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Business Analytics for Managers
- Taking Business Intelligence Beyond reporting
Introduction
Purpose of the PowerPoint…


These slides can be used for teaching: ”Business Analytics for Managers: Taking Business
Intelligence Beyond Reporting”
We don’t imagine that the slides are covering everything, but they are selected based on the
criteria below:


These slides contain the essential models of the chapters
In some cases exercises are suggested and examples included
Introduction
Purpose of the book…





A guide to fuel what we refer to as the analytical age
The ability to make an information strategy
An understanding of Business Analytics (BA) as a holistic information discipline
An understanding of the ever increasing role of BA
A reference to most used BI concepts, definitions and terms
Chapter Introduction
Content

What is business Intelligence?


The difference between BI and analytics


Defining the term
Defining more terms
What is an information system?

Business Intelligence is much more than a technical disciplin
Introduction
What is Business Intelligence

Two popular definitions are: ”Decision support” and ”The process of providing the right
people the right information at the right time”




Which customers should we send reminders (Credit department)
What advertising should we send to which customers (CRM)
What type of employees have the highest absence (HR)
Information about which products and customer segments are most profitable and
therefore deserves focus in the future (Marketing)
 This book definition of BI is a combination of the two definitions:
”Delivering the right decision support to the right people at the right time”
Introduction
What is Business Intelligence



This book focuses on information from data warehouses, however the same decision could
also be based on e.g. survey data, interviews with subject matter experts or external
consultants.
In general however, it is not the source that is important, it is too which degree it enables the
right persons to take the right decisions.
Good decisions are made; if the decision maker uses all the relevant input and analyzes this
correctly at the time of the decision.
Business intelligence is typical humans taking actions based on IT system input.
IT automation is typical when IT systems take actions based on IT system input.

Another definition could be: decision support used for business decisions.


Introduction
The difference between BI and Analytics
Types of information (BA vs. BI)…


This book typically takes it focus on processes – that is the routines we do and how they
interlink.
Processes typically evolves in steps over time




Horizontally when we manage it
Vertically when we improve it
The blue area represents a state
less than optimal
Can also be seen as the information
we need before we run a new
process vs. the information we use
during the process. Later in the book
we will look at learning information,
which is information we use after we
have learned from a process. E.g. a
marketing campaign.
Process Performance
Introduction
What is an information system?

BI-department produce information, but that in itself is not value creating. Value is not
created until people throughout the organization improve upon their decision making and
business processes

BI should be seen as a part of an information system



Business processes
Technical solutions
Human competencies
Introduction
What is an information system?

A value creating information system is characterized by three elements:
1. Some specific business processes are optimized by improved decision making –
compared to a situation without an information system
People must act differently and more efficient than they used to, before value is created by using
information. You can say that people must improve upon their work processes, that means the
way they act in daily work routines
2. The information system contains a technological element that collect, store and deliver
information
It can be IT-based, but also paper, papyrus scrolls, yellow sticky notes or heads with good
memories
3. Human competencies form part of the information system to
Someone must be able to retrieve data and deliver it as information in, say a front-end system.
Even more important, those who make the decisions, those who potentially should change their
behavior based on the decision support, are people who must be able to grasp the decision
support handed to them
Introduction
What is an information system?

When you create a new information system the order is the same:
1. Identify how you want the business process to work – who should do what and when, in
which order etc.
2. Then create an information system that can deliver the right decision support to the
right people at the right time
3. Train users in how to work the new process, including how to retrieve and use the
technical decision support system
Chapter 1
Content

The BIA model


Presenting the central concept in this book
An example

Make it more concrete
Chapter 1
The BIA model
Structure of the book

The BIA-model – from strategy
to data sources
Chapter 1
The BIA model
Structure of the book

The models five phases each of which is allocated a separate chapter

Ch. 2: The relationship between business strategies. The overall competitive position and strategy of
the company set requirements for the information to be delivered and used

Ch. 3: Based on the overall company strategy, requirements for information deliverables are specified
at the functional level , so they are able to reach their objectives. Business processes are improved in
this area.

Ch. 4: How data, information and knowledge is created by analysts.

