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MK99 – Big Data 
1 
Big data & cross-platform analytics 
MOOC lectures Pr. Clement Levallois
MK99 – Big Data 
2 
Integrating data in a multichannel environment 
1.Data: you don’t get it on tap 
2.A multichannel environment increases data fragmentation 
3.The business stake of data integration – why should we care? 
4.Practical aspects of data integration
MK99 – Big Data 
3 
Example: Customer data. 
Source: UNICA Corporation, in Multichannel Marketing, by A. Arikan (2008). 
1. Data: you don’t get it on tap 
Take away: data is fragmented by nature. 
You construct customer profiles by joining and assembling different sources of data into a meaningful synthesis.
MK99 – Big Data 
4 
2. Data gets even more fragmented in a multichannel environment 
•Basics: 
–Distribution, information and ad channels keep diversifying 
•POS, print, TV, radio, outdoor posters, mobile apps, mobile sites, emails, SMS, APIs, social networks, search engines, e- commerce platforms, e-commerce websites, blogs, content channels, … 
–Connections between these channels intensify and complexify 
•Social TV is TV delivered with Internet services, user profiles created on one platform are imported on another, orders taken online can be picked up on a variety of POS, ads circulating through one channel replicate on other channels, … 
–Underlying technologies evolve and fragment quickly, across channels 
•Cookies, SaaS, APIs, retargeting, HTML, Android, etc. 
–The expectations of customers on the quality of service elevate (realtime, seamless experience) 
-> Business stake: how to manage the complexity of this multichannel environment to deliver value to the market?
MK99 – Big Data 
5 
Example: French bank Societe Generale, up to early 2000s 
POS 
Outdoor 
Call center 
Radio, TV, Print media 
One-way communication, analog. 
1. No digital data collected 
Two-way communication, analog. 
2. Little (but important) digital data collected
MK99 – Big Data 
6 
Mobile app 
Twitter account 
POS 
Youtube channel 
Google Plus 
Online banking 
LinkedIn profile 
Facebook page 
Instagram 
Call center 
Outdoor 
Print media, including online version 
TV (including online TV) 
Example: French bank Societe Generale, in the 2010s 
One-way communication, analog and digital. 
1. Digital data collected in large volumes 
Two-way communication, digital. 
2. Digital data collected in large volumes 
Two-way communication, analog. 
4. Little (but important) digital data collected 
Two-way communication, digital. 
3. Digital data collected in large volumes
MK99 – Big Data 
7 
Example: Societe Generale, in 2020? 
Check the presentation on APIs to understand the stakes of this shift. 
1. Very large (extensive?) amounts of digital data collected.
MK99 – Big Data 
8 
The fragmentation of channels 
before 
today 
Source: “Multichannel Marketing Ecosystems”, by Stahlberg & Maila 2014). Chapter 1. 
Data integration now: 
-Offers more opportunities to create value and differentiate from your competitors 
-But is harder to manage
MK99 – Big Data 
9 
Multichannel = disintegration of data 
•The fragmentation of channel means the fragmentation of your data. 
•Data about your customers, products and campaigns is scattered in different places (channels). You don’t know precisely: 
–Who your customers are, how they behave with you 
–How your products are perceived, purchased and used 
–What are the results of your campaigns
MK99 – Big Data 
10 
3. Business stakes of data integration 
1.Providing a consistent customer experience across channels 
–How to provide the right services on the right channels 
–How to integrate the experience across channels (seamless, enriched experience) 
2.Managing communication campaigns 
–Which campaigns for which channel(s)? 
–How to coordinate campaigns across channels? 
–Which budgets should be spent, and how to spread them across channels? 
–How to measure the results of each campaign and the global result? 
3.Generating actionable insights for business 
–How to increase brand awareness, 
–How to segment the customer base, 
–How to improve the retention rate, 
–How to better measure default risk / fraud risk / …, 
–How to develop new products / services / channels
MK99 – Big Data 
11 
The pre-condition to achieve this goals 
We should be able to have a picture of all different channels and how they connect. 
This is hard.
