Implications of Business and Enterprise Social Networks, Data Representation for RFID, RFID + Sensors for Better BI

1. Emerging Trends
and Impacts in
IT & DSS
Dr. Kamal Gulati

2. Virtual (Internet) Communities
• A group of people with common interests who
interact with one another over a computer
network, mainly the Internet
• Similar to typical physical communities, such
as neighborhoods, clubs, or associations, but
people do not meet face-to-face
• It is a social network organized around a
common interest, idea, task, or goal
• Members interact across time, geographic
location, and organizational boundaries

3. Virtual (Internet) Communities
Elements of Interaction

4. Virtual (Internet) Communities
Interesting Characteristics
• Many thousands of communities exist on the
Internet, and the number is growing rapidly
– Pure-play Internet communities may have thousands, or
even hundreds of millions, of members
– Example - MySpace grew to 100 million members in just 1
year
• Two major difference from traditional purely physical
communities:
– The exponential speed of growth, and
– Not being confined to one geographic location

5. Virtual (Internet) Communities
Types of Virtual Communities
• Transaction and other business activities
– Buying/selling – (olx.in, quikr.com, clickindia.com etc)
• Purpose or interest
– Exchange of information – (godaddy.com, wikipedia.org)
• Relations or practices
– (ivillage.com, seniornet.com, isworld.com)
• Fantasy (e.g., starsports.com, espn.com)
• Social networks (e.g., facebook.com, orkut.com, Google+
etc)
• Virtual worlds (e.g., Secondlife.com, legendsofozworld.com,
games websites etc)

6. • Public Versus Private Communities
– Membership being open or close others
– Public: MySpace and Facebook
– Private: IBM's Virtual Universe Community
• Internal and External Private Communities
– Internal: limited to employees, retirees, suppliers, and
customers (e.g., Pfizer, FedEx, IBM, …)
– External: also include business partners (e.g., Sony
PlayStation 3 videogame network)
• There are other classifications of based on the
classification of members

7. Online Social Networking –
Basics and Examples
• A social network is a place where people create their
own space, or homepage, on which they write blogs;
post pictures, videos, or music; share ideas; and link
to other Web locations they find interesting
– The mass adoption of social networking Web sites points
to an evolution in human social interaction
• The size of social network sites are growing rapidly,
with some having over 100 million members – growth
for successful ones 40 to 50 % in the first few years and 15 to
25 % thereafter

8. Online Social Networking –
Social Network Analysis Software
• It is used to identify, represent, analyze, visualize, or
simulate networks with
– Nodes – agents, organizations, or knowledge
– Edges – relationships identified from various types of input
data (relational and non-relational)
• Various input and output file formats exist
• SNA (Social Network Analysis) software tools include
– Business-oriented social network tools such as InFlow and
NetMiner
– Social Networks Visualizer, or SocNetV, which is a Linux-
based open source package

9. Major Social Network Services
• Facebook: The Network Effect
– Launched in 2004 by Mark Zuckerberg (former Harvard
student)
– It is the 2nd largest social network service in the world with
more than 200 million active users worldwide (as of April
2009)
– Initially intended for college and high school students to
connected to other students at the same school
– In 2006 opened its doors to anyone over 13; enabling
Facebook to compete directly with MySpace

10. Implications of Business and
Enterprise Social Networks
• Survey shows that best-in-class companies use blogs
and wikis for the following applications:
– Project collaboration and communication (63%)
– Process and procedure document (63%)
– FAQs (61%)
– E-learning and training (46%)
– Forums for new ideas (41%)
– Corporate-specific dynamic glossary and terminology
(38%)
– Collaboration with customers (24%)

11. RFID
• Radio-Frequency Identification

12. RFID
• Wal-Mart's RFID mandate in June 2003
• DoD, Target, Albertson's, Best Buy,…
• RFID is a generic technology that refers to the
use of radio frequency waves to identify
objects
• RFID is a new member of the automatic
identification technologies family, which also
include the ubiquitous barcodes and magnetic
strips

13. • Radio-frequency identification (RFID) is the
wireless use of electromagnetic fields to
transfer data, for the purposes of
automatically identifying and tracking tags
attached to objects.
• The tags contain electronically stored
information. Some tags are powered by and
read at short ranges (a few meters) via
magnetic fields (electromagnetic induction).

