This document discusses several modern trends in information systems including online and real-time information systems, OLAP, data warehousing, data mining, business intelligence, business analytics, and knowledge management. It provides details on data warehousing such as how it combines data from multiple sources into a single database. It also discusses OLAP, data mining, business intelligence, and business performance management including key performance indicators, scoreboards, and dashboards.
1. MODERN TRENDS IN INFORMATION
SYSTEMS
1. Online & Real Time Information system
2. OLAP
3. Basic concept – Data Mining & Data Warehousing
4. Business Intelligence
5. Business Analytics
6. Knowledge Management
7. Business Performance Management – Scoreboards
and Dashboards
- Preeti Sontakke
2. Data Warehousing:
It is an electronic method of organising information.
A data warehouse essentially combines information
from several sources into one comprehensive
database.
- Bill Inmon defines: Data warehouse is a subject-
oriented, integrated, time variant and non volatile
collection of data in support of managements
decision making process.
3. Example:
In a business world, a data warehouse might
incorporate customer information from a company’s
point of sale system, its websites, its mailing list & its
comment cards.
Also it incorporate all information about employees,
including time cards, demographic data, salary
information etc.
--- By compiling all this information in one place, a
company can analyse its customers in a more holistic
way, ensuring that it has considered all the
information available.
4. How it works ?
Business
World
Demogra
phic data
Cash
Register
Time
cards
Websites
Salary
informati
on
Mailing
List
Comment
Cards
Data warehousing also makes Data Mining possible, which is the task of
looking
for patterns in the data that could leads to higher sales and profits.
5. Why it Matters ?
Companies with data warehouse can have an
advantage in:
Product development
Marketing
Pricing Strategy
Production time
Historical analysis
Forecasting
Customer satisfaction
However, data warehouses also can be very expensive to
design and implement.
6. Characteristics:
Subject Oriented: A data warehouse can be used to
analyse a particular subject area.
Example: sales
Integrated: A data warehouse integrates data from
multiple sources.
Example: Source “A” & “B” may have different ways of
identifying a product, but in a data warehouse, there will
be only a single way of identifying a product.
7. Cont….
Time variant: Historical data is kept in a data
warehouse.
Example: one can retrieve data from 3 months, 6
months, 12 months or even older data from a data
warehouse.
This contrast with a transaction system, where
often only the most recent data is kept.
Transaction system may hold most recent address of a
customer, where as a data warehouse can hold all
addresses associated with a customer.
Non – Volatile: Once data is in the data warehouse, it
will not changed or altered.
8. Other characteristics:
Some data is denormalised for simplification and to
improve performance.
Large amount of historical data are used.
Queries often retrieve large amount of data.
Both planned and ad hoc queries are common.
The data load is controlled.
9. Benefits:
A data warehouse maintains a copy of
information from the source transaction system:
It provides the opportunity to:
Maintain data history, even if the source transaction systems do
not.
Integrate data from multiple source systems, enabling a central
view across the enterprise.
Improve data, by providing consistent codes and descriptions,
flagging or even fixing bad data.
10. Cont….
Restructure the data so that it makes sense to the
business users.
Restructure the data so that it delivers excellent
query performance, even for complex analytic
queries without impacting the operational
systems.
Add value to operational business applications,
notably customer relationship management
system.
11. Limitations of Data Warehousing:
Limitations
Maintenance
Cost
Increased
demand of
users
Data
Ownership
Complica
-tions
Data
Rigidity
Hidden
Problems
Long
duration
project
Inability to
capture
required
data
12. Limitations:
Maintenance Cost: data warehouse for a huge IT
projects would involve high maintenance system which
may affect the revenue for medium scale organisations.
The cost benefit ratio is on lower side as it not only
involves systems with equipped technologies but also
longer hour’s as an investment from IT department.
Data Ownership: An important concern of data
warehouse is the security of data. Leaking of data
within the same organisation could cause problems for
the executives. This restricts your data security as the
data which has been implemented locally might be
sensitive only for certain department.
13. Cont….
Data Rigidity: The type of data imported into a data
warehouse is often static data sets which have the
least flexibility to generate specific solutions. For data
to be used, it has to be transformed and cleaned which
could take several days or weeks.
