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Leapfrog the Competition | © Mark Jeffery and Richard Winter 2011, All rights reserved.

Leapfrog the Competition: The Five Essential Questions to Ask for Every Data Warehouse Investment

Mark Jeffery, Ph.D.Kellogg School of Management and Agile Insights [email protected]

© Mark Jeffery and Richard Winter 2010, All rights reserved.

Richard WinterWinter [email protected]


Executive Summary

Can you expand or retain your market share without having deep insights into your customer’s needs and behaviors?

Can you fight margin erosion without supply chain transparency and optimization?

Can you afford to let your competitors beat you to the punch with their analytical insights?

1. What is the strategy for the data warehouse?

2. How do you engineer the business case?

3. Why are good business requirements essential for success?

4. How do you select the right platform and why is it critically important?

5. How do you execute for success?

In all likelihood, the answer to these questions is no. In today’s challenging business environment you need a flexible and scalable data warehouse (DW) platform to support your enterprise analytical needs.

This white paper is based upon our over 25 years of experience working with leading companies on DW and enterprise technology solutions. We share the secrets of leading organizations by answering five essential questions:

By answering these questions we show the path to leapfrog the competition and create a competitive advantage from your enterprise data.

As a first step, the leaders directly link their DW strategy to their business strategy. These organizations singularly focus on collecting and integrating data that is required to meet specific business goals. Many enterprise data warehouse failures can be attributed to collecting a massive amount of data without having a strategy to utilize it. In this white paper, we show how you can start small, scale fast, and grow while actively managing risk. The learning’s are applicable

to both single department solutions and much larger enterprise-wide initiatives.

A common theme of the leaders is that they systematically integrate data, derive complex business insights, and act on these insights either strategically or operationally to drive breakthrough business results. In this paper we show how to define the data warehouse strategy tied to the business strategy and engineer the ROI business case. You will also learn why understanding the business requirements is critically important since these requirements, today and in the future, define the optimal DW platform solution.


Selecting the right platform and technology vendor for your DW is the next crucial step. Leaders look three to five years down the road when selecting a platform: they see the organization vision and what types of business problems are expected, and think through how these challenges will impact the DW. It’s not possible to predict everything in advance, however, and the leaders understand the need for an agile infrastructure that can consistently unlock insights that beat the competition. Once the business problems are clearly defined, a business case has been made, and the right platform has been selected based on the business requirements, then you ensure success by project planning, risk management, and measuring success at each step of the journey.

This white paper provides best practices from our experience working with hundreds of clients on DW and enterprise technology solutions. Answering the five essential questions will enable you to develop a clear data management roadmap to the future, avoid the common pitfalls, and leapfrog your competition by creating a true strategic advantage from data and analytics.

For leaders in organizations who need to significantly

increase growth, profitability and/or efficiency of their

operations, Agile Insights is a professional services firm

that delivers measurable performance gains through

research, professional education, and management

consulting. Unlike other consulting companies, our

services are delivered by principals with deep experience

supported by cutting edge research. The managing

partner, Dr. Mark Jeffery, is an internationally recognized

expert in information technology (IT) management and

data-driven marketing.

www.agileinsights.com

WinterCorp is an independent consulting firm that

specializes in the performance and scalability of

data management systems throughout their lifecycle,

focusing on data warehousing. Since our inception in

1992, we have architected some of the largest and

most challenging databases in production today. Our

consulting services help users, system integrators and

vendors define business-critical database solutions,

select their platforms, engineer their implementations

and manage their growth to optimize business value.

www.wintercorp.com

Executive Summary Cont’d

About:

Agile Insights

About:

Winter Corp


Table of Contents

Introduction 1

The Five Essential Questions:

1. What is the strategy for the data warehouse? 3

2. How do you engineer the business case? 4

3. Why are good business requirements essential for success? 6

4. How do you select the right platform and why is it critically important? 9

5. How do you execute for success? 12

Summary 14

To Learn More 15

Author Biographies 16


Introduction

In 2008 the world was blindsided by the fall of Lehman Brothers and Bear Sterns. Repercussions of the financial meltdown exist today, years after the event. Volatility is at a historic high and senior executives more than ever before need fast and accurate answers in the face of great uncertainty: CFO’s are scrambling to understand their global sovereign debt exposure, COO’s need immediate supply chain transparency to accurately predict and manage volatile demand, CMO’s require deep customer insights and analytics to target marketing and grow revenues, and CEO’s need a real-time dashboard for corporate command and control. As a result, corporate data management has transitioned from a back office IT responsibility to a senior executive imperative.

In uncertain times leading organizations are able to rapidly respond to customer and market needs. DIRECTV, for example, can make a financially viable retention offer when a customer calls to cancel service, resulting in hundreds of millions of dollars in saved revenues each year and the lowest customer churn in its industry. In the current environment, the ability to understand and quickly react to changes in the market and customer behavior is essential to success.