Ch. 5: How information is stored over time in a data warehouse

Ch. 6: What sources systems typically deliver data to a data warehouse
Chapter 1
An example
Chapter 2
Content

Strategy and information


The relationship between strategy and business intelligence


Strategy is about solving short term issues and gaining long term competencies. Competing in the
information age means that you in the strategy creation process also must be aware of how
information can help you.
Varying degrees of integrating between company strategy and the usage of information
Which information do we prioritize?
Chapter 2
Strategy and information

Postulate: ”A company’s core competencies lie in the field of knowing how to
handle internal processes and knowing what customers want now and in the
future”

These competencies include things the company is especially good at, and which
can secure its survival, also an organization should be capable of evolving these
competencies to meet the future requirements in the marketplace

The point is, that companies competes on continuous creation of knowledge and
the ability to execute based on it
Chapter 2
The relationship between
strategy and business intelligence

Separated: In these companies, data are not used for decision making on a strategic level. The BI-function
only works on ad hoc basis. No prioritization in regard to what is relevant for the strategy.

Coordinated: The BI function performs monitoring of individual functions’ achievement of by using Dash
boards etc. BI is use reactively to monitor the execution of strategy.

Dialogue: A learning loop is facilitated when the BI function is reporting on business targets. Analyses
create learning from the differences between targets and actual. BI is used proactively to improve
operational processes when executing
the company strategy.

Holistic: Information is being treated as
a strategic asset, which can be used
to determine company strategy. The
organization constantly evaluates how
it can compete on information when
strategies are created.
Chapter 2
The relationship between
strategy and business intelligence
Coordinated

The BI function performs monitoring of individual functions’
achievement by using Dash boards etc.

Here the BI function is a reactive element, solely employed in connection
with the monitoring of whether the defined targets of the strategy are
achieved (performance management)
Chapter 2
The relationship between
strategy and business intelligence
Dialogue

A learning loop is facilitated when the BI function is reporting on
business targets.

Analyzing the differences between targets and actual create learning

BI is used proactively to improve upon operational processes when
executing the company strategy.
Chapter 2
The relationship between
strategy and business intelligence
Holistic

Information is being treated as a strategic asset, which can be used
to determine company strategy. How information, in combination with
strategies, can give them a competitive advantage

Competing on Analytics (Davenport) describes how a company can use information as a strategic
asset/resource
Chapter 2
Which information do we prioritize?

The strategy part of the book is primarily inspired by Treacy & Wiersema’s article: Treacy, M.
& Wiersema, F. (1993) Customer Intimacy and other Value Disciplines, Harvard Business
Review, Jan/Feb

The article explains that companies compete on the dimensions:
1.
Process Excellence (efficient in relation to production and delivery services, and which always
focuses on optimizing internal processes)
2. Customer Intimacy (strong customer relations, that is, about being able to establish a psychological
connection to customers)
3. Product innovation (strong in the field of product innovation and being a leading supplier of ’state
of the art’ products)
 If a company masters one of the three disciplines and matches its competition on the two others it
can become a market leader
Chapter 2
Which information do we prioritize?

Discuss which information for the following companies would be expected to focus
on when making a company strategy:




Dell – low cost web distribution of products
Apple – innovative and customer oriented
Siemens – world leading wind mills
Hennes & Mauritz, Boss and Wal-Mart clothing stores.
Chapter 3
Content

The relationship between this chapter and previous chapters


Establishing new business processes


If we start up a process from nothing, we have no data on it
Optimizing existing business processes


Keeping the BIA-model in mind
If optimizes and existing process, we have data to work with also
Which processes should we start with?

Depending on the overall company strategy, what internal information
systems must be expected a natural focus for the company
Chapter 3
The relationship between
this chapter and previous chapter

The previous chapter focused on how information is used at the overall strategic level

At the functional level, we identify
how to get from having some
overall objectives for a
department / function to
being able to specify the
information requirements.

The example contains 3 function
/ business processes
Chapter 3
Establishing new business processes

The Rockart model explains, how to identify information related to strategy and objectives, this is called
developing an information strategy

The department/function is given some objectives. To reach these objectives an operational strategy is
developed. The critical success factors are the elements of the plan that must have a successful outcome, if
the plan as a whole is to succeed. This
also defines what the Business Intelligence
department has to deliver
Lead and lag information

Lead information is information or knowledge that is
necessary for even beginning our new business activities.

