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Seth Familian
Founder + Principal, Familian&1
WORKING WITH
BIG DATA
FOLLOW ALONG!
familian1.com/wwbd
INTRODUCTION
SETH FAMILIAN
FOUNDER + PRINCIPAL, FAMILIAN&1
2
Corporate Strategy User Experience Design
Creative Procraftin...
AGENDA
‣ Context: What’s big data?
‣ Useful tools
‣ Building dashboards
‣ Inferring segments
‣ Final thoughts
3
WORKING WITH BIG DATA
CONTEXT:
WHAT’S BIG DATA?
4
CONTEXT: WHAT’S BIG DATA?
WELCOME TO DATA OBESITY!
5
http://www.datasciencecentral.com/profiles/blogs/basic-understanding-o...
CONTEXT: WHAT’S BIG DATA?
HOW BIG IS BIG?
6
http://www.domo.com/blog/2013/05/the-physical-size-of-big-data/
in 1 year!
cre...
CONTEXT: WHAT’S BIG DATA?
BIG IN GROWTH, TOO.
7
http://www.infosysblogs.com/brandedge/2013/04/20130419Infographc.html http...
CONTEXT: WHAT’S BIG DATA?
9 SOURCES
8
https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
CONTEXT: WHAT’S BIG DATA?
6 TYPES
9
{
"created_at": "Thu Sep 15 16:29:08 +0000 2016",
"id": 776457834095644700,
"id_str": ...
CONTEXT: WHAT’S BIG DATA?
6 TYPES
10
CONTEXT: WHAT’S BIG DATA?
6 TYPES
11
CONTEXT: WHAT’S BIG DATA?
THE FOUR V’S
12
http://www.slideshare.net/gschmutz/ukoug2013-big-datafastdata
9 Data Sources
6 D...
CONTEXT: WHAT’S BIG DATA?
COHESIVE ASSESSMENT
13
https://datafloq.com/read/understanding-sources-big-data-infographic/338
WORKING WITH BIG DATA
USEFUL TOOLS
FOR BIG DATA
14
USEFUL TOOLS
THE
ANALYTICS
PROCESS
15
USEFUL TOOLS
A BUSY
LANDSCAPE
16
USEFUL TOOLS
LET’S
SIMPLIFY
17
USEFUL TOOLS
AND LET’S
REFRAME IT
18
EVENT-BASED ANALYTICS
+TEXTUAL
VISUAL
ANALYTICS + INSIGHT
PROCESSING + NORMALIZATION
...
USEFUL TOOLS
POWER
PLAYERS
19
EVENT-BASED ANALYTICS
+TEXTUAL
VISUAL
ANALYTICS + INSIGHT
PROCESSING + NORMALIZATION
VISUAL ...
USEFUL TOOLS
SPLUNK
20
SPLUNK.COM
FOR ANY MACHINE DATA
USEFUL TOOLS
SPLUNK
21
SPLUNK.COM
FOR ANY MACHINE DATA
USEFUL TOOLS
CHARTED
22
CHARTED.CO
FOR SUPER SIMPLE CHARTS
USEFUL TOOLS
C3.JS
23
C3JS.ORG
FOR CUSTOM CHARTING
USEFUL TOOLS
QUID
24
QUID.COM
FOR UNSTRUCTURED ANALYSIS
USEFUL TOOLS
TAGUL
25
TAGUL.COM
FOR GORGEOUS WORD CLOUDS
USEFUL TOOLS
QUINTLY
26
QUINTLY.COM
FOR SOCIAL MEDIA DATA
USEFUL TOOLS
GOOGLE 