An agile enterprise uses business analytics to gain insights into its customers, internal operations, and market dynamics, and changes course before its competitors. As an example, by embedding analytics in its enterprise data warehouse and by targeting relevant offers in relevant geographies, Cabela’s, a leading outdoor sports outfitter, realized a 2.2% increase in

same-store sales and 60% increase in marketing campaign take rates (the fraction of customers who accept marketing offers). By selecting the right platform solution, it reduced 3.5 weeks of analysis time to 5 hours – speed to decision making and more targeted offers were the key to increased same-store sales.

DIRECTV and Cabela’s are great success stories, and there are many more we could discuss. However, our research of more than 400 firms shows that less than 22% have a true enterprise data warehouse capability, with integrated data and analytics infused throughout the business. So that while there are many stories of firms that have realized amazing results from analytics and DW systems, the vast majority of senior executives struggle with the question: ‘Where do I start?’

The road to a DW solution may seem daunting, but we show that the leaders follow a repeatable and relatively straightforward approach. And yes, you can easily apply these principles in your organization, for a department or enterprise-wide solution.

“Corporate data management has transitioned from a back office IT responsibility to a senior executive imperative.”

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Leapfrog the Competition | © Mark Jeffery and Richard Winter 2011, All rights reserved. 2

Figure 1 is a framework for thinking through the DW decision-making process, where each numbered step is the answer to an essential question:

What is the strategy for the data warehouse?

How do you engineer the business case?

Why are good business requirements essential for success?

How do you select the right platform and why is it critically important?

How do you execute for success?

Regardless of your objectives and business needs, a DW deployment requires careful planning and execution. If done right, the business impact can far exceed expectations, as an integrated and active data platform can create unexpected business opportunities – opportunities that may provide breakthrough results for your business. However, lack of planning can derail a project, leading to a disastrous outcome. We suggest that you enter into the decision to invest in a data warehouse with your eyes wide open, aware of both the likely benefits and the potential pitfalls. This white paper will provide you with a winning playbook to leapfrog your competition and create a true strategic advantage from your data.

Introduction Cont’d

Objective:

Leapfrog the competition by becoming agile through integrated and active data

Build the business case. Analyze cost drivers and revenue upside opportunities.

Define the requirements. Understand scope and complexity. Consider long term needs.

Select a platform that can meet the long term vision of an active enterprise and can support operational and strategic BI needs.

Execute for success. Avoid the common pitfalls. Manage risk and maintain executive support. Measure against defined success metrics.

Set the EDW vision. Define the strategy and objectives. Start small and scale fast. M

easure and Scale

Figure 1: A framework for unlocking value from EDW investments.

1.

2.

3.

4.

5.


To paraphrase Michael Porter, sustainable competitive advantage is created by the coordination of activities that are not easily duplicated. Leading organizations are finding that rapid business decision-making and the ability to provide analytical capabilities to front-line staff are significant sources of competitive advantage. Yet, as Peter Drucker said, “Management is doing things right; leadership is doing the right things.” We therefore take a pragmatic approach to executive decision-making starting with the overarching DW strategy.

Summary Insights:As a first step, define the vision for the DW following the mantra think big, start small, and scale fast.

In our experience, a primary reason that DW projects fail is that data is collected at great effort and expense across the enterprise and put into the database, but limited thought is given to what to do with the data once collected, so the project does not produce the desired results. So where do you start? The first essential step is to develop a vision and define a DW strategy and objectives that will lead to tangible returns. That is, the business problem you are trying to solve should be clearly defined.

We advocate thinking big, starting small, and scaling fast: Find the quick win that will build momentum for the initiative. A common myth is that you have to have all of the data to get started. In fact, the focus should be the 80/20 rule: Ask what are the 20% of opportunities that will produce 80% of the business value? Then you can create a focused plan, collecting the data to meet those 20% of opportunities.

For example, the Royal Bank of Canada (RBC), one of the world’s largest and most successful banks, wanted to increase the conversion rate for its end-of-tax year retirement plan offers (similar to US Individual Retirement Accounts). RBC started by building a model to analyze 12 months of data on

more than 1 million customers, and scoring the customers to find the 250,000 that had the most potential to contribute to a retirement plan. The results were impressive: Eight out of ten targeted customers accepted the offer to set up IRAs, this was an 800% improvement in performance. As a result of the quick win, the RBC initiative became the catalyst for a more than $10 million enterprise data warehouse initiative. The key to success was finding the right business opportunity and identifying the right data.

We recommend limiting the DW strategic deliverables to three to five concise objectives to maintain clarity and focus during the implementation. As an example, EarthLink, a midsize dial-up and broadband internet service provider, based its data integration and enterprise DW strategy on reducing the churn rate of its customers. Churn is the percentage of existing customers who stop purchasing products or services within a specified time. EarthLink developed an analytical model to predict churn and make optimal retention offers before customers called to cancel service. Over time and across all segments, the retained customers delivered a 20 times improvement in profitability. The business impact of the analytics was directly tied to the original strategic objectives of the data management solution.

To Test Your Team You Can Ask Questions Such As:

What is the strategy and objectives for the DW project?

