From: Subject: IntelligentEnterprise : Differences of Opinion (printable version) Date: Tue, 17 Oct 2006 10:32:07 +0200 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_0000_01C6F1D7.7ECC2AD0" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.2962 This is a multi-part message in MIME format. ------=_NextPart_000_0000_01C6F1D7.7ECC2AD0 Content-Type: text/html; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.intelligententerprise.com/print_article.jhtml;jsessionid=COFO2TMFNKHFIQSNDLRSKH0CJUNN2JVN?articleID=17800088 IntelligentEnterprise : Differences of Opinion = (printable version)

marzo 2004
http://www.intelligententerprise.com/showArticle.jhtml?articleI= D=3D17800088

Differences of Opinion

The Kimball bus architecture and the Corporate Information = Factory:=20 What are the fundamental differences?

By Margy Ross , Ralph Kimball=20

Based on recent inquiries, many of you are in the midst of = architecting=20 (or rearchitecting) your data warehouse. There's no dispute that = planning=20 your data warehouse from an enterprise perspective is a good idea, = but do=20 you need an enterprise data warehouse? It depends on your = definition. In=20 this column, we'll clarify the similarities and differences = between the=20 two dominant approaches to enterprise warehousing.

Common Ground

We all agree on some things related to data warehousing. First, = at the=20 most rudimentary level, nearly all organizations benefit from = creating a=20 data warehouse and analytic environment to support = decision-making. Maybe=20 it's like asking your barber if you need a haircut, but personal = bias=20 aside, businesses profit from well-implemented data warehouses. No = one=20 would attempt to run a business without operational processes and = systems=20 in place. Likewise, complementary analytic processes and systems = are=20 needed to leverage the operational foundation.

Second, the goal of any data warehouse environment is to = publish the=20 "right" data and make it easily accessible to decision makers. The = two=20 primary components of this environment are staging and = presentation. The=20 staging (or acquisition) area consists of extract-transform-load = (ETL)=20 processes and support. Once the data is properly prepared, it is = loaded=20 into the presentation (or delivery) area where a variety of query, = reporting, business intelligence, and analytic applications are = used to=20 probe, analyze, and present data in endless combinations.

Both approaches agree that it's prudent to embrace the = enterprise=20 vantage point when architecting the data warehouse environment for = long-term integration and extensibility. Although subsets of the = data=20 warehouse will be implemented in phases over time, it's beneficial = to=20 begin with the integrated end goal in mind during planning.

Finally, standalone data marts or warehouses in Figure=20 1 are problematic. These independent silos are built to = satisfy=20 specific needs, without regard to other existing or planned = analytic data.=20 They tend to be departmental in nature, often loosely = dimensionally=20 structured. Although often perceived as the path of least = resistance=20 because no coordination is required, the independent approach is=20 unsustainable in the long run. Multiple, uncoordinated extracts = from the=20 same operational sources are inefficient and wasteful. They = generate=20 similar, but different variations with inconsistent naming = conventions and=20 business rules. The conflicting results cause confusion, rework = and=20 reconciliation. In the end, decision-making based on independent = data is=20 often clouded by fear, uncertainty, and doubt.

Figure 1 Independent data = marts/warehouses.

So we all see eye to eye on some matters. Before turning to our = differences of opinion, we'll review the two dominant approaches = to=20 enterprise data warehousing.

Kimball Bus Architecture

If you've been regularly reading the last 100 or so columns, = you're=20 familiar with the Kimball approach in Figure=20 2. As we described in our last column "Data=20 Warehouse Dining Experience" (Jan. 1, 2004), raw data is = transformed=20 into presentable information in the staging area, ever mindful of=20 throughput and quality. Staging begins with coordinated extracts = from the=20 operational source systems. Some staging "kitchen" activities are=20 centralized, such as maintenance and storage of common reference = data,=20 while others may be distributed.

Figure 2 Dimensional data warehouse.

