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Alignment with the Business Community

  Article published in DM Direct Special Report
May 21, 2002 Issue
  By John Ladley

Many recent articles from data warehouse experts have revealed the need to align the data warehouse (DW) and business intelligence (BI) with the business. Suddenly cost of ownership, return on investment, sustaining value and increased business usage are important. The public verbalizations are seemingly new, but successful BI environment practitioners have long known that alignment with business drivers is key to success. Business drivers are now important. The basic concept seems simple ? without firm business drivers, DW/BI projects are technical projects vs. a business solution. Usability, cost of ownership and sustainability all suffer. These words resonate with many practitioners of DW development and design.

As usual, the devil is in the details, and this article endeavors to delve into that area between defining hard business drivers and acquiring the tools for building the DW. Far too many DW projects go from lists of business problems to tool selection without fleshing out the appropriate details. This is due to two reasons.

  1. CIOs or other executive see the benefit of DWs, but proceed without a business sponsor.
  2. Tools selections are frequently based on blind adherence to a particular design philosophy or are influenced by corporate standards that have no bearing on DW technology. The procurement process becomes a political issue vs. a business issue.

Our firm keeps tabs on this particular foible, and we that 90 percent of BI or related projects select tools and hardware before having any ideas what the first application will look like or what measurable benefit it will bring. As DW development has matured, it is apparent that key business requirements and business intelligence facilities must be aligned. Therefore, all tool selections must be predicated on a firm understanding of the business needs and how those needs translate into the DW framework. (Framework here means the nature and sequence in which the combination of granular data stores, summarized stores, operational stores etc. are implemented.) It is often mentioned that the DW architecture should not be based on business needs, but rather an idealized information factory concept. While the ultimate goals is certainly an efficient information factory, and the current Corporate Information Factory (CIF) hub and spoke-thinking is the most efficient topology, there is no need to acquire all of the tools and funding from the get go. It is also bad business to proceed toward the enterprise CIF with out a notion of the benefits and cost of the implemented frameworks. The business realities of many projects have caused this practitioner to develop a strategy for delivery based solely on business needs aligned with the correct DW framework. Bottom line is that CEOs are saying a firm "no" to any project that cannot predict value before it is built.

Focusing on the business requirements of a data warehouse must go beyond a list of needs. The key to developing a series of value-added projects that create an efficient intelligence infrastructure is a detailed understanding of business measures and information usage. High return DW projects require an emphasis on business improvement vs. delivery of reports and access to information.

Drivers to Requirements to Frameworks

The basic process is to decompose business drivers, or goals, into measurement-based requirements. These, in turn, are used to create data or parameters that suggest the type of infrastructure and project required to meet business needs.

The process starts, as do most, with business drivers. This means anything the business requires to solve a problem or meet an objective. Business driver at this point do not include examples such as:

  • Inability to share information across the organization;
  • Multiple, inconsistent sources of data; and
  • Lack of ability to generate reconcilable financial reports.

The term business drivers can be vague. Most organizations have goals and objectives or strategies. There are many layers to corporate and organizational strategies. Most list drivers as some sort of influencing factor. Therefore, be cautious of semantics. A great deal of the time, goals, objectives and/or drivers can be discerned from corporate documents. The key is to look for material that lists or implies measurable goals and objectives. There are several fundamental reasons to take this perspective.

  • The ability to share data is not of prime interest to a CEO. It is lower costs, more revenue. Period. If sharing data leads to those, fine; if not, don?t bother them.
  • Most executives have been interviewed into a stupor. However, few DW teams know enough about the business to replace executive insight ? so other techniques are called for.
  • Business metrics are objective and supply the same level of information to a DW architect that exhaustive interviews provide.

After business drivers are confirmed, business goals and objectives are nested within the drivers. See Figure 1.