What specific business problem are we solving?

How will we define success?

The best organizations define a one or two sentence vision statement for the DW program and follow this with three or four clearly articulated business objectives. The strategy is codified in a short written document, with both business and IT involvement, and then shared with everyone who will be impacted by the initiative. In our experience, too often the DW strategy is relegated to the IT team. We believe this is the kiss of death for a DW project. Business, not IT, must lead by defining a shared vision for the strategic initiative. Facilitated off-site workshops are a great way to create a shared strategic vision and disseminate best practices to business leadership.

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The Five Essential Questions:1. What is the strategy for the data warehouse?

“A common myth is that you have to have all of the data to get started.”


“May you live in interesting times” is an ancient Chinese curse. We are arguably living in the most challenging business environment since the Great Depression, and a solid return on investment (ROI) has become a prerequisite of all capital investments such as a DW. The good news is that data warehousing and analytics are relatively straightforward to quantify using hard financial metrics. But, as we will see at the end of this section, the true ROI will most likely be much greater than your initial projections.

Summary Insights:Look like a genius to your CFO and quantify the ROI for a DW investment using hard financials. However, the real value of the DW will be unpredicted insights unlocked from the data.

Like any other capital investment, the business case for a DW investment includes two components: (1) cost drivers, including the cost of implementation and potential cost savings, and (2) revenue upside opportunities. Ideally, the business discovery process to define the ROI should identify all possible opportunities to increase revenue and decrease costs, as well as define implementation costs. However if you are starting small, at least clearly understand the impact of the first-step smaller system and the way the benefits might scale.

In most cases, the DW implementation will reduce hardware, software and personnel costs due to system consolidations. The cost savings from consolidation alone can often offset or exceed investments in the new system. For example, when Continental Airlines consolidated 45 data marts into one DW, it realized a cost savings of $5 million per year.

The cost savings business case from consolidation is usually straightforward but could lead to unmanaged expectations by business executives. Yes, the consolidation will result in cost savings, but just consolidating data marts into one DW doesn’t enable a business to answer complex questions. You need to integrate data and ultimately make your data accessible in real-time – actionable integrated data is the key to driving revenue growth and competitive advantage.

The Five Essential Questions:2. How do you engineer the business case?

A Marketing Case Example:

Steps for Quantifying Revenue Impact

Identify the Opportunities: Define the existing profit

(revenue net of contact costs) based on the current mix

of inbound leads, contacts/offers, response rates, and

response values – this is the base case of the analysis.

Significant improvements could potentially be realized

by varying the mix of offer types for inbound leads and

by improving responses (take rates) and revenue lift for

targeted offers.

Analyze the Scenarios: Potential analyses may include

identification and management of inbound leads by offer

type mix and future profitability. Other scenarios may

include untargeted product centric marketing offers,

targeted product-centric offers, and event-based marketing

offers that are predefined, real-time or other.

Identify Actions: Potential actions may include

management of offer types for inbound leads contacted

in order to achieve a more favorable mix of take rates and

revenue lift.

Understand the Benefits and Impact: Net the total

increased profitability after improving the mix of lead/offer

types (from non targeted product centric to more targeted,

more event driven), improving channel optimization (for cost

and response rates), and driving higher response values

based on offer types.

Quantify the Financial Impact: Calculate the total

increased revenue and reduced contact costs, typically over

a three-year period. Then show the hard net present value

(NPV) and internal rate of return (IRR) numbers to your

CFO to justify the initiative.


Significant revenue and profitability increases are realized when integrated real-time data is combined with analytics. As an example, a major financial services company implemented an active and integrated data platform to improve the performance of marketing campaigns. The base case analysis included the value of existing campaigns based on the existing outbound leads, offer types, response rate, and response value. The projected revenue and costs were based on the expected change in campaign mix as a result of targeted campaigns. Ultimately, the company realized a 5% revenue increase, 14% cost savings, and 12% jump in response rates for outbound campaigns. (See the Marketing Case Example sidebar for the steps needed to complete a systematic revenue upside analysis for marketing. Similar steps apply to improving operations, but the specific focal points will be different.)

In summary, DW investments can by justified using hard financial analysis similar to any other capital expenditure in your organization. Questions you can ask to validate the ROI analysis:

What are the assumptions and what data do we have to support they are correct?

What is the range of possible ROI outcomes?

What are the follow-on opportunities once we have the DW?

We view DW ROI modeling as a collaborative exercise with conservative assumptions vetted by the key stakeholders and, where possible, validated by an independent third party. Furthermore, a best practice business case includes at least three scenarios for expected benefits: A best case, most likely case, and a worst case. Why is a range of ROI’s critically important? If the best and worst case net present value (NPV) and internal rate of return (IRR) are very different then there is a lot of business risk in the project. In this case we suggest

you go back and re-work the assumptions, and think through risk management strategies (see the last section).

Realize that as an executive you can have the biggest impact on the DW project in the business case and project planning stages. Why? This is a great opportunity to stress test the project so your eyes are open to all possibilities, both good and bad, before you start spending real money.

