain thatbusiness concepts have consistent,comparable definitions across theorganization. For example, a customermay be defined as a company in onesystem but as an individual placingan order in another. It also requiresmanagers to decide what to do aboutdata that is missing. These mundanebut critical tasks require an ongoingeffort, because new issues seem toconstantly arise.

Data repositories

Organizations have several options fororganizing and storing their analyticaldata:

Data warehouses are regularly updateddatabases that contain integrated datafrom different sources. They contain

time-series (historical) data to facilitatethe analysis of business performanceover time. A data warehouse may be amodule of an enterprise system or an


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independent database. Some companiesalso employ a staging database that isused to get data from many differentsources ready for the data warehouse.

A data mart can refer to a separaterepository or to a partitioned section of a complete data warehouse. Data martsare generally used to support a singlebusiness function or process and usuallycontain some predetermined analysesso that managers without statisticalexpertise can slice and dice some data.Data marts can result in the balkanizationof data and should only be used if the designers are confident that nobroader set of data will ever beneeded for analysis.

Ametadata repository contains technicalinformation and a data definition,including information about the source,how it is calculated, bibliographic infor-mation and the unit of measurement. Itmay include informationabout data reliability, accuracy andinstructions on how the data should beapplied. A common metadata repositoryused by all analytical applications iscritical to ensure data consistency.Consolidating all the information neededfor data cleansing into a single reposi-tory significantly reduces the timeneeded for maintenance.

Once the data is organized and ready, itis time to determine the analyticaltechnologies and applications needed.

Analytical tools and applications

Choosing the right software tools orapplications depends on several factors.

The first task is to determine howthoroughly decision making shouldbe embedded into business processes.Should a decision be automated ormade by a person? If it is automated,technologies exist that both structurethe workflow and provide decisionruleseither quantitative or qualitativeto make the decision.

Next, companies must decide whetherto use a third-party application or createa custom solution. The make or buydecision hinges upon whether a pack-aged solution exists and whether thelevel of skill required exists within theorganization. A growing number of functionally or industry-specificbusiness applications, such as capital-budgeting or mortgage-pricing models,now exist. Vendors of enterprise systemssuch as Oracle and SAP are buildingmore analytical applications into theirproducts. According to IDC, projectsthat implement a packaged analyticalapplication yield a median ROI of 140percent, while custom developmentusing analytical tools yields a medianROI of 104 percent.

Nevertheless, powerful tools have beencreated that allow organizations todevelop their own analyses (seeAnalytical technologies). Companiessuch as Business Objects and SAS offer

product suites consisting of integratedtools and applications. Some tools aredesigned to slice and dice or to drilldown to predetermined views of thedata, while others are more statisticallysophisticated. Some tools can accom-modate a variety of data types, while

others are more limited (to highlystructured data or textual analysis,for example). Some tools extrapolatefrom historical data, while others are

intended to seek out new trendsor relationships.

Whether a custom solution or off-the-shelf application is used, the businessIT organization must accommodate avariety of tools for different types of data analysis.

Employees naturally tend to preferfamiliar products, such as spreadsheets,even if they are ill suited for the analy-sis to be done. Another problem is pro-liferation of technologies. In a 2005survey, respondents from large organi-zations reported that their organiza-tions had, on average, 13 business intel-ligence tools from an average of 3.2vendors.In the past, different vendors haddifferent capabilitiesone might focuson financial reporting, another on adhoc query, and yet another on statisticalanalysis. Today, however, leadingproviders have begun to offer businessintelligence suites with stronger, moreintegrated capabilities.


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Analytical technologies

Executives in organizations that are planning to become analytical competitors should be familiar

with the key categories of analytical software tools:

data cubes to enable analysis acrosstime, geography, product lines and soon. Data cubes are simply collections of data in three variables or more that areprepackaged for reporting and analysis;they can be thought of as multidimen-sional spreadsheets. Unlike traditionalspreadsheets, OLAP tools must dealwith data proliferation, or the modelsquickly become unwieldy. For complexqueries, OLAP tools are reputed toproduce an answer in around 0.1 percentof the time that it would take forthe same query to be answered usingrelational data. Business Objects andCognos are among the leading vendorsin this category.

Statistical or quantitative algorithmsprocess quantitative data to arrive atan optimal target such as a price or aloan amount. In the 1970s, companiessuch as SAS and SPSS introducedpackaged computer applications thatmade statistics much more accessible.Statistical algorithms also encompasspredictive modeling applications,optimization and simulations.

