Only for educational purposes.

1. 4. Business Analytics
Business analytics (BA) refers to the skills, technologies, practices for continuous iterative
exploration and investigation of past business performance to gain insight and drive business
planning.
Business analytics focuses on developing new insights and understanding of business
performance based on data and statistical methods. Business analytics makes extensive use of
statistical analysis, including explanatory and predictive modeling, and fact-based management
to drive decision making. It is therefore closely related to management science. Analytics may be
used as input for human decisions or may drive fully automated decisions. Business intelligence
is querying, reporting, online analytical processing (OLAP), and "alerts."
4.2 Types of Business Analytics
Descriptive Analytics :The application of simple statistical techniques that describes what is
contained in a data set or database. Example: An age bar chart is used to depict retail shoppers
for a department store that wants to target advertising to customers by age.
Predictive Analytics: An application of advanced statistical, information software, or operations
research methods to identify predictive variables and build predictive models to identify trends
and relationships not readily observed in a descriptive analysis. Example: Multiple regression is
used to show the relationship (or lack of relationship) between age, weight, and exercise on diet
food sales. Knowing that relationships exist helps explain why one set of independent variables
influences dependent variables such as business performance.
Prescriptive Analytics: An application of decision science, management science, and operations
research methodologies (applied mathematical techniques) to make best use of allocable
resources. Example: A department store has a limited advertising budget to target customers.
Linear programming models can be used to optimally allocate the budget to various advertising
media
4.3 Business Analytics Process
The complete business analytic process involves the three major component steps applied
sequentially to a source of data (see Figure 4.1 ). The outcome of the business analytic process
must relate to business and seek to improve business performance in some way.
The logic of the BA process in Figure 4.1 is initially based on a question: What valuable or
problem-solving information is locked up in the sources of data that an organization has
available? At each of the three steps that make up the BA process, additional questions need to
be answered, as shown in Figure 4.1 . Answering all these questions requires mining the

2. information out of the data via the three steps of analysis that comprise the BA process. The
analogy of digging in a mine is appropriate for the BA process because finding new, unique, and
valuable information that can lead to a successful strategy is just as good as finding gold in a
mine. Many firms routinely undertake BA to solve specific problems, while other firms
undertake BA to explore and discover new knowledge to guide organizational planning and
decision-making to improve business performance.
The size of some data sources can be unmanageable, overly complex, and generally confusing.
Sorting out data and trying to make sense of its informational value requires the application of
descriptive analytics as a first step in the BA process. Also, incorporating some of the data into
spreadsheets like Excel and preparing cross tabulations and contingency tables are means of
restricting the data into a more manageable data structure. Simple measures of central tendency
and dispersion might be computed to try to capture possible opportunities for business
improvement. Other descriptive analytic summarization methods, including charting, plotting,
and graphing, can help decision makers visualize the data to better understand content
opportunities.
From Step 1 in the Descriptive Analytic analysis (see Figure 4.1 ), some patterns or variables of
business behavior should be identified representing targets of business opportunities and
possible (but not yet defined) future trend behavior. Additional effort (more mining) might be
required, such as the generation of detailed statistical reports(based on percentage analysis,
central tendency measure, dispersion) narrowly focused on the data related to targets of
business opportunities to explain what is taking place in the data (what happened in the past).
This is like a statistical search for predictive variables in data that may lead to patterns of
behavior a firm might take advantage of if the patterns of behavior occur in the future. For
example, a firm might find in its general sales information that during economic downtimes,
certain products are sold to customers of a particular income level if certain advertising is
undertaken. The sales, customers, and advertising variables may be in the form of any of
the measurable scales for data in Table 1.4 , but they have to meet the three conditions of BA
previously mentioned: clear relevancy to business, an implementable resulting insight, and
performance and value measurement capabilities.
To determine whether observed trends and behavior found in the relationships of the descriptive
analysis of Step 1 actually exist or hold true and can be used to forecast or predict the future,
more advanced analysis is undertaken in Step 2, Predictive Analytic analysis, of the BA process.
There are many methods that can be used in this step of the BA process. A commonly used
methodology is multiple regression, “Forecasting,” for a discussion on multipleregression and
ANOVA testing. This methodology is ideal for establishing whether a statistical relationship
exists between the predictive variables found in the descriptive analysis. The relationship
might be to show that a dependent variable is predictively associated with business value or
performance of some kind. For example, a firm might want to determine which of several
promotion efforts (independent variables measured and represented in the model by dollars in
TV ads, radio ads, personal selling,and/or magazine ads) is most efficient in generating customer
sale dollars (the dependent variable and a measure of business performance). Care would have to
betaken to ensure the multiple regression model was used in a valid and reliable way, which is
why ANOVA and other statistical confirmatory analyses are used to support the model

