2. Extensive experience in operations and management, with
research background in a variety of techniques and analysis. Taught
at several universities and colleges, including the University of
California, Berkeley, California State University- East Bay, San
Jose State University and University of Massachusetts. Recipient of
several awards, including Distinguished Tenured Staff Award of
2013, Business Program Instructor of the year for 2013 and 2014 and
the Parthenon award for best instructor in 2012, 2010 and 2003, and
Certificate of Honor for instructor of the year from the City and
County of San Francisco. Included in the 2000 to 2001 "Whos Who in
Finance and Industry." Ahmed Banafa
3. Some of my Publications
4. Now .lets talk Big Data !
5. Big Data? The simplest definition of big data is large and
complex structured and unstructured data (images posted on
Facebook, email, text messages, GPS signals from mobile phones,
tweets, and other social media updates, etc.) that cannot be
processed by traditional database tools.
6. Roots of Big Data
7. Starting from the basics statistics is using numbers to
quantify the data. Data mining is using statistics and programming
languages to find patterns hidden in the data. Machine learning
uses data mining to build models to predict future outcomes.
Artificial intelligence uses models built by machine learning to
make machines act in an intelligent way like playing a game or
driving a car (e.g., IBMs Watson supercomputer and the driverless
car by Google).
8. Big data analytics is the process of studying big data to
uncover hidden patterns and correlations to make better decisions
using technologies like NoSQL databases, Hadoop, and MapReduce. The
main goal of big data analytics is to help organizations make
better business decisions.
9. Three Vs of Big Data
10. Volume. Unstructured data streaming in from social media.
Increasing amounts of sensor and machine-to- machine data being
collected. Velocity. Data is streaming in at unprecedented speed
and must be dealt with in a timely manner. Variety. Data today
comes in all types of formats structured, numeric data in
traditional databases. Information created from line-of-business
applications.
11. Big Data Analytics 3.0 Analytics 1.0 : BI Analytics 2.0:
Used by online companies only (Google, Yahoo, Facebook, etc.).
Analytics 3.0: A new resolve to apply powerful data- gathering and
analysis methods not just to a companys operations but also to its
offeringsto embed data smartness into the products and services
customers buy.
12. Attributes of Analytics 3.0: The most important trait is
that not only online firms, but virtually any type of firm in any
industry, can participate in the data-driven economy. Multiple data
types: Organizations are combining large and small volumes of data,
internal and external sources, and structured and unstructured
formats to yield new insights in predictive and prescriptive
models.
13. Technologies and methods are much faster: Big data
technologies include a variety of hardware/software architectures,
including clustered parallel servers using Hadoop/MapReduce,
in-memory analytics, and so forth. All of these technologies are
considerably faster than previous generations.
14. Integrated and embedded: built into consumer- oriented
products and features. Data science/analytics/IT teams will work
together Chief analytics officers (CAO) are new leadership
positions.
15. Prescriptive analytics: There have always been three types
of analytics: descriptive, that report on the past; predictive,
that use models based on past data to predict the future; and
prescriptive, that use models to specify optimal behaviors and
actions. Analytics 3.0 includes all types, but there is an
increased emphasis on prescriptive analytics.
16. Old and New! Google announced acquisition of Nest (smart
home devices), a source of massive data from homes all over the
United States, confirming the direction of Analytics 3.0 by an
online company at the leading edge of Analytics 2.0.
17. Big Data has a dark side !
18. Dark Data Gartner defines dark data: as the information
assets organizations collect, process and store during regular
business activities, but generally fail to use for other purposes
(for example, analytics, business relationships and direct
monetizing). IDC, stated that up to 90 percent of big data is dark
data.
19. Similar to dark matter in physics, dark data often
comprises most organizations universe of information assets. Thus,
organizations often retain dark data for compliance purposes only.
Storing and securing data typically incurs more expense (and
sometimes greater risk) than value.
20. Dark data is a type of unstructured, untagged and untapped
data that is found in data repositories and has not been analyzed
or processed. It is similar to big data but differs in how it is
mostly neglected by business and IT administrators in terms of its
value. Dark data is also known as dusty data.
21. Dark data, unlike dark matter, can be brought to light and
so can its potential ROI. And whats more, a simple way of thinking
about what to do with the data - through a cost-benefit analysis -
can remove the complexity surrounding the previously mysterious
dark data.
22. So what is the future of Big Data?
23. Big Data as a Service: the next big thing ? Big data as a
service (BDaaS) is a term typically used to refer to services that
offer analysis of large or complex data sets, using the cloud
hosted services. Similar types of services include software as a
service (SaaS) or infrastructure as a service (IaaS), where
specific big data as a service options are used to help businesses
handle what the IT world calls big data, or sophisticated
aggregated data sets that provide a lot of value for todays
companies.
24. Examples of Big Data Analytics
25. Network Security Needs Big Data ZTM: "Zero trust model" is
an aggressive model of network security that monitors every piece
of data possible, assuming that every file is a potential threat
The convergence of Big Data and Network Security is a direct
product of Applied Big Data and its a prime example of using
analytics technologies to tackle a current business problem such as
cyberattacks
26. Using ZTM will generate enormous volume of real- time data
to analysis, which will have IT managers drowning in log files,
vulnerability scan reports, alerts, reports, and more, but the data
is not actionable at that stage.
27. The magic in using Big Data analytics is in analyzing this
data to give IT managers a comprehensive view of their security
landscape. Exposing what is at risk, how severe the risk, how
important the asset is, and how to fix it
28. Google They process 3.5 billion requests per day, and each
request queries a database of 20 billion web pages
29. Amazon Amazon has recently obtained a patent on a system
designed to ship goods to us before we have even decided to buy it
predictive despatch.