what kinds of data go into big data?

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What kinds of data go into Big Data?

Dan Wood, Solution Manager, HP Big Data

Mike Shaw, Director, HP Software Marketing

#mike_j_shaw

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Much higher volumes

Processed with more velocity

With much more variety

And a greater need to protect from vulnerabilities

What is big data versus normal data?

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Big data can give us the power of the 360-degree view… …combining structured and unstructured data

Structured data : 10%

Unstructured data : 90%

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Three types data can feed into big data

Machine to machine data

2 Human interaction data

3 Transaction data

1

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The three types of big data 1 - Transactions

Machine to machine data

2 Human interaction data

3 Transaction data

1

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Big data can analyze transactions faster

Retailer Guess is able to adjust shops’ layout in time for opening.

Kokubu is a able to optimize distribution from its 200+ centers.

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The HP.com team keeps transactionsfor 15 years. They look for ‘long-run affinities’ – buying patterns over long periods of time.

…and over a longer time period

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The three types of big data 2 – Machine to machine data

Machine to machine data

2 Human interaction data

3 Transaction data

1

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Kitchen appliances

Wearable monitors

Medical robots

Cars

Tvs

Automated factories

Exercise machines

Parking control

Shopping trolley

Security devices

Cooking Road-side

sensors

Smart power

Bikes

Poaching sensors

House control

Shopping displays

Smart phones

Wearable devices

Sensors

Smart devices

Tablets

Smart phones

The internet of things

Everyday devices are infused with intelligence that is updated in real time.

Embedded, connected computer power will soon be everywhere

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…much of it connected. In fact...

Mobile traffic increases 33X

A 33 times increase in mobile data traffic between 2010 and 2020.

2010 3.8 exabytes

2020 127 exabytes

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By 2020 there will be 6 billion mobile phones but 30 billion connected smart devices taking

42% of

the mobile bandwidth.

Machine generated data is estimated to reach 42% of mobile traffic by 2020

2020 42%

2013 33%

2005 11%

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How we will our use of machine-to-machine data evolve?

2010 2015

McKinsey : Big Data – The next frontier for innovation, competition and productivity

Automotive

Utilities

Travel / logistics

Security

Retail The internet of things

• Medical equipment

• Utility networks and meters

• Car and truck fleets

• Security sensors

• Home automation

• Touch-streams from games

• Drones

• Pollution sensors

• Transport sensors

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The three types of big data 3 – Human interaction data

Machine to machine data

2 Human interaction data

3 Transaction data

1

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Meaning from human interaction comes from many sources

Social media

Images

Video

Audio

Email

Documents

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We can analyze the calls made to call centers—looking for products customers do and don't like, for opportunities to up-sell and cross-sell, and for those calls where the customer is about to "churn".

Financial services companies use voice analysis to catch non-compliance behavior.

Audio

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During the London Olympics, British security services used HP technology to compare the photograph of

every visitor to the games against a list of known terror suspects.

We routinely perform number plate and car type

recognition, scene recognition, facial recognition and perimeter

enforcement at airports and military bases.

Images

Video

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Compliance departments can analyze company emails looking for non-compliant behavior…

…and for internal security breaches (e.g., sale of company assets to criminals).

Email

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Social media data can tell us all sort of things.

It can tell us about our products, about our competitors, about the likelihood of customers "churning" from us and about cheating and fraud.

Social media

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Automatically extracting meaning from legal documentation allows us to do legal discovery more quickly and cheaply.

Extracting meaning from case notes and then sharing this meaning between social care agencies might help to reduce interdepartmental failures of care.

Documents

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Companies can use human information and big data…

To catch cheaters We use micro-transactions analysis to catch those who cheat at online gambling games.

But cheats like to tell others about how clever they are on social media.

Combining micro-transactions with social media allows us to find cheats faster than any one data type alone would.

To get closer to customers

You can record every sale in every one of your retail stores and every transaction on your web site. This will tell you what items are trending and what items are being purchased together.

You can use sentiment analysis to tell you about "cool stuff" that maybe you don't yet stock but should; and about competitors trending up quickly.

You can record and analyze transactions to look for fraud and non-compliance of traders.

And analyze your company's emails and internal phone calls to get a “human interaction” view on non-compliance.

To improve compliance within financial trading

HP Operations Analytics records metric, event and log information—and from this, allows support staff to fix complex problems.

They could also analyze the “human” interactions between the support center and the app dev team, then correlate this with the structured information.

To solve problems with complex systems

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Are you ready to support the business’s big data needs?

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The survey says—probably not!

Source : IDG survey for HP, 2014 : “Do you feel ready to handle different forms of structured big data?”

8% Online clickstreams

15% Machine data

23% Transaction data

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Are you getting insight from human interaction data?

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The survey says—probably not!

Source : IDG survey for HP, 2014

51% To some extent

30% To little extent

2%

Not applicable To no extent

13%

To a great extent

5%

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Find out more…

Explore the whitepaper: See the big picture in Big Data

…or fill out the info form on the next page

Watch the SlideShare: Get closer to your customers with Big Data

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Get the insight you need to take action: www.hp.com/HAVEn

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