Lag information let us monitor if we reach the strategic
target
Chapter 3
Establishing new business processes
An example, page 64.
Chapter 3
Establishing new business processes
Exercise




Create 3 information wheels with lead and lag information for Human Resources (HR) in a IT consulting
company
Assumption: overall strategy at company level: we want to increase the market share from 15% to 25 %
Assumption: objective for HR: Hire 20 new good sales managers
Your task as a HR manager 
 Make your own local / operational
strategy
 Identify 3 critical success factors
 Identify lead and lag information
for each critical success factor
 Develop and present your information
wheels
Chapter 3
Optimizing existing business processes

The model focuses more on the lag information by collecting and analyzing it to understand correlations to
be able to improve processes in the future.

The model uses lag information to create lead information (maybe learning loops)
Chapter 3
Optimizing existing business processes
Exercise

Optimizing two existing business processes of your own choice (perhaps you local canteen)

Identify lag information to create lead information

Create learning loops

Present your information wheels
Chapter 3
Optimizing existing business processes
An example, page 71.
Chapter 3
Which processes should we start with?

Correlation between strategy and operational processes with significant analytical potential lead us to the
fact that some analytical disciplines are more relevant for some businesses than for others.


Exercise
Which analytical disciplines do you think the companies below should focus on?
 Dell – low cost web distribution of products
 Hennes & Mauritz, Boss and Wal-Mart clothing stores.
 Apple – innovative and customer oriented
 Siemens – world leader in wind mills
Chapter 4
Content







Data, information and knowledge
Analysts role in the BI model
Requirements the analyst must meet
How to select the analytical method (The three questions)
Reporting
Statistical testing
Data mining

This chapters focus in on what links the technical
part of BI and BA together with the commercial
part of the organization. The focal point here is
therefore the analyst and his or hers toolbox
Chapter 4
Data, information and knowledge
Exercise

Discuss the difference between data, information and knowledge – Are these concepts the same thing?
Chapter 4
Data, information and knowledge
The definitions in the book

Data is defined as the carrier of information. An example of a piece of data could be ‘‘bread’’ or
‘‘10.95.’’ Data is often too specific to be useful to us as decision support. Data rests in data
warehouses and typically describes a transaction, action, status etc.

Information is data that is aggregated to a level where it makes sense for decision support in the
shape of, for instance, reports, KPIs, alerts, tables, or lists. An example of information could be that
the sales of bread in the last three months have been respectively $18,000, $23,000, and $19,000.

Knowledge is information that has been analyzed and/or interpreted. This means that the BI
department, as an example, offers some suggestions regarding why bread sales have fluctuated in
the last three months. Reasons could be seasonal fluctuations, campaigns, new distributions
conditions, or competitors’ initiatives. It is therefore not a question of handing the user a table, but
instead of supplementing this table with a report or a presentation. This means, of course, that
when the BI department delivers knowledge, it is not a result of an automated process, as in
connection with report generation, but rather a process that requires analysts with quantitative
methods and business insight.
Chapter 4
Data, information and knowledge
Exercise

Give some examples of how data can be transformed to information and from information to knowledge.

You can reuse the Canteen case if you want to.
Chapter 4
Requirements the analyst must meet
The core skills of an business analyst

Business competencies
 First of all, the analyst must understand the business process he or she is supporting and how the
delivered information or the delivered knowledge can make a value-adding difference at a strategic
level

Tool kit must be in order (method competencies)
 Reporting
 Statistical tests (to show any correlations that might be present in the tables.)
 Data mining (spot pattern or correlation in data)

Technical understanding (data competencies)
 If, for instance, an analyst needs new data in connection with a task, it’s no good if he or she needs
several days to figure out how the Structured Query Language (SQL) works, what the different
categories mean, or whether value-added tax is included in the figures.
 Analysts spend about 80% of their time retrieving and presenting data, so we also have to place some
clear demands on the analysts’ competencies in connection with data processing.
Chapter 4
How to select the analytical method
Three question model

The aim is to present a model that can be used in the dialogue between management who wants
information and the analyst who must deliver it:
 Question 1: Determine with the process owner whether the quantitative analytical competencies, or
the data manager and report developer competencies are required.
 Question 2: Determine whether hypothesis-driven analytics, or data driven analytics can be expected
to render the best decision support.
 Question 3: Determine whether the data-driven method has the objective of examining the
correlation between
one given dependent
variable and a large
number of other
variables, or whether
the objective is to
identify different
kinds of structures
in data.
Chapter 4
Reporting
Three question model

Reporting (by using descriptive statistics) presents information and the individual viewer or business user
will be the person to interpret and transfer this information into knowledge.

