Intro to big data and applications

1. Introtobigdata
&applicationsDay -1
Oct 2020
Presented by: Parviz Vakili
parviz.vakili@gmail.com

2. Refences
[1]. DAMA-DMBOK (2017) Data Management Body of Knowledge (Second Edition)-DAMA
International
[2]. Data Strategy (2017) How to profit from a world of big data, analytics and the internet of things – By
Bernard Marr - Kogan Page
[3]. Big Data Analytics for Entrepreneurial Success (2019) – By Soraya Sedkaoui - IGI Global
[4]. https://www.eckerson.com/
[5]. https://www.lightsondata.com/
[6]. https://www.dataedo.com/
[7]. https://www.linkedin.com/in/denise-harders-4908a967/
[8]. http://www.fabak.ir/
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3. Data
Long-standing definitions of data emphasize its role in
representing facts about the world. In relation to
information technology, data is also understood as
information that has been stored in digital form (though
data is not limited to information that has been digitized
and data management principles apply to data captured
on paper as well as in databases). Still, because today we
can capture so much information electronically, we call
many things ‘data’ that would not have been called
‘data’ in earlier times – things like names, addresses,
birthdates, what one ate for dinner on Saturday, the most
recent book one purchased.

4. Data and Information
Much ink has been spilled over the relationship between
data and information. Data has been called the “raw
material of information” and information has been called
“data in context”. Often a layered pyramid is used to
describe the relationship between data (at the base),
information, knowledge, and wisdom (at the very top).
While the pyramid can be helpful in describing why data
needs to be well-managed, this representation presents
several challenges for data management.

5. BIGDATA
Early efforts to define the meaning of Big Data
characterized it in terms of the Three V’s: Volume,
Velocity, Variety (Laney, 2001). As more organizations
start to leverage the potential of Big Data, the list of V’s
has expanded

6. Volume
Refers to the amount of data. Big Data often has
thousands of entities or elements in billions of records.

7. Data storages challenges
This picture presents a visual
summary of the range of data
that has become available
through Big Data technologies
and the implications on data
storage options.

8. Velocity
Refers to the speed at which data is captured, generated,
or shared. Big Data is often generated and can also be
distributed and even analyzed in real-time.

9. Variety /Variability
Refers to the forms in which data is captured or
delivered. Big Data requires storage of multiple formats;
data structure is often inconsistent within or across data
sets.

10. Structured Data
The term structured data refers to the data that is
identifiable because it is organized or structured.
Structured data concerns all data which can be stored in
database SQL in a table with rows and columns. They
have the relational key and can be easily mapped into
pre-designed fields. It refers to the data that are stored in
relational databases or corporate data warehouses. In
another word, structured data are data which we
established the functional sense and life cycle (creation
of rules, values and possible technical means of
representation). Also, this type of data is relatively
simple to enter, store, query, and analyze.

11. Semi-Structured Data
This comes from the speed of arrival of the data, their
volume, making structuring impossible. The messages in
your mailboxes are a good example of semi-structured
data. Semi-structured data is information that doesn’t
reside in a relational database but that does have some
organizational properties that make it easier to analyze.
So, this kind of data contains a structured part and an
unstructured part, so it combines the two types
(structured and unstructured). And due to unorganized
information, the semi-structured is difficult to retrieve,
analyze and store as compared to structured data. It
requires software framework like Apache Hadoop to
perform all this.

12. Unstructured Data
This is the simplest abstract form, a series of bytes.
Unstructured data is also a description of a reality but
whose codification, meaning, is not directly exploitable
by the machine, for example, audio or video file, a text
contained in a document or an email. Belong to this
category, the data from social networks (Twitter and
Facebook). Such type of data becomes difficult and
requires advanced tools and software to generate value.
Today, there is a predominance of unstructured data
(they represent, according to the big data survey
conducted by New Vantage Partner, more than 85% of
digital data), which must be tried to organize a minimum
(via metadata for example) to give them meaning.

13. Metadata
Describe and enrich unstructured data. In other words,
the absence of structure is mitigated by the metadata.
For example, the title and the tags to describe the videos
on Dailymotion.