The presentation area is dimensionally structured, whether = centralized=20 or distributed. A dimensional model contains the same information = as a=20 normalized model, but packages it for ease-of-use and query = performance.=20 It includes both atomic detail and summarized information = (aggregates in=20 relational tables or multidimensional cubes) as required for = performance=20 or geographic distribution of the data. Queries descend to = progressively=20 lower levels of detail, without reprogramming by the user or = application=20 designer.

Dimensional models are built by business process (corresponding = to a=20 business measurement or event), not business departments. For = example,=20 orders data is populated once in the dimensional data warehouse = for=20 enterprise access, rather than being replicated in three = departmental=20 marts for marketing, sales, and finance. Once foundation business=20 processes are available in the warehouse, consolidated dimensional = models=20 deliver cross-process metrics. The enterprise data warehouse bus = matrix=20 identifies and enforces the relationships between business process = metrics=20 (facts) and descriptive attributes (dimensions).

Corporate Information Factory

Figure=20 3 illustrates the Corporate Information Factory (CIF) = approach, once=20 known as the EDW approach. Like the Kimball approach, there are=20 coordinated extracts from the source systems. From there, a third = normal=20 form (3NF) relational database containing atomic data is loaded. = This=20 normalized data warehouse is used to populate additional = presentation data=20 repositories, including special-purpose warehouses for exploration = and=20 data mining, as well as data marts.

Figure 3 Normalized data warehouse with = summary=20 dimensional marts (CIF).

In this scenario, the marts are tailored by business=20 department/function with dimensionally structured summary data. = Atomic=20 data is accessible via the normalized data warehouse. Obviously, = the=20 atomic data is structured very differently from summarized=20 information.

Fundamental Differences

There are two fundamental differentiators between the CIF and = Kimball=20 approaches. The first concerns the need for a normalized data = structure=20 before loading the dimensional models. Although this is a = requisite=20 underpinning of the CIF, the Kimball approach says the data = structures=20 required prior to dimensional presentation depend on the source = data=20 realities, target data model, and anticipated transformation. = Although we=20 don't advocate centrally normalizing the atomic data prior to = loading the=20 dimensional targets, we don't absolutely admonish it, presuming = there's a=20 real need, financial willpower (for both the redundant ETL = development and=20 data storage), and clear understanding of the two-step = throughput.

In the vast majority of the cases, we find duplicative storage = of core=20 performance measurement data in both normalized and dimensional = structures=20 is unwarranted. Advocates of the normalized data structures claim = it's=20 faster to load than the dimensional model, but what sort of = optimization=20 is really achieved if the data needs to undergo ETL multiple times = before=20 being presented to the business?

The second primary difference between the two approaches is the = treatment of atomic data. The CIF says atomic data should be = stored in the=20 normalized data warehouse. The Kimball approach says atomic data = must be=20 dimensionally structured. Of course, if you only provide summary=20 information in a dimensional structure, you've "presupposed the=20 questions." However, if you make atomic data available in = dimensional=20 structures, you always have the ability to summarize the data "any = which=20 way." We need the most finely grained data in our presentation = area so=20 that users can ask the most precise questions possible.

Business users may not care about the details of a single = atomic=20 transaction, but we can't predict the ways they'll want to = summarize the=20 transaction activity (perhaps for all customers of a certain type = living=20 in a range of ZIP codes that have been customers for more than two = years).=20 Anyone that's worked side by side with a business analyst knows = the=20 questions asked are unpredictable and constantly changing. Details = must be=20 available so that they can be rolled up to answer the questions of = the=20 moment, without encountering a totally different data structure. = Storing=20 the atomic data in dimensional structures delivers this = fundamental=20 capability. If only summary data is dimensional with the atomic = data=20 stored in normalized structures, then drilling into the details is = often=20 akin to running into a brick wall. Skilled professionals must = intervene=20 because the underlying data structures are so different.

