Business Intelligence Solution for an SME: A Case Study

Raghavendra Raj, Shun Ha Sylvia Wong and Anthony J. Beaumont

School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, U.K.

Keywords: Business Intelligence, Data Warehouse, Microsoft BI, SME.

Abstract: Business Intelligence (BI) leverages the usefulness of existing information. It equips business users with

relevant information to perform various analyses to make key business decisions. Over the last two decades,

BI has become a core strategy for the growth of many companies, in particular large corporations. However,

studies show that small and medium-sized enterprises (SMEs) lag behind in implementation and

exploitation of BI solutions. To stay ahead of the competition, SMEs must be able to monitor and

effectively use all of their resources, in particular information resources, to assist them in making important

business decisions. In this paper, we examine the challenges such as lack of technical expertise and limited

budget when implementing a BI solution within an SME in the UK. In light of our experiences in tackling

these issues, we discuss how these challenges can be overcome through applying various tools and

strategies and the potential benefits.

1 INTRODUCTION

In the last couple of decades, regardless of the size,

companies have generated large volumes of data

from internal and external environments. The ability

to efficiently manage, access and analyse large,

though seemingly unrelated, volumes of data is

essential to business decision-making, otherwise

potential business opportunities may be missed and

important lessons may not be learnt. The rapid

changes in business climate and conditions have

brought Business Intelligence (BI) into the limelight

even for small and medium-size enterprises (SMEs).

To bring about a competitive advantage, companies

have to think of new ways to improve their

businesses. One among the several strategies has

been the adoption of Data Warehouse (DW) and

Business Intelligence (BI). This has become an

essential component of contemporary business

decision support systems. However, amongst SMEs,

there is a general lack of technical expertise required

to convert data into information to facilitate an

informed business decision making process.

Furthermore, the lack of understanding of the

benefits of BI and budget also make SMEs reluctant

to invest in adopting any BI solutions.

Business Intelligence in itself is not a novel

concept. This term was first put forward by Luhn

(1958). As Burstein and Holsapple (2008) recalled,

this concept was reintroduced by Howard Dresner in

1989. Negash and Gray (2003) defined BI systems

as systems which “combine data gathering, data

storage, and knowledge management with analytical

tools to present complex and competitive

information to planners and decision-makers”. Such

systems typically utilise data from a centralised data

repository that were brought together from different

data sources. Business Intelligence has grown

significantly since then due to the advancement of

BI tools and also the readiness of companies to

adopt new strategies to stay ahead of their game.

Some of the technologies such as Decision Support

Systems (DSS), Executive Information Systems

(EIS), Data Warehouse (DW), Online Analytical

Processing (OLAP) and Data Mining have had a

significant impact on the increase in popularity of

BI.

According to a survey of CIOs in 2012 (Gartner,

2012), BI was ranked as the top technology priority

by the companies as BI enabled them to create new

capabilities. Howson (2008) also noted that BI

helped employees in all cadres to interact with, and

analyse, business data in order to manage and

increase the efficiency of the company, to identify

any new opportunities and to facilitate operation

reengineering. Watson and Wixom (2007) pointed

out that information retrieval could be faster and

cheaper by implementing BI solutions. They also

mentioned that BI solutions could help organisations

achieve their business goals. Chugh and Grandhi

Raj, R., Wong, S. and Beaumont, A.

Business Intelligence Solution for an SME: A Case Study.

DOI: 10.5220/0006049500410050

In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 3: KMIS, pages 41-50

ISBN: 978-989-758-203-5

(2013) summarised key benefits of implementing BI

within an enterprise, which include:

 Ability to analyse data from multiple sources

and using various dimensions;

 Enabling managers to make intelligent business

decisions by identifying important patterns in

the business transaction data;

 Improved accuracy in predictions of business

trend;

 Increased operational efficiency through

identifying root causes of problems.

1.1 BI Components

To generate business intelligence, data from

business transactions needs to undergo an ETL

(Extract-Transform-Load) process which cleanses,

transforms and restructures the data into a data

warehouse or a data mart. Data warehouses and data

marts are essentially databases specially designed

for facilitating data analysis and knowledge

discovery. The data analyses are guided by the Key

Performance Indicators (KPIs) of the organisation

and they vary from business to business. A data

visualisation tool is used to present the results of

data analyses to BI users. These results may appear

in the form of standard reports based on regular

queries or as on-demand reports which show

information about business performance and

efficiency. Figure 1 summarises the key components

of a BI solution.