14. BIGDATA another v’s
Viscosity: Refers to how difficult the
data is to use or integrate.
Volatility: Refers to how often data
changes occur and therefore how long
the data is useful.
Veracity: Refers to how trustworthy
the data is.

15. WHEREDOBIG DATA COMEFROM?
The answer is clear, they come from everywhere!
Because so much of human activity is executed
electronically, massive amounts of data are
accumulating every day as we move through the
world, interact with each other, and transact
business. Big Data is produced through email,
social media, online orders, and even online
video games. Data is generated not only by
phones and point-of-sale devices, but also by
surveillance systems, sensors in transportation
systems, medical monitoring systems, industrial
and utility monitoring systems, satellites, and
military equipment.

16. Bigdata is itjusta simplebuzzword?
Not at all! Our lives are already concerned in all their
aspects by the uses of big data. Big data is already in
several fields that the author lists as examples.
Companies want to understand the behaviors and
expectations of their customers to better target their
proposals. They create predictive models to anticipate
the departure of a client or sales of a product.

17. BIGDATA APPLICATIONS
▪ Industry
▪ Telecom
▪ Manufacturing
▪ Retail
▪ Sport and Fitness
▪ Insurance and Bank
▪ Tourism
▪ Government
▪ Health
▪ Transport
▪ Energy
▪ Agriculture
▪ Science and Research

18. Data asan Organizational Asset
An asset is an economic resource, that can be
owned or controlled, and that holds or produces
value. Assets can be converted to money. Data
is widely recognized as an enterprise asset,
though understanding of what it means to
manage data as an asset is still evolving.
Today’s organizations rely on their data assets
to make more effective decisions and to operate
more efficiently. Businesses use data to
understand their customers, create new
products and services, and improve operational
efficiency by cutting costs and controlling
risks.

19. business model

20. business model

21. business model

22. business model

23. business model

24. Conceptual DW/BIand BigData Architecture

25. UDAP

26. UDAPArchitucture

27. BigData &Digital Transformation
.1‫توصیفی‬ ‫تحلیل‬(Descriptive( )‫است‬ ‫افتاده‬ ‫اتفاقی‬ ‫چه‬)
.2‫تشخیصی‬ ‫تحلیل‬(Diagnostic( )‫است‬ ‫افتاده‬ ‫اتفاق‬ ‫چرا‬)
.3‫گویانه‬‫پیش‬ ‫تحلیل‬(Predictive( )‫افتاد‬ ‫خواهد‬ ‫اتفاقی‬ ‫چه‬)
.4‫تجویزی‬ ‫تحلیل‬(Prescriptive)(‫بیافتد‬ ‫اتفاق‬ ‫این‬ ‫تا‬ ‫دهیم‬ ‫انجام‬ ‫کاری‬‫چه‬)
‫از‬ ‫وقتی‬‫داده‬ ‫تحلیل‬‫گوییم؟‬‫می‬ ‫چه‬ ‫از‬ ‫گوییم‬‫می‬
•‫مدیریت‬ ‫تیم‬ ،‫ارشد‬ ‫مدیران‬
‫ها‬‫سیستم‬ ‫کاربران‬ ،‫استراتژیک‬‫ی‬
‫و‬ ‫استراتژیک‬...
‫استراتژیک‬
•‫و‬ ‫داخلی‬ ‫کاربران‬ ،‫میانی‬ ‫مدیران‬
‫و‬ ‫خارجی‬...
‫تاکتیکی‬
•‫کاربران‬ ،‫عملیاتی‬ ‫مدیران‬
‫ن‬ ‫برنامه‬ ،‫تراکنشی‬ ‫های‬‫سیستم‬‫ویسان‬
‫تراکنشی‬ ‫های‬‫سیستم‬
‫عملیات‬

28. BigData &Digital Transformation
‫بر‬‫اساس‬‫مدل‬‫ارزیابی‬20200‫ُبدگی‬‫ز‬،‫دیجیتال‬‫دو‬‫مولفه‬«‫تحلیل‬‫داده‬‫و‬‫بیگ‬‫دیتا‬»‫از‬‫ابعاد‬‫مهم‬‫سنجش‬‫بلوغ‬‫دیجی‬‫تال‬‫است‬.

