5th February 2014

This morning…

1.Intro’s etc

2.Lifting the lid (on your data)

3.DataShaka; go on then, impress us…

4.Further questions, next steps etc

Intro’s…

The Data Journey…

The agency problem…

Client A

A/C Team

Solution

Vendor

Client B

A/C Team

Solution

Vendor

Client D

A/C

Team

Solution

Vendor

Client C

A/C Team

Solution

Vendor

Client E

A/C

Team

Solution

Vendor

Client A

A/C Team

Solution

Client B

A/C Team

Solution

Client A

A/C Team

Solution

Client B

A/C Team

Solution

Client A

A/C Team

Solution

Client B

A/C Team

Client A

A/C Team

Solution

Client B

A/C Team

Client C

A/C Team

Solution

Client A

A/C Team

Solution

Client B

A/C Team

Client C

A/C Team

Solution

Client D

A/C Team

Solution

Client E

A/C Team

Client A

A/C Team

Solution

Client B

A/C Team

Client C

A/C Team

Solution

InternalClient A

IT

Solution

Internal Client B

Finance

Solution

The brief.

1. Ticketing - Database +

Analysis Tool

2. More sources

3. Future needs…

The Ticketing Journey…

AudienceAgents Theatres

ENTAAudience View

Ticket MasterTessitura

Clients

The Brief.

The Brief. 1. Creating a Database

1. Creating an analysis tool

2. More Sources

3. Future needs

Evolving SourcesEvolving Query Space

Evolving AKA User NeedsEvolving Client Needs

Lifting the lid…

A small sample came with the brief

A bigger example set requested

LearningsAction

1. Multiple Formats for ‘the same’ conceptual set

2. Different formats from the same system

3. Data quality/format issues1. Ticket data all delivered by email2. Data in CSV, XLS and PDF3. A lot of manual work4. Historical data stored in folders in Excel5. Local server ‘almost full’

Examining what we received

1. MSG files can be automatically extracted

2. Data can be automatically extracted from PDF

3. All three formats must be handled4. Email must be handled5. There is duplication in the files

Core domain data set exploration 1. Waste within data acquisition

Duplication and Waste: an efficiency opportunity…

• 30th December 2013 to 26th January 2014

(28 days)

• 4 different report types split overo Advanceo Dailyo Wrap

• CSV and PDF file format

• 115 files

• 47.6mb

• Most detailed level:

o Price Type

• Waste due to pivots and aggregates:

approx. 25%

o 3 report areas, 4 reports = 1

unnecessary

Duplication and Waste: an efficiency opportunity…

• 30th December 2013 to 26th January 2014

(28 days)

• 4 different report types split overo Advanceo Dailyo Wrap

• CSV and PDF file format

• 115 files

• 47.6mb

• Most detailed level:

o Price Type

• Waste due to pivots and aggregates:

approx. 25%

o 3 report areas, 4 reports = 1

unnecessary

• 30th December 2013 to 27th January 2014 (29

days)

• 12 different report types split overo Advanceo Maturedo On-Day sales

• XLS and PDF file format

• 256 files

• 45.7mb

• Most detailed level: o Seat Type by Price Band by Discount

type by Performance Type

• Waste due to pivots and aggregates: approx.

75%

o 3 report areas, 12 reports = 9

unnecessary

LearningsAction

1. Multiple Formats for ‘the same’ conceptual set

2. Different formats from the same system

3. Data quality/format issues1. Ticket data all delivered by email2. Data in CSV, XLS and PDF3. A lot of manual work4. Historical data stored in folders in Excel5. Local server ‘almost full’

1. MSG files can be automatically extracted

2. Data can be automatically extracted from PDF

3. All three formats must be handled4. Email must be handled5. There is duplication in the files1. Waste within data acquisition2. A rich and valuable core set3. Many connection points for other sets

