Big data refers to the collection and analysis of extremely large data sets to reveal patterns, trends, and associations. It allows for more accurate analyses, confident decision making, and greater efficiencies. Some key points: - Big data comes from various sources like web browsing, social media, sensors, and can provide insights into customer engagement, retention, and optimizing marketing programs. - Many large companies are using big data to reduce costs, optimize operations, develop new data-driven products and services, and support internal business decisions. - Technologies used for big data include Hadoop, HDFS, NoSQL, MapReduce, MongoDB, and Cassandra. - Challenges remain around overreliance

1. Big data
By: Amin Salimian

2. Big data
More data -more accurate analyses- more confident decision making -greater operational
efficiencies, cost reductions and reduced risk.
collection of large amounts of data from web-browsing -social network communications, sensor
and surveillance ,that is then searched for patterns, new revelations and insights.

3. Big data in marketing
Customer engagement. who your customers are, where they are, what they want, how they
want to be contacted and when.
Customer retention and loyalty. help you discover what influences customer loyalty and what
keeps them coming back again and again.
Marketing optimization/performance. continuously optimize marketing programs through
testing, measurement and analysis.

4. Big data category in marketing
Customer: include behavioral, attitudinal and transactional metrics from such sources as
marketing campaigns, points of sale, websites, customer surveys, social media, online
communities and loyalty programs.
Operational: includes objective metrics that measure the quality of marketing processes relating
to marketing operations, resource allocation, asset management, budgetary controls, etc.
Financial: include sales, revenue, profits and other objective data types that measure the
financial health of the organization.

5. Position
$15 billion on software firms specializing in data management and analytics.
In 2010, $100 billion on software firms specializing in data management and analytics.
,growing at almost 10 percent a year: about twice as fast as the software business as a whole.
The world's effective capacity to exchange information through telecommunication networks

6. Technologies
Hadoop- HDFS- NoSQL- Map Reduce -MongoDB –Cassandra- PIG- HIVE- R- HBase

7. Why using big data in big companies?
*Cost Reduction from Big Data Technologies
*Time Reduction from Big Data: Macy: The department store chain has been able to reduce the
time to optimize pricing of its 73 million items for sale from over 27 hours to just over 1 hour

8. Developing New Big Data-Based Offerings: online firms, which have an obvious need to employ
data-based products and services. LinkedIn, People You May Know, Groups You May Like, Jobs
You May Be Interested In, Who’s Viewed My Profile, and several others. These offerings have
brought millions of new customers to LinkedIn
The testing firm Kaplan ,advising customers on effective learning and test-preparation strategies.

9. Supporting Internal Business Decisions: What offers should be presented to a customer? Which
customers are most likely to stop being customers soon? How much inventory should be held in
the warehouse? How should we price our products?
Three major banks we interviewed - Wells Fargo, Bank of America, and Discover - are also using
big data to understand aspects of the customer relationship that they couldn’t previously get at

10. UPS :the world’s largest package delivery
company
big data, however, comes from telematics sensors in more than 46,000 vehicles. The data in not
only used to monitor daily performance, but to drive a major redesign of UPS drivers' route
structures.
savings in 2011 of more than 8.4 million gallons of fuel by cutting 85 million miles off of daily
routes. UPS estimates that saving only one daily mile per driver saves the company $30 million.

11. HERTZ
With over 8300 locations worldwide in 146 countries, Hertz keeps its finger on the pulse of its
customers with customer satisfaction. By applying advanced analytics solutions, the company
was able to process the information much more quickly–in half the time it previously took
surveys and measurements indicated that delays were occurring for returns during specific
times of the day. By investigating this anomaly, Hertz was able to quickly adjust their staffing
levels at the Philadelphia office during those peak times, ensuring a manager was present to
resolve any issues. This enhanced Hertz’s performance, and increased customer satisfaction…all
by parsing the volumes of data being generated from multiple sources

12. SETON HEALTHCARE
to reduce the occurrence of high cost Congestive Heart Failure (CHF) readmissions By proactively
identifying patients likely to be readmitted on an emergent basis, they applied predictive models
and examined analytics .For Seton, a reduction in costs and risks associated with complying with
Federal readmission targets. For Seton’s patients, fewer visits to the hospital and overall
improved patient care. Seton is able to identify patients likely for re-admission and introduce
early interventions to reduce cost, mortality rates, and improved patient quality of life.

13. H&R BLOCK
Every question that is not answered immediately is a lost sale. Tax preparation is a highly
seasonal business. H&R Block had a heavy paid media scheduled but they also used Facebook
and Twitter. The effort secured 1,500,000 unique visitors and answered 1,000,000 questions for
a 15% lift in business versus the prior year when there was no social media in the marketing mix.

14. Ford
The most obvious example of data influencing the driving experience might be the types of data
car companies are actually giving back to drivers. At Ford, its energy line of plug-in hybrid cars
generate 25 gigabytes of data per hour that’s then processed and given back to drivers via a
mobile app. It tells them about battery life, the nearest charging stations and other data about
the vehicle’s performance. The goal is to better understand how drivers are using the vehicles
and use that information to continuously improve the vehicles and the overall experience

15. Wal-Mart Stores Inc.
The mega-retailer's latest search engine for Walmart.com includes semantic data. Polaris, a
platform that was designed in-house, relies on text analysis, machine learning and even
synonym mining to produce relevant search results. Wal-Mart says adding semantic search has
improved online shoppers completing a purchase by 10% to 15%. "In Wal-Mart terms, that is
billions of dollars," Laney said.

16. 5 Ways Big Data Will Change Sales and
Marketing in 2015
Large enterprises will be the first , small and medium businesses will benefit even more.
Marketing spend will become significantly more precise.
Salespeople will gradually adopt data-driven methodologies to target high-value prospects.
Sales forecasting accuracy will improve dramatically
Real-time sales data visualization technologies will emerge

17. The future of big data
From BMW’s self-driving car to smart door locks and bells, and Wi-Fi tea pots. The IoT is
ultimately about connecting devices to people, and allowing them to remotely control and
monitor.
the Industrial Internet. about machine intelligence and allowing things like wind turbines,
locomotives and jet engines to talk and understand each other.

18. When big data goes bad
Big Data and the cloud are putting supercomputer capabilities into everyone’s hands. But what’s
getting lost in the mix is that the tools we use to interpret and apply this tidal wave of
information often have a fatal flaw. Much of the data analysis we do rests on erroneous models,
meaning mistakes are inevitable. And when our outsized expectations exceed our capacity, the
consequences can be dire

19. Big data hubris: Google Flu Trends
Google relied too much on simple searches, and hit what the authors of a new report in
Science call a “Big Data hubris”:
“Big data hubris” is the often implicit assumption that big data are a substitute for, rather
than a supplement to, traditional data collection and analysis.”

20. Data mining
is the computational process of discovering patterns in large data sets involving methods at the
intersection of artificial intelligence, machine learning, statistics, and database systems. The
overall goal of the data mining process is to extract information from a data set and transform it
into an understandable structure for further use