Big Data Analytics The Next Frontier in Logistics?
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1 Big Data Analytics The Next Frontier in Logistics?
2 Today s presentation
3 Today s presentation What is Big Data about?
4 Today s presentation What is Big Data about? How is it used?
5 Today s presentation What is Big Data about? How is it used? What s happening now in Logistics
6 Today s presentation What is Big Data about? How is it used? What s happening now in Logistics How can it help me?
7 What is Big Data?
8 What is Big Data? It s data where, quite simply, there is rather a lot of it.
9 What is Big Data? It s data where quite simply, there is rather a lot of it. In addition, it s often not very well structured.
10 What is Big Data? It s data where quite simply, there is rather a lot of it. In addition, it s often not very well structured. Sometimes its just text
11 What is Big Data? It s data where quite simply, there is rather a lot of it. In addition, it s often not very well structured. Sometimes its just text And might even be images
12 Big Data Sources Where does it all come from?
13 Big Data Sources Where does it all come from? 1. People generated: people talking to people. There are one billion Facebook users.
14 Big Data Sources 1. People generated. People talking to people. There are one billion Facebook users. 2. Machine Generated. Machines talking to machines. The Bankers Automated Clearing Service processes billions of transactions every week.
15 Big Data Sources 1. People generated. People talking to people. There are one billion Facebook users. 2. Machine generated. Machines talking to machines. The Bankers Automated Clearing Service processes billions of transactions every week. Electronic Data Interchange processes similarly large volumes of transactions.
16 Big Data Sources 1. People generated. People talking to people. There are one billion Facebook users. 2. Machine generated. Machines talking to machines. The Bankers Automated Clearing Service processes billions of transactions every week. Electronic Data Interchange 3. The Internet of Things (IoT) Sensors collecting and sharing data. Several million vehicles report their geographical position every day.
17 Processing Big Data Practical implications of this volume and structure (or lack of it)
18 Processing Big Data Ordinary data processing
19 Processing Big Data Ordinary data processing Simply doesn t work
20 So now what?
21 So now what? A little bit of history to explain where this technology has it s origins
22 So now what? First came the web (the late 90s) and the accompanying super exponential growth in the number of pages available
23 So now what? First came the web (the late 90s) and the accompanying super exponential growth in the number of pages available then technology needed to exploit it
24 So now what? First came the web (the late 90s) and the accompanying super exponential growth in the number of pages available then technology needed to exploit it
25 Processing Big Data - Volume 1. Massively Parallel Processing
26 Processing Big Data 1. Massively Parallel Processing Technology that allows large numbers of small computers to work on the same problem. Google has more than 3 million servers!
27 Processing Big Data 1. Massively Parallel Processing: Technology that allows large numbers of small computers to work on the same problem. The databases, operating systems and server stuff
28 Processing Big Data - Structure 1. Massively parallel processing: Technology that allows large numbers of small computers to work on the same problem. The databases, operating systems and server stuff. 2. New Development Environments for Natural Language Processing
29 Processing Big Data 1. Massively parallel processing: Technology that allows large numbers of small computers to work on the same problem. The databases, operating systems and server stuff. 2. Natural Language Processing Technology invented to process data that goes beyond adding numbers or looking for occurrences of text strings in a name field.
30 Textual Analysis 1. Count the occurrences of words
31 Textual Analysis 1. Count the occurrences of words 2. Rank them
32 Textual Analysis 1. Count the occurrences of words 2. Rank them 3. Score them (e.g. positive negative)
33 Textual Analysis Do something useful..
34 Textual Analysis 1. Do something useful.. By analysing social media communications a TV client knows what is likely to become a breaking story in it s area of coverage
35 Textual Analysis 1. Sentiment Analysis Movie Reviews The movie was actually neither that funny nor super witty
36 Textual Analysis The movie was actually neither that funny nor super witty Advanced textual analysis correctly scores this review as negative.