ANALYTICS
27
ANALYTICS.GOOGLE.COM
FOR WEBSITE TRAFFIC
USEFUL TOOLS
MIXPANEL
28
MIXPANEL.COM
FOR USER-EVENT DATA
USEFUL TOOLS
GOOGLE SHEETS
30
SHEETS.GOOGLE.COM
MIND MELTED YET? LET’S TAKE 15.
BREAK TIME!
33
‣ Stretch your legs
‣ Hydrate or grab a snack
‣ We’ll start again in 15 min...
Seth Familian
Founder + Principal, Familian&1
WORKING WITH
BIG DATA
FOLLOW ALONG!
familian1.com/wwbd
WORKING WITH BIG DATA
BUILDING DASHBOARDS
FROM BIG DATA
35
BUILDING DASHBOARDS
SMALL DATA EXAMPLE
36
via
Raw 

“Header”

File
BUILDING DASHBOARDS
BIG DATA VISUALIZATION
37
26Mrows
250Kaffiliate IDs
28sub-channels
“Long” Data
BUILDING DASHBOARDS
THE “OLD SCHOOL” APPROACH
38
Raw 

“Header”

File
2
1
Affiliates
Lookup File
Update Loop
S...
BUILDING DASHBOARDS
ANATOMY OF A SPLUNK SEARCH
39
BUILDING DASHBOARDS
SCHEDULED SEARCHES + INDICES
40
by sourceoforder by af_type
by af_source by af_name
by af_name 

+ ppc...
BUILDING DASHBOARDS
NESTED CHARTS + SMALL MULTIPLES
41
BUILDING DASHBOARDS
SEGMENT
42
SEGMENT.COM
FOR DATA ROUTING
BUILDING DASHBOARDS
CLEARBIT
43
CLEARBIT.COM
FOR DATA ENRICHMENT
BUILDING DASHBOARDS
AUTOMATED BIG DATA FLOW (EXAMPLE 1)
45
RAW DATA EXTRACT LOADTRANSFORM
Traffic Sources 

& Session Stat...
BUILDING DASHBOARDS
AUTOMATED BIG DATA FLOW (EXAMPLE 2)
46
LOAD
Custom
dashboards
synced with
70+ APIs
Traffic Sources 

&...
WORKING WITH BIG DATA
INFERRING SEGMENTS
FROM BIG DATA
47
INFERRING SEGMENTS FROM BIG DATA
Frequency (F)
Ranking
Recency (R)
Ranking
Monetary (M)
Ranking
1 2 3 4 5 1 2 3 4 5 1 2 3 ...
INFERRING SEGMENTS FROM BIG DATA 49
WHY QUANTILES?
NORMAL DISTRIBUTION SKEWED-DISTRIBUTION
INFERRING SEGMENTS FROM BIG DATA 50
X11 X21 X31 X41
X12 X22 X32 X42
X13 X23 X33 X43
X14 X24 X34 X44
X15 X25 X35 X45
X51
X5...
INFERRING SEGMENTS FROM BIG DATA
$0
$1,500
$3,000
$4,500
$6,000
51
Average total spent ($) by new MFR quantiles rerun for ...
INFERRING SEGMENTS FROM BIG DATA 52
High Value Customers Low Value Customers
Still
Loyal
Once
Loyal
New
Old
M1 M2 M3 M4 M5...
INFERRING SEGMENTS FROM BIG DATA
PRICE-POINT
CUTOFFS
53
Best Camera/Lens Purchased
DSLR Body DSLR Lens DSLR Body + Lens Po...
INFERRING SEGMENTS FROM BIG DATA
CROSSING RFM
W/ CATEGORIES
54
Low Value High Value
Still
Loyal
Once
Loyal
New Old
Still
L...
WORKING WITH BIG DATA
FINAL THOUGHTS
57
FINAL THOUGHTS
A NEW TYPE OF
KNOWLEDGE
WORKER
58
http://www.doclens.com/87922/think-issue-7-2014/
FINAL THOUGHTS
AN INCREDIBLY VALUABLE SKILL
59
https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-h...
FINAL THOUGHTS
THE CORNERSTONE OF A DAUNTING FUTURE?
60
https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolu...
FINAL THOUGHTS
DATA AS INTERFACE
61
for
using
Made Visual
BACKGROUND TITLES + BUTTONS TEXT + LINES
Your data + brand
up to...
FINAL THOUGHTS
DATA AS INTERFACE
62
for
using
FINAL THOUGHTS
DATA AS INTERFACE
63
FINAL THOUGHTS
START HERE
64
CHARTED.CO
FINAL THOUGHTS
OR HERE
65
SEGMENT.IO
MIXPANEL.COM
FINAL THOUGHTS
OR HERE
66
HBR.ORG
FINAL THOUGHTS
OR HERE
67
FINAL THOUGHTS
OR HERE
68
DISCUSSION TIME
WORKING WITH BIG DATA 69
QUESTIONS · FEEDBACK · IDEAS · INSIGHTS
THANK YOU
KEEP IN TOUCH!
70
SETH@FAMILIAN1.COM · @SETHFAM1
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Working With Big Data - Nov 2016

