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Big data

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Jiawei Zhang

on 9 December 2014

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Transcript of Big data

Every 60 seconds
Big Data Definition
"Big data"is data whose scale,diversity,and complexity require new architecture,techniques,algorithms,and analytic to manage it and extract value and hidden knowledge from it...
Big data and Social media
Part 1-Big data
Presentation prepared by students of the first year of Applied Economics
Zhang Jiawei and Yang Kaiyuan
(Zoli) and (David)

98,000 + tweets
695,000 status updates
11 million instant messages
698,445 Google searches
168 million + emails sent
1,820 TB of data created
217 new mobile web users
Key terms
What is big data ?
What is big data used for?
How do we recognize big data ?
"Big Data (big data)", or a huge amount of data, said, referring to the amount of data involved are too great to pass the current mainstream software tools, within a reasonable time to reach the capture, management, treatment, and help organize business decisions become the purpose of a more positive information.
Big data-4V
Big data implies enormous volumes of data. It used to be employees created data. Now that data is generated by machines, networks and human interaction on systems like social media the volume of data to be analyzed is massive. Yet, Inderpal states that the volume of data is not as much the problem as other V’s like veracity.
Variety refers to the many sources and types of data both structured and unstructured. We used to store data from sources like spreadsheets and databases. Now data comes in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured data creates problems for storage, mining and analyzing data. Jeff Veis, VP Solutions at HP Autonomy presented how HP is helping organizations deal with big challenges including data variety.
Big Data Velocity deals with the pace at which data flows in from sources like business processes, machines, networks and human interaction with things like social media sites, mobile devices, etc. The flow of data is massive and continuous. This real-time data can help researchers and businesses make valuable decisions that provide strategic competitive advantages and ROI if you are able to handle the velocity. Inderpal suggest that sampling data can help deal with issues like volume and velocity.
Big Data Veracity refers to the biases, noise and abnormality in data. Is the data that is being stored, and mined meaningful to the problem being analyzed. Inderpal feel veracity in data analysis is the biggest challenge when compares to things like volume and velocity. In scoping out your big data strategy you need to have your team and partners work to help keep your data clean and processes to keep ‘dirty data’ from accumulating in your systems.
E-promotions:Based on your current location,your purchase history,what you like send promotions right now for store next to you
Healthcare monitoring:sensors monitoring your activities and body any abnormal measurements require immediate reaction
When dealing with larger datasets, organizations face difficulties in being able to create, manipulate, and manage big data. Big data is particularly a problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets.

As research from Webopedia parent company QuinStreet demonstrates, big data initiatives are poised for explosive growth. QuinStreet surveyed 540 enterprise decision-makers involved in big data and found the datasets of interest to many businesses today include traditional structured databases of inventories, orders, and customer information, as well as unstructured data from the Web, social networking sites, and intelligent devices.

This data, when captured, formatted, manipulated, stored, and analyzed can help a company to gain useful insight to increase revenues, get or retain customers, and improve operations.
Why is it interesting for Business
"Big data" is NOT only large amount, but also, there is another aspect, namely that it is non-numeric, textual and visual data - or in general (and most importantly), big data may be interpreted as data without context.
1. Build a corporate culture that’s savvy about big data and analytics.
Organizations are becoming much more data driven, applying insights to everything from key business processes and decisions to the way they fundamentally operate.
2. Make security, privacy and governance a requirement.
Businesses need to embrace innovation — while managing risk — by thinking and acting more quickly on insight derived from analyzing big data. They will need to embed security, privacy and governance policies directly into all of their daily processes.
3. Invest in a big data platform.
Company-wide big data platforms allow organizations to address the full spectrum of big data business challenges. The real benefit of a platform (versus point products) is the ability to start with one capability and easily add others over a big data journey, whether it’s based upon transactions, social media or mobile computing.
4. Appoint a Chief Data Officer.
They will be an organization’s champion of data, focusing on the best ways to analyze big data to transform their businesses.
5. Infuse cognitive intelligence into a new generation of apps.
We are seeing a new ecosystem of startups, ISVs and developers who want to accelerate innovation, creativity and an entrepreneurial spirit around cognitive computing. This will spark an entirely new class of applications that will learn from experience, improve with each interaction and outcome, and assist in solving the most complex questions facing industries and society today.

How companies use
Big Data ?
Harnessing Big Data
: Online Transaction
Processing (DBMSs)
: Online Analytical
Processing (Data Warehousing)
: Real Time Analytics
Processing (Big Data
Architecture &
Thanks for your attention !
Full transcript