A small sample came with the brief

A bigger example set requested

Examining what we received

Core domain data set exploration

Report Date

Unpaid

Count

Unpaid

Net

UnpaidCharges

Unpaid

Total

Seats Comps Value Capacity TargetReserved

SeatsReserved

Value…*Potential

Value

Source

Show Theatre

ShowType

ShowMonth

ShowYear

ShowDay

ShowDOW

Discount Type Price

BandSeat Type

PaymentChannel Agent

CardType

Show/Theatre

Location/Theatre

Segment

Ticketing

PaidMedia Social

Media

Email

Print Media

BroadcastMedia

Weather

Events

Out of homeMedia

And we only looked at 2 shows over 30 days…

Analysis Tool

Database

DataShaka…

Store

Harvest

“Everything is a source...”

http

file

FTP

email

API

market

place

secure

server

Unify

DeliverTimeT

UnifiedData

Context

C

SignalS

ValueV

ConsilientConnection

istChaordicRobustFlexible

Any question of any data.

32

Time Context Signal Value

Context Type

Ct

Sales

Ct

34

{ "name":"Nutrigum" "followers_count": 39061, "friends_count": 12986, "listed_count": 917,}

Harvested at 2013-10-01 17:35:00

{ "category": "Company", "talking_about_count": 58550, "username": "healthyx", "likes": 1985655, "link": "http://healthyx"}

Harvested at 2013-10-01 19:12:00

<performance> <account> Healthy X Limited </account> <cam>nutrigum – branding</cam> <data> <date v=“2013-10-01”> <impr>14000</impr> <clk>1500</clk> <cnv>10</cnv> </date> </data></ performance >

Adserver

Sales <filterTags>Nutrigum</filterTags><tagStats> <tag>~SOURCE~t</tag> <tagDisplayName> TWITTER </tagDisplayName> <matchCount>71</matchCount> <popularity> <popularityCount> <timeInterval> 2013-10-01 </timeInterval> <count>2.0</count> <normalizedCount> 2.0</normalizedCount> </popularityCount> </popularity> </tagStats>

eCRMDate,productId,userId,number,ppu2013-10-01,123,321,2,5.002013-10-01,123,521,1,5.002013-10-01,333,444,2,15.002013-10-01,854,111,1,20.00

Some Data…

Store

Harvest

“Everything is a source...”

http

file

FTP

email

API

market

place

secure

server

Unify

DISQ

Unstructured

Relational

Graph

In Memory

Document Store

File System

Big Table

Deliver

Enterprise Data Store

TimeT

UnifiedData

Context

C

SignalS

ValueV

Your data in TCSV…

Lots of TCSV

Viewing & analysing……

Other ways to view…

Clients

Ways of working

Partnershi

p

An Agile Approach

Lean

Change

Collaboration Communication

Agile

Pitch

Statement of Work signed

off

Follow-up:Tech Deep Dive

SurgeryWorkshop

1.Analysis Tool

1.TicketingDatabase

2.More

Sources

3.The Future

6-8 weeks 8-10 weeks

How Much?

Phase Configure Monthly Timing1. Ticketing £25k £5k 6-8 weeks

2. More sources £15k Incl. 8-10 weeks

3. Future TBC TBC TBC

Total £40k £5k

There is not one answer.

Solutionapproach Pro’s Con’s

Re-Seller

Custom Build

Data As A Service

Solutionapproach Pro’s Con’s

Re-Seller• Off the shelf ‘modules’• Polished• Safe buy

• Lack of influence• Take ‘as is’• ‘just’ a sales team

Custom Build

Data As A Service

Solutionapproach Pro’s Con’s

Re-Seller• Off the shelf ‘modules’• Polished• Safe buy

• Lack of influence• Take ‘as is’• ‘just’ a sales team

Custom Build• Get what you want• Direct involvement• Can be cheaper

• Get what ‘only you’ want• Focussed on ‘now’• Or more expensive

Data As A Service

Solutionapproach Pro’s Con’s

Re-Seller• Off the shelf ‘modules’• Polished• Safe buy

• Lack of influence• Take ‘as is’• ‘just’ a sales team

Custom Build• Get what you want• Direct involvement• Can be cheaper

• Get what ‘only you’ want• Focussed on ‘now’• Or more expensive

Data As A Service• Proven specialist platform• Fully configurable• Future proof

• Data vs DV specialists• Requests not instructions• Active involvement