Figure 1: Confirmed Business Drivers

Driver ? general business influence, direction

Goal ? specific strategy

Documented objectives ? measurable target

Measurable attributes ? used to monitor objectives

Strategic Enterprise Goals

Customer Loyalty

Reward customer loyalty

Target high-value customers

Increase market share of high-value customer 3 points


Keep enterprise financially strong

Grow the business

Increase new client sales by 15 percent; increase renewals by 5 percent

Market Loyalty

Maintain a consistent view of company products and services

Commonly aligned messages throughout the brand experience

80 percent of customers express intent to renew

Business ? Goals Related to Business Targets

Service Capability

Increase ability to keep up with sales volumes

Distribute information to all touchpoints

Point-of-sale information collection

Customer Loyalty

Be consistent across product divisions

Common aligned messages throughout the product experience

Increased speed of case resolution to average of three days


Reduce costs

Maximize investment in customer programs

Customer related spending YTD

Market Loyalty

Make customer experience a competitive advantage

Emphasize distribution channel sales effectiveness

Increased sales growth year over year 15 percent

Process - Goals Related to Process Improvement

Customer Loyalty

Improve service representative efficiency

Reduce response time

Respond within eight hours 90 percent of time

Service Capability

Reduce cycle times

Consistent experience at every touchpoint

Increased customer satisfaction, more accurate information

Service Capability

One-stop shopping experience

Single point of contact for all interactions

Increased customer satisfaction, quicker resolution


Increase marketing campaign effectiveness

Increase response rate to marketing programs

Reduce mailing costs

Technology ? Goals Related to Enabling Technology

Service Capability

Support business growth

Design flexible and high performance data components

Database scalability, performance and ease of use

Customer Loyalty

Leverage customer data asset

Create appropriate customer data stores and enable customer data analytics

Customer data integrity

Customer Satisfaction

Leverage customer experience data

Manage consistent customer data by implementing data asset management

Use of common customer files, policies for meta data management

People ? Goals Related to Human Capital

Customer Loyalty

Improve effectiveness of each customer touch

Enable employees to use customer data effectively

Increased self-sufficiency in retrieval and use of customer data

Market Loyalty

Develop passion for product

All employees demonstrate product knowledge

Increase customer satisfaction via survey

Service Capability

Become advocate for each customer

Empower service reps to understand the customer

Increased rep decision-making capacity

Note that the traditional people, process, technology categories of can be used to trigger goals and objectives should interviews or end-user participation be scarce. A preliminary list of measures is created to allow the business to confirm the efficacy of using information to help meet business objectives.

Requirements Topology

Once business drivers, goals and objectives are listed, then business requirements are refined through the measures, or business metrics, and an information model. Again, this is an apolitical approach. It is designed for the prevalent situation where the DW team is creating architecture and infrastructure before the business case unfolds.


Measures are key to alignment. Measures express whether goals and objectives are being met. A measure is a calculation, a count or snapshot number that indicates achieving an objective. Capturing the enterprise measures creates a foundation for capturing 80 percent of the analytical data elements and dimensions required for most organizations. Once captured. Measures are decomposed into their component parts.

Measure name: Complaint response time

Description: Amount of time a customer query is addressed and resolved

Business objectives measure supports: Reduce response time, common message through experience

Attributes used: Complaint number, representative, complaint date time, complaint code, complaint description, complaint notes, assigned resolution employee

Algorithm: Count number of complains by month and calculates arithmetic mean and median of average days from start to resolution.

Key Dimensions: Product line, service rep, region, customer type, customer category

Latency: Must be available within one day of monthly close

Volume: Approximately 500 complaints per month

Granularity requirement: Individual complaints need to be counted, no drill through for this measure

Historical Requirements: Year over year required for three years

Dynamic nature: The metric is stable


From the measures, a conceptual data model is derived (or enhance an existing one). This provides a foundation for two other types of analysis.

  1. Confirmation of the information requirements. There is a chance the measures will miss some dimensions or primary facts. If the scope of the analysis is not at an enterprise view, the data the model helps as an enterprise-level check and balance. Creating entities and primary keys usually generates more information requirements.
  2. Data quality is of paramount concern. The conceptual model allows the team to perform, at minimum, an anecdotal survey of source data quality and do a conceptual mapping. As we know, poor data quality can radically affect the types of tools and process required to manage the DW.


The final step in creating a business-aligned architecture is to develop a framework from the requirements (measures and models). Again, framework here means the nature and sequence in which the combination of granular data stores, summarized stores, operational stores etc. are implemented. This is to have a documented rationale for the kinds of tools and hardware to buy, without guessing or overbuying. It is irresponsible to buy capacity or fund a framework that won?t be fully used for a period, just because it will eventually be needed; for example, to buy a large server for a large accessible atomic layer when the initial apps will present data at a summarized layer. It is equally irresponsible to start with mart-type structures that you know will run out of gas or offer performance challenges as soon as latency requirements change (and they will). The key is know what the ultimate structure will look like (most likely a CIF hub and spoke) but create incremental projects that build toward the framework efficiently while adding value.

Developing the framework starts by using two techniques to analyze the measures.

  1. Affinity grouping of measure by latencies and granularity. This gives an indication of type of framework. For example, clustering a series of measures together that have low latency, and having another cluster o f higher latency measures indicates the need for a multiple, iterative projects. One project will most likely address some or all of the lower latency measures, another the higher. The same type of analysis is applied to granularity.

If the business goals and objectives are linked to a specific cluster, then that portion of the framework that supports the cluster is a likely candidate to meet those business needs. There are many other analyses that can be done, but space is limited in this article.

  • Analyze the types of data movement that will be required to use the measures; for example, low latency measures my require messaging to produce timely answers, whereas higher latency measures could use traditional ETL approaches.
  • Develop a picture of how the various types of structures will roll out to produce the CIF or hub and spoke framework. This is a function of clustering, and the types of data movement required to satisfy the requirements in those clusters.
  • Use the data associated with the measures and models to provide requirements to the tool and meta data creation processes.

The key is satisfying business needs. Too many wonderful DW structures are going begging because of disregard of a simple principle ? businesses need to make money, not spend it. Remember most DW projects start with technology. This is a sure fire way to buy too much, overbuild, underbuild or create an environment that has too few users for the amount spent. Alignment with the business reduces the risk of non-usage and excessive spending. Alignment with the business while developing an efficient DW topology requires old-fashioned business analysis and reliance on apolitical techniques.

John Ladley is President of KI Solutions (formerly Knowledge InterSpace and short for Knowledge and Information Solutions,) a management consulting firm specializing in knowledge and information asset management and strategic business intelligence planning and delivery. He can be reached at

John Ladley presented his keynote address for the Online Conference and Expo. You can hear his keynote at

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