37 How is Big Data used? Just a few examples: Online advertising predicting intent and interest
38 How is Big Data used? Just a few examples: Online advertising predicting intent and interest Gauge consumer sentiment and predicting behaviour
39 How is Big Data used? Just a few examples: Online advertising predicting intent and interest Gauge consumer sentiment and predicting behaviour Detecting adverse events and predicting their impact
40 How is Big Data used? Just a few examples: Online advertising predicting intent and interest Gauge consumer sentiment and predicting behaviour Detecting adverse events and predicting their impact Categorising and recognising places, faces, people
41 How is Big Data used? Just a few examples: Online advertising predicting intent and interest Gauge consumer sentiment and predicting behaviour Detecting adverse events and predicting their impact Categorising and recognising places, faces, people Personalised genomic medicine
42 How is Big Data used? Just a few examples: Online advertising predicting intent and interest Gauge consumer sentiment and predicting behaviour Detecting adverse events and predicting their impact Categorising and recognising places, faces, people Personalised genomic medicine of the future Building more intelligent public services: energy, water
43 How is Big Data used? Just a few examples: Online advertising predicting intent and interest Gauge consumer sentiment and predicting behaviour Detecting adverse events and predicting their impact Categorising and recognising places, faces, people Personalised genomic medicine of the future Building more intelligent public services: energy, water Security
44 What s Happening in Logistics? Large Retail: Small Parcel Savings Large retailers using DHL. Fed Ex, UPS et al Focus in on parcel costs Found they paid overnight premium for parcels going next door Realised parcel spend was not within contract Ts & Cs Employee compliance with company procedures was deficient (depot to depot costs unreasonable)
45 What s Happening in Logistics? Deere s Technology Use Solves Insolvable Problems Analysing the inventory of parts and other supplies in real time, allowing it to allocate resources appropriately. Equipment ships with sensors that monitor wear and anticipate replacement cycles. Land area and usage along with yield forecasts are factored into demand forecasts using Big Data
46 What s Happening in Logistics? Fuel Retailer: Using sensor data from its network of service stations and convenience stores, a fuel retailer knows in near real time what its fuel product mix and consumption rates are in each area of its geographical territories Incoming point of sale (POS) data also tells it which mixes of products (besides fuel) work best in different geographical areas so it can tailor offers accordingly The end business result? Faster time to market with offers, and better revenue positioning and agility because offers can be instantaneously changed to respond to customer demand shifts.
47 What s Happening in Logistics? Large Manufacturer: To minimize exposure to risk in its global supply chain, a large manufacturer overlays geographical supplier locations with weather statistics for tornadoes, hurricanes, earthquakes Then calculates the probabilities of natural disasters occurring through a predictive analytics program The end business result? The company now has a way to orchestrate its suppliers so that it has backup plans and failover to suppliers in other areas of the world if a key supplier gets hit by a disaster and the incident takes down production. By proactively managing its perceived risks, the company now has a way to avoid disruption to its supply chain that can endanger revenue capture and even impair customer perceptions of the company.
48 Global News Media Analysis with Big Data
49 Trends in Media: Analysis with Big Data Application collects news from more than 50 original news sources
50 Trends in Media: Analysis with Big Data Application collects news from more than 50 original news sources Includes most major US and UK media outlets (e.g. Fox, BBC News, and press releases of the major mobile outlets
51 Trends in Media: Analysis with Big Data Application collects news from more than 50 original news sources Includes most major US and UK media outlets (e.g. Fox, BBC News, and press releases of the major mobile outlets Involves Structured Crawling : article, title, author and tags taken from the text
52 Trends in Media: Analysis with Big Data Application collects news from more than 50 original news sources Includes most major US and UK media outlets (e.g. Fox, BBC News, and press releases of the major mobile outlets Involves Structured Crawling : article, title, author and tags taken from the text During processing articles are divided into ten categories: business, culture, economics, education, entertainment, health, politics, science, showbiz, sports, technology, world and other.
53 Trends in Media: Analysis with Big Data Application collects news from more than 50 original news sources Includes most major US and UK media outlets (e.g. Fox, BBC News, and press releases of the major mobile outlets Involves Structured Crawling : article, title, author and tags taken from the text During processing articles are divided into ten categories: business, culture, economics, education, entertainment, health, politics, science, showbiz, sports, technology, world and other. So what is the world talking about? Geo view
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56 Trends in Media: Analysis with Big Data Application collects news from more than 50 original news sources Includes most major US and UK media outlets (e.g. Fox, BBC News, and press releases of the major mobile outlets Involves Structured Crawling : article, title, author and tags taken from the text During processing articles are divided into ten categories: business, culture, economics, education, entertainment, health, politics, science, showbiz, sports, technology, world and other. So what is the world talking about? Trend view
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58 Should you be using Big Data? Big Data sounds brilliant, where do I start?
59 Should you be using Big Data? X Big Data sounds brilliant, where do I start?
60 Should you be using Big Data? Are any of the challenges I face right now capable of being addressed by Big Data?
61 Should you be using Big Data?
62 Should you be using Big Data? Bring in your IS dept. and start small You probably already have a lot of data Remember, access to large datasets is available and sometimes free.
63 John O Brien Business Development Director DataArt Technologies UK John.OBrien@DataArt.com +44 (0)
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