Figure 1: Key Components of BI.

1.2 BI Vendors

As mentioned earlier, BI solutions comprise multiple

technological components. In the past, each BI

component was built and delivered by specialised

vendors. Selecting an appropriate combination of BI

components to meet a business’s need requires

technical expertise which is often not readily

available in a business setting. The need to integrate

various BI components also pushes up the overall

implementation cost of a BI solution. Nowadays,

suppliers have developed BI components that could

be easily integrated. This massively reduces the cost

to implement BI. However, there remains the need

to identify a suitable strategy to integrate BI

components with a company’s existing systems.

Some of the major players in the BI field are

well-known names like Oracle, Microsoft, IBM,

SAS and SAP. Over the last few years, some smaller

vendors like Qlik and Tableau are gaining

popularity. Amongst the major suppliers of BI

solutions, Microsoft and IBM are two suppliers

whose BI products cover the entire stack of BI

components. Suppliers like Microsoft also deliver

these BI components as a part of the Microsoft

Developer Network (MSDN) license (Microsoft,

2016c) without any extra cost.

1.3 BI Usage

Existing business sectors which make use of BI to

make some key business decisions include: banking,

health service, IT companies, insurance companies,

manufacturing industry, financial services and many

more. As mentioned, the majority of large

organisations are already drawing benefits from

using BI and BI has become one of the major

strategies to maintain a competitive edge.

A survey conducted by McCabe (2012) found

that 33% of medium-size companies already used BI

solutions and a further 28% planned to use BI

solutions. However, amongst smaller organisations,

only 16% were using BI solutions and 16% planned

to use BI solutions. Though McCabe’s (2012)

survey showed that the adoption of BI amongst

SMEs was slow, there was an upward trend in the

awareness of the need for BI.

Studies (Horakova and Skalska, 2013) have

shown that different departments and business units

within a company use some kind of BI tools, and the

major users of BI include managers from: sales,

marketing, purchasing, accrual, finance, accounting,

human resource and IT. It is therefore clear to see

that BI is not just for top-level management such as

Managing Directors. A wide range of business

decision-making processes can also be benefited

from using BI.

1.4 BI Solutions

Given the cost and complexity involved in most BI

solutions, a large number of SMEs simply adopt a

BI solution which utilises spreadsheets such as MS

Excel integrated with a database (McCabe, 2012;

Tutunea and Rus, 2012). However, as typical

spreadsheet software does not come with a rich set

of data analysis and visualisation tools to facilitate

KMIS 2016 - 8th International Conference on Knowledge Management and Information Sharing

knowledge discovery, the resulting level of BI is

rudimentary.

Another contemporary BI solution comes in a

form of Software as a Service (SaaS). SaaS is

essentially a software application deployed in the

cloud. Rather than purchasing the software

application from a vendor and installing it in the

company’s server, companies pay monthly or yearly

subscriptions to gain access to the application via the

Internet. With SaaS, company data is typically

stored in the cloud and is managed by the service

provider (Papazoglou, 2003). One typical

application of BI supported by SaaS is to provide

intelligence on customer satisfaction in order to

bring about an improved Customer Relationship

Management (CRM). BI as a Service could be very

beneficial for SMEs because they are cost effective

and also relatively easy to implement. The major

concern of using a cloud-based service is data

security, privacy and data ownership.

While data ownership and privacy are not issues

for a locally-hosted BI solution, there is the need to

consider how data visualisation reports can be

shared with other stakeholders. BI using web

technologies, e.g. Google Chart (Google, 2016),

should enable web users to maintain reports in the

web, distribute the reports to interested users,

annotate the content and also generate ad-hoc reports

from web applications dynamically.

