6. Example of Big Data
Hadron Collider
• 150 million sensors deliver data…
• 40 million times per second…
• 600 million collisions per second, of which…
• 100 are of interest
8. Wikipedia
• “…is a collection of data sets so large that it
becomes difficult to process using traditional
data processing applications.”
• Challenges include
“capture…storage…analysis…sharing…”
• “…requiring massively parallel software
running on tens, hundreds, or THOUSANDS of
servers!!!”
25. Be Knowledgeable
AVOID SQUARE PEGS IN ROUND HOLES
• Don’t expect web analytics from order
management system
• Don’t ask Email software to provide customer
profiles
26. Be Focused
AVOID ANALYSIS PARALYSIS
• Rejoice in imprecision
• Be Inclusive; include your SME’s
• Ask targeted questions
37. Who is Big Data
9 Attributes of a Data Rockstar
38. #1 –Knows The Tools
• How well do they know the tools you’ve
provided?
• Excel
• Databases
• Tableau
• Other
39. #2 – Communicator
• Often overlooked!
• S/he is a liason
• Good = Efficient
• Communication is 2 way street
40. #3 – Inquisitive
• Asks good questions
• About the business
• About the data
41. #4 – Enjoys Data
• Tough to teach
• Have it or you don’t
• Evident after limited exposure to data
• Afflicts less than 10% of humans
42. #5 – Primed for Improvement
• Continuous quest for Better
• Better performance
• Better understanding
• Better outputs and results
43. #6 – Self Aware
• Understands not everyone possesses this mix
of traits
• Embraces mission to make things simple and
useful for others
44. #7 – Versed in the Business
• Absolutely Crucial
• Think of this person as a “techie” at your own
peril!
• Displays capacity to become deeply versed if
not already so
45. #8 – Aesthetic Sensibilities
• Often ignored trait
• Supports Communication (see #2!)
• Supports Usefulness & Utility Mission (see #6!)
46. #9 – Empathetic
• Can place themselves in the shoes of the
“consumers”
• Understands that others may use their work,
not just look at it
• Sees through the eyes and workflows of others
48. Case Study
The Challenge
• Small Business marketing skilled labor
• Individuals billed out at different hourly rates
depending on project
• Needed to see projected revenue by individual
and project, by week
49. Case Study
Resources
• 4 data sources
• People, Projects, Rates, Projected Hours
• Excel
• Solution Designer (fancy word for human being)
This is the challenge.The good news is that at SMB scale, there’s no need for massive servers and software.
This is not a size of data challenge, this is a diversity of data challenge as shown by all the different KPI’s that matter.
So this is really about bringing together a diversity of potentially useful indicators to understand what is driving the business.The good news is you probably don’t need huge technology and softwareBut you do need to be clever, and you do need a game plan
Focused = asking the right questions to stay efficient
HAVE A PLAN
The trick to extracting value is really in effective summarization of data
The trick to extracting value is really in effective summarization of data
What useful analytics do each of these support?
Difficulty framing the right question among biggest contributors to disappointment with big data issues
1.8% response rate without mailing a catalog.What’s going on here?Where do you go next to try to find out?
Cross channel analysisOnly possible when we narrowed the line of questioning to the context of the specific testBy the way, total number of orders in this analysis? 500. Hardly a Big number.
So this is really about bringing together a diversity of potentially useful indicators to understand what is driving the business.The good news is you probably don’t need huge technology and softwareBut you do need to be clever, and you do need a game plan
Here’s one in its natural habitat
Seems obvious, but…
2 way street means good listener as well
Others = those whose heart doesn’t skip a beat at the sight of three tables that can be joined together in a database
#2 = good communicator#6 = self-aware, recognizes need to make information accessible for others
Consumers of data, insights, findings etc, not buying consumers
So, which one is it?
Only difference in the path to useful reporting was the human being
That’s me, taking a break in college.From my studies as a french lit major.I’m a 17 year direct marketing data nerd
Consumers of data, insights, findings etc, not buying consumers