3. development. Exploring a database using advanced statistical procedures to verify and confirm
the best predictive variables is an important part of this step in the BA process. This answers the
questions of what is currently happening and why it happened between the variables in the
model.
A single or multiple regression model can often forecast a trend line into the future. When
regression is not practical, other forecasting methods in Time series (exponential smoothing,
smoothing averages) can be applied as predictive analytics to develop needed forecasts of
business trends. The identification of future trends is the main output of Step 2 and the
predictive analytics used to find them. This helps answer the question of what will happen. If a
firm knows where the future lies by forecasting trends as they would in Step 2 of the BA process,
it can then take advantage of any possible opportunities predicted in that future state.
In Step 3, Prescriptive Analytics analysis , operations research methodologies can be used to
optimally allocate a firm’s limited resources to take best advantage of the opportunities it found
in the predicted future trends. Limits on human, technology, and financial resources prevent any
firm from going after all opportunities they may have available at any one time. The
implementation of statistical results are based on the business environment variables seeing
the validity and effectiveness of the organization . Using prescriptive analytics allows the firm
to allocate limited resources to optimally achieve objectives as fully as possible. For example,
linear programming (a constrained optimization methodology) has been used to maximize the
profit in the design of supply chains This third step in the BA process answers the question of
how best to allocate and manage decision-making in the future.
In summary, the three major components of descriptive, predictive, and prescriptive analytics
arranged as steps in the BA process can help a firm find opportunities in data, predict trends that
forecast future opportunities, and aid in selecting a course of action that optimizes the firm’s
allocation of resources to maximize value and performance.
4.4 BA process and Decision Making
The BA process can solve problems and identify opportunities to improve business performance.
In the process, organizations may also determine strategies to guide operations and help achieve
competitive advantages. Typically, solving problems and identifying strategic opportunities to
follow are organization decision-making tasks. The latter, identifying opportunities, can be
viewed as a problem of strategy choice requiring a solution. It should come as no surprise that
the BA process closely parallels classic organization decision-making processes. As depicted in
Figure 4.2 , the business analytic process has an inherent relationship to the steps in typical
organization decision-making processes. The organization decision-making process (ODMP)
developed by Elbing (1970) and presented in Figure 4.2 is focused on decision making to solve
problems but could also be applied to finding opportunities in data and deciding what is the best
course of action to take advantage of them. The five-step ODMP begins with the perception of
disequilibrium, or the awareness that a problem exists that needs a decision. Similarly, in the BA
process, the first step is to recognize that databases may contain information that could both
solve problems and find opportunities to improve business performance.

4. Then in Step 2 of the ODMP, an exploration of the problem to determine its size, impact, and
other factors is undertaken to diagnose what the problem is. Likewise, the BA descriptive
analytic analysis explores factors that might prove useful in solving problems and offering
opportunities. The ODMP problem statement step is similarly structured to the BA predictive
analysis to find strategies, paths, or trends that clearly define a problem or opportunity for an
organization to solve problems.
Finally, the ODMP’s last steps of strategy selection and implementation involve the same kinds
of tasks that the BA process requires in the final prescriptive step (make an optimal selection of
resource allocations that can be implemented for the betterment of the organization). The
decision-making foundation that has served ODMP for many decades parallels the BA process.
The same logic serves both processes and supports organization decision-making skills and
capacities.
4.5 Advantages of Business Analytics
Enhancing product value for total customer satisfaction is every company’s end goal. Target
markets of different industries are dynamic with their needs often changing according to
society’s standards, issues, and fads. Knowing this, it is necessary to pull ahead of the
competition through unique innovation of ideas and products which would readily attract
customers.
Before business analytics became the version we know today, businessmen had to make do with
error-ridden analytic models which also potentially damaged their plans. Since there was no
constructive way to initialize organized data extraction, earlier versions of analytics did not work
well for its first generation of users. Traditional analytics teams had to dig hard and deep
whenever they tried to gather information about their consumers.
The data gathered is vital in statistical analysis, which in turn is essential for decision making.
Decision making is the critical process which makes or breaks a company’s goals. A slight
mistake or an overlooked factor can delay a decision and may even put the business plan to a
halt.
But how does analytics really work? What benefits can your company gain from it?
1. Analytics helps you measure how much of your mission statement is accomplished
A good business has its own missions statement, which is a set of values presented to their
consumers either as a marketing plan or as basis of checking in on their own development. Many
businesses retain or promote employees using the values in their mission statements as
guidelines. Although this is helpful in determining who helps your company succeed, it isn’t
strategic enough to leave it at that. Values must also be quantified and expressed in a tangible
way such as generating more profit for the company.
Quantified values can help the business improve their analytical process because it defines a
common goal that should be followed by everyone involved in the business. When these values
are quantified, they will be evaluated by the employees in order to gain a clearer view of what is
expected from them. The more informed they are, the more productive they will become.