We want to emphasize, however, that no matter how hard you try the initial ROI analysis will not be the full value equation. Invariably, the DW system will unlock value through follow on opportunities that are not predictable in advance. Continental Airlines, for example, found quite unexpectedly that fraud detection and management enabled by the DW delivered a 500% greater return than anticipated. Similarly, Lowes found that sales representatives were giving away free appliance delivery. Steve Stone, SVP and CIO of Lowes told us: “We had the DW platform so that putting reporting in place was not hard to do. The result was $25 million net to the bottom line.”

For the value of the DW we encourage you to prepare for the unexpected. You absolutely need the hard ROI to justify the initial DW investment, but the ultimate ROI will come from the many new and unanticipated insights gained across the enterprise as you unlock the value of your data – these are the game changing business opportunities that enable you to leapfrog the competition.

The Five Essential Questions:2. How do you engineer the business case? Cont’d

“You absolutely need the hard ROI to justify the initial DW investment, but the ultimate ROI will come from the many new and unanticipated insights gained across the enterprise as you unlock the value of your data.”


The strategic business objectives (Essential Question 1) establish where you want to go. The business requirements define what you are going to need to get there, from a business point of view. A common pitfall when starting a DW initiative is that executive teams think the solution is relatively simple (with complexity, for example, similar to a residential home), but the solution they need may have a scale with 1000 times more complexity (that is, they actually need the Empire State Building instead of a residential home). This mistake can be avoided by carefully thinking through the business requirements before laying a single brick to build the system.

Summary Insights:There are four things every business executive should know about business requirements when building a data warehouse:

1. You need the top level business requirements before you start selecting a platform.

2. The business requirements must be quantified roughly, to establish scale.

3. You need to get concrete on how the business is going to use the data warehouse to achieve the business objectives. You don’t need every detail, but you do need to sketch out scenarios of use in common sense business terms.

4. Your data warehouse needs to handle rapidly changing, unpredicted requirements – often including the ability to answer virtually any business question at any time, whether or not it is anticipated.

How is it possible to get the scale wrong by a factor of 1000? Because a database is not tangible, you can’t touch it or see it, and most databases IT builds are small so that scale is not an issue. That is, what is to be done with the data, the business

requirements, has as large an impact on system performance as the data volume.

If the business requirements are vague or unknown, it is impossible to project either the data volume or the workload reasonably. So as surprising as it seems, even companies with a lot of IT skill and experience often trip up when it comes to correctly gauging the scale or complexity of a data warehouse in advance. Hence the requirements, driven by the business strategy and objectives, should be well defined in advance.

Beyond lack of strategy and business objectives, the next major driver of DW failure is implementing a solution that solves today’s problem, but is not flexible for changing business needs. Again, this gap arises primarily because the true requirements of the system are not understood in advance. When the business requirements are vague, often the data warehouse gets built with: (1) data that lacks crucial details, has poor quality or is not timely, (2) data that is not – and cannot be – integrated, or (3) a platform that cannot perform at the required level, cannot scale or cannot keep the data available as needed. These problems can be fatal to the DW effort because they tend to show up when the system is near, or even beyond, apparent completion.

For example, a specialty grocery chain consisted of more than 500 stores and $10 billion revenues had the business goal of reducing stock outs. The retailer sold organic fresh produce and learned from a market study that reducing stock outs would significantly increase profits and customer satisfaction. The solution required analyzing point-of-sales data from each store every day and integrating these data with the supply chain to replenish the required products each day. For this example, the strategy is to reduce daily stock outs. The business requirements include the need for inventory levels at each store at closing, product specific demand in each store each day, and the requirement to complete the analysis on all 500 stores in a few hours after the stores close, in order to enable new product delivery by the next morning.

The Five Essential Questions:3. Why are good business requirements essential for success?


For this example, the system was built only to find that the entire system capacity was needed to calculate the restocking of a single store in one day, but calculations had to be done for more than 500 stores. That is, the management team had built a ranch house-size DW when they needed the Empire State Building to solve this problem. Of course, you won’t be able to predict all business requirements and every data warehouse need for the future, but defining the key goals for the next few years and then using them in a systematic and forward-looking DW development process tremendously increases the prospects for success.

The data you need are defined by the business questions you want to answer and the complexity of the business questions drives the complexity of requirements. For example, for an airline a simple business question is: How many people stopped flying in a particular sector? This would need simpledata from a single database. However, a complex businessquestion is: What other customers are likely to stopflying for the same reasons, what marketing campaign oroffers could we design to stop the loss of these customers,and how would the campaign offer affect profitability?This question needs enterprise data from multiple systems. The difference in requirements driven by the business questions can have massive implications; the ranch house compared with the Empire State Building infrastructure, a few hundred thousand dollars versus tens of millions of dollars. See the sidebar at right, Data Volume vs. Complexity.