A deep-dive workshop at General Assembly. (Note: some pages missing to protect confidentiality)

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Working With Big Data - Nov 2016

  1. 1. Seth Familian Founder + Principal, Familian&1 WORKING WITH BIG DATA FOLLOW ALONG! familian1.com/wwbd
  2. 2. INTRODUCTION SETH FAMILIAN FOUNDER + PRINCIPAL, FAMILIAN&1 2 Corporate Strategy User Experience Design Creative ProcraftinationTeaching + Education Growth Hacking
  3. 3. AGENDA ‣ Context: What’s big data? ‣ Useful tools ‣ Building dashboards ‣ Inferring segments ‣ Final thoughts 3
  4. 4. WORKING WITH BIG DATA CONTEXT: WHAT’S BIG DATA? 4
  5. 5. CONTEXT: WHAT’S BIG DATA? WELCOME TO DATA OBESITY! 5 http://www.datasciencecentral.com/profiles/blogs/basic-understanding-of-big-data-what-is-this-and-how-it-is-going
  6. 6. CONTEXT: WHAT’S BIG DATA? HOW BIG IS BIG? 6 http://www.domo.com/blog/2013/05/the-physical-size-of-big-data/ in 1 year! creates enough data to fill
  7. 7. CONTEXT: WHAT’S BIG DATA? BIG IN GROWTH, TOO. 7 http://www.infosysblogs.com/brandedge/2013/04/20130419Infographc.html https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
  8. 8. CONTEXT: WHAT’S BIG DATA? 9 SOURCES 8 https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
  9. 9. CONTEXT: WHAT’S BIG DATA? 6 TYPES 9 { "created_at": "Thu Sep 15 16:29:08 +0000 2016", "id": 776457834095644700, "id_str": "776457834095644672", "text": "I love @glip because it makes me more productive and reliant on far fewer tools! #gliplove #goglip #gliptastic :)", "truncated": false, "entities": { "hashtags": [ { "text": "gliplove", "indices": [ 82, 91 ] }, { "text": "goglip", "indices": [ 92,
  10. 10. CONTEXT: WHAT’S BIG DATA? 6 TYPES 10
  11. 11. CONTEXT: WHAT’S BIG DATA? 6 TYPES 11
  12. 12. CONTEXT: WHAT’S BIG DATA? THE FOUR V’S 12 http://www.slideshare.net/gschmutz/ukoug2013-big-datafastdata 9 Data Sources 6 Data Types
  13. 13. CONTEXT: WHAT’S BIG DATA? COHESIVE ASSESSMENT 13 https://datafloq.com/read/understanding-sources-big-data-infographic/338
  14. 14. WORKING WITH BIG DATA USEFUL TOOLS FOR BIG DATA 14
  15. 15. USEFUL TOOLS THE ANALYTICS PROCESS 15
  16. 16. USEFUL TOOLS A BUSY LANDSCAPE 16
  17. 17. USEFUL TOOLS LET’S SIMPLIFY 17
  18. 18. USEFUL TOOLS AND LET’S REFRAME IT 18 EVENT-BASED ANALYTICS +TEXTUAL VISUAL ANALYTICS + INSIGHT PROCESSING + NORMALIZATION DATA TRANSFORMATION (ETL) ACTIVITY MODALITY DATA DISPLAY + DASHBOARDING STATISTICAL ANALYTICS VISUAL ANALYTICS
  19. 19. USEFUL TOOLS POWER PLAYERS 19 EVENT-BASED ANALYTICS +TEXTUAL VISUAL ANALYTICS + INSIGHT PROCESSING + NORMALIZATION VISUAL ANALYTICSSTATISTICAL ANALYTICS DATA TRANSFORMATION (ETL) DATA DISPLAY + DASHBOARDING ACTIVITY MODALITY
  20. 20. USEFUL TOOLS SPLUNK 20 SPLUNK.COM FOR ANY MACHINE DATA