5. 2.Analytics Encourages Smart Decision-Making
Accessibility to important data gives companies the power to make accurate decisions that could
leverage businesses. Not only does it provide useful data, it also allows companies to make
decisions faster and more efficiently than before.
Companies can maximize the use of analytics when they share the discussion to as many
employees as needed. Ever heard of the saying “two heads are better than one?” A group is
usually able to analyze data better and reach objective and informed decisions compared to just
one person.
3. Analytics Provides Clearer Insights Through Data Visualization
Recent versions of analytics care about how you present your data to your analytics team.
Comprehensive charts and graphs can be used to make sure that decision-making is more
interesting. Through visual representations of extracted data, relevant and useful insights can be
extracted in a much clearer way.
With analytics’ data visualization, information that you need about your market is there on your
table, presented in a visually appealing and organized manner.
4. Analytics Keep You Updated
Modern consumers change their mind easily as fads come and go, and they are easily swayed by
“better” offers. Analytics can give you insight about how your target market thinks and acts. You
will be prompted to be dynamic at all times to serve the needs of your ever-changing consumers.
Changes in the industry can occur at a very rapid pace. It is not unusual to see larger companies
being devoured by promising start-ups. Protect your business from unpredictability with
analytics so that you may be able to innovate and pre-empt your products according to your
consumer’s needs and preferences.
5. Analytics Offer Efficiency
Efficiency for businesses has been improving since the advent of business analytics. With the
ability to gather a large amount of data at a fast rate and present it in a visually appealing way,
companies can now formulate decisions to help achieve specified goals. Analytics encourages a
company culture of efficiency and teamwork where employees are able to express their insights
and share in the decision-making process.
Analytics also provides companies with better choices on such matters like where to take the
business as well as determining the steps needed to achieve new goals.

6. 5. Types of Digital Data - Structured Data, Unstructured, Semi structured
80% unstructured, 10% structured, 10% semi structured
Structured Data
Data which are in an organized form (in rows and columns) and can be easily used by computer programme.
Relationship exists between data items in a structured data. Data stored in databases is an example of structured
data.
Example of structured data
Structured data is often managed using Structured Query Language (SQL) – a programming language created for
managing and querying data in relational database managementsystems. Structured data was a huge improvement
over strictly paper-based unstructured systems, but life doesn't always fit into neat little boxes. As a result, the
structured data always had to be supplemented by paper or microfilm storage. As technology performance has
continued to improve, and prices have dropped, it was possible to bring into computing systems unstructured and
semi-structured data.
Structured data has the advantage of being easilyentered,stored,queried and analyzed. At one time,because of the
high costand performance limitations of storage, memory and processing, relational databases and spreadsheets
using structured data were the only way to effectively manage data. Anything that couldn't fit into a tightly organized
structure would have to be stored on paper in a filing cabinet.

7. Sources of Structured data – Data Base, Spreadsheets,SQL,Tables
Characteristics of Structured data
a. Conforms to a data model
b. Similar entities are grouped
c. Attributes in a group are same
d. Data stored in the form of rows and columns
e. Fixed sized records within a file
Ease with structured data
a. Better storage
b. Scalability – Capable of handling enormous data
c. Security
d. Easy insert, edit(update) , and delete
Unstructured Data
Data that does not conform to any data model or not in the form that can easily used by a computer program. 80-90
percent data comes in this category.
Unstructured data is all those things that can't be so readily classified and fit into a neat box: photos and graphic
images, videos, audio , streaming instrument data, webpages, PDF files, PowerPoint presentations, emails, blog
entries, wikis and word processing documents, social media data
Characteristics of Un- Structured data
a. Does not conform to any data model
b. Cannot stored in the form of rows or columns in the form of data base
c. Not in a particular format or sequence
d. Not easily usable by a programme
e. Does not follow any rule or semantics
f. Has no easily identifiable structure
Challenges in storing unstructured data