Once the business requirements have been defined and understood, the next step is to document the technical requirements. One technical requirement that needs special mention is scalability. Successful use of the data warehouse is infectious and often results in significant new usage. For example, initially the focus may be on calculating product profitability, but this can lead to the realization that optimizing the supply chain can have big gains. As uses proliferate, data sources, data volumes, and workloads will grow.

The Five Essential Questions:3. Why are good business requirements essential for success? Cont’d

Data Volume vs. Complexity

Data Volume: The size of the data warehouse, typically

measured in terabytes (TB) or petabytes (PB). In sales,

marketing, and service applications, data volume is

primarily a function of the number of customers. That is,

the larger the customer base, the larger the data volume.

For example, a regional retailer with 10 stores and 10,000

customers will have a data warehouse with approximately a

terabyte of data, while a national retailer with 5,000 stores

and 100 million customers will need a DW platform

of several hundred terabytes or more. In supply and

manufacturing applications, the primary driver of data

volume is the variety and volume of products.

Complexity: The complexity of a DW environment is driven

by several factors. How large and how active is the user

population? How rapidly must new data be incorporated

in the system? How rapidly must user queries be answered?

Are the queries predictable or unpredictable? Are the

queries simple or complex? Must the system perform

different types of work at the same time, satisfying different

service level objectives for each?

High

Low

Departmental Data Volume Enterprise

Com

plex

ity

Complex Businessquestions, extensive ad-hoc

reporting, strategic and operational intelligence, 100s of users and small

data volume

Simple Businessquestions, pre-defined reports, some ad-hoc reporting, strategic

intelligence, 100s of users and small data volume

Complex Businessquestions, strategic and operational intelligence, real-time BI, predictive

analytics, 1000s to 100,000s front line users

and large data volume

Simple Businessquestions, pre-defined reports, some ad-hoc reporting, strategic

intelligence, 1000s of users and large data volume


In our experience, DW scalability problems surface at the worst possible times, and are created by vague requirements. For the specialty grocery chain example described above the system was logically correct but the management team had not understood the requirements correctly. Understanding the scalability performance requirement is also the key to procuring and purchasing the right platform. If you underestimate the scalability performance requirements, you will probably end up with a DW appliance that will not meet your business needs in the very near future. On the other hand, if you overestimate the scalability requirements, you could end up procuring a solution that is much larger and more expensive than your actual need.

So why are the DW requirements so critically important? The ultimate value of a DW investment comes from having the freedom to adopt new strategies without fear that they will be compromised by an insufficiently flexible or scalable DW platform. Therefore defining the business requirementsconcretely is critical to the success of the DW.

To confirm that your stakeholder team has completed the crucial business requirement definition step, ask the following questions before you proceed to platform selection:

For the business results we are depending on have weenvisioned the new business processes and scenariosthat we expect to yield these results?

Have we identified, at a high level, the major components of business information needed to support each scenario?

Have we defined the time-sensitive aspects of each scenario?

What capability do we require to provide the businessagility we need? That is, do we need the ability toanswer any business question at any time? And do weneed to integrate data across the enterprise on demand?

In our experience, the answers to these questions require just a few scenarios per business objective, capturing the essence of how the business operations will change. The goal is to make the business needs as concrete as you can, for example, by projecting future user growth and usage patterns. No one can perfectly predict what will be needed to support a dynamic business in a fast changing world. But you can communicate concretely about what you need to reach the known goals and what you are likely to need to harvest the upside potential. If you take a systematic approach, you will greatly increase the odds for success and the ability to deliver on the promise of the DW.

“If the business requirements are vague or unknown, it is impossible to project either the data volume or the workload reasonably.”

“The ultimate value of a DW investment comes from having the freedom to adopt new strategies without fear that they will be compromised by an insufficiently flexible or scalable DW platform.”

The Five Essential Questions:3. Why are good business requirements essential for success? Cont’d


Think about planning a trip by boat. The business requirements are the length of the voyage, whether it is inland, coastal or across the open sea, the likely range of weather conditions, the opportunities for refueling, etc. You wouldn’t buy the boat without knowing these requirements. The business requirements define what your boat needs to be able to do for you, in business terms.Platform selection is about picking the right boat. This step is the most critical link in the decision making chain. Why? Sometimes you need a speedboat and sometimes you need a container ship – the wrong choice just won’t work.

Summary Insights:What every business executive should know about platform selection:

• Your business needs and interests should drive the platform choice.

• You must choose a DW platform you won’t outgrow.

• You want to weigh total cost of ownership (TCO) over several years much more than initial acquisition cost. That is, don’t pay less now in order to pay more later.

• You are entitled to convincing proof that your critical requirements can be satisfied.

The mechanics of platform selection will be technical and will be about the trade-offs concerning performance, scalability, data availability, cost, risk and other factors. Even though you may not have a technical background, you want to sponsor and encourage a systematic process that evaluates these

factors for each platform you consider. But you want to see the results applied back to your business needs and interests.