Reporting is typically based on a part of the available data and is cross tabulated (e.g. sales per week per
region)
Chapter 4
Reporting
Types of reporting





Ad hoc reports (one time only)
Manually updated reports (normally used in connection with projects and therefore have a limited
lifetime)
Automated Reports: On Demand (standard reports)
Automated Reports: Event Driven
(relevant information presents
itself to the individual user at the
right time)
Reports should be internally aligned
(one version of the truth)
Chapter 4
Statistics

When working with hypothesis-driven methods, we use statistical tests to examine the relationship
between some variables in, let’s say, gender and age. We must have some initial idea of the relationship

The result of the test will be a number between 0 and 1, describing the risks of being wrong, if we conclude
based on the data material that there is a relationship between gender and lifetime. The rule is then that if
the value we find is under 0.05, that is, 5%, then the likelihood of our being wrong is so insignificant that
we will conclude that there is a relationship.

Since the 5% also means that if we make 20 random test between variables which has nothing to do with
each other, will in average 1 time (in 5% of the cases) find a significant relationship which is a false true.
For the same reason you must before you
test make a sanity check in regard to
whether it theoretical possible that there
is a relationship between the two variables.
Chapter 4
Data mining


Where statistics is a hypothesis driven process with the aim of creating knowledge.
Data mining is more of a data driven process with the aim of finding actionable patterns in the data.

This means that we scan the data for patterns and we evaluate the quality of the decision support the
process generates via testing the model on an unknown data set. In statistics we evaluate the quality of
the decision support via monitor the level of significance and screen for whether it is a relevant test in the
first place.

Data mining is typically used to give decision support on questions like:





Which of our engines will break down when and why?
Which customers will leave us when and why?
Which customers will buy what and when?
Which customers have high credit risk?
What is the price of our products next year?
Chapter 4
Exploratory techniques
Four typical examples

In BI, we typically see four types of explorative analyses. These are methods for data reduction, cluster
analysis, cross-sell models, and up-sell models.




Data reduction - We take all the information in a large number of variables and condense it into a
smaller number of variables.
The cluster analysis also simplifies data structures by reducing a large number of rows of individual
customers to a smaller number of segments. For this exact reason, the two methods are often used in
combination with questionnaires, where data reduction identifies the few dimensions that are of
great significance, and the cluster analysis then divides the respondents into homogenous groups or
segments.
Cross-sell models are also known as basket analysis models. These models will show which products
people typically buy together
Up-sell models are not looking at
what’s in the shopping basket once;
instead, they are looking at the
contents of the shopping basket
over time
Chapter 4
Data mining
A typical data mining process

Step 1: Development of different models

Step 2: Selecting the best model based on criteria such as :
 The model can be interpreted
 The model’s prediction power on an ”unknown”
dataset

Step 3: Score a data set
– Implement model
(production)
Chapter 4
Data mining
Example

Question to be answered: “Is there a correlation between the inquiries from corporate customers and
whether they canceled their subscriptions shortly after?”
Base table :
Canceled subscription
Yes
No
Yes

Customer name
YMCA
Maersk Shipping
General motors
Contract up
yes
yes
no
Inquiries
Get discount
Good deal on new phones
Get discount
Based on the model, an automated electronic service was generated that scanned the data warehouse of
the call center every five minutes. If any ‘‘critical’’ calls were found in the log readings from conversations
with customers—which could be that they had called in for a good deal combined with the fact that their
contract was up—then the person who was responsible for this customer would automatically receive an
email. With reference to the reporting section of this chapter, this is essentially an event driven report
which is generated based on a data mining algorithm.
Chapter 4
Data mining
About data mining projects

A data mining project often takes several weeks to carry out, partly because we are often talking about
large volumes of data (both rows and columns) as input for the models.