2 BI AND THE SME

The European Commission (EC) defined a small and

medium-size enterprise (SME) as a business which

has less than 250 staff, with a turnover of no more

than €50m and 99% of businesses in the EU fall in

this category (European Commission, 2016). With a

relatively small turnover, SMEs typically do not

have additional financial and human resources to

invest in new technologies such as BI systems that

are not core to their businesses. Hence, it comes as

no surprise that the adoption of BI amongst SMEs

has been slow. Tutunea and Rus (2012) noted that

cost-effective cloud-based solutions to BI exist and

an efficient BI solution can give SMEs a competitive

advantage. However, the issue of lacking expertise

and technical know-how to select and adopt a

suitable BI solution still poses a challenge for most

SMEs.

For SMEs, the term Business Intelligence is

often misperceived as a technology that is applicable

to large organisations only. This misconception

about BI means that most decisions made by the top

level management in SMEs are based on the results

from various Information and Communications

Technology (ICT) tools built as a part of the

company’s infrastructure (Tutunea and Rus, 2012).

There is a general opinion that SMEs are trailing

behind in adopting Business Intelligence to assist in

decision-making. There are a lot of studies and

articles describing successful adoption and

utilisation of BI amongst large organisations but

similar successful stories amongst SMEs are

uncommon (Fink and Ploder, 2009).

Irrespective of size and sector, companies

appreciate the necessity for, and the benefits of, BI

to assist in the business decision-making process.

However, with the complexity involved in a typical

BI implementation process, there is a high risk for

the project to fail in SMEs. Hence, SMEs need a

simple, flexible and efficient solution.

2.1 Challenges of BI Adoption in SMEs

There are various factors that affect SMEs’ planning

to adopt BI solutions:

 BI solutions are often expensive, for example, a

cloud-based BI solution typically costs at least

USD$500 per month per user.

 While off-the-shelf BI tools are available, the

learning curve of such tools for non-technical

business users is often steep.

 Hosting a BI solution requires the support of a

non-trivial, and often costly, hardware

infrastructure.

 While there are a wide range of BI solutions

available, SMEs often lack in-depth

knowledge of BI to select the most appropriate

solution for addressing the business’s needs.

 Generating BI is often a non-trivial task which

requires good knowledge and understanding of

database modelling and data warehousing.

Such technical know-how is often not readily

available within most SMEs.

In summary, the challenges can be narrowed

down to two main factors: cost involved and lack of

knowledge among the available resource pool to

handle complicated BI tools. While these factors are

legitimate barriers for SMEs to adopting a BI

solution, they should not prove detrimental in

promoting the implementation of BI solutions in

SMEs. To overcome the budget issue, there is a need

to identify low cost BI solutions. For example, a

standard BI solution is shipped as part of

Microsoft’s flagship product. Companies owning an

MSDN licence can have access to Microsoft’s

Business Intelligence Solution for an SME: A Case Study

standard BI solution at no extra cost. However,

many SMEs may not even be aware of such an

opportunity nor have the expertise required to start

utilising it.

2.2 Benefits of Adopting BI in SMEs

According to a research by Scholz et al. (2010),

there are lots of benefits SMEs could realise by

using BI tools. Some of the most important benefits

identified in the research include:

 Improvements in Data Support through

providing easy access to data. Improved data

validation and cleansing process also leads to

reports generated using high-quality data.

 Improvements in Decision Support through

providing rich visuals to support an accelerated

decision-making process. In addition, BI tools

could help identify risks and rectify them in a

timely manner.

 Cost and Time Saving can be achieved by

various BI tools such as dashboards and

scorecards because the retrieval of the data will

be faster and it is easier to interpret the visual

output of business data analysis.

The cost involved could be subsidised by

utilising technologies that are already part of the

existing company infrastructure. For example, if a

company adopts the Microsoft suite of business

products, no extra cost is required for purchasing

standard BI components. Furthermore, the cost for

purchasing data visualisation tools can be reduced

when lightweight web technologies are used to

present the business intelligence. This, not only

minimises the overall cost, it also removes the need

for extensive training.

3 BI IMPLEMENTATION IN AN

SME

3.1 About the Company

The current Business Intelligence project is carried

out for a UK based company known as AGGORA

Group which is specialised in catering equipment

solutions (AGGORA Group, 2016). Their core

business is in sales and service of catering

equipment and they perform asset management for

their clients who range from major corporates to

small businesses. The company has approximately

150 staff. The company turnover is approximately

£25m a year.

