SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS World

Reaktor Mannerheimintie 2 00100, Helsinki Finland

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garbage in, garbage out Data quality in a TMS world

Simo Ahava Senior Data Advocate

Simo AhavaSenior Data Advocate, Reaktor

Google Developer Expert, Google Analytics

Blogger, developer, www.simoahava.com

Twitter-er, @SimoAhava

Google+:er, +SimoAhava

Data quality isn’t fixed. Depending on the

hypothesis, a single data set can shift from

useless to incredibly insightful without a

single datum changing shape, size, form, or

function.

#1 Data is subjective

Plug-and-play Analytics

@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

Plug-and-play AnalyticsData quality isn’t acquired — it’s earned.


from online-behavior.com

from online-behavior.com

Claim 1: Data quality is destroyed

by laziness and lack of ambition.

Claim 2: A TMS empowers

developers more than others.

The root of all evil


The root of all evilThe "project"


Your organization is creating absurd

amounts of data with every passing second,

and it’s very difficult to adapt to the fluctuations

without an agile, process-driven mindset.

#2 Data is a process

The project is often a series of handovers,

breeding non-involvement.

Specification Implementation Analysis Results


Businessowner


Businessowner

Marketing


Businessowner

Marketing

Developer

This leads necessarily to silos, which have entry

and exit conditions.

Implementation Analysis Results

Businessowner

Marketing

Developer

Specification


Businessowner

Marketing

Developer

Specification


Businessowner

Marketing

Developer

Specification


Businessowner

Marketing

Developer

Specification


Businessowner

Marketing

Developer

Specification


Businessowner

Marketing

Developer

Specification

Silos, so what?


Silos, so what?As long as the work gets done, right?


Data is the lifeblood of the organization. It flows

through all departments, across job titles,

permeating the very fabric of the organization,

reinforcing its foundations for growth.

#3 Data abhors silos

Do these sound familiar:

Monthly reports which lack relevance, are rife with generic suggestions that lack research in the context of your business, reiteration of previous month’s points, even if there are

solid reasons why they weren’t addressed.

Ridiculously ugly and ineffective JavaScript hacks for measurement points which should be tackled in the Data Layer.

Hiding behind data, and passing blame to other silos.

Could someone fix the Bounce Rate metric on our

site?

Analytics feature requests are deprioritized, and deployed extremely infrequently.

Fix transactionRevenue to show revenue, not

customer weight.

Communication is difficult due to the overhead of meeting face-to-face, project plans are set in stone during sales, and it’s difficult to change existing project goals or set new ones

due to consultants being hired as "extra pairs of hands" rather than advisors.

These are symptoms of data being treated as a

project outcome.

Cure I: The Data Layer


Cure I: The Data LayerUsing technology to solve communication problems


Typically, there are three definitions

of Data Layer that we use in the digital world.

1. Set of business requirementsfor tracking digital assets,visits, and visitors.


2. Encoded, global data structure, accessed and modified by connected platforms.



2. Data model of a connected platform, which copies or digests information in the global structure.



2. Data model of a connected platform, which copies or digests information in the global structure.

dataLayer.push({ 'pageType' : 'home' });

google_tag_manager['GTM-123'] .dataLayer .set('pageType', 'home');

Across all three definitions, the purpose of a Data Layer

is simple:

DMP / DWH / TMS / etc.

X X

Actions Presentation

Data Layer


X X

Actions Presentation

Data Layer

The purpose of a Data Layer is to provide a bilateral layer on the digital asset, which decouples, normalises, and uniformly encodes semantic

information passed through and stored within.

The Data Layer is a joint venture, where people and

systems communicate across silos.


let tracker = GANTracker.sharedTracker() tracker.trackEvent("revenue", action:"Q1", value:"15678000") tracker.trackEvent("revenue", action:"Q2", value:"16888000") tracker.trackEvent("revenue", action:"Q3", value:"15991000") tracker.trackEvent("revenue", action:"Q4", value:"19133000")

rq12014,rq22014,rq32014,rq42015 15677998,16887988,15990988,19133400

analytics.collect({ ' revenueQ1' : ' 15677998.00', ' revenueQ2' : ' 16887988.00', ' revenueQ3' : ' 15990988.00', ' revenueQ4' : ' 19133400.00' })


let dataLayer = new Array()dataLayer.push({ "revenue_Q1_2014" : "15677998.00", "revenue_Q2_2014" : "16887988.00", "revenue_Q3_2014" : "15990988.00", "revenue_Q4_2014" : "19133400.00"})

Cure II: The Process


Cure II: The ProcessInvolve, involve, involve


An iterative, agile process is necessary for

optimal utilization of a TMS.

Definition of Done

Definition of DoneDeveloped features do not impede measurement. Developed features are trackable.

Sprint


Sprint

If necessary, feature is encoded with tracking attributes.

If necessary, feature is linked to a Data Layer object.

Feature


Sprint

If necessary, feature is encoded with tracking attributes.

If necessary, feature is linked to a Data Layer object.

Feature

Attribute syntax is correct for tracking.

Data Layer object syntax is correct.

Task

Constant participation

Constant participationTransparency

Cure III: Empowerment


Cure III: EmpowermentWe are all hybrid beings


The entire life cycle of a single data point, from collection to reports,

requires knowledge and expertise to manage.

#4 Data is difficult

Developer facilitation is crucial to data quality

and optimized data collection.

1: Education

1. JavaScript: www.codecademy.com, www.codeschool.com, Professional JavaScript for Web Developers, DOM Enlightenment…

2. Digital analytics: www.kaushik.net, www.simoahava.com, Successful Analytics, Practical Google Analytics and Google Tag Manager for Developers…

3. Training, courses, certifications: Digital Analytics Association, Digital Analytics Fundamentals (Google), Market Motive…

4. Conferences: MeasureCamp, SMX, eMetrics, Digital Analytics Hub, ConversionXL, Superweek, All Things Data…

http://www.codecademy.com

http://www.codeschool.com

http://www.kaushik.net

http://www.simoahava.com

treat content as a product2: hybrid skills

"Business owner"- No operational skills

+ Strategic

"Developer"- Uncooperative

+ Methodical

"Marketer"- Bully

+ Consultative

+ Passionate, actively interested+ Understands ever-changing requirements+ Good grasp of digital tech+ Statistical mindset+ Knows the product / service inside and out+ Critical about the present, curious about the future

treat content as a product3: Passionate interest

+ Dedicated sandbox

+ Website or blog to test new ideas on

+ Test and debug setups in Google Analytics and Google Tag Manager

+ Utilization of GTM environments

Hire to educate, not to delegate

PO

Developer Analyst

Hire to educate, not to delegate

PO

Developer Analyst

Data is difficult


Data is difficultData quality is earned, not acquired


Thank [email protected]

www.simoahava.com

Twitter: @SimoAhava

Google+: +SimoAhava

Data is difficult - http://goo.gl/53aFUU

The Schema Conspiracy - http://goo.gl/o2Pwys

Further reading:

10 Truths About Data - http://goo.gl/EpesEj

SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS World

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