Rule engines process a series of businessrules that use conditional statements toaddress logical questionsfor example,If the applicant for a motorcycle insur-

ance policy is male and under 25, anddoes not either own his own home orhave a graduate degree, do not issue apolicy. Rule engines can be part of alarger automated application or providerecommendations to users who need tomake a particular type of decision. FairIsaac, ILOG and Pegasystems are someof the major providers of rule enginesfor businesses.

Data mining tools draw on techniquesranging from arithmetic computationto artificial intelligence, statistics,decision trees, neural networks andBayesian network theory. Their objec-tive is to identify patterns in complexand ill-defined data sets. Sprint, forexample, uses neural analytical technologyto predict which customers are likely toswitch wireless carriers and take theirexisting phone numbers with them.SAS offers both data and text miningcapabilities and is a major vendor inboth categories.

Text mining tools can help managersquickly identify emerging trends innear-real time. A spider (or datacrawler) that identifies and countswords and phrases on Web sites, is anexample. Text mining tools can beinvaluable in sniffing out new trendsor relationships. For example, by moni-toring technical-user blogs, a vendorcan recognize that a new product hasa defect within hours of being shippedinstead of having to wait for com-plaints to arrive from customers.

Simulation tools model businessprocesses with a set of symbolic,mathematical, scientific, engineeringand financial functions. Much as

computer-aided design systems areused by engineers to model the designof a new product, simulation tools areused in engineering, R&D and a surpris-ing number of other applications. For

Spreadsheets such as Microsoft Excelare the most commonly used analyticaltools because they are easy to use andreflect users mental models. Managersand analysts use them for the lastmile of analyticsthe stage rightbefore the data is presented in reportor graphical form for decision makers.But too many users attempt to usespreadsheets for tasks for which theyare ill suited, leading to errors or incor-rect conclusions. Spreadsheet programsgenerally provide the ability to managedata arrays across three dimensions(down, across, and worksheet pages),while OLAP models (see below) canhave seven or more, and are thereforesuited to more complex problems. Evenwhen used properly, spreadsheets areprone to human error; more than 20percent of spreadsheets have errors,and as many as 5 percent of allcalculated cells are incorrect.

Online analytical processors are gener-ally known by their abbreviation, OLAP,and are used for semi-structured deci-sions and analyses. While a relationaldatabase (or RDBMS)in which datais stored in related tablesis a highlyefficient way to organize data fortransaction systems, it is not particularlyefficient when it comes to analyzing

array-based data (data that is arrangedin cells like a spreadsheet), such as timeseries. OLAP tools are specificallydesigned for multidimensional, array-based problems. They organize data in


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example, simulations can be used tohelp users understand the implicationsof a change to a business process. Theycan also be used to help streamline the

flow of information or productsforexample, to help employees of healthcare organizations decide where tosend donated organs according to suchcriteria as blood type and geography.

Emerging analytical technologiesThese are some of the leading-edgetechnologies that will play a role inbusiness intelligence over the nextfew years:

Text categorization is the process of using statistical models or rules to ratea documents relevance to a certain

topic. For example, text categorizationcan be used to dynamically evaluatecompetitors product assortments ontheir Web sites.

Genetic algorithms are a class of stochastic optimization methods thatuse principles found in natural geneticreproduction (crossover or mutationsof DNA structures). One commonapplication is to optimize deliveryroutes.

Expert systems are not a new technol-ogy but one that is finally coming of age. Specialized artificial intelligenceapplications are capable of makingexpertise available to decision makersimagine a Warren Buffett in a boxthat gives advice on investment deci-sions in changing market conditions.

Audio and video mining are much liketext or data mining tools, but they lookfor patterns in audio or images, partic-ularly full-motion images and sound.

Swarm intelligence, as observed in thecomplex societies of ants and bees, istechnology used to increase the realismof simulations and to understand howlow-level changes in a system can havedramatic effects.

Information extraction culls and tagsconcepts such as names, geographicalentities and relationships from largelyunstructured (and usually) textual data.