AGGORA Group wanted to improve their ability

to manage, analyse and utilise data. They also

wanted to have a data management system that is

flexible and can adapt to future changes so that they

can manage their business effectively and

efficiently. Though the existing IT team had

capabilities to generate reports for the management

team to analyse, the report generation was a

laborious and inflexible task and it slowed down the

processing of company’s core IT system because the

database was not designed to facilitate report

generation. Furthermore, as the company has been in

business for over ten years, there were also some

legacy IT issues that affected the efficiency of report

generation. The leadership team has therefore

decided to implement a BI solution to support their

expansion plans for the next decade. However, the

key challenge for implementing a BI solution for

AGGORA Group was that they did not possess

sufficient expertise in BI technologies. BI

technologies such as data warehousing need

advanced knowledge in database design and it is a

demanding task. Furthermore, the IT staff within the

company were already tied up to the day-to-day

support and enhancement of the existing IT

infrastructure. Hence, retraining existing staff was

not a possible option.

The company secured funding from the UK

government through the Knowledge Transfer

Partnerships (KTP) scheme (InnovateUK, 2016) to

investigate possible solutions for their business

challenges. This enables an IT graduate to be

employed and work on implementing a BI solution

with guidance from a UK university. The IT

graduate is employed by the university, but is based

in the company and hence able to interact with the

company employees daily. This enables all project

stakeholders to be involved in the project

development, with all views and concerns

considered and addressed, from the beginning of the

project. This helped to ensure a smooth roll out of

the BI solution.

3.2 Project Objectives

The main source of the company’s business data is

generated by an in-house software developed for

managing field services. This BI project focuses on:

 Analysing Client KPIs: Since the major

business of the company is servicing catering

equipment, it is important to understand

whether the Service Level Agreements (SLAs)

are met, whether the customers are satisfied,

etc.

KMIS 2016 - 8th International Conference on Knowledge Management and Information Sharing

 Analysing Engineers’ Performance and

Productivity: The company employs over 80

field service engineers. To better manage the

engineers’ time and the quality of their work,

the company would like to measure and

analyse the performance and the productivity

of the engineers.

 Analysing the Financial Figures: As the

growth rate of the company increases,

analysing the costs and sales figures will

enable the users to establish the economic state

of play of the company.

 Asset Management: The company holds large

volumes of historical information about the

clients’ equipment such as service history and

overall spend. The ability to utilise this

information will enhance the service provided

to the clients.

In terms of maintainability and sustainability, as

this is a KTP project and each KTP project is

required to include a detailed plan for embedding

new skills and knowledge into the company. Our

project plan therefore has included provision for

knowledge transfer and embedding through

workshops and training.

3.3 Implementation Methodology

There are different approaches for implementing a

BI solution. This could vary significantly based on

the size and the IT budget of the company, as well as

the company’s knowledge in the BI field. For

example, larger organisations with ample budget

may purchase one of the several BI solutions

available from a leading vendor. Such a BI solution

may include staff training and supports. Such an

organisation can also afford to employ a dedicated

team to implement and support the system.

However, SMEs are unlikely to be able to afford a

high profile BI system bundle and the capabilities of

such solutions are likely to exceed the needs of an

average SME.

We have adopted an iterative, five-stage

implementation process as advocated by McGonagle

(2007):

 Planning

 Data Collection

 Data Analysis

 Data Distribution

 Feedback

3.3.1 Planning

Our BI implementation project starts with the

planning phase. In this phase, the management team

that would be consuming the intelligence articulate

their requirements and establish the KPIs required to

be measured. This forms the foundation for the

subsequent phases.

Based on the objectives stated in Section 3.2,

SQL Server Management Studio (SSMS) and SQL

Server Data Tools (SSDT) were used for developing

the required BI solution. In addition, the end user

visualisation is achieved using Microsoft Power BI

to generate self-servicing reports while the SQL

Server Reporting Services (SSRS) was used to

generate standard reports.

This decision was based on the existing IT

infrastructure available in the company. Since the

company already uses Microsoft SQL Server 2012

as its backend database, the Microsoft BI suite was a

convenient option to implement this BI project.