8. a. Storage space
b. Scalability
c. Retreival process
d. Security and Privacy
e. Update and delete
f. Indexing and searching
Some solutions in retrieval
a. Tags
b. Text Mining
c. Classification/Clustering/Taxonomy
d. Naming Conventions
e. Natural Language Processing
f. Mahchine learning
g. Artificial Intelligence
Semi-Structured Data
Semi-structured data is a cross between the two. It is a type of structured data, but lacks the strict data model
structure. With semi-structured data, tags or other types of markers are used to identify certain elements within the
data, but the data doesn't have a rigid structure. For example, word processing software now can include metadata
showing the author's name and the date created, with the bulk of the document just being unstructured text. Emails
have the sender, recipient, date, time and other fixed fields added to the unstructured data of the email message
content and any attachments. Photos or other graphics can be tagged with keywords such as the creator, date,
location and keywords, making it possible to organize and locate graphics. XML and other markup languages are
often used to manage semi-structured data.
Examples: HTML, XML,marup languages, TCP/Ippackets,Zipped files, Integration of data from hetrogeneous
sources.

9. Characteristics of Semi-Structured Data
a. Not conform to a data model but contains tags and elements
b. Cannot stored in the form of rows and columns as in a data base
c. The tags and elements describe the data is stored
d. Attributes in a group may not be the same
e. Similar entities are groupd.
6. Balanced Score card
The balanced scorecard is a strategy performance management tool – a semi-standard
structured report, that can be used by managers to keep track of the execution of activities by the
staff within their control and to monitor the consequences arising from these actions.
The phrase 'balanced scorecard' primarily refers to a performance management report used by a
management team, and typically this team is focused on managing the implementation of a
strategy or operational activities – in a recent survey 62% of respondents reported using
Balanced Scorecard for strategy implementation management, 48% for operational management.
Balanced Scorecard is also used by individuals to track personal performance, but this is less
common – only 17% of respondents in the survey using Balanced Scorecard in this way,
however it is clear from the same survey that a larger proportion (about 30%) use corporate
Balanced Scorecard elements to inform personal goal setting and incentive calculations.
The critical characteristics that define a balanced scorecard are
 its focus on the strategic agenda of the organization concerned
 the selection of a small number of data items to monitor
 a mix of financial and non-financial data items.
Balanced scorecard is an example of a closed-loop controller or cybernetic control applied to the
management of the implementation of a strategy Closed-loop or cybernetic control is where
actual performance is measured, the measured value is compared to a reference value and based
on the difference between the two corrective interventions are made as required. Such control
requires three things to be effective:
 a choice of data to measure,
 the setting of a reference value for the data,
 the ability to make a corrective intervention.
Within the strategy management context, all three of these characteristic closed-loop control
elements need to be derived from the organisation's strategy and also need to reflect the ability of
the observer to both monitor performance and subsequently intervene – both of which may be
constrained.]
Balanced Scorecard was initially proposed as a general purpose performance
management system. Subsequently, it was promoted specifically as an approach to strategic
performance management. Balanced scorecard has more recently become a key component of
structured approaches corporate strategic management. ]

10. Two of the ideas that underpin modern balanced scorecard designs concern making it easier to
select which data to observe, and ensuring that the choice of data is consistent with the ability of
the observer to intervene.
Example Score Card
Advantages
a. Clarify and update the organization´s strategy and vision (obtain consensus on the
strategy and vision to follow and a greater degree of uniformity between the different
opinions);
a. Translate the mission and strategy of an organization into concrete actions and a
set of indicators that inform of the achievement of the objectives and the causes
that motivated the results obtained;

