Business Value of the “Data Warehouse Appliance” Technology

Business Value of the “Data Warehouse Appliance” Technology

Affärsvärde med tekniken "Data Warehouse Appliance"

Saga Undén Eric Westerlund

Examensarbete inom teknik och management, grundnivå

Kandidat

Degree Project in Engineering and Management, First

Level

Stockholm, Sweden 2012

Kurs IK120X, 15hp

TRITA-ICT-EX-2012:98

Business value of the Data Warehouse Appliance technology Saga Undén & Eric Westerlund

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ABSTRACT

The recent increase in the amount of stored company data and exceeding interest in data analysis

has resulted in new requirements on Data Warehousing solutions. This has led to the development

of Data Warehouse Appliances, which this research project aims to investigate the business value

of. The result is intended to support companies that are considering an investment, and give them

an understanding of the technology’s benefits.

The research project was conducted in two parts. Vendors of the Appliance technology were

interviewed, as well as their customers. The results from the vendor interviews together with a

literature study provided a knowledge base for the analysis of the user companies’ interviews. The

results clearly indicate that there is value in the technology for larger companies.

The research shows that although the main benefits advocated by the vendors match the perceived

ones of the user companies, there are other aspects which they value even more. Examples of this

include a reduced amount of administrative tasks and support from a single source. The research

also reveals that the benefits estimated by the customer at the time of purchase were not their most

valued benefits in hindsight.

Keywords: Data Warehouse Appliance, Business Intelligence, Data Warehousing, Business Value


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SAMMANFATTNING

Företag lagrar allt större datamängder och låter dessa ligga till grund för komplicerade

dataanalyser, vilket ställer nya krav på deras befintliga Data Warehouse-‐lösningar. Detta har lett till

utvecklingen av Data Warehouse Appliance, vars affärsnytta detta projekt syftar till att utreda.

Resultatet kommer tillhandahålla beslutsunderlag för de företag som överväger en investering i

tekniken.

Undersökningen genomfördes i två steg. Intervjuer genomfördes med leverantörer som

tillhandahåller tekniken såväl som med deras användande kunder. Resultaten från

leverantörsintervjuerna tillsammans med en omfattande litteraturstudie låg sedan till grund för

den analys som gjordes av intervjuerna med de användande företagen. Resultaten visar på ett

verkligt värde i tekniken för företag med stora datamängder.

Undersökningen visar att de fördelar som framhålls som teknikens främsta av leverantörerna

bekräftas av deras användande kunder, men att det finns andra vinster de värdesätter ännu mer.

Dessa inkluderar en minskad teknisk komplexitet, en minskad mängd administrativa uppgifter

samt support från en enda källa. Undersökningen visar även att de faktorer som spelat störst roll

vid investeringen inte är desamma som tillskrivs störst värde i efterhand.

Nyckelord: Data Warehouse Appliance, Business Intelligence, Data Warehousing, Business Value


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PREFACE

This thesis is written for companies considering an investment in the Data Warehouse Appliance

technology, in an attempt to provide them with objective information on the subject. It might also

be of interest to professionals within the field of Business Intelligence, as well as any novice who is

curious about and looking for an introduction to Business Intelligence, Data Warehousing or Data

Warehouse Appliances.

Working with this thesis has been very interesting, enjoyable and worthwhile. We would like to

thank Affecto for their support and confidence in us -‐ a special thanks goes out to our tutor (and

mentor) Tomas Nabel who has acted as an excellent sounding board and with whom we have had

many interesting and valuable discussions during the project. We would also like to thank our

examiner Anders Sjögren who has been of great help in all administrative formalities, and Richard

Nordberg who has provided guidance and support throughout the writing process.

Finally, we would like to thank the house of Nymble, which has provided us with not only great

coffee, lunch and ‘fika’, but also super comfortable arm chairs and ‘Musikrummet’ which has acted

as our office these two months.


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TABLE OF CONTENTS 1. Introduction 7 1.1 Problem definition 7 1.2 Purpose and goal 8 1.3 Scope and delimitation 8 1.4 Project method 8

2. Theoretical background 9 2.1 Business Intelligence 9 2.1.2 Data Warehousing 9 2.1.3 Data Warehouse Appliances 12 2.1.4 Data Warehouse Appliance architecture 13

2.2 Measuring business value 14 2.2.1 Business value of an IT investment 15 2.2.2 Value of Business Intelligence 15 2.2.3 Cost and value of information 17

3. Research Method 19 3.1 Choice of method 19 3.2 Seven stages of interview investigation 19 3.3 Question types – when to ask what and how 20 3.4 How to conduct an interview of great quality 21 3.5 What to consider when conducting an interview 22 3.6 What to consider when analyzing the interview results 22

4 Results 24 4.1 Vendor interview results 24 4.1.1 Top business values of Data Warehouse Appliances 24 4.1.1.1 Performance 25 4.1.1.3 Scalability 27 4.1.1.4 Simplicity 28

4.2 User interviews 28 4.2.1 Thoughts on Data Warehouse Appliance before implementation 29 4.2.2 Thoughts on Data Warehouse Appliance after implementation 30

5. Analysis 31 5.1 Vendor interviews 31 5.1.1 Vendor truths 31 5.1.1 Analysis of vendor truths 32

5.2 User companies interviews 32 5.2.1 The top business values of Data Warehouse Appliances 33


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5.2.2 The difference between expected and perceived business value of Data Warehouse Appliances 33 5.2.3 What drives an investment in Data Warehouse Appliance technology 33 5.2.4 Delivering value is more important than lowering costs 34 5.2.5 Focus on information rather than technology 34

6. Conclusion 35 6.1 Considerations 36 6.2 Further research 36

7. References 37 7.1 Further reading 37 7.2 Figures 38

Appendix A 40 1 Vendor interview question framework 40 2 Vendor question form 40 3 Using companies interview question framework 42

TABLE OF FIGURES Figure 1: Data Warehouse architecture 11 Figure 2: Shared everything architecture 13 Figure 3: Shared nothing architecture 13 Figure 4: Business value of Business Intelligence 16 Figure 5: Avantages of Data Warehouse Appliances according to the vendors 24 Figure 6: Factors that contribute to the performance of Data Warehouse Appliances, according to the vendors 25 Figure 7: Hardware components of a Data Warehouse Appliance 27 Figure 8: Pricing of a Data Warehouse Appliance 28 Figure 9: Administrative tasks, before and after an implementation of Data Warehouse Appliances 30


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1. INTRODUCTION Over the years, companies have come to increasingly value their stored information. This

realization is related to the fact that today, almost all company information is stored electronically

in databases. The companies strive towards using this accumulated information as a source and

base for various decision support tools. This has led to the development of Business Intelligence

(BI) tools and Data Warehousing (DW), which helps companies get more out of what they already

possess, by analyzing data and transforming it into information. The very best results are obtained

when implementing a customized solution which fits in to the companies’ unique business

processes. Among other things, this enables ad hoc reports and forecasts that supports employees

at all levels in their decision-‐making. While a couple of years ago, the usage of Business Intelligence

tools gave your company business leverage, today it has become nearly mandatory.