  21. 21. USEFUL TOOLS SPLUNK 21 SPLUNK.COM FOR ANY MACHINE DATA
  22. 22. USEFUL TOOLS CHARTED 22 CHARTED.CO FOR SUPER SIMPLE CHARTS
  23. 23. USEFUL TOOLS C3.JS 23 C3JS.ORG FOR CUSTOM CHARTING
  24. 24. USEFUL TOOLS QUID 24 QUID.COM FOR UNSTRUCTURED ANALYSIS
  25. 25. USEFUL TOOLS TAGUL 25 TAGUL.COM FOR GORGEOUS WORD CLOUDS
  26. 26. USEFUL TOOLS QUINTLY 26 QUINTLY.COM FOR SOCIAL MEDIA DATA
  27. 27. USEFUL TOOLS GOOGLE 
 ANALYTICS 27 ANALYTICS.GOOGLE.COM FOR WEBSITE TRAFFIC
  28. 28. USEFUL TOOLS MIXPANEL 28 MIXPANEL.COM FOR USER-EVENT DATA
  29. 29. USEFUL TOOLS GOOGLE SHEETS 30 SHEETS.GOOGLE.COM
  30. 30. MIND MELTED YET? LET’S TAKE 15. BREAK TIME! 33 ‣ Stretch your legs ‣ Hydrate or grab a snack ‣ We’ll start again in 15 mins!
  31. 31. Seth Familian Founder + Principal, Familian&1 WORKING WITH BIG DATA FOLLOW ALONG! familian1.com/wwbd
  32. 32. WORKING WITH BIG DATA BUILDING DASHBOARDS FROM BIG DATA 35
  33. 33. BUILDING DASHBOARDS SMALL DATA EXAMPLE 36 via
  34. 34. Raw 
 “Header”
 File BUILDING DASHBOARDS BIG DATA VISUALIZATION 37 26Mrows 250Kaffiliate IDs 28sub-channels
  35. 35. “Long” Data BUILDING DASHBOARDS THE “OLD SCHOOL” APPROACH 38 Raw 
 “Header”
 File 2 1 Affiliates Lookup File Update Loop Summary Index saved searches scheduled searches TRANSFORM Instantly
 Generates EXTRACT LOAD Channel
 Dashboards
  36. 36. BUILDING DASHBOARDS ANATOMY OF A SPLUNK SEARCH 39
  37. 37. BUILDING DASHBOARDS SCHEDULED SEARCHES + INDICES 40 by sourceoforder by af_type by af_source by af_name by af_name 
 + ppc_s SUMMARY INDEX INDEX_MAIN_SOURCES INDEX_A.COM INDEX_AFFILS INDEX_PAID_SEARCH INDEX_SHOPPING_ENGINES 12M+ TRANSACTIONSFULL DB METRICS SAVED SEARCHES DATA DELTAS METRICS DATA DELTAS METRICS DATA DELTAS METRICS DATA DELTAS METRICS DATA DELTAS
  38. 38. BUILDING DASHBOARDS NESTED CHARTS + SMALL MULTIPLES 41
  39. 39. BUILDING DASHBOARDS SEGMENT 42 SEGMENT.COM FOR DATA ROUTING
  40. 40. BUILDING DASHBOARDS CLEARBIT 43 CLEARBIT.COM FOR DATA ENRICHMENT
  41. 41. BUILDING DASHBOARDS AUTOMATED BIG DATA FLOW (EXAMPLE 1) 45 RAW DATA EXTRACT LOADTRANSFORM Traffic Sources 
 & Session Stats RAW DATA Behavioral Segments, Funnels, Retention & LTV EXTRACT Additional aggregation and data refinement Core Website Social Engagement Footprint Unified social
 footprint metrics Enrichment of email addresses CRM data store for easy segmentation + analysis Additional context 
 on Twitter followers More flexible segments, funnels + retention metrics
  42. 42. BUILDING DASHBOARDS AUTOMATED BIG DATA FLOW (EXAMPLE 2) 46 LOAD Custom dashboards synced with 70+ APIs Traffic Sources 