11. b. Facilitate internal communication of the strategy, vision and strategic objectives
(communicate clearly the way to follow and how to improve performance
throughout the entire organization, and link strategic objectives and indicators
through cause-effect relationships);
c. Improve the use of available resources;
d. Encourage the achievement of objectives without causing imbalances between
potential success factors;
e. Align personal objectives and those of the departments and units with the business
strategy (favouring the achievement of synergies and development of the spirit of
cooperation);
f. Link strategic objectives to long, medium and short-term goals and to the
respective annual budgets (assigning the necessary resources to achieve the
objectives);
g. Promote improvement programmes, such as process re-engineering and total
quality management (without the BSC, these programmes could focus on
processes that are not critical to strategic success, not helping to achieve the
expected financial result);
h. Promote the visualization process and monitor the results achieved
i. Allow the teams and all the members of the organization to concentrate on its
strategic priorities;
j. Obtain feedback that encourages continuous learning and improves strategic
formulation, identifying new initiatives and favouring the review of strategies on
a regular and systematic basis;
k. Identify and align the initiatives that lead to strategic objectives by creating a
coherent structure of strategies, objectives, goals and indicators that enable to
build a dynamic strategic plan;
l. Build a sense of confidence in the performance compensation system (quantifying
and clarifying the evaluation criteria
m. Translate the mission and strategy of an organization into concrete actions and a
set of indicators that inform of the achievement of the objectives and the causes
that motivated the results obtained;

12. n. Facilitate internal communication of the strategy, vision and strategic objectives
(communicate clearly the way to follow and how to improve performance
throughout the entire organization, and link strategic objectives and indicators
through cause-effect relationships);
o. Improve the use of available resources;
p. Encourage the achievement of objectives without causing imbalances between
potential success factors;
q. Align personal objectives and those of the departments and units with the business
strategy (favouring the achievement of synergies and development of the spirit of
cooperation);
r. Link strategic objectives to long, medium and short-term goals and to the
respective annual budgets (assigning the necessary resources to achieve the
objectives);
s. Promote improvement programmes, such as process re-engineering and total
quality management (without the BSC, these programmes could focus on
processes that are not critical to strategic success, not helping to achieve the
expected financial result);
t. Promote the visualization process and monitor the results achieved
u. Allow the teams and all the members of the organization to concentrate on its
strategic priorities;
v. Obtain feedback that encourages continuous learning and improves strategic
formulation, identifying new initiatives and favouring the review of strategies on
a regular and systematic basis;
w. Identify and align the initiatives that lead to strategic objectives by creating a
coherent structure of strategies, objectives, goals and indicators that enable to
build a dynamic strategic plan;
x. Build a sense of confidence in the performance compensation system (quantifying
and clarifying the evaluation criteria

13. 7. Key Performance Indicator(KPI)
A performance indicator or key performance indicator (KPI) is a type of performance
measurement. KPIs evaluate the success of an organization or of a particular activity (such as
projects, programs, products and other initiatives) in which it engages.
Often success is simply the repeated, periodic achievement of some levels of operational goal
(e.g. zero defects, 10/10 customer satisfaction, etc.), and sometimes success is defined in terms
of making progress toward strategic goals. Accordingly, choosing the right KPIs relies upon a
good understanding of what is important to the organization. What is deemed important often
depends on the department measuring the performance – e.g. the KPIs useful to finance will
differ from the KPIs assigned to sales.
Since there is a need to understand well what is important, various techniques to assess the
present state of the business, and its key activities, are associated with the selection of
performance indicators. These assessments often lead to the identification of potential
improvements, so performance indicators are routinely associated with 'performance
improvement' initiatives. A very common way to choose KPIs is to apply a management
framework such as the balanced scorecard
Examples
Key performance indicators are the non-financial measures of a company's performance - they
do not have a monetary value but they do contribute to the company's profitability. [4]
Accounts
Some examples are:
1. Percentage of overdue invoices
2. Percentage of purchase orders raised in advance
3. Number of retrospectively raised purchase orders
4. Finance report error rate (measures the quality of report)
5. Average cycle time of workflow
6. Number of duplicate payments
Marketing and sales
1. New customer acquisition
2. Demographic analysis of individuals (potential customers) applying to become
customers, and the levels of approval, rejections, and pending numbers
3. Status of existing customers
4. Customer attrition
5. Turnover (i.e., revenue) generated by segments of the customer population
6. Outstanding balances held by segments of customers and terms of payment