The Business Intelligence concept of Data Warehousing aims to collect data from multiple sources

and store it in one common database, used for reporting and other BI tools (Porter & Rome, 1995).

Today, as the amount of collected data grows, some companies are growing out of their Data

Warehouse solutions. For them, a pre-‐packaged, optimized, large scale Data Warehouse solution –

Data Warehouse Appliance -‐ might be of interest.

1.1 PROBLEM DEFINITION

In businesses such as finance, telecommunication and retail, extremely large amounts of data is

generated every day. This could serve as a perfect source for Business Intelligence tools and

applications, which analyze data and create analyses that can provide support in business decision

situations. However, a problem arises when the generated data amounts to a level where it is no

longer possible to load into the system quickly enough. For example, this could result in that the

weekly sales statistics are not completely loaded into the BI applications during the weekend. This,

in turn, would mean that the upcoming results from the BI tools would never be based on fresh

data, but instead on an older and in some cases irrelevant base.

A performance issue of this type can be solved with the Data Warehouse Appliance technology,

allowing decisions to be made based on current data and information.


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1.2 PURPOSE AND GOAL

This thesis aims to investigate the business value of the Data Warehouse Appliance technology, in

order to help companies that are considering an investment in making their decision.

1.3 SCOPE AND DELIMITATION

The study will focus on the Data Warehouse Appliance market in Sweden. The following suppliers

and their respective products will be considered:

● Teradata Enterprise Data Warehouse, Teradata 13.10

● IBM Netezza

● Oracle Exadata Database Machine

● Microsoft/HP Enterprise Data Warehouse Appliance

● SAP HANA

Other suppliers of the technology, that does not hold market in Sweden, has been set as out of scope

for this research project.

1.4 PROJECT METHOD

The project consists of a qualitative study in which interviews are conducted with professionals

within the area of Data Warehousing Appliances, both at the supplier and customer side. The

interviews are performed semi-‐structurally, based on a question framework, and thereafter

analyzed with regards to a literature study.


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2. THEORETICAL BACKGROUND

In order to provide relevant background information on the research subject, this section presents

information compiled from a literature study. The first section presents the Business Intelligence

and Data Warehousing areas. The second deals with business value -‐ its definition and ways it can

be assessed. The information about Business Intelligence, Data Warehousing and business value

has been collected from books and academic articles. The information about Data Warehouse

Appliances is based on interviews with Appliance vendors as well as their documentation.

2.1 BUSINESS INTELLIGENCE

Business Intelligence (BI) is a concept that can be described as the usage of business information

and business analysis in key business processes in order to take actions and make decisions that

increase performance or profit. It is not a specific product, technology or methodology but rather a

combination of the three (Williams & Williams, 2007).

There has been an increased interest in Business Intelligence over the past few years. What was

business leverage five or ten years ago is today mandatory in order to keep up with the

competition. Every year since 2004, Business Intelligence has been among the top ten priorities of

CIO’s. This year, 2012, it is the very top one (Gartner, 2004-‐2012).

Today, as more and more information is stored electronically, the foundation on which BI tools rely

becomes greater. One reason for this is the fact that prices on hardware has dropped, allowing

companies to not only store their current data, but historical as well (Chaudhuri, Dayal &

Narasayya, 2011). The technology for storing this historical data is commonly called Data

Warehousing.

2.1.1 DATA WAREHOUSING

Data Warehousing (DW) is a term for the collection of decision support technologies enabling

companies to make better and faster decisions (Chaudhuri & Dayal, 1997). In order to understand

its definition, one must first know the basics of operational databases.


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Operational databases are digital storage areas for computer applications. It is a solution for

handling lots of data for many users. When new data is created within an application, it is sent to

the database which writes it to its memory. When a user wants information in an application, a

request -‐ or query -‐ for the relevant data is sent to the database. The read and write operations of

an operational database are typically simple and many. Every single query that is sent costs a bit of

the database’s capacity, meaning that the amount of capacity needed is based on the number of

queries and their complexity. Therefore, companies with many users or a large amount of complex

queries need a database with a lot of capacity (Abiteboul et al, 1995).

In the 1990’s when companies were starting to analyze data stored in their databases, they realized

some important differences between operational and analytical needs:

● The data serving needs were physically different

● The supporting technology needs were fundamentally different

● The user communities were different

● The processing characteristics were fundamentally different

These findings led to the separation of operational databases and databases with historical data

intended for analysis. These databases were named Data Warehouses and its main characteristics

are (Inmon, 2005):

• It has a longer time horizon than operational databases

• It integrates data from many heterogeneous sources

• It is organized around subjects such as customer, product or sales

• Its data is not changed over time, the only permitted change is to add new data

In later years Data Warehousing has come to mean different things. One meaning is the database

itself and another, broader meaning is the entire Data Warehouse environment. The reason for this

is that in the beginning a Data Warehouse consisted of just one database. As it often ended up

overly complicated and hard to understand and navigate, it evolved into an architecture consisting

of both a large integrated database and smaller databases targeted only to support a few

applications. These smaller databases are called Data Marts (Adelman & Moss, 2000).


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Surrounding this architecture are processes to handle the flow of data from operational systems to

analytic applications. This is needed because the data stored often differs between the source

systems. Examples of differences are:

• Label of the information,

such as a person being labeled as a customer in one system and a user in another

• Structure of data,

such as forename and surname stored separately in one system and together in another

• Formatting of data,

such as a zip code saved as a number in one system and as a text string in another

The term used to describe this flow of information is the Extract-Transform-Load (ETL) process.

Figure 1 displays a typical Data Warehouse architecture with source systems, Data Warehouse,

Data Marts, analytical tools, as well as the ETL process.

Figure 1: Data Warehouse architecture


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2.1.2 DATA WAREHOUSE APPLIANCES

This section is a compilation of information extracted from interviews with Data Warehouse

Appliance vendors and a number of published documents. As an introduction, here is the definition

of appliance by the New Oxford American Dictionary:

appliance |əәˈplīəәns| noun 1 a device or piece of equipment designed to perform a specific task, typically a domestic one. See note at TOOL . • an apparatus fitted by a surgeon or a dentist for corrective or therapeutic purpose : electrical and gas appliances. 2 Brit. the action or process of bringing something into operation : the appliance of science could increase crop yields.

The definition of Data Warehouse Appliance is, according to one vendor, a complete and optimized

software and hardware solution for large-‐scale Data Warehousing purposes. Others referred to an

analogy of a kitchen appliance, and argued that any two appliances have one thing in common: it is

not defined by what it consists of, but by what it is meant to do. While you could describe a toaster

as a metal box containing heating elements and a spring timer, the common way is to say it's a tool

for toasting bread. Ergo, an appliance is a tool or product with a specific purpose.

According to vendors, companies that have invested in Appliance technology are in one of the

following categories:

● Companies with large amounts of data

● Companies with complex queries

● Companies with many queries

Targeted areas are retail, telecommunications and banking. What they have in common is the large

amount of operational data that is generated every day. Banks register every transaction from

every customer, retail companies register every item sold in every store and telephone companies

register every call and message of every customer.