Presentation tools and applications

Business intelligence will only work if people can impart their insights to othersthrough reporting tools, scorecards andportals. Presentation tools should allowusers to create ad hoc reports, to inter-actively visualize complex data, tobe alerted to exceptions throughcommunication tools (such as e-mail,PDAs, or pagers), and to collaborativelyshare data. (Business intelligence vendorssuch as Business Objects, Cognos, SAS,and Hyperion sell product suites thatinclude data presentation and reportingsolutions. As enterprise systems becomemore analytical, vendors such as SAPand Oracle are rapidly incorporatingthese capabilities as well.)

Commercially purchased analyticalapplications usually have an interface tobe used by information workers, managers

and analysts. But for proprietary analyses,the presentation tools determine howdifferent classes of individuals can usethe data. For example, a statistician could

directly access a statistical model, butmost managers would hesitate to do so.

A new generation of analytical toolsfrom vendors such as Spotfire, VisualSciences and SASallow the manipulationof data and analyses through an intuitivevisual interface. A manager, for example,could look at a plot of data, excludeoutlier values, and compute a regressionline that fits the dataall without anystatistical skills. Because they permitexploration of the data without the riskof accidentally modifying the underlyingmodel, visual analytics tools increasethe number of users who can employsophisticated analyses. At VertexPharmaceuticals, for example, CIOSteve Schmidt estimates that only 5percent of his users can make effectiveuse of algorithmic tools, but another 15percent can manipulate visual analytics.

Operational processes

This element of the BI architecture answersquestions about how the organizationcreates, manages and maintains data andapplications. It details how a standardset of approved tools and technologiescan be used to ensure the reliability,scalability and security of the IT envi-ronment. Standards, policies and processesmust also be defined and enforcedacross the entire organization.

Issues such as privacy and security aswell as the ability to archive and auditthe data are of critical importance todata integrity. This is a business as wellas a technical concern, because lapsesin privacy and security (for example, if customer credit card data is stolen) canhave dire consequences. Executives canbe found criminally negligent if theyfail to establish procedures to docu-ment and demonstrate the validity of data used for business decisions.


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Conclusion

Top management can help the IT archi-tecture team plan a robust technical

environment by establishing guidingprinciples for analytical architecture.Those principles can help to ensure thatarchitectural decisions are aligned withbusiness strategy, corporate culture andmanagement style.

About the authors

Jeanne G. Harris ([email protected]) is a senior executiveresearch fellow at the Institute forHigh Performance Business inWellesley, Massachusetts.

Thomas H. Davenport is the formerdirector of the Accenture Institute forHigh Performance Business. He holdsthe Presidents Chair in InformationTechnology and Management atBabson College in Wellesley,Massachusetts.

About Accenture

Accenture is a global managementconsulting, technology services andoutsourcing company. Committedto delivering innovation, Accenturecollaborates with its clients to helpthem become high performance busi-nesses and governments. With deepindustry and business process expertise,broad global resources and a proventrack record, Accenture can mobilizethe right people, skills and technologiesto help clients improve their performance.With approximately 146,000 people in49 countries, the company generatednet revenues of US$16.65 billion forthe fiscal year ended August 31, 2006.Accentures home page iswww.accenture.com.

About the Accenture Institutefor High Performance Business

The Accenture Institute for HighPerformance Business creates strategicinsights into key management issuesthrough original research and analysis.Its management researchers combineworld-class reputations with Accenture'sextensive consulting, technology andoutsourcing experience to conductinnovative research and analysis intohow organizations become and remainhigh-performance businesses.

To make that happen, senior manage-ment must be committed to the process.

Working with IT, senior managersmust establish and rigorously enforcecomprehensive data management poli-cies, including data standards andconsistency in data definitions. Theymust be committed to the creation anduse of high-quality data that is scalable,integrated, well documented, consistentand standards-based. And they mustemphasize that the business intelligencearchitecture should be flexible and ableto adapt to changing business needsand objectives. A rigid architecturewont serve the needs of the businessin a fast-changing environment.

Top management can help the IT architecture team plan a robust technical environmentby establishing guiding principles for analytical architecture. Those principles can helpto ensure that architectural decisions are aligned with business strategy, corporateculture and management style.

To make that happen, senior management must be committed to the process. Workingwith IT, senior managers must establish and rigorously enforce comprehensive datamanagement policies, including data standards and consistency in data definitions.They must be committed to the creation and use of high-quality data that is scalable,integrated, well documented, consistent and standards-based. And they must emphasizethat the business intelligence architecture should be flexible and able to adapt tochanging business needs and objectives. A rigid architecture will not serve the needsof the business in a fast-changing environment.

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