14. 7. Collection of bad debts within customer relationships
8. Profitability of customers by demographic segments and segmentation of customers by
profitability
Manufacturing
Overall equipment effectiveness is a set of broadly accepted non-financial metrics which reflect
manufacturing success.
 Availability = run time / total time, by definition this is the percentage of the actual
amount of production time the machine is running to the production time the machine is
available.
 Performance = total count / target counter, by definition this is the percentage of total
parts produced on the machine to the production rate of machine.
 Quality = good count / total count, by definition, this is the percentage of good parts out
of the total parts produced on the machine.
 Cycle time ratio (CTR) = standard cycle time / real cycle time
 Utilization
 Rejection rate
Professional Services
 Utilization rate = the percentage of time employees spend generating revenue
 Project profitability = the difference between revenue generated by a project and the
cost of delivering the work
 Project success rate = the percentage of projects delivered on time and under budget
System operations
 Availability / uptime
 Mean time between failure
 Mean time to repair
 Unplanned availability
 Average time to repair
Project execution
 Earned value
 Estimate to complete
 Manpower spent / month
 Money spent / month
 Planned spend / month

15.  Planned manpower / month
 Average time to delivery
 Tasks / staff
 Project overhead / ROI
 Planned delivery date vs actual delivery date
Supply chain management
 Automated entry and approval functions
 On-demand, real-time scorecard measures
 Rework on procured inventory
 Single data repository to eliminate inefficiencies and maintain consistency
 Advanced workflow approval process to ensure consistent procedures
 Flexible data-input modes and real-time graphical performance displays
 Customized cost savings documentation
 Simplified setup procedures to eliminate dependence upon IT resources
8 Dash Boards
Dashboards often provide at-a-glance views of key performance indicators (KPIs) relevant to a
particular objective or business process. In the other, "dashboard" has another name for "progress
report" or "report."
The "dashboard" is often displayed on a web page which is linked to a database that allows the
report to be constantly updated. For example, a manufacturing dashboard may show numbers
related to productivity such as number of parts manufactured, or number of failed quality
inspections per hour. Similarly, a human resources dashboard may show numbers related to staff
recruitment, retention and composition, for example number of open positions, or average days
or cost per recruitment.The term dashboard originates from the automobile dashboard where
drivers monitor the major functions at a glance via the instrument cluster.

16. Digital dashboards allow managers to monitor the contribution of the various departments in
their organization. To gauge exactly how well an organization is performing overall, digital
dashboards allow you to capture and report specific data points from each department within the
organization, thus providing a "snapshot" of performance.
Benefits of using digital dashboards include:
 Visual presentation of performance measures
 Ability to identify and correct negative trends
 Measure efficiencies/inefficiencies
 Ability to generate detailed reports showing new trends
 Ability to make more informed decisions based on collected business intelligence
 Align strategies and organizational goals
 Saves time compared to running multiple reports
 Gain total visibility of all systems instantly
 Quick identification of data outliers and correlations
 Watch any time any where
Classification
Dashboards can be broken down according to role and are either

17. a. strategic,
b. analytical,
c. operational,
d.
Informational
Strategic dashboards support managers at any level in an organization, and provide the quick
overview that decision makers need to monitor the health and opportunities of the business.
Dashboards of this type focus on high level measures of performance, and forecasts. Strategic
dashboards benefit from static snapshots of data (daily, weekly, monthly, and quarterly) that are
not constantly changing from one moment to the next. Dashboards for analytical purposes often
include more context, comparisons, and history, along with subtler performance evaluators.
Analytical dashboards typically support interactions with the data, such as drilling down into the
underlying details. Dashboards for monitoring operations are often designed differently from
those that support strategic decision making or data analysis and often require monitoring of
activities and events that are constantly changing and might require attention and response at a
moment's notice.
Types of dashboards
Digital dashboards may be laid out to track the flows inherent in the business processes that they
monitor. Graphically, users may see the high-level processes and then drill down into low level
data. This level of detail is often buried deep within the corporate enterprise and otherwise
unavailable to the senior executives.
Three main types of digital dashboard dominate the market today: stand alone software
applications, web-browser based applications, and desktop applications also known as desktop
widgets..
Specialized dashboards may track all corporate functions. Examples include human resources,
recruiting, sales, operations, security, information technology, project management, customer
relationship management and many more departmental dashboards. For a smaller organization
like a startup a compact startup scorecard dashboard tracks important activities across lot of
domains ranging from social media to sales.[citation needed]
Digital dashboard projects involve business units as the driver and the information technology
department as the enabler. The success of digital dashboard projects often depends on the metrics
that were chosen for monitoring. Key performance indicators, balanced scorecards, and sales
performance figures are some of the content appropriate on business dashboards.
Dashboards and scoreboards
a. Balanced Scoreboards and Dashboards have been linked together as if they were
interchangeable. However, although both visually display critical information, the
difference is in the format: Scoreboards can open the quality of an operation while