MK99 – Big Data 
12 
4. Practical aspects of data integration
MK99 – Big Data 
13 
Organizational culture 
Business culture 
Software Application 
Data type 
Infrastructure 
Organizational culture 
Business culture 
Software Application 
Data type 
Infrastructure 
Servers in Canada 
Clicks 
NoSQL DB 
Web agency 
Startup mentality, data driven, fast to react 
Servers in France 
Number, duration and subject of customer calls to call center 
ERP 
Call center: B2B services 
Mature industry, not data driven 
Example: Your company has a website generating clicks from your customers. Your customers can also use your call center. How do you integrate these two datasets about yours customers? 
? 
Call center 
Web agency
MK99 – Big Data 
14 
The layers of data integration 
1.Organizational culture 
–Attitudes towards data must be compatible 
–Organizations / execs don’t have the same sensitivity to the priority of data projects. 
2.BU 
–How will different datasets be made compatible / convertible? (clicks and calls?) 
–What is the revenue sharing agreement on data, if any? 
–What are the acceptable levels of investment to generate / curate / share data? 
3.Software application 
–If the two parties agree to share data, how to do the sharing work in practice? (see video on APIs) 
4.Data type 
–Are datasets of a textual, numerical type? Are they time series, if so what is the frequency? Can identifiers be reconciled? 
5.Infrastructure 
–The volume of data, the place it is stored, and the servers and connections available: do they permit the integration to take place?
MK99 – Big Data 
15 
Options to manage the integration 
Option 
Pros 
Cons 
Delegate to IT department 
- Resources already there. 
- Conformity to existing procedures is assured. 
-Innovation levels remain low 
-Ad hoc solutions 
External provider – domain specialist 
-Experimented on data integration for a single domain of expertise 
-Cover all layers of data integration 
-Knowledgeable of your business needs 
-Coordination costs 
External provider – 
- Large scope of data integration, across domains of expertise 
-Coordination costs 
-Diseconomies of scale? 
-Lack of / costly customization
MK99 – Big Data 
16 
The jargon of data integration 
DMP 
Data Management Platform 
CRM Customer Relationship Management 
DSP 
Demand-Side Platform 
SSP 
Supply-Side Platform 
ERP 
Enterprise Resource Planning 
ETL or API 
ETL or API 
ETL or API 
ETL or API 
: you should research the meaning of these terms.
MK99 – Big Data 
17 
Next steps 
•Watch the video lecture on APIs 
•Go through the reading list
MK99 – Big Data 
18 
This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) 
Contact Clement Levallois (levallois [at] em-lyon.com) for more information.

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The business stakes of data integration

  • 1. MK99 – Big Data 1 Big data & cross-platform analytics MOOC lectures Pr. Clement Levallois
  • 2. MK99 – Big Data 2 Integrating data in a multichannel environment 1.Data: you don’t get it on tap 2.A multichannel environment increases data fragmentation 3.The business stake of data integration – why should we care? 4.Practical aspects of data integration
  • 3. MK99 – Big Data 3 Example: Customer data. Source: UNICA Corporation, in Multichannel Marketing, by A. Arikan (2008). 1. Data: you don’t get it on tap Take away: data is fragmented by nature. You construct customer profiles by joining and assembling different sources of data into a meaningful synthesis.
  • 4. MK99 – Big Data 4 2. Data gets even more fragmented in a multichannel environment •Basics: –Distribution, information and ad channels keep diversifying •POS, print, TV, radio, outdoor posters, mobile apps, mobile sites, emails, SMS, APIs, social networks, search engines, e- commerce platforms, e-commerce websites, blogs, content channels, … –Connections between these channels intensify and complexify •Social TV is TV delivered with Internet services, user profiles created on one platform are imported on another, orders taken online can be picked up on a variety of POS, ads circulating through one channel replicate on other channels, … –Underlying technologies evolve and fragment quickly, across channels •Cookies, SaaS, APIs, retargeting, HTML, Android, etc. –The expectations of customers on the quality of service elevate (realtime, seamless experience) -> Business stake: how to manage the complexity of this multichannel environment to deliver value to the market?