Hidden Problems of the Source: It arise when an
organisation finds themselves with the problems
related to the original source systems which were
involved in the importing of data into the ware house
often several years of operation. Practically, a human
error while entering data could be considered as a
void.
14. Cont….
Inability to capture required data: There is always the
probability that the data which was required for
analysis by the organisation was not integrated into
the warehouse leading to the loss of information.
Increased demand of the users: After success with the
initial few queries, user of the facility may ask more
complicated queries which would increase the
workload on the system & server. With awareness of
the features of the data warehouse, there might be an
increase in the no. of queries posed by the staff which
also increase the server load.
15. Conti….
Long duration projects: A comprehensive
warehouse project might take up to three years to
complete. Not all organisations are able to
dedicate themselves entirely & are hence more
reluctant in investing a data warehouse.
Complications: The integration feature is one of
the most important aspects of the warehouse
which is why it is recommended that an
organisation pays special attention to the
disparate and equally compelling data warehouse
tools & their results to arrive at a proper business
conclusion and make their decision.
16. DATA MINING
It is the process used by companies to turn
raw data into useful information.
Data mining depends on effective data
collection and warehousing as well as on
computer processing.
It is not only applied in business environment
but also in other fields such as weather
forecast, medicine, transportation, healthcare,
insurance, government etc.
17. Data Mining Process:
Organisations collect data and load it into their data
warehouse
Storing and Managing of data, either on in house
server or the cloud
Assessment of data and organising of data
Application software sorts the data based on the users
result
Presentation of data in easy to share formats, such as
a graph or table
18. Advantages of Data Mining:
Marketing / Retail: Data Mining helps marketing
companies build models based on historical data to
predict who will respond to the new marketing
campaigns such as direct mail, online marketing etc.
Through the results, marketers will have an appropriate
approach to selling profitable products to target
customer. Data mining brings a lot of benefits to the
retail companies in the same way as marketing.
Through market basket analysis, a store can have an
appropriate production arrangement in a way that
customers can buy frequent buying products together
with pleasant. It also helps retail companies offer certain
discounts for particular products that will attract more
19. Cont.….
Finance / Banking: data mining gives financial institutions
information about loan information & credit reporting. By
building a model from historical customers data, the bank
& financial institution can determine good & bad loans. In
addition, data mining help banks to detect fraudulent credit
transactions to protect credit owner.
Manufacturing: By applying data mining in operational
engineering data, manufacturers can detect faulty
equipment & determine optimal control parameters.
Governments: Data mining helps government agency by
digging & analysing records of the financial transaction to
build patterns that can detect money laundering or
criminal activities.
20. Disadvantages of Data Mining:
Disadvantages
Privacy Issue:
personal
information
shared by e-
commerce,
forums, blogs
etc., can be used
as an unethical
way that can
cause troubles.
Security
Issue: hackers
accessed &
stole big data of
customers from
corporations.
Personal &
financial
information,
credit card
stolen & identity
theft become a
big problem.
Misuse of
information /
inaccurate
information:
Data are collected
from warehouse
through mining, if the
technique is not
perfect or accurate, it
will cause serious
consequence.
21. OLAP: ONLINE ANALYTICAL
PROCESSING
It is a computer processing that enables a user to
easily and selectively extract and view data from
different points of view.
OLAP designates a category of applications &
technologies that allows collection, storage and
reproduction of multidimensional data.
Multidimensional analysis is the analysis of data based
on more than one factor.
22. Basic Components of OLAP:
Two basic components of OLAP are:
a) Dimension
b) Measures
Dimensions includes in the analysis are time,
location, product and customers.
Measures are the quantitative representation of
dimensions like revenues, costs, unit sold etc.
23. Why OLAP?
It is becoming popular because of the following reasons:
Companies are migrating to relational databases i.e. data is
stored in the form of rows and columns.
The ability to view data in different formats makes the system
flexible.
OLAP provides multidimensional analysis. E.g. Apart from
answering questions like “who” & “what” OLAP provides answers
to “what if” and “why”.
Main task of OLAP is to transform relational or non – relational
data into highly exportable structure which means data can be
broken down into small units to derive meaningful information.