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However, the vendors differentiate as they target companies of various sizes. While one vendor

states that those who consider the DW Appliance technology usually are among the five largest in

their industry, others imply that their solutions fit the needs of smaller sized companies as well.

Another vendor claims that there are clear breaking points in data volume that indicate that an

Appliance is applicable. This vendor states that at six to ten terabytes of stored data, it becomes

more beneficial in terms of hardware price and performance -‐ while other vendors mention one

terabyte as this breaking point.

2.1.3 DATA WAREHOUSE APPLIANCE ARCHITECTURE

When DW Appliance vendors are asked how the technology works, it is clear that the solution is

complex. One component that is essential to the concept of Data Warehouse Appliance is the overall

architectural design.

Data Warehouse Appliances focus on two architectural types of design: Symmetric Multi-‐Processing

(SMP) and Massively Parallel Processing (MPP). Both intend to speed up the input/output (I/O) of

the database but they work in slightly different ways. The SMP design revolves around multiple

processing units connected to a single shared memory and storage area. This design is often called

a shared everything design, and is shown in figure 2. The MPP design has parallel processing units

which all have their own data source and memory. This is called shared nothing architecture, and is

shown in Figure 3.

Figure 2: Shared everthing architecture Figure 3: Shared nothing architecture


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Both designs use a query planner, which distributes the incoming tasks on the different processing

units. Each unit does its part of the work and the result is then assembled at the end. On top of the

query planner is an interface, which typically is able to understand most database query languages.

All DW Appliance systems use some kind of security for handling hardware malfunctions. The

most common setup is RAID 1, which means that every disc has a mirror somewhere, containing

the exact same information. The system is usually configured in a way that prevents two mirror

partitions from being on the same physical machine. The risk of inaccessible data is therefore

further reduced.

2.2 MEASURING BUSINESS VALUE

In order to investigate how an investment in Data Warehouse Appliances can be valued, it is

important to first understand what business value is. The economic formula for defining value is

rather straight forward: “Economical value occurs when the benefit derived from a resource’s

application is greater than the costs incurred from its planning, acquisition, maintenance, and

disposition.” This means that value roughly can be translated into benefits minus costs (English,

1999).

The possible outcomes of any successful investment are lowered costs, improved productivity and

increased revenue, all leading to that more money will be generated than what was spent. This is

called return on investment (ROI) (Adelman & Moss, 2000).

Benefits can be divided into two categories: tangible and intangible. Tangible benefits are those that

are considered easily quantifiable, such as higher productivity or fewer returned products.

Intangible benefits are harder to measure and creates value indirectly. Examples of intangible

benefits are goodwill and customer relationships. Costs are also usually divided into two categories:

fixed and variable. Fixed costs are described as the costs involved with creating the capacity to

produce something. This may include infrastructure, machinery and other things required to

produce. As the name implicates, fixed costs do not vary with the amount produced, given that the

amount is within capacity. Variable costs do however vary incrementally, as they are the costs

required to produce a set item or record. Examples are costs for materials consumed or personnel


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time (English, 1999). These concepts should be kept in mind while reading further about value in IT

and Business Intelligence.

2.2.1 BUSINESS VALUE OF AN IT INVESTMENT

Business value is the difference between perceived value of the company's product or service and

the cost for it. In order to sell a product or service, a company will need to create business value and

then capture it. There have been many attempts to try to describe the value of IT in an organization,

and the main issue is to describe how a general IT infrastructure contributes to the overall benefits

and costs.

There are several reasons to why companies wish to do value assessments of their IT investments -‐

it can not only help justify the money spent, but can also function as a way of engaging the

employees and future users. The assessment process focuses on what creates value and is

important for the company. This thought process is said to create creativity and motivation

(Dahlgren & Lundgren & Stigberg, 1998) (Keeney, 1994). But there is a real challenge in assessing

the value of an IT investment. Studies indicate that there is no absolute method of measuring the

value of an IT investment which is applicable for all companies. Instead, while some companies try

to quantify the value and make everything into dollars and cents, others consider a list of intangible

values as a reason for an investment (Renkema, 2000).

One reason that assessments of IT investments are difficult to conduct is the fact that different parts

of an organization might not consider the same things to be of value. From the business

management point of view, factors such as higher margins and improved efficiency are prioritized.

But from a technological perspective, availability, performance and security is of higher interest

(Gammelgård, 2007).

2.2.2 VALUE OF BUSINESS INTELLIGENCE

The true value of Business Intelligence occurs when business information is combined with

business analysis in a way that makes it possible to make well informed decisions. For this to occur,

both the business and technical departments of an organization needs to prepare and deliver input

to the Business Intelligence tools, as seen in Figure 4. A BI tool is used differently by every company

to create business leverage, but it requires explicit knowledge of the working processes (English,

1999).


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Figure 4: Business value of Business Intelligence

Business Intelligence affects the business value to a very large extent. Companies and organizations

have been using information as a foundation for decisions and performance control for a long time.

This comes from the basic assumption that an informed decision tends to have a higher chance of

leading to good results than an uninformed one. This is a straightforward reason for gathering

information in a business. The less uncertainty we have about the current state and future

outcomes, the better chance we have to make decisions with good outcomes (Clemen & Reilly,

2011).

To assess the value of information that will influence decisions and actions, Clemen & Reilly (2011)

introduce the term expected value of information. This term describes what we expect to gain from

acquiring more information on how to act. Only by considering the expected value of information

can we decide whether to invest in obtaining it. The worst-‐case scenario is that no new input is

acquired on how to make the decision, and in this case the expected value of the new information is

zero. The best case is when the acquired information always leads to a decision with the best

possible outcome. This is according to Clemen & Reilly called perfect information. Putting this

together, the expected value of any information source is somewhere between zero and the value of

perfect information. Additionally, the expected value of information is critically dependent on the

particular decision or problem at hand. This means that different people, in different situations,

place different value on the same information.

There have been many studies trying to find evidence of the value of information. In one research

paper published by Brynjolfsson, Mitt & Kim (2011), business processes and technology


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investments of 179 larger companies were studied. The findings were that companies that had

adopted data driven decision-making had 5-‐6% higher output and productivity. They also found a

correlation between making decisions based on data and asset utilization, return on equity and

market value. In a study made by Park (2006), it was concluded that a full data warehouse solution

increases the performance of Decision Support System users.

The main focus of BI is to enable profit making and to make non-‐profit making business processes

more efficient. This is done through identifying the information which the business processes need

and obtaining that very information. Therefore, every BI environment should be developed around

the company's business processes (Willams & Williams, 2007).

2.2.3 COST AND VALUE OF INFORMATION

A common approach to Data Warehousing is that all stored data is valuable, meaning that the more

information is saved, the more valuable it is. This is not entirely true. Although a Data Warehouse

could potentially be more valuable when filled with a greater amount of information, it is not until

the information is used in the organization it becomes valuable (English, 1999).

In business there are typically two types of costs, fixed and variable. However when discussing the

cost of information there are two other areas that categorize costs: the cost basis and the value

basis. The cost basis of information is the cost of developing and maintaining the infrastructure that

supports collecting information. This includes developing information and technology architecture,

as well as the cost of designing applications and databases. The value basis of information is the

cost of applying information. This means the cost for applications that access or retrieve data and

use it to perform work or to solve a business problem (English, 1999).