29. bigdata and analytics criteria
‫های‬‫شاخص‬‫هشت‬‫گانه‬‫مورد‬‫بررسی‬‫در‬‫ُعد‬‫ب‬«‫تحلیل‬‫داده‬‫و‬‫بیگ‬‫دیتا‬»(‫بر‬‫اساس‬‫مدل‬‫ارزیابی‬20200‫ُبدگی‬‫ز‬‫دیجیتال‬)
.1‫استراتژی‬‫داده‬
.2‫مدیریت‬‫داده‬
.3‫حاکمیت‬‫داده‬
.4‫تیم‬‫تخصصی‬‫تحلیل‬‫داده‬
.5‫یکپارچگی‬‫داده‬
.6‫گستردگی‬‫تحلیل‬‫داده‬‫در‬‫سازمان‬
.7‫داشبوردهای‬‫سازمانی‬
.8‫امنیت‬‫و‬‫محرمانگی‬‫داده‬

30. Data Governance
‫حاکمیت‬‫داده‬‫یک‬‫برنامه‬‫مدیریتی‬‫چندین‬‫وظیفه‬‫ای‬‫است‬‫که‬‫مه‬‫مترین‬
‫هدف‬‫آن‬‫برخورد‬‫با‬‫داده‬‫به‬‫عنوان‬‫یک‬‫سرمایه‬‫سازمانی‬‫است‬.‫این‬‫کار‬
‫از‬‫طریق‬‫بکارگیری‬‫مجموعه‬‫ای‬‫از‬‫سیاست‬‫ها‬،‫استانداردها‬‫،فرآی‬،‫ندها‬
‫افراد‬‫و‬‫فناوری‬‫که‬‫برای‬‫مدیریت‬‫داده‬‫حیاتی‬‫است‬،‫دنبال‬‫می‬‫شو‬‫د‬.
‫حاکمیت‬‫داده‬‫در‬‫مرکز‬‫های‬‫فعالیت‬‫مدیریت‬‫داده‬‫قرار‬‫شده‬‫داده‬‫است‬،
‫چراکه‬‫حاکمیت‬‫داده‬‫برای‬‫برقراری‬‫ثبات‬‫بین‬‫اجزاء‬‫و‬‫ایجاد‬‫تعادل‬‫بین‬
‫توابع‬‫الزم‬‫هست‬.‫دیگر‬‫های‬‫حوزه‬‫دانش‬‫در‬‫اطراف‬‫چرخ‬‫قرار‬
‫اند‬‫شده‬‫داده‬‫که‬‫نوعی‬‫به‬‫بیانگر‬‫تمام‬‫های‬‫بخش‬‫ضروری‬‫یک‬‫عملی‬‫ات‬
‫مدیریت‬‫داده‬‫بالغ‬‫هست‬.

31. Data strategy
‫استراتژی‬‫داده‬‫چشم‬‫انداز‬‫سازمان‬‫شما‬‫برای‬‫مدیریت‬‫داده‬‫به‬
‫عنوان‬‫یک‬‫سرمایه‬‫و‬‫تالش‬‫هدفمند‬‫برای‬‫تقویت‬‫آن‬‫در‬‫ج‬‫هت‬
‫ایجاد‬‫یک‬‫مزیت‬‫رقابتی‬‫است‬.‫استراتژی‬‫داده‬‫یک‬‫برنام‬‫ه‬‫و‬‫یا‬
‫طرح‬‫بلندمدت‬‫و‬‫یا‬‫میان‬‫مدت‬‫برای‬،‫بهبود‬‫مدیریت‬‫و‬‫بهر‬‫ه‬
‫برداری‬‫از‬‫داده‬‫در‬‫کسب‬‫و‬‫کار‬‫و‬‫نحوه‬‫تحقق‬‫آنها‬‫است‬.‫داده‬
‫یکی‬‫از‬‫منابع‬‫حیاتی‬‫هر‬‫سازمان‬‫است‬‫که‬‫با‬‫مدیریت‬‫صح‬‫یح‬‫و‬
‫تبعیت‬‫از‬‫یک‬‫استراتژی‬‫مدون‬‫می‬‫توان‬‫آن‬‫را‬‫به‬‫یک‬‫مزی‬‫ت‬
‫رقابتی‬‫برای‬‫کسب‬‫و‬‫کار‬‫تبدیل‬‫کرد‬.