Both approaches advocate enterprise data coordination and = integration,=20 but the implementations differ. CIF says the normalized data = warehouse=20 fills this role. While normalized models communicate data = relationships,=20 they don't inherently apply any pressure to resolve data = integration=20 issues. Normalization alone doesn't begin to address the common = data keys=20 and labels required for integration.

From the earliest planning activities, the Kimball approach = uses the=20 enterprise data warehouse bus architecture with common, conformed=20 dimensions for integration and drill-across support. Common, = conformed=20 dimensions have consistent descriptive attribute names, values, = and=20 meanings. Likewise, conformed facts are consistently defined; if = they=20 don't use consistent business rules, then they're given a distinct = name.

Conformed dimensions are the backbone of any enterprise = approach as=20 they provide the integration "glue." We've provided detailed = design and=20 implementation guidance regarding conformed dimensions in our = books and=20 columns. Conformed dimensions are typically built and maintained = as=20 central persistent master data in the staging area, then reused = across the=20 enterprise's presentation marts and warehouses to ensure data = integration=20 and semantic consistency. It may not be practical or useful to get = everyone to agree to everything related to a dimension; however,=20 conformity directly correlates to an organization's ability to = integrate=20 business results. Without conformity, you end up with isolated = data that=20 cannot be tied together. This situation perpetuates incompatible = views of=20 the enterprise, diverting attention to data inconsistencies and=20 reconciliations while degrading the organization's decision-making = ability.

Hybrid Approach?

Some organizations adopt a hybrid approach, theoretically = marrying the=20 best of both the CIF and Kimball methods. As shown in Figure=20 4, the hybrid combines Figure=20 1 and 2.=20 There's a normalized data warehouse from the CIF, plus a = dimensional data=20 warehouse of atomic and summary data based on the Kimball bus=20 architecture.

Figure 4 Hybrid of normalized data = warehouse and=20 dimensional data warehouse.

Given the final presentation deliverable, this approach is = viable.=20 However, there are significant incremental costs and time lags = associated=20 with staging and storing atomic data redundantly. If you're = starting with=20 a fresh slate and appreciate the importance of presenting atomic = data to=20 avoid presupposing the business questions, then why would you want = to ETL,=20 store, and maintain the atomic details twice? Isn't the value = proposition=20 more compelling to focus the investment in resources and = technology into=20 appropriately publishing additional key performance metrics for = the=20 business?

Of course, if you have already built a normalized data = warehouse and=20 now recognize the need for robust presentation capabilities to = deliver=20 value, then the hybrid approach lets you leverage your preexisting = investment.

Success Criteria

When evaluating approaches, people often focus strictly on IT's = perception of success, but it must be balanced with the business'=20 perspective. We agree that data warehouses should deliver a = flexible,=20 scalable, integrated, granular, accurate, complete, and consistent = environment; however, if it's not being leveraged by the business = for=20 decision-making, then it's impossible to declare it successful. = Does the=20 business community use the data warehouse? Is it understandable to = them=20 (including nontechnical users)? Does it answer their business = questions?=20 Is the performance acceptable from their vantage point?

In our opinion, the success of any data warehouse is measured = by the=20 business's acceptance of the analytic environment and their = benefits=20 realized from it. You should choose the data warehouse = architecture that=20 best supports these success criteria, regardless of the label.

Margy Ross, [margy@ralphkimball.com] = is=20 president of the Kimball Group and an instructor with Kimball = University.=20 She cowrote The=20 Data Warehouse Lifecycle Toolkit (Wiley, 1998) and The = Data=20 Warehouse Toolkit, 2nd Edition (Wiley, 2002).

Ralph Kimball, founder of the Kimball Group, teaches = dimensional=20 data warehouse design through Kimball University and critically = reviews=20 large data warehouse projects. You can reach him through his Web = site, http://www.ralphkimball.com/.