Before the information can create value, it must go through a process containing various steps

which all are tied to costs. This process is called the Resource life cycle. IT systems designed to

capture data and turn it into information is looked upon as a company resource. The first step of

this cycle is the planning. This step consists of planning what software and hardware to buy. The

second step is the acquisition step, where the company buys and installs its purchase in the

workspace. To make sure this installation will continue to work, a process of constant maintenance

and improvement is needed. This is the third step of the life cycle. The final step is termination,

where the resource is discarded to make room for a new resource, thus completing the cycle.


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However, in the life cycle there is another step that is not tied to costs: the application step. This is

where the resource is applied to the business processes in order to add value. (English, 1999).


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3. RESEARCH METHOD

This section presents the research and interview methods used in the study, to vindicate the

correctness of the conducted interviews and their function as research material.

3.1 CHOICE OF METHOD

The interviews were conducted according to the principles stated by Kvale in ’Interviews – an

introduction to qualitative research interviewing’ (1996). A qualitative research method was

chosen because of a number of reasons. First -‐ since the existing research is extremely limited, the

interviews serve as the main source of information on the subject and therefore needs to be in-‐

depth. Second -‐ the target interviewees were too few to serve as a reasonable ground for a

quantitative research. Third -‐ since a comparison of the answers from the different interviewees

was to be conducted, reasons existed for using a predefined set of questions.

However, it is important to create a comfortable interview environment where the interviewee

feels secure and comfortable and therefore answers the questions openly. Therefore, the interviews

were conducted in a semi-‐structured way, using a framework of topics that were to be discussed

instead of questions being answered. This is found in Appendix A. Prior to each interview; these

topics were changed to fit the specific interviewee. The interview was then recorded, which allowed

the researchers to participate actively and take notes when specific subjects of interest were

discussed to enable revisits to them later. The transcription of the interviews was facilitated by

performing it the very day of the interview, while fresh in mind.

3.2 SEVEN STAGES OF INTERVIEW INVESTIGATION

Kvale introduces the following seven stages of interview investigation:

1. Thematizing

2. Designing

3. Interviewing

4. Transcribing

5. Analyzing


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6. Verifying

7. Reporting

The first stage, thematizing, results in a well-‐formulated purpose of the investigation and a

description of the main topic. This is to be done before any of the interviews takes place, in order to

gain an understanding of what is to be done during the research, and why. It is followed by a

designing phase where the research study is planned in detail with regards to all of the seven stages

as a whole. The interviewing is then conducted in the chosen manner, according to the interview

guide that was developed during the previous designing phase. The transcribing phase follows,

which aims to prepare the material for analysis. Kvale stresses the importance of this stage,

claiming that rather than being a simple clerical task, transcription is itself an interpretative

process. Through careful analyzing, conclusions can be drawn. This is done systematically, using a

chosen method that is in line with the previously stated purpose of the project. By verifying the

collected material, the generalizability, reliability and validity of the conducted interviews are

ascertained. Finally the reporting is done to communicate the findings.

3.3 QUESTION TYPES – WHEN TO ASK WHAT AND HOW

Kvale also introduces how and when different types of interview questions are asked:

● Introducing questions are used to open up a conversation broadly, e.g. ’can you tell me

something about…’

● Follow-up questions are used to keep the conversation going. Either by asking a direct

question on the already touched subject, repeating keywords or agreeing: nodding, making

affirmative sounds

● Probing questions are used to make the interviewee elaborate on the already touched

subject

● Specifying questions are used to drill down into a detailed subject and the opinions of the

interviewee, e.g. ’what did you think then?’

● Direct questions are used to openly introduce a new topic or dimension to the discussion

● Indirect questions can be used either to discretely introduce a new topic or dimension to the

discussion or to confirm something you suspect is true


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● Structuring questions are used to close an already exhausted topic or disrupt a long answer

which is not relevant to the research

● Silence in between the questions are used to make the interviewee more comfortable and

get time to collect his/her thoughts without feeling rushed

● Interpreting questions are asked to confirm that what you have interpreted from the

answers really is what the interviewee meant, e.g. ’So it is true that you mean that…?’

Moreover, the aspects of how and when to use leading questions are discussed. According to Kvale it

suits qualitative research interviews particularly well as it is not only is important to repeatedly

check the reliability of the interviewees’ answers, but also to verify the interviewers’

interpretations. As a qualitative research study generally comprises a smaller number of interviews

than a quantitative, this is of especially great importance. Kvale stresses that the interviewer should

not put focus on whether to lead or not, but rather where the interview questions should lead – in

important directions, which results in relevant findings for the research study.

3.4 HOW TO CONDUCT AN INTERVIEW OF GREAT QUALITY

A great quality interview requires not only well planned and asked questions, but also an

interviewer who possesses the following qualities:

● Knowledgeable

● Structuring

● Clear

● Gentle

● Sensitive

● Open

● Steering

● Critical

● Remembering

● Interpreting

Kvale presents a number of criteria and guidelines that needs to be fulfilled and followed in order to

conduct an interview of high quality. These are as follows:


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● The answers should be spontaneous, rich, specific, and relevant

● The questions asked should be short and clear, allowing the answers to be long and in focus

● The interviewer should take care to clarify the meanings of relevant terms used in the

interview

● The interpretation of the answers should begin already during the interview

● The interviewer should strive to verify his or her interpretations of the interviewee’s

answers during the interview

● The interview should be self-‐communicative and therefore be understandable without

extensive knowledge of or introduction to the subject

3.5 WHAT TO CONSIDER WHEN CONDUCTING AN INTERVIEW

While performing the analysis, there are two crucial factors of which the researchers need to be

aware. First – their own theoretical presuppositions and the role these play in the interpretation of

the material. Second – the usage of either miners' or travelers' approach. When using the former, the

researcher must take care not to affect the interviewee’s answer in any way – much like a botanic

collecting flowers in the nature without damaging the environment. When using the latter, the

opposite applies and the questions asked are answered collaboratively.

3.6 WHAT TO CONSIDER WHEN ANALYZING THE INTERVIEW RESULTS

To gain a high level of reliability, validity and generalizability, there are a number of things to

consider when analyzing the interview results, especially when they are qualitative and conducted

semi-‐structurally.

Generalizability tells to which degree the conclusions that are drawn from the analysis apply in

general. This is crucial when a small number of interviews are conducted, as they will represent a

much larger group. According to Kvale, this is achieved through examining relevant attributes only.

Reliability concerns the consistency of the research findings. The more sources tell the same, the

higher the probability that it is true. When only a small number of interviews are conducted,

contradictions will be increasingly noticeable and may damage the reliability.


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Validity regards the degree to which the observations reflects the variables that are of true

importance to the research. This is achieved through the researchers’ capabilities and

craftsmanship, and concerns agreeing with the interviewee on the meanings of the terms that are

used. It also concerns the truth and correctness of the interviewee’s statements, which must be

carefully evaluated by the researcher.


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4 RESULTS

This section presents a summary of what was said during the interviews with the vendors and user

companies.