 & Session Stats Realtime (RT) TRANSFORMRAW DATA EXTRACT Core Website Social Engagement Footprint heavy-duty query tools already in place App Databases Custom aggregation scripts Postgres or Redshift DB Daily Pull Internally-reported metrics summarized for triangulation Daily CSV Behavioral segmentation 
 + in-app messaging RT Behavioral Segments, Funnels, Retention & LTV RT Unified social
 footprint metrics Unified app downloads & ratings metrics App Store Activity email address enrichment
  43. 43. WORKING WITH BIG DATA INFERRING SEGMENTS FROM BIG DATA 47
  44. 44. INFERRING SEGMENTS FROM BIG DATA Frequency (F) Ranking Recency (R) Ranking Monetary (M) Ranking 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1. all customers are independently ranked into equal-sized “tiles” three times over 48 1,164,927 customers 2. M scores are multiplied by 100 and F scores are multipled by 10 to create unique ranking values 100 200 300 400 500 10 20 30 40 50 1 2 3 4 5 3. MFR scores are added up for each customer to yield 125 unique MFR segments: 111 121 131 141 151 112 122 132 142 152 155 211 221 231 241 251 212 222 232 242 252 255 511 521 531 541 551 512 522 532 542 552 555 Most recent, frequent, and highest-value customers Least recent, frequent, and lowest-value customers RFM SCORING
  45. 45. INFERRING SEGMENTS FROM BIG DATA 49 WHY QUANTILES? NORMAL DISTRIBUTION SKEWED-DISTRIBUTION
  46. 46. INFERRING SEGMENTS FROM BIG DATA 50 X11 X21 X31 X41 X12 X22 X32 X42 X13 X23 X33 X43 X14 X24 X34 X44 X15 X25 X35 X45 X51 X52 X53 X54 X55 High Frequency High Recency Low Frequency Low Recency Still Loyal Once Loyal New Old F + R = LOYALTY INSIGHTS
  47. 47. INFERRING SEGMENTS FROM BIG DATA $0 $1,500 $3,000 $4,500 $6,000 51 Average total spent ($) by new MFR quantiles rerun for non-outlier M1 + M2 customers M1 M2 M3 M4 M5 percent: top 20% of M1+2 2nd 20% 3rd 20% 4th 20% Bottom 20% segment size: 93,134 93,139 92,861 93,406 93,143 avg. $ spent: $3,337 $1,137 $642 $412 $276 total $ spent: $345,234,826 $105,573,528 $59,348,459 $38,398,553 $25,537,936 % of total revs: 53% 32% 18% 11% 8% High-Value Customers Low-Value Customers M = VALUE 
 INSIGHTS
  48. 48. INFERRING SEGMENTS FROM BIG DATA 52 High Value Customers Low Value Customers Still Loyal Once Loyal New Old M1 M2 M3 M4 M5 212111 121 112 122 113 123 211 221 222 311 321 312 322 411 421 412 422 511 521 512 522 114 124 115 125 213 223 214 224 215 225 313 323 314 324 315 325 413 423 414 424 415 425 513 523 514 524 515 525 131 132 141 142 151 152 231 232 241 242 251 252 331 332 341 342 351 352 431 432 441 442 451 452 531 531 541 542 551 552 133 134 143 144 153 154 135 145 155 233 234 243 244 253 254 235 245 255 333 334 343 344 353 354 335 345 355 433 434 443 444 453 454 435 445 455 533 534 543 544 553 554 535 545 555 1 2 3 4 5 6 7 8 COMBINING INSIGHTS