14. How does RFID work?
• RFID system –
– a tag (an electronic chip attached to the product
to be identified)
– an interrogator (i.e., reader) with one or more
antennae attached
– a computer (to manage the reader and store the
data captured by the reader)
• Tags –
– Active tag versus Passive tags

15. Data Representation for RFID
• RFID tags contain 96 bits of data in the form of
serialized global trade identification numbers
(SGTIN)

16. RFID for Supply Chain BI
• RFID in Retail Systems

17. RFID Data Sample

18. RFID for BI in Supply Chain
• Better SC visibility with RFID systems
– Timing/duration of movements between different
locations – especially important for products with
limited shelf life
– Better management of out-of-stock items (optimal
restocking of store shelves)
– Help streamline the backroom operations:
eliminate unnecessary case cycles, reorders
– Better analysis of movement timings for more
effective and efficient logistics

19. RFID + Sensors for Better BI
• Knowing the location and health of goods (i.e.,
exception) during transportation

20. Business intelligence (BI)
• Business intelligence (BI) is the transformation of
raw data into meaningful and useful information
for business analysis purposes.
• BI can handle enormous amounts of unstructured
data to help identify, develop and otherwise
create new strategic business opportunities.
• BI allows for the easy interpretation of volumes
of data.
• Identifying new opportunities and implementing
an effective strategy can provide a competitive
market advantage and long-term stability.

21. • BI technologies provide historical, current and
predictive views of business operations.
• Common functions of business intelligence
technologies are reporting, online analytical
processing, analytics, data mining, process
mining, complex event processing, business
performance management, benchmarking,
text mining, predictive analytics and
prescriptive analytics.

22. • Business intelligence as it is understood today
is said to have evolved from the decision
support systems (DSS) that began in the 1960s
and developed throughout the mid-1980s.
• DSS originated in the computer-aided models
created to assist with decision making and
planning.
• From DSS, data warehouses, Executive
Information Systems, OLAP and business
intelligence came into focus beginning in the
late 80s.

23. Types of Systems
• Data mart
• Online analytical processing (OLAP)
• Online Transaction Processing (OLTP)
• Predictive Analysis

24. Data Mart
• A data mart is the access layer of the data
warehouse environment that is used to get
data out to the users.
• The data mart is a subset of the Data
Warehouse that is usually oriented to a
specific business line or team.
• Data marts are small slices of the Data
Warehouse.

25. • A data mart is a simple form of a data
warehouse that is focused on a single subject
(or functional area), such as sales, finance or
marketing.
• Data marts are often built and controlled by a
single department within an organization.
• Given their single-subject focus, data marts
usually draw data from only a few sources.
• The sources could be internal operational
systems, a central data warehouse, or external
data.

26. • Whereas data warehouses have an enterprise-
wide depth, the information in data marts
pertains to a single department.
• In some deployments, each department or
business unit is considered the owner of its
data mart including all the hardware, software
and data.
• This enables each department to use,
manipulate and develop their data any way
they see fit; without altering information inside
other data marts or the data warehouse.

27. • In other deployments where conformed
dimensions are used, this business unit
ownership will not hold true for shared
dimensions like customer, product, etc.
• The reasons why organizations are building
data warehouses and data marts are because
the information in the database is not
organized in a way that makes it easy for
organizations to find what they need.

28. • Also complicated queries might take a long
time to answer what people want to know
since the database systems are designed to
process millions of transactions per day.
• Transactional database are designed to be
updated, however, data warehouses or marts
are read only. Data warehouses are designed
to access large groups of related records.