The modelling fase of the project only takes up a fraction of the total time it takes to establish a new data
mining process

After the data mining process is established and automated, it can be performed in a matter of hours next
time.
Chapter 4
Choose appropriate analytical method for
information wheels
Exercise

HR wants' to optimize their future hiring process by using analytics. As analyst you shall help them choose
appropriate analytical methodologies for their information wheels







HR wants to compare the new sales managers performance with the ’old’ sales managers
performance – What’s the method?
HR wants to identify a profile of a successful sales manager – What’s the method?
HR wants to identify if there is a correlation between salary and sales results – What’s the method?
HR wants to identify a good 10 year carrier path for the new sales managers – What’s the method?
HR wants to know the average sales manager’s historical sales result – What’s the method?
Which of the above questions/answers are lead information and which are lag information?
Which of the above questions/answers belong to the information domain and which belong to the
knowledge domain?
Chapter 5
Content

Why a Data Warehouse?

Architecture and processes in a Data Warehouse

How should you access your data?

BI Portal: Functions and examples

Exercise in Data Warehouse

This chapter will look into what a data warehouse or data repository is, and how it extract, transforms and
loads data to other systems and front ends
Chapter 5
Why a Data Warehouse?

To increases the usability and availability of source data

To ensure consistency and valid data definitions across business areas and countries (this principle is called
one version of the truth).

To hold documentation of metadata centrally upon collection of data

To avoid information islands

To create a historical data foundation

To avoid overloading of source systems with daily reporting and analysis
Architecture and processes
in a data warehouse
Data floats from source systems to the BI Portal
Chapter 5
Chapter 5
How should you access your data?
How does users get data out of a data warehouse.
Chapter 5
BI Portal: Functions and examples
Exercise

Explain what information the 4 MicroStrategy front-ends displays and how business people can use them
to improve upon decision making and processes?
Chapter 5
BI Portal: Functions and examples
Exercise

Explain what information the 4 MicroStrategy front-ends displays and how business people can use them
to improve upon decision making and processes?
Chapter 5
BI Portal: Functions and examples
Exercise

Explain what information the 4 MicroStrategy front-ends displays and how business people can use them
to improve upon decision making and processes?
Chapter 5
BI Portal: Functions and examples
Exercise

Explain what information the 4 MicroStrategy front-ends displays and how business people can use them
to improve upon decision making and processes?
Chapter 5
Exercises
Some of the most common terms in data warehousing











What are dimensions and how can you use them? Examples?
What are metrics/facts? Examples?
Why have a metadata repository?
What does an ETL job do? Examples?
What is a OLAP cube?
What is a data mart?
It is often said in data warehouse projects that you should: ”Think big, start small and deliver fast” - Why is
that?
What is SQL and how can you use it?
How do business people normally access BI information?
What does ’data quality’ mean and why is it important?
What is an ODS and why have it?
How many did you get right?
Chapter 6
Content

Some examples of data-generating source systems

What data source do we prioritize?

How to store data in the best way

How to combine data in the best way

This chapter is about the different data that often can be found in an organization. You will learn about
which types of decision support it can be used for and principle for how the data should be stored.
Chapter 6
Some examples of data-generating source systems
- and how this data can be useful

Billing systems. These systems print bills to named customers. By analyzing this data, we can carry out
behavior-based segmentations, value-based segmentations, etc.

Reminder systems. These systems send out reminders to customers who do not settle their bills on time. By
analyzing this data, we can carry out credit scoring and treat our customers based on their payment
records.

Debt collection systems. These systems send statuses on cases that have been transferred to external debt
collectors. This data provides the information about which customers we do not wish to have any further
dealings with, and which should therefore be removed from customer relationship management (CRM)
campaigns, until a settlement is reached.