24. Architecture of OLAP:
Main
Memory
Data base
Missing data
interpretation
Automated
process
Sensor
data input
Prediction Aggregation(cluster
of things bought
together)
OLAP
Analysis
Archive data
base
25. OLAP Functions:
Drilling:
breaking up of data
into dimensions to
facilitate analysis
Slicing & Dicing:
process of changing
the dimension of
analysis to suit the
analyst requirements
Changing displays:
information may be
available in tabular
forms but it can be
transformed into graphs
& charts if required
E.g. while performing quarterly sales
analysis, the analyst may use information
regarding monthly, weekly or daily sales in
that quarter.
E.g. the region-wise monthly sales
Report can be changed into monthly
Sales report.
E.g. in production
department, OLAP
can be used to track
the
Material
requirements, the
no. of units
produced &
26. Characteristics of OLAP:
Multidimensional views: All business models have a
minimum of three dimensions; time, location & product. These
dimensions may vary according to the analysis. Managers
should have the flexibility to use the data for analytical
processing irrespective of the database design.
Complex Modelling: The most important use of OLAP is its
ability to perform complex calculations. OLAP systems are
rated on the basis of their ability to create information & data.
The OLAP software should provide powerful but simple tools
so that individual handle it alone.
Time Intelligence: It is very important factor in analytical
processing. It is unique because it is the only dimension that
follows a sequence. E.g. Managers may seek breakup of
sales in a week, a month or quarter etc.
27. Benefits of OLAP:
OLAP software can be very useful to an organisation
especially with respect to data management.
A well designed OLAP increases productivity.
Complex modelling is made easy by the use of OLAP.
Faster application development will reduce application
backlog.
Helps organisation to respond quickly to market demand
by modelling real business problems & using human
resource efficiently.
28. BUSINESS PERFORMANCE
MANAGEMENT
What it is?
An area of Business
intelligence which
monitors & manages
an organisation’s
performance,
according to Key
Performance
Indicators (KPIs)
A framework for
organising,
automating and
analysing business
methodologies,
metrics, processes
and systems to drive
all the performance
of the organisation
It helps organisations
to translate a unified
set of objectives into
plans, monitor
execution, and
deliver critical insight
into improve financial
and operational
performance
29. WHY
BPM?
Executives can get real
time access to key
performance indicators,
interact & drill down on
data as against a static
number.
Impacts
functions
across org. to
meet legal
needs
30. Scoreboard vs Dashboard
Scoreboard illustrate
performance data over a
period of time.
Dashboards are series of
graphs, charts and other
visual indicators that
illustrate performance in
real time.
31. Scorecard vs Dashboard : These two buzzwords cause confusion among business
professionals as they are used synonymously.
From the first look one might have an impression that both are interchangeable, but there are
some differences:
Basis Dashboard Balance Scorecard
Is used for… performance measurement / monitoring performance management
As a measurement tool is… Metric KPI (Metric + Target)
Measure is linked to
business objectives…
doesn’t link Links
It measures… Performance progress (the current value
versus the target value)
It is updated… in real-time periodically (monthly)
It focuses on… operational (short-term) goals strategic (long-term) goals
Its purpose is to… give a high-level idea of what is
happening in the company
plan and execute a strategy,
identify why something is
happening and what can we do
about that
Its helps… visualize the performance to
understand the current state
align KPI, objectives, and actions
to see the connection between
them
In automobile it is… automobile dashboard (shows how
your car is operating)
GPS (shows when and how you
will arrive?)
32. Common Features of DB & SB:
Who uses a dashboard and a scorecard?
It is hard to distinguish who uses the dashboard and who
uses the Balanced Scorecard.
Some companies reported that their Balanced Scorecard is
available only for executives, other prefer to share it with all
of their employees.
A Dashboard is supposed to be available for supervisor
roles only, but some companies think that this valuable
information can help line-level employees in their daily job
as well.
Generally speaking both tools are historically business
measurements and management tools of executives and
top managers.
33. Dig into cause and effect
What happens when a supervisor receives a
warning signal generated by a dashboard?
After having a first look at what is going on a
supervisor is supposed to understand the
cause and effect relation between business
objectives, actions and measures.
That’s sounds very close to what the Balanced
Scorecard framework suggests doing with
business objectives on the strategy map.
34. Measure and KPI
Although many sources tend to differentiate
measures (no target) and KPIs (with target), in
practice most companies follow the idea of the
KPI in the dashboard as well
by assigning some synthetic benchmark.