  • 5. MK99 – Big Data 5 Example: French bank Societe Generale, up to early 2000s POS Outdoor Call center Radio, TV, Print media One-way communication, analog. 1. No digital data collected Two-way communication, analog. 2. Little (but important) digital data collected
  • 6. MK99 – Big Data 6 Mobile app Twitter account POS Youtube channel Google Plus Online banking LinkedIn profile Facebook page Instagram Call center Outdoor Print media, including online version TV (including online TV) Example: French bank Societe Generale, in the 2010s One-way communication, analog and digital. 1. Digital data collected in large volumes Two-way communication, digital. 2. Digital data collected in large volumes Two-way communication, analog. 4. Little (but important) digital data collected Two-way communication, digital. 3. Digital data collected in large volumes
  • 7. MK99 – Big Data 7 Example: Societe Generale, in 2020? Check the presentation on APIs to understand the stakes of this shift. 1. Very large (extensive?) amounts of digital data collected.
  • 8. MK99 – Big Data 8 The fragmentation of channels before today Source: “Multichannel Marketing Ecosystems”, by Stahlberg & Maila 2014). Chapter 1. Data integration now: -Offers more opportunities to create value and differentiate from your competitors -But is harder to manage
  • 9. MK99 – Big Data 9 Multichannel = disintegration of data •The fragmentation of channel means the fragmentation of your data. •Data about your customers, products and campaigns is scattered in different places (channels). You don’t know precisely: –Who your customers are, how they behave with you –How your products are perceived, purchased and used –What are the results of your campaigns
  • 10. MK99 – Big Data 10 3. Business stakes of data integration 1.Providing a consistent customer experience across channels –How to provide the right services on the right channels –How to integrate the experience across channels (seamless, enriched experience) 2.Managing communication campaigns –Which campaigns for which channel(s)? –How to coordinate campaigns across channels? –Which budgets should be spent, and how to spread them across channels? –How to measure the results of each campaign and the global result? 3.Generating actionable insights for business –How to increase brand awareness, –How to segment the customer base, –How to improve the retention rate, –How to better measure default risk / fraud risk / …, –How to develop new products / services / channels
  • 11. MK99 – Big Data 11 The pre-condition to achieve this goals We should be able to have a picture of all different channels and how they connect. This is hard.
  • 12. MK99 – Big Data 12 4. Practical aspects of data integration
  • 13. MK99 – Big Data 13 Organizational culture Business culture Software Application Data type Infrastructure Organizational culture Business culture Software Application Data type Infrastructure Servers in Canada Clicks NoSQL DB Web agency Startup mentality, data driven, fast to react Servers in France Number, duration and subject of customer calls to call center ERP Call center: B2B services Mature industry, not data driven Example: Your company has a website generating clicks from your customers. Your customers can also use your call center. How do you integrate these two datasets about yours customers? ? Call center Web agency
  • 14. MK99 – Big Data 14 The layers of data integration 1.Organizational culture –Attitudes towards data must be compatible –Organizations / execs don’t have the same sensitivity to the priority of data projects. 2.BU –How will different datasets be made compatible / convertible? (clicks and calls?) –What is the revenue sharing agreement on data, if any? –What are the acceptable levels of investment to generate / curate / share data? 3.Software application –If the two parties agree to share data, how to do the sharing work in practice? (see video on APIs) 4.Data type –Are datasets of a textual, numerical type? Are they time series, if so what is the frequency? Can identifiers be reconciled? 5.Infrastructure –The volume of data, the place it is stored, and the servers and connections available: do they permit the integration to take place?
  • 15. MK99 – Big Data 15 Options to manage the integration Option Pros Cons Delegate to IT department - Resources already there. - Conformity to existing procedures is assured. -Innovation levels remain low -Ad hoc solutions External provider – domain specialist -Experimented on data integration for a single domain of expertise -Cover all layers of data integration -Knowledgeable of your business needs -Coordination costs External provider – - Large scope of data integration, across domains of expertise -Coordination costs -Diseconomies of scale? -Lack of / costly customization
  • 16. MK99 – Big Data 16 The jargon of data integration DMP Data Management Platform CRM Customer Relationship Management DSP Demand-Side Platform SSP Supply-Side Platform ERP Enterprise Resource Planning ETL or API ETL or API ETL or API ETL or API : you should research the meaning of these terms.
  • 17. MK99 – Big Data 17 Next steps •Watch the video lecture on APIs •Go through the reading list
  • 18. MK99 – Big Data 18 This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com) Contact Clement Levallois (levallois [at] em-lyon.com) for more information.