CRM systems. These systems contain history about customer calls and conversations. This is key
information about customers, which can provide input for analyses of complaint behavior and thus what
the organization must do better. It can also provide information about which customers draw considerably
on service resources and therefore represent less value. It is input for the optimization of customer
management processes (see ‘‘Optimizing Existing Business Processes’’ in Chapter 3). It’s used in connection
with analyses of which customers have left and why.
Chapter 6
Some examples of data-generating source systems
- and how this data can be useful

Product and consumption information. This information can tell us something about which products and
services are sold out over time. If we can put a name to individual customers, this information will closely
resemble billing information, only without amounts. Even if we are unable to put a name to this
information, it will still be valuable for multi purchase analyses, as explained in ‘‘The Product and
Innovation Perspective’’ in Chapter 2.

Customer information. These are names, addresses, entry time, any cancellations, special contracts,
segmentations, and so forth. This is basic information about our customers, for which we want to collect all
market information. This point was explained in the customer relations perspective in Chapter 3.

Business information. This is information such as industry codes, number of employees, or accounting
figures. It is identical to customer information for companies operating in the business-to-business (B2B)
market. This information can be purchased from a large number of data suppliers, such as Dun &
Bradstreet. It is often used to set up sales calls.

Accumulation of KPIs. These are used for monitoring processes in the present, but can later be used for the
optimization of processes, since they reveal the correlations between activities and the resulting financial
performance.
Chapter 6
Some examples of data-generating source systems
- and how this data can be useful

Campaign history. Specifically, who received which campaigns when? This is essential information for
marketing functions, since this information enables follow-up on the efficiency of marketing initiatives. If
our campaigns are targeted toward named customers, and we subsequently are able to see which
customers change behavior after a given campaign, we are able to monitor our campaigns closely. If our
campaigns are launched via mass media, we can measure effect and generate learning through statistical
forecasting models. If this information is aggregated over more campaigns, we will learn which campaign
elements are critical and we will learn about overall market development as well.

Web logs. This is information about user behavior on the company’s Web site. It can be used as a starting
point to disclose the number of visitors and their way of navigating around the Web site. If the user is also
logged in or accepts cookies, we can begin to analyze the development of the use of the Web site. If the
customer has bought something from us, it constitutes CRM information in line with billing information.
Chapter 6
Some examples of data-generating source systems
- and how this data can be useful

Questionnaire analyses performed over time. If we have named users, this will be CRM information that
our customers may also expect us to act on. Questionnaire surveys can be a two-edged sword, however; if
we ask our customers for feedback on our service functions, for instance, they will give us just that,
expecting us to then adjust our services to their needs.

Production information. This kind of information can be used to optimize production processes, stock
control, procurement, and so on. It is central to production companies competing on operational
excellence, as described in Chapter 2. & Accumulation of KPIs. These are used for monitoring processes in
the present, but can later be used for the optimization of processes, since they reveal the correlations
between activities and the resulting financial performance.

Data mining results. These results, which may be segmentations, added sales models, or loyalty
segmentation, provide history when placed in a data warehouse. Just as with KPIs, this information can be
used to create learning about causal relations across several campaigns and thus highlight market
mechanisms in a broader context.
Chapter 6
Some examples of data-generating source systems
- and how this data can be useful

Human resources information about employees, their competencies, salaries, history, and so on. This
information is to be used for the optimization of the people side of the organization. It can also be used to
disclose who has many absences due to illness and why. Which employees are proven difficult to retain?
Which employees can be associated with success as evaluated by their managers? This information is
generally highly underrated in large organizations, and public enterprises in particular, which we will
substantiate by pointing out that all organizations have this information and that the scarce resource for
many organizations is their employees. Similarly, hour registration information can be considered human
resources-related information. When hour registration information (consumption of resources) is combined
with output information from, for instance, the enterprise resource planning (ERP) system, we can develop
a number of productivity key performance indicators (KPIs).

Information from ERP systems. This information includes accounting management systems in which
entries are made about the organization’s financial transactions for the use of accounting formats. It can
be related to KPI information, if we want to disclose correlations between initiatives, and whether results
were as expected.
Chapter 6
What data source do we prioritize?
- depends on what you want to use the data for


I takes a long time to make a data warehouse, often you will therefore include some data sources at the
time
The same with data mining projects, time limitations might make us discard some data sources
Chapter 6
How to store data in the best way
- depends on what you want to use the data for

Depending on what you want to use the data for, it can be stored more or less optimally
Chapter 6
How to combine data in the best way
- depends on what you want to use the data for

Through merging data sources we can gain synergies’

We can merge internet behavior and customer info and get improved decision support on the different
behavior across age groups, gender etc.
Chapter 6
Exercises

It is a well known fact that men save more for retirement than women – A new law is passed in Denmark in
2009 that allow men to keep their pension 100% in case of a divorce.