Microsoft SQL Server 2012 has a database system

and other components needed for BI

implementation.

SQL Server 2012 (Microsoft, 2016b) includes

five core server components providing services

ranging from data storage, management and security

to specialist tools for data analysis, e.g. OLAP, and

data mining. Apart from the Analysis Service, we

have used the other four core components in this

iteration.

3.3.2 Data Collection

Once the requirements are gathered and objectives

are set, we then source the data and transform it into

a form that will provide the required intelligence to

the users. It is important to identify the data

available in the source that can meet the project

objectives.

As mentioned earlier, the source business data is

generated by an in-house field service management

system, the data is stored in the form of a relational

database using the SQL server. The database stores

information such as equipment management, cost

and sales and engineers’ timesheet information.

Based on the project objectives, it was decided

that the data from the source would be transformed

and stored into a data warehouse. One of the most

commonly used technique for designing a data

warehouse is dimensional modelling (Kimball and

Ross, 2013). The main reasons for its popularity are

that it results in a database schema which supports

fast query performance and allows presentation of

data in a user consumable format.

Though the dimensional model holds the same

data as the source database, the dimensional model

Business Intelligence Solution for an SME: A Case Study

deals with un-normalised tables for extracting

summarised and aggregated data. The main building

blocks of building a dimensional model are: facts

and dimensions. We have followed Kimball’s four

steps to dimensional modelling (Kimball and Ross,

2013):

1. Select the business process.

2. Declare the grain.

3. Identify the dimensions.

4. Identify the facts.

As per Kimball, a data warehouse could be

designed based on the two kinds of schemata:

 Star Schema usually consists of fact tables

linked to dimension tables using

primary/foreign key relationships.

 Snowflake Schema consists of hierarchical

relationships in a dimension table, with

normalised, low-cardinality attributes

appearing as secondary tables connected to the

base dimension table by an attribute key.

For the purpose of this project, we have opted for

a hybrid approach which is a combination of both

star and snowflake schema. As we have used an

iterative approach to build the data warehouse, some

of the iterations utilised a star schema and others

followed the snowflake schema. In order to maintain

the data consistency among the shared dimensions,

we have used the hybrid approach. The trade-off for

utilising a hybrid approach is that the performance

of the resulting data warehousing is slightly lower.

However, due to the nature of our data, the slight

degradation in performance was not noticeable by

end users.

3.3.3 Data Analysis

Once the data warehouse is designed, it is populated

with data from the live transaction database. This is

known as an ETL (Extract-Transform-Load)

process. Our ETL process uses Microsoft SQL

Server Data Tools (SSDT) which provides a

graphical interface that eases the design and

implementation of data transformation and

integration steps. As discussed earlier, our source

data is from a single source, the ETL process

involves mapping the data from the live transaction

database to the data warehouse. The transformations

performed in our ETL process include (Lacko,

2009):

 An Aggregate function which applies

aggregation (minimum, maximum, average,

sum, etc.) on the incoming set of data.

 A Conditional Split function which divides the

set of data into more subsets.

 A Copy Column function which adds new

columns, which are the copies of columns from

input data set.

 A Data Conversion function which enables

changing the data type of a column during the

transformation.

 A Derived Column function which creates new

columns derived from the values in the input

columns.

Power BI (Microsoft, 2015) provides business

users with useful data analysis and rich visualisation

to facilitate informed decision-making. The desktop

version of Power BI provides the ability to create

powerful data connection, models and reports for

data analysis. In this project, we have used Power BI

desktop version to connect to the data warehouse.

Once we have connected to the data source, we can

adjust the data to meet our needs. We did some

transformations using query editor in Power BI such

as renaming column headers and changing the text

to numbers. It is important to note that, the original

data source is not affected; only this particular view

of the data is modified. We have also created new

measures using Data Analysis Expression (DAX)

queries (Microsoft, 2016a) to provide aggregated

data.

3.3.4 Data Dissemination

The output of the data analysis process is presented

as business intelligence to the end users. We utilised

the cloud-based version of Power BI to disseminate

the results of data analysis to company managers.

Users can quickly generate and deploy reports and

dashboards and share them with other users.