For example, if one platform scans data faster than another, that’s interesting, but how does it relate to your business goals? Perhaps for you it is really important to be able to answer any business question at any time and it is not that critical how fast you can scan a terabyte of data. When technical specifications come up you can ask, how do they compare interms of your specific business interest? As another example, perhaps you need to be absolutely sure that inventory data is loaded for 500 stores and available every night, without fail, within an hour of closing. And you mean, without fail. In this case you can ask, what will it take with each platform option to satisfy this requirement, without exception, 365 days a year?

Your business needs will grow and change, and your data warehouse has to be able to grow and change with your needs. This means the DW platform has to handle increases in scale and complexity, without forcing your technical staff to do back flips, without disproportionate increases in cost, and without disruptive conversions to a different vendor or database engine.

System architecture is the next essential platform consideration to ensure you are making the right decision. Architecture is a term that is sometimes mysterious to business executives. Think of the system architecture asdefining the big picture of how everything is connected together, just like the architecture for a building. If you aren’t a technical expert, how can you evaluate system architectures?We suggest you can ask some common sense questions and demand answers in plain language.

The Five Essential Questions:4. How do you select the right platform and why is it critically important?

9


You know that you are likely to encounter a variety of different business requirements as your use of the data warehouse and analytical technologies grows. For example,you may need to retain large volumes of data but use that data infrequently, say, for compliance. In the next 18 months you may also need to support a large population of users sharing the same data and requiring very fast, very predictable responses – say, when ten thousand customer service agents each needs to be able to look up live customer or accountdata at the moment the customer asks a question. Realize that these two situations have very different architectural requirements. As another example, an enterprise data warehouse, a system that can support sharing of all your enterprise data across all your present and future uses, has yet another different, architectural requirement. The essential question to ask is does the platform and vendor support thearchitecture we need to meet our specific business requirements?

You therefore need to explore if the vendor has the architectural flexibility and range of platforms to meet yourbusiness requirements not just today, but in the future as well. If you believe three or four different scenarios are likely to arise in your business in the next few years, then you’ll wantto find out whether a vendor has the range of platforms to satisfy your spectrum of needs. Furthermore, if the vendor has a range of products you should ask, are they compatible? Or does deploying their different platforms entail learningmultiple software environments and database engines? Supporting duplicate environments and database engines will add significantly to the overall system cost.

Regarding the cost of the system, total cost of ownership (TCO), not initial acquisition cost, should be the key cost

metric. TCO includes support, upgrades, and the personnel cost of operation and administration. Usually, the expected payback is many times the system cost – this is why data warehouses are built. You don’t leapfrog the competition by paying the lowest possible price for your boat. You leapfrog the competition by having a boat that can do things others can’t do.

Business value, business opportunity and business risk are most likely much more important to your company than system cost. These factors also relate to agility. You should ask for example, how rapidly and how simply will you be able to react to changing and growing business requirements? And, when a more complex or larger scale requirement comes along, will you suddenly be stuck, because you can’t add more users, more data, or a more complex workload?

Realize that when you invest in a DW platform you are investing in more than the current product capabilities. You are also entering into a partnership of at least several years with a vendor, and you are relying on the vendor tosustain an R&D program to provide a stream of new DW capabilities, typically anticipating your needs as they grow. If your vendor falls behind because it lacks the necessary revenue, product focus, or vision, your DW program islikely to fall behind your competitors. If your goal is to lead your market – through innovation, low prices, speed, or other strategies – you can’t simply imitate what everyone else can do. You will need a DW infrastructure that provides a way to create and sustain a competitive advantage. To do that, youwill need a DW platform vendor that thinks ahead and leads innovation in the DW field – not only when you buy, but five years in the future as well.

The vendor you select should have industry expertise in data warehousing and a proven track record. Independent research and testimony from existing clients with requirements like yours should weigh heavily in your evaluation. Your vendor should also address all of your concerns during the selection process.

The Five Essential Questions:4. How do you select the right platform and why is it critically important? Cont’d

“You don’t leapfrog the competition by paying the lowest possible price for your boat. You leapfrog the competition by having a boat that can do things others can’t do.”

10


In summary, following are key questions to ask beforecommitting to a platform and vendor:

Are we satisfied that we can meet our key businessrequirements with this platform?

Does the platform support the architecture we need tomeet our specific business requirements?

Do we have proof that our key technical criteria – those we depend upon for business results – will be satisfied?

Are the proofs based on solid references with similarrequirements and/or realistic tests?

Are we focusing on the cost metric that matters most, TCO?

Does our platform choice give us the right balance ofbusiness value, business risk and TCO?

Does the vendor have the focus and staying power tocontinue to deliver value to us over the expected life of this investment?

Can this platform handle our needs as our datastructures, queries, and workloads become morecomplex and more varied over time? Do the costs scale proportionately?

