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Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
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Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
Two of the most important questions data quality analysts ask
ourselves is what to audit and when to audit.In past blog posts,
weve covered some tactical methods such as auditing cadence,
creating alerts, qualifications of a data quality analyst, and
characteristics of a good deployment. This three-part series builds
on these foundational concepts and ties them together into the data
quality management process: a strategic framework that you can use
to add value to your business through data quality improvement.
Understanding the ToolsAs an ObservePoint user, it helps to
clearly understand the difference between audits and
simulations.
Tactical methods
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Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
A Tag Audit is a periodic scan of a particular segment of a web
site. Audits should be segmented by content area or type,
technology, or architecture. Audits can be run on both regular,
calendared basis and on a one-off basis.
The data generated by an audit is moment-in-time view of the
included pages and tag deployment of specific content under
specific conditions at a specific time. Tag audits can collect data
for OnPageLoad and OnClicktags, and track some site performance
data such as page load time and HTTP status codes.
Tag Audits
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Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
Simulations are targeted, high-frequency tests of specific
content under specific conditions. They differ from audits in that
you must specify exactly what content to load and what conditions
to simulate on each test. When any parameter fails to verify, the
system generates an alert. Simulations are best used on content
that is proximate to reportable metrics attribution, ROI, KPIs.
Since a majority of operational data relies on some key content,
youll be informed if and when tracking performance is degraded.
Simulations
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Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
Data Quality Management (DQM) combines the tools, strategy, and
processes required to measure, monitor, and improve the quality of
digital marketing data and in effect, its utility and value in the
enterprise.
The key to improving data quality for digital marketing tools is
recognizing the need for active data quality management. This means
applying proper focus and segmentation techniques to target the
correct results, performing prescriptive analysis, making
recommendations, then realizing and reporting the improvements.
Although few people may pay tribute to data quality for data
qualitys sake, attention to this process will keep your
organization on the solid foundation of reality.
The Data Quality Management Process
-
Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
The Data Quality Management Process will help you create a
problem statement that encompasses when, what, and why you are
conducting a data quality investigation. Following the diagram from
left to right, you should eventually be able to form problem
statements that look like this:Because our web team rolled out a
new home page and primary content area for our corporate brand, we
need to audit this section of the web site to ensure our Tag
Management System, Primary Analytics System of Record and other
essential tags have been successfully migrated to the production
environment without any errors that will cause data corruption. and
In anticipation for an updated dynamic booking flow that uses
[insert tool name] to recommend upsells in step two based on input
in step 1 and data from [insert tool name], we need to confirm
that: 1) on-click variables are firing correctly in step 1 when
specific paramatersare chosen; 2) tags from [tools 1, 2, 3, and 4]
fire correctly OnClick; 3) Confirm that the test creative ad is
served in Step 2; and 4) [tags 1, 2, 3, 4] fire OnClick to track
the conversion.
Forming a Problem Statement
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Data Quality Management Process Part 1: Data Collection
Triggers
ObservePoint ObservePoint DataAssuranceData Quality
Management
Data collection triggers are your signal that an investigation
into a marketing channel should be conducted. Triggers come in two
types, as each type will inform your segmentation technique in the
next step.A business condition is an external trigger something
that has happened that necessitates an audit or simulation. For
example, the web site was redesigned, so you need to confirm that
all tags made it through the migration process. An external trigger
could also be a question about data quality, and a need to confirm
that a deployment is correct.
An internal campaign is an internal trigger deploying a new tag,
running an email / ad / testing campaign, or changing an
architecture. An internal trigger gives you the opportunity to have
proactive conversations about data quality. Many internal campaign
triggers will call for the use of simulations to actively monitor
data quality on specific metric-proximate content.
Data Collection Trigger as a Signal of When to Audit
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