last modified: 2017-09-08

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Reading list

Find the reading list for this session on Pinterest: https://fr.pinterest.com/seinecle/what-is-the-cloud/

1. Data: you don’t get in on tap

A naive vision of data would get that it’s all fluid. Don’t we talk about "data streams"? Working with data would be as simple as opening a tap and "getting customer data", for instance.

tap
Figure 1. Data streams, as fluid as water on tap?

 

Actually, data is more like a complex patchwork: many different pieces which must be stiched together - and this is hard.

patchwork
Figure 2. Data streams make a patchwork

 

Take customer data.

It is not a given. Instead, this is a design made of multiple primary data sources:

Multiple sources of customer data
Figure 3. Multiple sources of customer data

 

Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work.

Take away: Data is fragmented by nature. It comes from different sources and presented in different formats. You (the marketer in collaboration with data scientists) wrangle to construct customer profiles by joining and assembling different sources of data into a meaningful synthesis.

(if you are interested, data scientists actually have whole books on the subject of wrangling with the mess of different data sources)

wrangling
Figure 4. Data wrangling

 

2. Sources of fragmentation

a. Channels keep diversifying

Point of sale, 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, …

→ all these channels can provide relevant data.

b. 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 point of sales

  • Ads circulating through one channel replicate on other channels, …​

→ It is very complex to trace the "customer journey" on all these channels and to keep an updated view of a customer profile.

It is even more difficult to explore causality (which action on which channel caused which subsequent action by the user?)

c. Underlying technologies fragment and keep evolving, across channels

Browsers, Cookies, APIs, mobile OS (Android or iOS?), etc…​ All these different techs evolve and need continuous effort and expertise to integrate.

Example: did you notice that on a mobile device, the url of the pages you visit can now start with an AMP url?

Like, to visit the page of the New York Times the url should be http://www.nyt.com but it looks like: http://google.com/amp/www.nyt.com

This http://google.com/amp prefix is a new tech by Google to accelerate the display of web pages on mobiles. Fine. But then, as a marketing data analyst, how to count visits to:

and

→ It is important to count visits to these two urls as a visit to the same page.

In Sept 2017 major services of web data analytics were still struggling with this issue.

This illustrates that to just count visits to a web page (something which should be classic and robust) and integrate this data to a larger analysis, big issues can arise and be hard to fix even in 2017, because of the evolution of techs and standards.

d. In the meantime, customers have growing expectations about the quality of service

Difficulties posed by data integration do not slow or decrease customers expectations. To the contrary, we see an elevation of expectations. Customers increasingly expect:

  • realtime contact

  • two-ways interaction (they want to be able to voice their opinion, and get a response)

  • seamless experience (no glitch, modern UI, consistence of the UX across channels)

  • personalized experience (customization of the message they receive)

e. Example: A French bank going through the 2010s

Before   a couple of data sources across a few channels
Figure 5. Before - a couple of data sources across a few channels

 

Now   many data sources across a variety of channels
Figure 6. Now - many data sources across a variety of channels

 

3. Tools for data integration: DMPs and more

a. Data Management Platform (DMP)

In 2015/2016 a new acronym started to trend: "DMP", standing for "Data Management Platform".

Basically a DMP is an information system dedicated to solving the issues of data integration:

  • it can store a large amount of data

  • it can receive data from a variety of sources, in a variety of formats

  • it offers functions to reconcile records from different data sources and generate a unique identifier for each reconciled entry.

  • it offers segmentation / classification functions

  • it provides security and analytics capabilities on the data

  • it makes this data available for execution by other software.

b. DMP in relation to other information systems

DMPs are relatively new. They integrate with 3 other information systems in the firm:

  • CRM (Customer Relationship Management)

    • This is the software gathering data related to customers and sales. It is a major source of input data for a DMP.

  • ERP (Enterprise Resource Planning)

    • Large software synchronizing information systems from finance, sales, logistics and more. The CRM can be independent or part of the ERP.

How can data circulate across these software and with the external world? The next lesson is devoted to APIs, another important concept.

The end

Find references for this lesson, and other lessons, here.

round portrait mini 150 This course is made by Clement Levallois.

Discover my other courses in data / tech for business: http://www.clementlevallois.net

Or get in touch via Twitter: @seinecle

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