35. BUSINESS INTELLIGENCE:
BI is the processes, technologies and tools that
helps executives to change data into information,
information into knowledge and knowledge into
plans that helps the organisation in decision
making.
BI is about getting the right information, to the
right decision makers at the right time.
It is an enterprise – wide platform that supports
reporting, analysis and decision making.
36. Technology that
allows:
• Gathering, storing,
accessing &
analysing data to
help business
users make better
decisions.
Set of
Applications that
allow:
• DSS
• Query & Reporting
• OLAP
• Statistical analysis,
forecasting & data
mining.
Helps in
analysing
business
performance
through data
driven insight:
• Understand the
past and predict
the future.
CONCEPT OF BI:
39. BI applications and technologies can
help companies analyse:
Changing trends in market share.
Changes in consumer behaviour and
spending patterns.
Customer’s preferences.
Company capabilities.
Market conditions.
40. Modules of Business Intelligence:
Dashboards (Snapshot of daily operations)
Key Performance Indicators (tracking and
comparing performances over a period of time)
Graphical OLAP (visualize the information with
eye catching, stunning displays and valuable
indicators)
Forecasting (year, quarter, month, week, day)
Graphical Reporting (create a report to
summarize your performance metrics)
41. BUSINESS ANALYTICS:
Monitoring and tracking metrics / KPIs in the
form of reports / dashboards is “Business
Intelligence”, but making meaningful sense of
these metrics, co-relating them with other
factors that influence them, understanding the
trends and using statistical algorithms to
predict outcomes is where the bang for the
buck is… and that is “Business Analytics”.
42. Advantages of Business Analytics:
Eliminate guess work
Get faster answer to your question
Get insight into consumer behaviour
Identify cross selling and up selling opportunities
Get key business metrics reports when and where
you need them.
43. USERS of Business Analytics:
Students
Business Man
Accountants
Organisations
Companies
Group of industries
Auditors
Small firms
44. KNOWLEDGE MANAGEMENT:
What is Knowledge?
Data
Human
Interpretation
Informatio
n
Information
Human
Use
Knowledge
47. It means “from before” or “from earlier”. It id depend upon what a person
can derive from the world without needing to experience it.
It means “from what comes later”. This type of knowledge is based
on observation, firstly having an experience and then using logic.
It is similar to prior knowledge, rather it is more reliable. This
type of knowledge is recorded and transmitted through different
mediums. Example – databases and libraries.
This knowledge is closely resemble a Posteriori knowledge, as
it can only be achieved through experience.
It is simply knowing something or having knowledge of something.
It is acquired by more conservative forms of learning.
It is the knowledge that can be used, can be applied to something, such
as any problem. This kind of knowledge is acquired by doing. Here the
hands on experience is more valuable.
Priori
Poster
-iori
Explicit
Tacit
Descr
-iptive
Proce
-dural
48. What is KM?
Efficient handling of information & resources within a
commercial organisation.
It is the systematic management of an organisations
knowledge assets for the purpose of creating value &
meeting tactical and strategic requirements.
It is the process of creating, sharing, using and
managing the knowledge and information of an
organisation.
It refers to the multidisciplinary approach to achieving
organisational objectives by making the best use of
knowledge.
49. Knowledge Management
Technologies:
Groupware
Software that
facilitates
collaboration &
sharing of
organisational
Information.
E.g. : Lotus Notes
–it provides tools
for threaded
discussions,
document sharing,
organisation wide
uniform emails
etc.
Workflow
systems
Systems that
allow the
representation
of processes
associated with
the creation ,
use and
maintenance of
organisational
knowledge.
E.g. : the
process to
create & utilise
forms &
documents
Content &
Document
Managemen
t system
Enterprise
Portals
Software
systems that
automates the
process of
creating web
contents or
documents.
E.g. : Role such
as editors,
graphic
designers, and
writers etc.
Software that
aggregates
information
across the
entire
organisation or
for groups
such
as project
teams.
E.g.: Microsoft
Share point
50. E-learning
Software that
enables
organisations to
create
customised
training &
education.
This can include
lesson plans,
monitoring
progress and
online classes.
Planning &
Scheduling
Software
Software that
automates
schedule
creation &
maintenance.
E.g. : Microsoft
outlook
Telepresence
Software that
enables
individuals to
have virtual
“face to face”
meetings without
assembling at
one location.
E.g. : Video
conderencing