For large systems, we highly recommend you undertake a benchmark or proof-of-concept test in making your selection. Be sure to make the proof-of-concept realistic with respect to the scope and complexity of the business problem you are aiming to solve. Don’t be satisfied with a test in which a vendor runs a handful of queries on a mockup of your data. You should define your requirements carefully and make the tests realistic with respect to data, workload, performance requirements, and scale. If you test multiple vendors, make sure you get comparable results, and if you don’t have the skills in your organization to define and evaluate such a testrealistically, get outside help. Finally, don’t let the “wow” factor of a simple demo obscure your need to find a platform and vendor that will position you to deal with the full complexity and challenge of your critical business needs and interests now and in the future.

The Five Essential Questions:4. How do you select the right platform and why is it critically important? Cont’d

“Don’t let the ‘wow’ factor of a simple demo obscure your need to find a platform and vendor that will position you to deal with the full complexity and challenge of your critical business needs and interests now and in the future.”

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For readers who own a house, the story of the typical re-modeling project is all too familiar: when the dust settles the project is double the cost and time to complete compared to the original estimate. Unfortunately, IT projects have similar characteristics. Research by The Standish Group shows that less than 28% of IT projects are completed on time and within budget, and the typical IT project is 186% over the original budget. The larger a project, the higher the risk of failure, and DW deployments are often large projects. This section is particularly important since, from our perspective, strategy is relatively easy – it’s the execution that is difficult.

Summary Insights:All large IT projects have significant risks. For DW projects the risks are well known and you can develop systematic strategies to dramatically increase your odds of success. A key success strategy is to design the DW project to be measured.

For DW projects the risks are well known and you can plan in advance to engineer success into your project. Research from Barbara Wixom and Hugh Watson identified the following key risk factors for DW projects:(1) Lack of focus, vision, and senior management support and sponsorship.(2) Lack of a champion to promote the project and provide information, material resources, and political support.(3) Organizational politics and cultural issues.(4) Lack of resources – funding, time, and/or personnel.(5) Improper scalability of the solution – building a ranch house when the Empire State Building was needed.(6) Poor development technology – inadequately tested or inappropriate.(7) Lack of skills and reliance on outside IT help.(8) Quality of existing databases.(9) No end-user participation.(10) Lack of training - the system is built but there is no instruction on how to use it.

All these factors are important, but the critical ones are lack of focus, vision, and senior executive support, organizational politics, lack of resources, improper scalability, and the quality of databases. Lack of vision and executive support go hand in hand. For example, a Fortune 500 company spent $30 million creating a data warehouse but had no strategy for using the data they collected to produce the desired business results. The outcome was the system did not meet the business expectations. Starting small and learning by doing is a good approach for organizations that don’t have a strong appetite for risk taking and failure.

Synchronizing and cleaning data from different systems can sometimes be perceived as trivial tasks. However, the reality is very different. For example, one company we worked with had 70 systems to keep its customer data, and analysis showed that it had 200 million customer IDs for its 20 million customers. We recommend an up front analysis to clearly define the magnitude of the data cleanliness problem and to identify the data that needs to be sequentially cleaned and integrated. That is, apply the 80/20 rule to focus on solving the real data problem, instead of undertaking a massive enterprise data cleaning project that might be expensive and not produce any business value for a long time.

A true enterprise DW spans the organization and requires cooperation from divisional stakeholders. In some cases the new system will result in a loss of division managers’ control over the siloed data. The result can be resistance that translates into low user acceptance, difficult integration of data, and lack of resources to get the project completed. To overcome this, you should obtain senior management’scommitment very early in the project.

Scalability issues are often associated with the wrong platform selection. Again, starting small and showing quick wins is a good strategy, but it is important to have a long-term DW vision. The selected platform should provide an easy path to grow into the enterprise scale platform.

The Five Essential Questions:5. How do you execute for success?

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The best risk management approach is to have both a technology and a consulting partner with a proven track record. Industry research, customer testimonials, and your own evaluation of the vendor’s capabilities should all be considered when making a decision. A partnership with a DW vendor involves more than just buying a platform. It is about utilizing the vendor’s industry expertise and models of success to realize the desired business results. For example, a vendor with 100% deployment across a given industry has likely developed skills and expertise that go beyond configuring and installing a data platform.

Show me the detailed project plan and explain thespecific risks?

How will you manage these risks?

What is the range of possible outcomes for the projectin cost and time?

We often find that the back of the IT project-planning document has a risk section, but the text is identical in all project plans. Another hint: If the risk section was cut and pasted, then there is no risk management strategy for the DW project. There is one thing you know for sure about both the ROI business case and the project plan – the single point estimate of NPV/ IRR for the ROI, or cost and time to complete the project, will not be the final result. So we recommend always asking for the range of possible outcomes.You can then decide if the worst case is acceptable, and if additional planning is necessary to reduce the downside risk.

Finally, there is one business process that differentiates the leaders from the rest of the pack: the leaders systematically keep score, measuring success each step of the way. One of the authors has studied 179 Fortune 1000 firms and 44 federalgovernment agencies. That research captures more than $70 billion in annual IT spending, and shows that organizations that systematically measure the realized ROI of their IT projects have better returns on assets and long-term shareholder equity than competitors. We recommend that all DW project should be ‘designed to be measured’ from both a business and an IT perspective.