4.1 VENDOR INTERVIEW RESULTS

Vendor interviews were conducted in order to gain an insight into what the technology aims to

solve as well as to analyze the current position of the appliance technology vendors.

In-‐depth interviews were conducted semi-‐structurally and in person. The question framework can

be found in Appendix A. Follow-‐up questions were asked, when necessary, via email and telephone.

Afterwards, a form was sent out to enable comparisons between the vendors and attain and collect

short, clear and specific answers. The question form and the collected answers can be found in

Appendix A.

4.1.1 TOP BUSINESS VALUES OF DATA WAREHOUSE APPLIANCES

According to Data Warehousing Appliance vendors there are many reasons to invest in data

warehouse technology. They mention the benefits seen in Figure 5.

Figure 5: Advantages of Data Warehouse Appliances according to the vendors


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● Cost of hardware, the cost that occurs when buying hardware to support the Data Warehouse

Appliance

● Cost of maintenance, the cost of administration and development tasks

● Performance, the speed at which the Data Warehouse Appliance operates

● Support, the external help received when maintaining or troubleshooting

● Time of implementation, the duration of setting up and configuring the Data Warehouse

Apppliance

● Read performance, the speed with which a question to the Data Warehouse Appliance is

retrieved

● Write performance, the speed with which an update batch is inserted into the Data

Warehouse Appliance

● Scalability, the Data Warehouse Appliances’ ability to expand or contract in order to fit the

changing needs of the user company

● Other, including shortened ‘latency’ in information which means the reduced time taken for

information flow between operational system and analysis, and fewer systems to administrate

The following sections cover what the vendors say about these benefits.

4.1.1.1 PERFORMANCE In terms of performance every vendor has numerous reasons why appliances are fast. The vendors

mention many factors that make up the Appliance performance, as seen in Figure 6.

Figure 6: Factors that contribute to the performance of Data Warehouse Appliances, according to the vendors


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When discussing performance, vendors explain that the main issue with large scale Data

Warehousing is the input/output (I/O). I/O can be described as the flow of data between processing

and storage. Today the processing speed is much higher than the reading speed of storage discs. In

order to make up for the slow reading speed, Appliance products use parallel processing of data.

This is, as shown in Figure 6, an essential part of why appliances have high performance. The goal

has been to retrieve as little unnecessary data as possible from the database. To achieve this, the

DW Appliance has several processing units directly linked to the location of the data. These

processing units each process their own part of a query and filter out unneeded rows and columns.

Many Appliance products also use compression to further reduce the traffic between processing

units and storage. This parallel processing technology is controlled by software developed

especially for Appliances. Vendors say that software that handles query planning and optimizing is

central in building a parallel Data Warehouse solution.

Because of the highly increased performance in Appliances, the structure of the Data Warehouse

can be changed. The potential benefit is shortened latency between registered information in

source systems and information ready for analysis. Vendors explain that with increased

performance of queries, the traditional architecture with a large Data Warehouse and several Data

Marts can be changed. The result is a structure where all of the data is stored in the Data

Warehouse and the Data Marts are built as views of that data. According to a vendor this has

several benefits, such as less duplicated data, less development effort and more flexibility in report

design.

When talking about DW Appliance business value, one of the benefits most commonly mentioned

by vendors is the change in maintenance. Since the architecture can be changed and compressed to

one place, the administrative work is reduced. Vendors argue that since less physical modeling is

needed to create Data Marts, indexes and aggregated views, less development is required from a

Business Intelligence perspective. The eliminated need to construct Data Marts also contributes to a

more flexible environment for the developers.

While the software has been developed especially for Data Warehouse Appliances for optimal

performance, the hardware situation is quite reversed. Only one vendor mentions specialized

hardware, which can be seen in Figure 7. Another common feature among is the unified source of

hardware. All vendors have built their products with hardware from one company. Their comment


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on this is that it is easier building an optimized solution with hardware that fit well together.

Another mentioned reason is that prices can be lowered.

Figure 7: Hardware components of a Data Warehouse Appliance

All vendors provide a unified source of support. Since large scale Data Warehouse architectures can

be very complex, it is often hard to specify exactly what is causing errors or performance issues.

This problem lies in the many different components that constitute the architecture. Vendors argue

that with a standardized product, it is far easier to duplicate the environment and run tests to find a

solution. There is also an issue with responsibility, where in a solution with many vendors low

performance or errors could be blamed on others.

4.1.1.2 SCALABILITY Appliance solutions are in many ways targeted for companies with large amounts of data. This

means that the products must be able to grow. Vendors talk about the concepts and linear

scalability and modular expansion. Linear scalability means that performance, price, and

administration will increase linearly when expanding the Data Warehouse Appliance. Modular

expansion means that expansion of the Data Warehouse Appliance is done in modules – a company

that needs to expand will buy another set of discs and processors, instead of individual units. Every

set is in itself an appliance that automatically synchronizes with the ones already bought. The

purpose of modular expansion is to avoid complexity in buying and expanding a Data Warehouse.

Investigating how many new processors or hard drives to buy in order to have sufficient capability

can be both costly and time consuming. Therefore, Appliance product modules have been designed

to maximize performance of all hardware components.


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4.1.1.3 SIMPLICITY There are a number of benefits which are less tangible. One reason to invest in an Appliance is -‐

according to the vendors -‐ the lowered amount of systems included in the Data Warehouse

architecture. This benefit is accompanied by fewer administrative tasks. A result of these impacts

would be a less complex Data Warehouse environment. Another aspect taken into consideration

when marketing DW Appliances is the pricing. Vendors have learned that pricing of large scale

systems can be very confusing to the customer. They have therefore developed a simple pricing

method with either price per complete product, or per amount of storage needed. This is shown in

Figure 8. The column ‘Other’ represents the answer that a combination of the pricing methods can

be offered.

Figure 8: Pricing of a Data Warehouse Appliance

4.2 USER INTERVIEWS

User interviews were conducted in order to gain an insight into the decision process of a Data

Warehouse Appliance investment:

What where the grounds for the investment? How was the vendor chosen? How was the

implementation managed? And most importantly: what is the perceived business value?

In-‐depth interviews were conducted semi-‐structurally and in person. A framework of questions and

topics was sent to the interviewees beforehand. This can be found in Appendix A. Follow-‐up

questions were asked, when necessary, via email and telephone.


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This section presents a summary of the answers received from interviews with companies with

Data Warehouse Appliance solutions within various business areas. The interviewees are from

various parts of the organizations, ranging from Data Warehouse project managers to technical

consultants.

4.2.1 THOUGHTS ON DATA WAREHOUSE APPLIANCE BEFORE IMPLEMENTATION

When asked about the reason for the investment in a Data Warehouse Appliance, the users gave a

single term answer: performance. In most cases the companies already had a Data Warehouse

solution running, which had grown to the extent where its performance no longer was adequate.

This became evident to the interviewed companies in different ways: one could not respond to

newly established legal requirements, another could not perform complex enough customer

analyses and a third ended up with outdated reports due to the weekly maintenance taking too long

to be completed in the weekend. In some cases of early adoption, an Appliance was initially

acquired instead of a standard Data Warehouse since a future need of capacity and performance

was identified. Another reason for considering an Appliance was architectural -‐ the possibility of

replacing several products with a single one seemed appealing.