  49. 49. INFERRING SEGMENTS FROM BIG DATA PRICE-POINT CUTOFFS 53 Best Camera/Lens Purchased DSLR Body DSLR Lens DSLR Body + Lens Point-and-Shoot Segment Name Relationship 
 to Photography Memory Keepers Use cameras to record family memories and milestones less than 
 $650 less than 
 $300 less than 
 $950 less than 
 $450 Hobbyists Enjoy the picture-taking process; understand and use camera controls $650 - $1725 $300 - $750 $950 - $2300 $450 - $700 Prosumers Advanced skills, but do not make a living from photography $1725 - $2750 $750 - $3000 $2300 - $4200 $700 - $2500 Pros Rely on photography as a profession $2750+ $3000+ $4200+ $2500+
  50. 50. INFERRING SEGMENTS FROM BIG DATA CROSSING RFM W/ CATEGORIES 54 Low Value High Value Still Loyal Once Loyal New Old Still Loyal Once Loyal New Old Memory Keepers 1 2 3 4 5 6 7 8 Hobbyists 9 10 11 12 13 14 15 16 Prosumers 17 18 19 20 21 22 23 24 Professionals 25 26 27 28 29 30 31 32 3. Cross-Tabulate 
 Top customers and categories to create behavioral and 
 loyalty-based segments 9 
 key categories
 account for 81% of sales 2. Isolate 
 the top customers and categories by total dollars spent, frequency, and recency (RFM) measures 465,683 
 top customers
 account for 88% of sales
 1,164,927 customers 807 categories 1. Aggregate 
 72 months of Internet channel transaction data, organizing by key variables 2,246,094 Internet Channel transactions 4. Generate
 Segment-specific marketing recommendations which can be further targeted by brand YIELDS SOLID TARGETS 
 FOR TACTICAL PLANNING
  51. 51. WORKING WITH BIG DATA FINAL THOUGHTS 57
  52. 52. FINAL THOUGHTS A NEW TYPE OF KNOWLEDGE WORKER 58 http://www.doclens.com/87922/think-issue-7-2014/
  53. 53. FINAL THOUGHTS AN INCREDIBLY VALUABLE SKILL 59 https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
  54. 54. FINAL THOUGHTS THE CORNERSTONE OF A DAUNTING FUTURE? 60 https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
  55. 55. FINAL THOUGHTS DATA AS INTERFACE 61 for using Made Visual BACKGROUND TITLES + BUTTONS TEXT + LINES Your data + brand up to 
 100,000 
 objects Anywhere on the Web using 
 1 line
 of code
  56. 56. FINAL THOUGHTS DATA AS INTERFACE 62 for using
  57. 57. FINAL THOUGHTS DATA AS INTERFACE 63
  58. 58. FINAL THOUGHTS START HERE 64 CHARTED.CO
  59. 59. FINAL THOUGHTS OR HERE 65 SEGMENT.IO
  60. 60. MIXPANEL.COM FINAL THOUGHTS OR HERE 66
  61. 61. HBR.ORG FINAL THOUGHTS OR HERE 67
  62. 62. FINAL THOUGHTS OR HERE 68
  63. 63. DISCUSSION TIME WORKING WITH BIG DATA 69 QUESTIONS · FEEDBACK · IDEAS · INSIGHTS
  64. 64. THANK YOU KEEP IN TOUCH! 70 SETH@FAMILIAN1.COM · @SETHFAM1
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A deep-dive workshop at General Assembly. (Note: some pages missing to protect confidentiality)

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