32. Data Management
‫مدیریت‬‫ها‬‫داده‬‫عبارت‬‫است‬‫از‬،‫توسعه‬‫اجرا‬‫و‬‫نظارت‬‫بر‬
،‫ها‬‫برنامه‬،‫ها‬‫سیاست‬‫ها‬‫طرح‬‫و‬‫هایی‬‫شیوه‬‫است‬‫که‬‫در‬‫طول‬
‫چرخه‬‫عمر‬،‫خود‬‫ارزش‬‫های‬‫دارایی‬‫از‬‫جنس‬‫داده‬‫و‬‫اطالعات‬‫را‬
،‫تحویل‬،‫کنترل‬‫پشتیبانی‬‫و‬‫تقویت‬‫کند‬‫می‬.
‫به‬‫طور‬‫معمول‬،‫یک‬‫استراتژی‬‫داده‬‫نیاز‬‫به‬‫یک‬‫برنامه‬‫مد‬‫یریت‬
‫داده‬‫پشتیبانی‬‫دارد‬‫که‬‫شامل‬‫طرحی‬‫برای‬‫حفظ‬‫و‬‫بهبود‬‫ک‬‫یفیت‬
‫داده‬‫ها‬،‫یکپارچگی‬‫داده‬‫ها‬،‫دسترسی‬‫و‬‫امنیت‬‫در‬‫حالی‬‫ک‬‫ه‬
‫خطرات‬‫شناخته‬‫شده‬‫و‬‫ضمنی‬‫را‬‫کاهش‬‫می‬‫دهد‬‫است‬.‫مدیری‬‫ت‬
‫داده‬‫باید‬‫کلیه‬‫حوزه‬‫های‬‫دانش‬‫دانش‬‫چارچوب‬‫مدیریت‬‫داده‬
‫های‬DAMA‫را‬‫که‬‫مربوط‬‫به‬‫سازمان‬‫است‬‫را‬‫شامل‬‫شود‬.

33. Data Governance
‫حاکمیت‬‫داده‬‫یک‬‫برنامه‬‫مدیریتی‬‫چندین‬‫وظیفه‬‫ای‬‫است‬‫که‬‫مه‬‫مترین‬
‫هدف‬‫آن‬‫برخورد‬‫با‬‫داده‬‫به‬‫عنوان‬‫یک‬‫سرمایه‬‫سازمانی‬‫است‬.‫این‬‫کار‬
‫از‬‫طریق‬‫بکارگیری‬‫مجموعه‬‫ای‬‫از‬‫سیاست‬‫ها‬،‫استانداردها‬‫،فرآی‬،‫ندها‬
‫افراد‬‫و‬‫فناوری‬‫که‬‫برای‬‫مدیریت‬‫داده‬‫حیاتی‬‫است‬،‫دنبال‬‫می‬‫شو‬‫د‬.
‫حاکمیت‬‫داده‬‫در‬‫مرکز‬‫های‬‫فعالیت‬‫مدیریت‬‫داده‬‫قرار‬‫شده‬‫داده‬‫است‬،
‫چراکه‬‫حاکمیت‬‫داده‬‫برای‬‫برقراری‬‫ثبات‬‫بین‬‫اجزاء‬‫و‬‫ایجاد‬‫تعادل‬‫بین‬
‫توابع‬‫الزم‬‫هست‬.‫دیگر‬‫های‬‫حوزه‬‫دانش‬‫در‬‫اطراف‬‫چرخ‬‫قرار‬
‫اند‬‫شده‬‫داده‬‫که‬‫نوعی‬‫به‬‫بیانگر‬‫تمام‬‫های‬‫بخش‬‫ضروری‬‫یک‬‫عملی‬‫ات‬
‫مدیریت‬‫داده‬‫بالغ‬‫هست‬.

34. THANKS
Does anyone have any questions?
parviz.vakili@gmail.com
+98 912 444 2418
https://www.linkedin.com/in/parvizvakili/