In general, a request for proposal was sent to several of the Data Warehouse Appliance vendors.

Several factors were considered when the decision was made -‐ cost and ease of transition being two

of the important ones. When the decision had been made, a proof of concept was often performed

even though it added extra costs and risk for the vendors, who spends time on an implementation

which is not yet sold. The reason for this was that vendors felt confident that customers who got to

run the machine with their own data, in their own environment, would clearer see the benefits of

the product.

As previously mentioned, most companies already had a Data Warehouse in place when

considering the Appliance technology. In these cases, the current data models and data was

migrated directly into the new environment. Many companies chose to start with this approach, to

quickly see results that justified their investment. They then returned to the data models, to make

changes that further enhanced the performance of the Data Warehouse Appliance.

The companies that invested in the Appliance technology expected a great increase in performance.

They also expected it to be quick to implement, require a low amount of maintenance and include


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vendor support able to solve problems over distance. In some cases, layoffs in the database

administration team were expected.

4.2.2 THOUGHTS ON DATA WAREHOUSE APPLIANCE AFTER IMPLEMENTATION

All interviewees state that their implementation of a Data Warehouse Appliance has led to a great

increase in performance. In some cases, this increase has been so significant it has opened for new

possibilities -‐ analyses that were previously based on measures on a daily base could instead be

measured by minutes and seconds. There is no question that the various Appliances met the

performance expectations of the interviewees.

However, there is something else which the Appliance customers came to appreciate even more

than they expected: the benefit of having a ‘black box that manages itself’. After the implementation,

they no longer have to consider the hardware structure of their Data Warehouse. They can expect it

to run as it should -‐ and if it does not, all they have to do is make a single call to their vendor

support contact. For the customers, this amounts to a great reduction in the complexity of handling

their Data Warehouse, which can be seen in Figure 9.

Figure 9: Administrative tasks, before and after an implementation of Data Warehouse Appliances


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5. ANALYSIS

This section presents a discussion of the results of the vendor interviews, vendor form, and user

companies interviews. It aims to answer the following questions:

Do the vendors share one view on the subject?

Do the customers share one view on the subject?

Do the vendors and customers share one view on the subject?

5.1 VENDOR INTERVIEWS

This section provides a discussion of the results from interviews with the DW Appliance vendors. .

From the answers that were collected from the participating vendors, a number of common

statements were found. These are listed below as vendor truths.

5.1.1 VENDOR TRUTHS

Vendor truth 1: an Appliance is built with hardware from a single source

This reduces complexity by eliminating needs for integration within the Appliance as well as

facilitating the external integration. It may also help in keeping the costs down and make it easier to

replace spare parts. Furthermore, it could simplify the maintenance and enable support from a

single source, which are two factors that provide value to the user companies.

Vendor truth 2: an Appliance is pre-installed and pre-configured upon delivery

This greatly contributes to the ease of implementation and allows the customer to quickly gain the

benefits of the investment. Although database migration projects tends to be complex, a pre-‐

configured Data Warehouse Appliance can both lower costs and shorten the time of

implementation.

Vendor truth 3: an Appliance automatically optimizes according to different workloads and data

This might be a truth with modification. A system that automatically optimizes to any situation

might be a little too perfect to be true. There are however some functionalities that make this truth

relevant. The automatic categorization and placement of ‘hot’ data that is used often eliminates

many tuning and optimizing tasks.


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Vendor truth 4: an Appliance is priced on a ‘pay for what you use’ basis

There is no standard price for an Appliance and the reason for this is that there is no such thing as a

standard Appliance. Instead, they come in different sizes and configurations that all affect the price.

Most vendors, however, use a pricing model that takes the amount of data (terabyte), in

consideration. This makes it possible for companies of different sizes to all consider an investment

in an Appliance solution.

Vendor truth 5: the top benefit of an Appliance is performance, closely followed by a low cost of

maintenance and a great scalability

The fact that the vendors name the same top benefits indicates homogeneity of the technology.

Vendor truth 6: the most important factor contributing to the performance of an Appliance is its

parallel processing in combination with its purpose-built software

The fact that the vendors name the same top benefits indicates homogeneity of the technology.

5.1.2 ANALYSIS OF VENDOR TRUTHS

These similarities show that Appliance products are designed to be simple. This does not mean that

the technology itself is simple -‐ but that it is percieved as this by the customers, who will only notice

the effects of the product. This is very much in line with how an Appliance should work. What needs

to be considered by customers investing in this technology is that there are in fact differences

between the products, and they will fit different types of companies in different situations. One

noticeable difference is the scope of the appliance. Just as kitchen appliances can be of single as well

as multipurpose character, Data Warehouse Appliances have different intentions. Some Data

Warehouse Appliances aim to serve a specific type of purpose while others have a combination of

purposes. This difference in scope affects the price-‐performance relation.

5.2 USER COMPANIES INTERVIEWS

This section provides a discussion of the results of the interviews with the companies using a Data

Warehouse Appliance.


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5.2.1 THE TOP BUSINESS VALUES OF DATA WAREHOUSE APPLIANCES

The vendors and user companies fully agree that the main business value of the Data Warehouse

Appliance technology is performance. However, in hindsight the users perceive the Appliances ease

of administration and the overall reduction of Data Warehouse complexity as another very

important benefit of the technology. While this upside is somewhat addressed by the vendors as

they promote a reduced cost of maintenance, it seems to be of even greater value to the customers

than expected. Due to this benefit being intangible, it might have been difficult for the vendors to

promote. But as the number of Data Warehouse Appliance implementations increases, the number

of reference cases does too, and they might compose a plausible source of information later on.

5.2.2 THE DIFFERENCE BETWEEN EXPECTED AND PERCEIVED BUSINESS VALUE OF DATA WAREHOUSE APPLIANCES

The user companies are unanimous in the perception that their respective DW Appliance product

does not only live up to, but exceed, their expectations in terms of performance, ease of

implementation, ease of administration, as well as in reduction of Data Warehouse complexity. One

reason for this might be that they were partially unaware of the amount of work they put into

continuous maintenance and tuning with their previous solutions.

5.2.3 WHAT DRIVES AN INVESTMENT IN DATA WAREHOUSE APPLIANCE TECHNOLOGY

For all user companies that were interviewed, they experienced various events that led to an

investment in Data Warehouse Appliance technology. These events were of different kinds, internal

as well as external, but all led to that their current Data Warehouse solution became acutely aged.

They can even be referred to as compelling events:

• For one company within the banking industry, certain legal requirements were enforced

which increased their need for reliable, complex, and timely analytical reports.

• For one company within the retail industry, the amount of data that was collected during

the weeks had grown to the point where the update window, which was Friday night to

Monday morning, simply was not big enough. This led to that the reports generated from

the Data Warehouse always lacked the most recent data.

• For one company within the insurance industry, a need arose to perform increasingly

complex analysis, to be able to tailor insurances and target customer groups.


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It is important to note, however, that the interviewed companies are all early investors in the Data

Warehouse technology. The fact that compelling events has led to their investments might

therefore indicate that the maturity level of the technology is low, rather than that it will precede all

future investments.