As a business intelligence analyst you are asked by the banks pension department (sales) to develop a
questionnaire to create source data for lead information for a campaign targeting women with insufficient
pension schemes.

In accordance to their information wheels, the pension department needs to know how many women are
in the target group and a profile of the potential customers for the campaign. Also sales asks you to
develop lag information to measure the success of the campaign.

Task: Develop the questionnaire for the lead information and the lag information
Chapter 7
Content

What is a BICC

The relationship with the technical part of the organization

Competencies in a BICC

Organizing your BICC

Ambitions with a BICC

This chapter will discuss what a Business Intelligence Competence Centre is and why it is important to
have this business unit to govern a cross functional process.
Chapter 7
Organizing your BICC

If you place your BICC unit – preferably close to the strategy department – it will be more likely to have a
direct impact on strategy


Holistic: Information is being treated as a strategic resource, which can be used to determine company strategy. The organization
constantly evaluates how it can compete on information when creating strategies.
If you make virtual business unit close to where the analysts are used, you are likely to increase the
performance
Chapter 7
What is a BICC

A BICC is a business unit mastering analytical, business and IT-competencies

A BICC can give BI and BA the needed critical mass to become successful

A BICC is the owner and developer
of the information wheels in an
organization
Chapter 7
The relationship with the technical part of
the organization

First it uncovers what the organizational wide information need is – which is what you information
strategy must be able to deliver on

This include how the data and information must be transported around in the organization – the
information architecture

Finally you identify the technology that can make all this happen considering costs and alternative
solutions – the technology strategy
Chapter 7
Competencies in a BICC

The three knowledge domains will together create the needed synergies to make BI and BA successful
Chapter 7
Organizing your BICC

If you place your BICC unit – preferably close to the strategy department – it will be more likely to have a
direct impact on strategy


Holistic: Information is being treated as a strategic resource, which can be used to determine company strategy. The organization
constantly evaluates how it can compete on information when creating strategies.
If you make virtual business unit close to where the analysts are used, you are likely to increase the
performance
Chapter 7
Ambitions with a BICC

Where are you and where do you want to be?
 Improved performance – move horizontally
 Improved strategic impact – move vertically
Chapter 8
Content

Strategic project or not

Estimating the value of a project

Qualitative description of a project

Project seen as a part of a greater whole

This chapter will give you an idea about how you can priorities which BI, BA or other project
to focus on first
Chapter 8
Strategic project or not

Putting all the projects into a cost-benefit matrix might make it easier for you to prioritize

Large projects must always be supported by strategic arguments

If the project is not strategic, it should be prioritized based on a cost-benefit analysis
Chapter 8
Estimating the value of a project

Elements in the costs benefit analysis
 One off costs – implementation costs
 Additional maintenance costs
 Increased value for the users or price for customers
 Improved resource utilization- what you will save from using the new solution

See also the SIPOC example next slide
Chapter 8
Estimating the value of a project

By comparing the process in a SIPOC model before and after you will be able to put value on the changes
Chapter 8
Estimating the value of a project

Alternatively you can make a qualitative description

Useful if there is uncertainty about the financial consequence and a need to link to the organizational
strategy
Chapter 8
Project seen as a part of a greater whole

Try to see the project
as a part of a greater whole

What is you strategic
objective vs. where are
you today
(maturity analysis)

Can you make the leap
(readiness analysis)
Chapter 8
Project seen as a part of a greater whole

When to use which way to prioritize a project
Chapter 9
BI in the future

Decision support will be
centered around the individual
person as a mean of optimizing
the overall performance of
the process they are a part of.
Chapter 9
BI in the future

We define BI as delivering the right decision support to the right people at the right time.

We believe that the quality of the decision support will improve
 Suggested solutions rather than issue flagging

People will receive the information individually in a way and form that is customized to the individuals
need
 Based on what you prefer, you will be informed

The information will be given when relevant to the user
 forecasting will increased the lead time for critical
decisions