How do football coaches keep score? Yes they measure wins and losses, but during the game the key metric is not the points on the scoreboard, but the number of first downs. Given enough first downs coaches know they will score enough points to win the game. DW projects are similar. Once the team has the right strategy, business requirements, platform and partners, winning the game requires systematicexecution on the field through good project planning, measurement, and active risk management.

The Five Essential Questions:5. How do you execute for success? Cont’d

“Once the team has the right strategy, business requirements, platform and partners, winning the game requires systematic execution on the field through good project planning, measurement, and active risk management.”

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Summary

Leading organizations are finding that speed to business decision-making, and the ability to provide analytical capabilities to their front-line staff are significant sources of competitive advantage. A realtime DW and analytics platform capability enables this competitive advantage. To start the journey we advocate starting small and realizing quick wins as a good strategy to embark on a larger enterprise-wide initiative.

However, in selecting a platform, it is critical to consider your long-term vision. Your initial successes will likely lead to the widespread use of integrated and active data, and you can’t just replace the DW platform as your needs and usage grow. Your selected platform should be adequate to meet your eventual enterprise needs or should have a seamless migration path into the future. Also consider that unforeseen business opportunities will present themselves, and you will want a platform that positions your business to take full advantage of these opportunities in order to achieve breakthrough results.

Along with avoiding the common pitfalls of a DW deployment, we suggest focusing on selecting the right partner: A simple prototype can’t prove that a platform will meet your needs today and in the future. Besides a platform that has superior technical capabilities, the vendor should also have a record of customer success stories, proven results within your industry, a clear product strategy and commitment to R&D investments, and a culture of enabling customer successes. Finally, you should define success criteria and metrics before embarking on the journey. This will ensure accountability and measured business results that will justify your future investments.

We are living in difficult times, but now is the time to embrace the opportunity of enterprise data to cut costs and drive revenue growth for your firm. You need to act now to ensure your competitors do not outpace your business in the marketplace. By asking and answering the five essential questions, this white paper should enable you to develop a clear data management roadmap to the future, avoid the common pitfalls, and leapfrog your competition with a true strategic advantage.

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To Learn More

Michael Porter (1998), Competitive Strategy: Techniques for Analyzing Industries and Competitors, Free Press.

Mark Jeffery (2010), Data-Driven Marketing – The 15 Metrics Everyone in Marketing Should Know, John Wiley & Sons.

Mark Jeffery (2010), Return on Investment Analysis, in The Handbook of Technology Management, first edition, Hossein Bidgoli, editor, John Wiley & Sons.

Mark Jeffery and Robert Sweeney (2006), ROI for a Customer Relationship Management Initiative at GST, Harvard Business School Publishing, Case 5-404-766.

Barbara Wixom and Hugh Watson (2001), An Empirical Investigation of the Factors Affecting Data Warehousing Success, MIS Quarterly 25(1) (March):17-41.

Mark Jeffery and Ingmar Leliveld (2004), Best Practices in IT Portfolio Management, Sloan Management Review,45( 3): 41-49, reprint 45309.

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Author Biographies

Mark Jeffery is the Director of Technology Initiatives in the Center for Research in Technology and Innovation at the Kellogg School of Management, and the managing partner of Agile Insights LLC, a technology and marketing consultancy. His research, teaching, and consulting focus on enterprise performance management, unlocking business value from marketing and IT, and data-driven marketing strategy and execution. Mark has an active research program at Kellogg and has recently surveyed 252 Fortune 1000 firms on strategic marketing performance management, capturing $53 billion of annual marketing spending. He previously surveyed 179 Fortune 1000 firms and 44 federal government agencies about IT management best practices, capturing $70 billion of IT spending.

Mark directs several Kellogg executive programs, including Strategic Data-Driven Marketing and Driving Strategic Value from IT. He also teaches in custom executive programs at organizations including Microsoft, DuPont, Sony, Nissan, and Philips. In 2008 Mark launched the Kellogg Technology Strategy Summits, an intense knowledge-sharing forum for CIO/CTO and VP level executives.

Mark is the author of the book Data-Driven Marketing, published by Wiley in 2010, and 26 case studies distributed by Harvard Case Publishing. He also has more than 30 publications in scientific and technology journals, and three book chapters, including a chapter on return-on-investment analysis in Wiley’s Handbook of Technology Management (2010). Mark holds a Ph.D. in theoretical physics from Drexel University (1991) and an MBA from the Kellogg School of Management (2001).

Email: [email protected]

Richard Winter is a specialist in large database technology and implementation with over twenty five years of experience. As President of Winter Corporation, he advises executives on their strategies and critical projects, focusing on data warehousing. He directs the Winter Large Database Research Program, which investigates the issues, practices, technologies, tools, and techniques used in the world’s largest and most heavily used databases. With decades of experience in large-scale data warehouse implementations and in-depth knowledge of database products, Richard delivers unmatched insight into the issues that impede performance and the technologies and strategies that enable success.

Email: [email protected]

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