29. • Data marts improve end-user response time
by allowing users to have access to the
specific type of data they need to view most
often by providing the data in a way that
supports the collective view of a group of
users.

30. Online analytical processing (OLAP)
• Is characterized by a relatively low volume of
transactions. Queries are often very complex and
involve aggregations.
• For OLAP systems, response time is an
effectiveness measure.
• OLAP applications are widely used by Data
Mining techniques.
• OLAP databases store aggregated, historical data
in multi-dimensional schemas (usually star
schemas). OLAP systems typically have data
latency of a few hours, as opposed to data marts,
where latency is expected to be closer to one day.

31. Online Transaction Processing (OLTP)
• Is characterized by a large number of short on-
line transactions (INSERT, UPDATE, DELETE).
• OLTP systems emphasize very fast query
processing and maintaining data integrity in
multi-access environments.
• For OLTP systems, effectiveness is measured
by the number of transactions per second.
OLTP databases contain detailed and current
data.

32. Predictive analysis
• Predictive analysis is about finding and
quantifying hidden patterns in the data using
complex mathematical models that can be used
to predict future outcomes.
• Predictive analysis is different from OLAP in that
OLAP focuses on historical data analysis and is
reactive in nature, while predictive analysis
focuses on the future.
• These systems are also used for CRM (Customer
Relationship Management).

33. Data Warehouse Vs Data Mart
Data Warehouse
• Holds multiple subject areas
• Holds very detailed
information
• Works to integrate all data
sources
• Does not necessarily use a
dimensional model but feeds
dimensional models.
Data Mart
• Often holds only one subject
area- for example, Finance, or
Sales
• May hold more summarized
data (although many hold full
detail)
• Concentrates on integrating
information from a given
subject area or set of source
systems
• Is built focused on a
dimensional model using a
star schema.

34. Reasons for creating a data mart
• Easy access to frequently needed data
• Creates collective view by a group of users
• Improves end-user response time
• Ease of creation
• Lower cost than implementing a full data
warehouse
• Potential users are more clearly defined than in a
full data warehouse
• Contains only business essential data and is less
cluttered.

35. Data Warehouse
• In computing, a data warehouse (DW, DWH), or
an enterprise data warehouse (EDW), is a system
used for reporting and data analysis.
• Integrating data from one or more disparate
sources creates a central repository of data, a
data warehouse (DW).
• Data warehouses store current and historical data
and are used for creating trending reports for
senior management reporting such as annual and
quarterly comparisons.

36. ODS: Operational Data Store, ETL: Extract, Transform and Load

37. • The data stored in the warehouse is uploaded
from the operational systems (such as
marketing, sales, etc.)
• The data may pass through an operational
data store for additional operations before it is
used in the DW for reporting.

38. Generic data warehouse environment
The environment for data warehouses and marts includes
the following:
• Source systems that provide data to the warehouse or
mart.
• Data integration technology and processes that are
needed to prepare the data for use.
• Different architectures for storing data in an
organization's data warehouse or data marts.
• Different tools and applications for the variety of users.
• Metadata, data quality, and governance processes must
be in place to ensure that the warehouse or mart meets
its purposes.

39. Level of sophistication of a data
warehouse:
• 1. Offline operational data warehouse
– Data warehouses in this stage of evolution are
updated on a regular time cycle (usually daily,
weekly or monthly) from the operational systems
and the data is stored in an integrated reporting-
oriented data

40. • 2. Offline Data Warehouse
– Data warehouses at this stage are updated from data
in the operational systems on a regular basis and the
data warehouse data are stored in a data structure
designed to facilitate reporting.
• 3. On time Data Warehouse
– Online Integrated Data Warehousing represent the
real time Data warehouses stage data in the
warehouse is updated for every transaction
performed on the source data.
• 4. Integrated Data Warehouse
- These data warehouses assemble data from different
areas of business, so users can look up the
nformation they need across other systems.