5.2.4 DELIVERING VALUE IS MORE IMPORTANT THAN LOWERING COSTS

All the user companies that were interviewed made their investment in Data Warehouse Appliance

technology with the intention of creating and adding value to their business processes. The

expected increase in performance was intended to improve the existing reports and analyses, and

in some cases even enabled entirely new business ideas. As opposed to many other types of IT

investments, it was not done primarily in order to cut costs – something that is shown by the fact

that none of the user companies has performed reviews the return of their investment.

5.2.5 FOCUS ON INFORMATION RATHER THAN TECHNOLOGY

Companies that have invested in Data Warehouse Appliance technology did so because of a need for

powerful technological solutions. But this does not necessarily mean that what they were interested

in the technology itself. Instead, what the user companies emphasize during the interviews was the

quality of analyses, reports, metadata and master data – and all processes that govern them is made

possible with the technology. They consider the technology as a business enabler, and the most

important aspect for them is that their Data Warehouse Appliance functions – not how it functions.


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6. CONCLUSION

Companies that have implemented Data Warehouse Appliance products have found considerable

value in the technology being presented in an accessible way. To manually design an optimized

solution for large scale Data Warehousing is a complex project that requires a large amount of

system design resources for an extended period of time. As organizations look to store and analyze

more data, they find the idea of a pre-‐tested appliance, that is purposely designed for their needs,

attractive.

The companies that have invested in a Data Warehouse Appliance have done so because of lack of

performance in their current Data Warehouse solutions. What they look for is a product that solves

their problems – and does so quickly. How this is done is of less importance, what the customers

want is something that works. The vendors have made the concept of Data Warehouse Appliance

clear and simple to grasp, by offering:

• a limited number of different sizes

• a single source of support

• a machine made up with hardware components from a single source

• a machine which is pre-‐installed and pre-‐configured upon delivery

• a solution that requires little administration and tuning

Together, these factors lower the complexity of investing and maintaining a Data Warehouse

solution. There is less focus on technical issues and products are designed to be convenient for the

customer. This convenience itself has proved to be a highly valuable intangible benefit, powerful

enough to potentially spread to other IT areas and products.

The technology used in the Data Warehouse Appliance products is not revolutionizing. In fact, the

hardware components are standard and some of the software can be found in large scale database

management systems. Instead, what Data Warehouse Appliances offer customers is an easy to

manage, ‘black box’ solution that, as the current users put it, “simply works”. It is a productification

of an entire complex system environment – a solution equivalent to that of buying a fully equipped

house instead of drawing, planning and building it yourself.


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6.1 CONSIDERATIONS

Since the interviewed user companies all experienced compelling events which led to their

implementation, their views might not represent those of a standard Data Warehouse Appliance

customer. Also, the relationships between vendors and companies using their product must be

considered. Since the interviewed companies may have agreed to be a reference customer for their

vendor, the objectivity of their answers might be questionable.

The rather low number of performed interviews might put the generalizability of the research

results at risk, as not even unanimous answers can be assumed to apply in general. It has also

affected the reliability of the research results, as it grows with the number of sources telling the

same. However, since the interviewees in this research have been unanimous to a large extent, the

highest possible generalizability and reliability for this research has been reached.

Since the researchers have little experience in conducting interviews in the purpose of research, the

validity of the research might also be questioned. However, with this in mind, a lot of care has been

put into letting the interviewees clearly define relevant terms and concepts. Also, many interpreting

questions have been asked and all statements have been carefully evaluated.

6.2 FURTHER RESEARCH

To verify the results of this report, further research is required. Primarily, a larger number of

companies using the technology should be interviewed in order to increase the reliability of the

answers. A possible alternative could be to interview more people within the same companies, to

get a broader input for the analysis.

Future research may also include a mapping of tangible benefits of the Data Warehouse Appliance,

identified by companies that have implemented the technology. This could lay the groundwork for a

framework for estimating profitability, for companies facing an investment decision in Data

Warehouse Appliance products.


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7. REFERENCES Abiteboul, S, Hull, R, Vianu, V, (1995). Foundations of Databases. 1st ed. USA: Addison-‐Wesley. Adelman, S, Moss, L. T, (2002). Data Warehouse Project Management. 1st ed. USA: Addison Wesley. Brynjolfsson, E, Hitt, L, Kim, H, (2011). Strength in numbers: how does data-driven decision making affect firm performance?. 1st ed. USA: MIT Sloan School of Management. Chaudhuri, S., Dayal, U., (1997). An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD. 26 (1), pp.65-‐74 Chaudhuri, S. Dayal, U, Narasayya, V, (2011). An Overview of Business Intelligence Technology. Communications of the ACM. 54 (8), pp.88-‐98 Clemen, R. T, Reilly, T, (2001). Making Hard Decisions. 1st ed. USA: South-‐Western Cengage Learning. Dahlgren, L.E.; Lundgren, G; Stigberg, L. (1998). Gör IT lönsamt!. 2nd ed. Sweden: Ekerlids English, L.P. (1999). Improving Data Warehouse and Business Information Quality. 1st ed. USA: Wiley Gammelgård, M. (2007). Business Value Assessment of IT Investments. Doctoral Thesis in Industrial Information and Control Systems. Sverige: Kungliga Tekniska Högskolan Inmon, W. H., (2005). Building the Data Warehouse. 4th ed. Indiana: Wiley. Keeney, R.L, (1994). Creativity in Decision Making with Value-‐Focused Thinking. Sloan Management Review. Summer, pp.33-‐41 Kvale, S, (1996). Interviews - an Introduction to Qualitative Research Interviewing. 1st ed. USA: Sage publications, Inc. Park, Y, (2006). An empirical investigation of the effects of data warehousing on decision performance. Information & Management. 43, pp.51-‐61 Porter, J. D, Rome, J. J, (1995). Lessons from a Successful Data Warehouse Implementation. CAUSE/EFFECT. Winter, pp.43-‐50 Renkema, T. J, (2000). The IT value quest: how to capture the business value of IT-based infrastructure. 1st ed. England: Wiley. Williams, S, Williams, N, (2007). The Profit Impact of Business Intelligence. 1st ed. San Francisco: Elsevier.