18. dashboards provide calculated direction. A balanced scoreboard has what they called a
"prescriptive" format. It should always contain these components (Active Strategy)...
b. Perspectives – group
c. Objectives – verb-noun phrases pulled from a strategy plan
d. Measures – also called Metric or Key Performance Indicators (KPIs)
e. Spotlight Indicators – red, yellow, or green symbols that provide an at-a-glance view of a
measure’s performance.
f. Each of these sections ensures that a Balanced Scorecard is essentially connected to the
businesses critical strategic needs.
g. The design of a dashboard is more loosely defined. Dashboards are usually a series of
graphics, charts, gauges and other visual indicators that can be monitored and interpreted.
Even when there is a strategic link, on a dashboard, it may not be noticed as such since
objectives are not normally present on dashboards. However, dashboards can be
customized to link their graphs and charts to strategic objectives]
Design
Good dashboard design practices take into account and address the following:
 the medium it is designed for (desktop, laptop, mobile, tablet)
 use of visuals over tabular presentation of data
o bar charts: to visualize one or more series of data
o line charts: to track changes in a number of dependent data sets over a period of
time
o sparklines: to show the trend in a single data set
 use of legends anytime more than one color or shape is present on a graph
 spatial arrangement: place your most important view on the top left (if the language is
written left to right) then arrange the following views in a Z pattern with the most
important information following the top-to-bottom, left-to-right pattern[4]
 color palettes to be colorblind friendly
A good information design will clearly communicate key information to users and makes
supporting information easily accessible.]
Assessing the quality of dashboard
There are a few key elements to a good dashboard:.]
a. Simple, communicates easily
b. Minimum distractions...it could cause confusion
c. Supports organized business with meaning and useful data
d. Applies human visual perception to visual presentation of information
e. It can be accessed easily by its intended audience

19. Assignment
Business Analytics in Functional Domain
 HR Analytics
 Financial Analytics
 Marketing Analytics
 Operational Analytics
Discuss successful BA implementation using a Case Study.
Measuring the success of Business Analytics
The LOFT Effect
As we analyzed the trends from the successful BI companies, a consistent theme emerged. Many
had been plugging along at business intelligence to varying degrees for years. What catapulted
them from BI mediocrity to success were multiple aspects. A few people described the change
as “an aligning of the stars” or a “perfect storm”. When we look closely at these factors that led
to the change from mediocre business intelligence to greater success, there were varying degrees
of Luck, Opportunity, Frustration, and Threat: LOFT.
BI initiatives can be boiled down to four different motivational factors, collectively referred to as
LOFT. That stands for luck (i.e. a change of leadership), opportunity (such as new business
opportunities), frustration (for example, a company “flying blind” or dealing with data chaos),
and threat (competition, the threat of bankruptcy, etc.) “A certain level of pain can force you to
work smarter”. “Where are your opportunities? Where are your pain points?” Answering those
questions can help lead BI project in the right direction.
The role of Luck
The funny thing about Luck is that you never really know if a positive outcome truly arises from
luck or if it is from fortuitous timing and exceptional insight. While working at Crystal
Chemicals, there were times I felt luck played a big role in our BI efforts, but in hindsight,
perhaps it was not luck at all
“Luck is what happens when preparation meets opportunity”.
Opportunity
Identifying the business opportunity and acting on time to utilize it
Frustration
When companies first embark on BI, a frequent starting point is to address the biggest pains.
Sometimes the degree of frustration has to reach a boiling point before BI becomes a priority.
Frustration can come in many forms, whether it is the inability to answer simple questions, being
held accountable for things without the right tools to do a job well, or, as many managers
describe, the frustration at managing blindly without facts to support their decisions.
At Continental Airlines, the data warehouse began in 1998 driven by two key initiatives:
Revenue Management and Customer Relationship Management. Continental had only recently

20. emerged from its second bankruptcy. Part of the airline’s turnaround strategy was a Go Forward
plan that promised to transform the customer’s flying experience and to appeal to more business
travelers. Mike Gorman, Senior Director of Customer Relationship Management, recalls trying
to understand one thing about a single customer. “ We could not. We had 45 different databases
with customer information. It took a few years to get to a single view of the customer but now,
detailed customer information is available within seconds of an event.
Threat
Threat from competitor, Policy Makers, Government, Tax Consultants, Vendors