Figure 2: Sample reports created in Power BI.

Any Excel user who is familiar with using pivot

tables and charts could be potential users of Power

BI because the learning curve is not steep. With

some basic training, we found that the end users

were generally able to generate required business

KMIS 2016 - 8th International Conference on Knowledge Management and Information Sharing

intelligence using Power BI without much technical

assistance. Our Power BI users can edit their own

reports, but they cannot edit the underlying data.

This helps ensure data integrity.

We have chosen Power BI over the other self-

service BI options available in the market mainly

because Power BI is fairly easy to use even for non

IT experts and it has the ability to integrate with

Office 365 (Microsoft, 2016d). Figure 2 shows some

of our sample reports and dashboards created using

Power BI. To ensure confidentiality, the clients’

name have been removed in the sample reports.

Apart from Power BI, Microsoft SQL Server

Reporting Services (SSRS) was used to create

standard reports that could be delivered to relevant

users on a regular basis. SSRS is a server based

report generating software that comes as part of the

Microsoft SQL Server suite. The SSRS provides a

unique user interface based on Visual Studio that

enables developers to connect to the relevant data

(Microsoft, 2016e).

In this project, we used SSRS to create some

standard KPI reports such as Engineer Performance

and Productivity. These reports are embedded into

an email which is sent to the respective managers for

their analysis.

3.3.5 Feedbacks

At the end of each implementation iteration,

feedback is collected from the end users. This

feedback is used to inform the next iteration of

implementation.

In this project, the feedback we obtained is from

a steering group consisting of managers from

various levels in the company who are also users of

our BI solution. Our steering group members include

commercial and technical directors as well as

business operation managers. The steering group

verifies the quality of the data and the accuracy of

the intelligence provided and also provides feedback

on how the solution can be improved in the future.

4 BENEFITS

Our experience shows that adopting BI has been

beneficial to the company. The key beneficiaries so

far have been the top and middle level management

as well as the IT department. Since the introduction

of the BI solution, the management gained the

freedom to explore business information without

requiring IT support and the IT team has been freed

up to improve the quality and the granularity of

information available. As a result, the management

is able to make more informed business decisions as

they have a clearer picture of the state of the

business.

We have also carried out tangible benefit

analysis of implementing a BI solution in the

company. This analysis revealed that there were

clear tangible benefits within six months of

implementing the first iteration of the BI solution.

The self-servicing reporting solution using Power BI

has enabled top and middle level management to

explore the business data and create their own on-

demand BI reports without reliance on the IT

department. This has led to a significant amount of

time saving in the IT department as there is no

longer the need for them to produce reports using

queries and pivot tables in an ad hoc manner. This

has also opened up more opportunities for the IT

department to widen the scope of information

available. The use of a specialised data warehouse

has also reduced the processing load of the live

database, making the core business system more

efficient, reliable and effective.

The following table lists some of the key benefits

identified so far:

Table 1: Tangible Benefits.

Tangible Benefits Value

Achieved

within 6

months (£)

Projected

Value within

12 months (£)

Cost saving resulted from

the introduction of Power

BI for self-service

reporting

£4,202 £8,404

Increased efficiency,

productivity and revenue

through new reports

generated using data

warehouse and Power BI

£18,060 £43,000

Time saving achieved for

IT team by using data

warehouse for generating

report

£7,000 £14,000

Total: £29,262 £65,404

5 DISCUSSION

In this section, we will summarise our experiences in

implementing the first iteration of a BI solution

within a UK SME. As a first remark, it is important

to understand that implementing a BI solution is an

iterative process and it is not trivial. Thorough

Business Intelligence Solution for an SME: A Case Study

understanding of the existing systems is required.

The objectives and KPIs of the business should be

well-defined before any implementation commen-

ces. In addition, this requires technical knowledge of

BI concepts such as data warehousing, the ETL

process and available data visualisation tools.

In terms of implementation strategy, we have

chosen to deliver BI in a top-down manner, with the

first set of BI solutions rolled out to top-level

managers. This allowed top-level management to

better understand the benefits of BI, and hence be

more supportive of a wider exploitation of BI within

the company.