=A9 2006 CMP Media LLC

Return=20 to Article

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s_trackExternalLinks=3Dtrue var s_trackInlineStats=3Dtrue var = s_linkDownloadFileTypes=3D"exe,zip,wav,mp3,mov,mpg,avi,wmv,doc,pdf,xls,rm= " var s_linkInternalFilters=3D"javascript:,." var s_linkLeaveQueryString=3Dfalse var s_linkTrackVars=3D"None" var s_linkTrackEvents=3D"None" /* Plugin Config */ var s_usePlugins=3Dtrue function s_doPlugins() { /* Add calls to plugins here */ /* plugin code created October 7, 2004 by Ryan Filimoehala--Omniture IE = */ /* updated November 22, 2004 by Ryan Filimoehala--Omniture IE */ /* updated January 5, 2005 by Ryan Filimoehala--Omniture IE */ function getParam( pn ) { var qs =3D location.search.toLowerCase(); // to lower case for case = insensitivity of name if( qs.indexOf( pn ) !=3D -1 ) { var vi =3D qs.indexOf( pn ) + pn.length + 1; // + 1 for =3D qs =3D location.search; // reset to keep case sensitivity of value if( qs.indexOf( '&', vi ) !=3D -1 ) { return( qs.substring( vi, qs.indexOf( '&', vi ) ) ); } else { return( qs.substring( vi, qs.length ) ); } } else { return( null ); } } =09 s_vpr( 's_server', document.domain.toLowerCase() ) // set s_server = variable to page domain var hrefArray =3D location.href.split( '/' ) var dirStruct =3D "" // declare directory structure variable for( i=3D0; i < hrefArray.length-1; i++ ) { if( i > 1 && // include domain name in value i < hrefArray.length ) { dirStruct +=3D hrefArray[i] + '/' } } =09 s_vpr( 's_channel', dirStruct ) // set s_channel to directory structure = =09 if( getParam( 'kw' ) =3D=3D null ) { s_vpr( 's_campaign', getParam( 'cid' ) )=09 } else { s_vpr( 's_campaign', "KW:" + getParam( 'kw' ) ) } =09 =09 /* if statement condition for querytext added on 5/18/05 as requested = by CMP // Mark Stringham -- Omniture IC */ =09 if( s_vp_getValue( 's_prop1' ) =3D=3D '' ) { s_vpr( 's_prop1', getParam( 'querytext' ) ) // set s_prop1 from = query string } if( s_vp_getValue( 's_prop2' ) =3D=3D '' ) { s_vpr( 's_prop2', getParam( 'articleid' ) ) // set s_prop2 from query = string } /* getCGI added on 5/18/05 for DartMail Testing by Mark Stringham -- = Omniture IC */ if(!s_vp_getValue('s_campaign')) { s_vp_getCGI('s_campaign', 'ssdmh') } /* core data collection */ if(window.s_prop3) var s_eVar14=3Ds_prop3; if(window.s_prop4) var s_eVar15=3Ds_prop4; if(window.s_prop7) var s_eVar17=3Ds_prop7; var s_prop =3D "s_prop13"; var repPrefix =3D "KW:"; /* an optional prefix for the campaign landing = page */ var tp =3D s_vp_getValue("s_pageName"); /* replace pageName with url = param name here */ var tc =3D s_vp_getValue("s_campaign"); =09 if(tc) { =09 s_vpr(s_prop, tc) // set s_prop to campaign + kw on landing page only } if(document.cookie.indexOf('s_p_s_path')=3D=3D-1) { s_c_w("s_p_s_path",tc,0); } if (s_c_r('s_p_s_path') && (!tc)) { s_vpr(s_prop,tp) } /* setValOnce added on 10/12/05 to set single instance of query param = value by Mark Stringham -- Omniture IC */ s_vp_setValOnce('s_campaign'); } /************************** PLUGINS SECTION *************************/ /* You 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