7.1 FURTHER READING

Ahmed, N; Akhtar, W. (2010). Enterprise Wide Data Warehouse. Master of Science Thesis in Engineering and Management of Information Systems. Sweden: Royal Institute of Technology Bonifati, A, Cattaneo, F, Ceri, S, Fuggetta, A, Paraboschi, S, (2001). Designing data marts for data warehouses. ACM Transactions on software engineering an methodology. 10 (4), pp.452-‐483 Gartner Inc. (2012). Magic Quadrant for Data Warehouse Database Management Systems. [ONLINE] Available at: http://www.gartner.com/technology/reprints.do?id=1-‐196T8S5&ct=120207&st=sb. [Last Accessed 23 April 2012]. Gray, P, Watson H. J, (1998). Present and future directions in data warehousing. The data base for advances in information systems. 29 (3), pp.83-‐90


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Hopkins, M.S , Kruschwitz, N, LaValle, S, Lesser, E, Shockley, R, (2012). Analytics: The New Path to Value. 1st ed. USA: MIT Sloan Management Review. Hwang, M. I, Xu, H, (2007). The effect of implementation factors of data warehousing success: an exploratory study. Journal of information, information technology, and organizations. 2, pp.1-‐14 ISACA, (2010). The business case guide: using val IT 2.0. 1st ed. USA: ISACA. Kimball, R, Ross, M, (2002). The Data Warehouse Toolkit. 2nd ed. USA: John Wiley and Sons, Inc.. Lee, S. C, (2001). Modeling the business value of information technology. Information & Management. 39, pp.191-‐210 Manyika, J, Chui, M, Brown, B, Bughin, J, Dobbs, R, Roxburgh, C, Byers, A.H, (2011). Big data: The next frontier for innovation, competition, and productivity. 1st ed. USA: McKinsey Global Institute. Melville, N, Kraemer, K, Gurbaxani, V, (2004). Review: Information technology and organizational performance: an integrative model of business value. MIS Quarterly. 28 (2), pp.283-‐322 Pezzini, M, Sholler, D, (2011). SAP Throws Down the Next-Generation Architecture Gauntlet With HANA. 1st ed. USA: Gartner, Inc. Pokorny, J, (2006). Database architectures: current trends and their relationships to environmental data management. Environmental Modelling & Software. 21, pp.1579-‐1586 Sammon, D, Finnegan, P, (2000). The ten commandments of data warehousing. The data base for advances in information systems. 31 (4), pp.82-‐91 Scofield, T.C. , Delmerico, J.A. , Chaudhary, V, Valente, G, (2010). XtremeData dbX: An FPGA-‐Based Data Warehouse Appliance. Computing in Science & Engineering. 12 (Issue 4), pp.66-‐73 Sybase, an SAP Company, (2012). Intelligence for Everyone: Transforming Business Analytics Across the Enterprise. 1st ed. USA: Sybase, an SAP Company. Symons , C, (2006). Best Practices: Measuring The Business Value Of IT. 1st ed. USA: Forrester Research, Inc. Watson, H. J, Goodhue, D. L, Wixom, B. H, (2002). The benefits of data warehousing: why some organizations realize exceptional payoffs. Information & Management. 39, pp.491-‐502

7.2 FIGURES

Figure 1 Data Warehouse architecture

Chaudhuri, S., Dayal, U., (1997). An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD. 26 (1), pp.65-‐74

Figure 2 Shared everthing architecture

Introduction to parallelism, [ONLINE], Available at https://www1.columbia.edu/sec/acis/db2/db2d0/db2d006.htm

[Last Accessed 27 June 2012]

Figure 3 Shared nothing architecture

Introduction to parallelism, [ONLINE], Available at https://www1.columbia.edu/sec/acis/db2/db2d0/db2d006.htm

[Last Accessed 27 June 2012]


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Figure 4 Business value of Business Intelligence

Williams, S, Williams, N, (2007). The Profit Impact of Business Intelligence. 1st ed. San Francisco: Elsevier.

Figure 5 Advantages of Data Warehouse Appliances according to the vendors

Undén, S, Westerlund, E, (2012)

Self produced chart based on interviews with vendors of Data Warehouse Appliance technology

Figure 6 Factors that contribute to the performance of Data Warehouse Appliances, according to the vendors



Figure 7 Hardware components of a Data Warehouse Appliance



Figure 8 Pricing of a Data Warehouse Appliance



Figure 9 Administrative tasks, before and after an implementation of Data Warehouse Appliances


Self produced chart based on interviews with companies using the Data Warehouse Appliance technology


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APPENDIX A

1 VENDOR INTERVIEW QUESTION FRAMEWORK

• What is Data Warehouse Appliance technology?

• Which industries have an interest in the Data Warehouse Appliance technology?

o Why do companies within these industries need an Appliance?

• How does your Data Warehouse Appliance work?

• How do your Data Warehouse Appliance differ from the other on the market?

• Are the tools used for working with your Data Warehouse Appliance developed by you?

• What are the technical requirements for a company that wishes to implement your Data

Warehouse Appliance?

• How is a migration performed from a customers’ current Data Warehouse to your Data

Warehouse Appliance?

• How does your Data Warehouse Appliance handle parallel processing?

• Which interface is used when interacting with your Data Warehouse Appliance?

• How does your Data Warehouse Appliance handle data loading? By batch or transaction?

• Does your Data Warehouse Appliance handle ETL, ELT or both?

• Are there any possible bottle necks in your Data Warehouse Appliance?

• Does your Data Warehouse Appliance handle anything in-‐memory?

• What are your thoughts on the future or Data Warehouse Appliance?

2 VENDOR QUESTION FORM

A. YES-‐NO QUESTIONS

Yes 100% Are all hardware components from the same source? No 0% Yes 20% Are any of the hardware components built especially for this

Appliance? No 80%


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Yes 100% Is the product pre-‐configured when delivered to the customer?

No 0% Yes 100% Does the product automatically optimize according to different

workloads and data? No 0%

B. CHECKBOX QUESTIONS

Per terabyte 80%

Per user 20% Per unit 40%

How is your product priced?

Other 60% Cost of hardware 40% Cost of maintenance 80% Performance 100% Support 20% Time of implementation 40% Read performance 40% Write performance 20% Scalability 80%

Choose the biggest upsides to your Appliance product, compared to a traditional Data Warehouse.

Other 40% Parallel processing 100% Compression 60% Fast discs (such as SSD) 0% High bus bandwidth 20% Columnar database 60% Purpose-‐built hardware 20% Purpose-‐built software 80% Lots of RAM 20%

Choose the factors that contribute the most to the performance of your Appliance

Other 40%


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C. FREE TEXT QUESTIONS

How long, approximately, does it take to perform a POC (proof of concept) for a client?

Answers vary between the different vendors as well as for a single vendor -‐ how long a specific POC takes depends on the clients and its needs. Most common answer: 2-‐3 weeks.

Is a POC performed at the client’s site or in another environment?

Answers vary – it depends on the client’s wishes. Most common answer: Whichever fits the client.

How many employees are, in general, involved in the implementation of a POC?

Most common answer: Not many people are needed. A POC is performed together with the client; therefore at least two people are required

3 USING COMPANIES INTERVIEW QUESTION FRAMEWORK

● Before the implementation

○ What made you consider the Appliance technology, and when?

○ What did the decision process look like?

○ Was a prestudy/case study/proof of concept conducted before the decision was made?

○ Was there an existing Data Warehouse solution when the Appliance was implemented? If so,

how was it used?

○ What was the expected business value of the Appliance investment?

● During the implementation

○ What was the general attitude towards the Appliance technology and the related business

changes?

○ How does the Appliance fit into the general IT architecture?

○ How did the implementation go?

○ How long did the implementation take?

● After the implementation

○ What changes has the Appliance technology led to?

○ How is the Appliance used today?

○ How is the maintenance managed?


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○ Has the Appliance investment been assessed? How?

○ Did the implementation live up to your expectations?

Business Value of the “Data Warehouse Appliance” Technology

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