To keep the costs down and to make the solution

more accessible to a wide range of end users, we

have deliberately used only a limited set of BI tools.

Based on our experience, the Microsoft suite of BI

tools can be used to support all aspects of business

intelligence required in this project. Microsoft also

provides tools for more advanced business data

analysis such as creating data cubes and mining data

for prediction analysis.

It is vital for an IT solution to be user-friendly so

that there will be more buy-in from end users. The

SSRS tools used to generate standard reports require

some technical skills to work efficiently with it. On

the other hand, Power BI supports an intuitive

process for creating on-demand reports and it

provides rich data visualisations. Most Excel users

will be able to work efficiently with Power BI

without much training. Furthermore, there are

several tutorials available for demonstrating how to

use Microsoft BI tools.

Finally, our experience shows that:

 BI solutions empower SMEs to understand

their current performance and evaluate it

against the company’s KPIs.

 While many SMEs understand the benefits of

implementing BI solutions, they are often put

off by the seemingly complex and expensive

implementation process. Our experience shows

that affordable and relatively simple BI

solutions exist and they can easily fit in SMEs’

existing IT solutions.

 As BI solutions become more readily available,

SMEs should possess better understand of their

existing IT infrastructure before making any

new investments.

 Implementing a BI solution involves various

phases. A good understanding of these phases

is needed to bring about success.

 In order to generate more accurate business

intelligence, it is important to have high-quality

data. This can be achieved using various

transformation and cleansing steps in the ETL

process.

 To facilitate efficient data retrieval and

analysis, data warehouses cannot be created by

simply joining tables from the live transaction

database. It should be properly designed by

following guidelines on dimensional

modelling.

 The Microsoft suite of BI tools could be a

perfect candidate for SMEs wanting to

implement BI solutions, especially when they

are already using other Microsoft business

products.

 While technical barriers do exist, there are

government schemes, in the case of UK and

Europe, available to help SMEs to overcome

such barriers through funding, recruitment and

knowledge transfer.

6 CONCLUSION

BI tools provide analytic data and key performance

information which enables organisations of all sizes

to be managed efficiently. The usage of BI tools in

SMEs was generally lower than that in large

organisations. Studies show that SMEs appreciate

the importance of streamlining their information

resource to help them make important business

decisions. They are also aware of the availability of

many BI tools in the market. However, the technical

expertise required to select the right combination of

tools is not readily available in many SMEs.

Furthermore, the general lack of sufficient IT budget

prevents SMEs from investing time and money in

adopting BI solutions.

In light of a BI implementation project, we have

described how an SME can reap the benefits of BI

using the Microsoft BI suite: Microsoft SQL Server

for data modelling and Power BI for data analysis

and visualisation. Based on our experience, we have

also made a recommendation of how SMEs may

approach implementing a BI solution, highlighting

the need for good understanding of the existing data

source, cleansing and transforming the data, creating

a data warehouse based on Kimball’s approach for

storing the transformed data and presenting the data

to the end users using various visualisation tools. A

possible limitation of our work is that it is based on a

company which has already adopted a Microsoft-

based IT infrastructure and has accumulated a

significant amount of business transaction data. We

recognise that if our approach were to be applied in

a company with a different IT infrastructure, the

KMIS 2016 - 8th International Conference on Knowledge Management and Information Sharing

costs, benefits and time scales could be different.

However, with Microsoft being the market leader in

both operational database management systems and

BI and analytics platforms (Gartner, 2015; 2016),

our findings are relevant to a large number of

businesses.

With the advancement of data mining and big

data analysis, there are a wide range of algorithms

and tools available for generating advanced business

intelligence. This paper describes our first step in

implementing a BI solution to an SME in the UK.

Our next step includes using BI to: (1) achieve

further potential enhancements within the

organisation, (2) analyse the productivity of sub-

teams within the enterprise and (3) perform margin

analysis or implementing a more sophisticated

account statement model. With the option available

to integrate R scripts with Power BI, we also plan to

investigate using predictive time series analysis or

other advanced data analysis to provide forecasts.

ACKNOWLEDGEMENT

This project is supported by Innovate UK under the

Knowledge Transfer Partnership (KTP) scheme

[KTP number KTP009714].

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