Your Marketing First-Party Data: Asset or Liability?
December 11, 2020 -
Recently, Napkyn Analytics’ Chief Technologist, Nick Bennett, was invited to speak at Google’s annual, invite-only Data & Analytics Summit. Held November 12th & 13th, the focus of the Summit was two-fold. The theme for Day one was “Shining a light on Diversity, Equity, and Inclusion (DEI) in the Analytics Community”, and on day two, the focus shifted to how customers are successfully using the Google Marketing Platform to accelerate their digital strategies. Nick shared how Napkyn partnered with two large-scale brands to monitor, investigate and remediate data quality – across elements such as privacy compliance, financial normalization, measurement infrastructure, AdTech tools, and analytics instrumentation..to create more trust in their first-party data.
The presentation – “Your First-Party Data: Asset or Liability” – attracted more than 330 attendees. Not surprising considering Data Quality is a top of mind issue for marketing leaders. In an October 2020 Gartner survey – Marketing Analytics Fails to Meet Leadership Expectations – 44% of CMOs and VPs of Marketing identified poor data quality as one of the top two reasons why analytics is not used in informing decisions, highlighting that data quality is determining how data-driven marketing leaders can or can’t be.
Napkyn has responded to this issue by creating dNOC (Data Network Operations Centre) a service designed to monitor key measures of Marketing First Party Data Quality. Here is a summary of the presentation:
“Only 16% of companies characterize the data they are using as “very good.” – ChiefMarketing
In today’s ever more complex and volatile business environment, brands are increasingly relying on being data-informed and data-driven to survive and compete. However, a lack of confidence in data quality is undermining a brand’s ability to leverage first-party data to drive insights to take action.
“Poor data quality impacts marketing analytics’ usefulness”. In fact, poor data quality was ranked as the number one reason by marketing leaders and analytics practitioners as to why analytics is not used in informing decisions.” – Gartner
The ability to accurately assess data quality on an ongoing basis is critical to building that trust and ultimately determines how effectively a brand is able to put its data to use – how data-driven they can be.
“Every year, 25-30% of data becomes inaccurate, leading to less effective sales and marketing campaigns.” – MarketingSherpa
The data-driven brand relies on high data quality to drive actionable analysis, insight generation, and decision making. The ability to accurately assess Data Quality on an ongoing basis is the foundation and differentiator between first-party data as an asset or a liability.
“Many CMOs struggle to quantify the relationship between insights gathered and their company’s bottom line,” says Lizzy Foo Kune, Senior Director Analyst, Gartner.
The most important characteristic of an asset is the Quality of its contents. For first-party data, quality can be measured in 4 distinct ways:
- Complete — Are we certain we’re capturing all relevant or important data?
- Current — Is the data up-to-date?
- Compliant — Is the data we’re capturing and the means of capture aligned with relevant corporate policies and government regulations?
- Accurate — Does the data correctly represent what’s actually happening?
Unfortunately, measuring data quality in a consistent way can be extremely challenging in the real world. Initial quality assessments may have to process very large historical data sets, and once complete, new data needs to be monitored to ensure no new issues are introduced. Plus, timeliness is critical since remediation of any issues can have a big impact on the completeness, currency and accuracy of collected data.
“Businesses lose as much as 20% of revenue due to poor data quality.” – Kissmetrics
A proven approach to addressing this problem is to apply a cycle of 3 continuous steps:
- Monitor the data being collected for any issues that can be detected
- Investigate issues to characterize both type and root cause of identified issues, and to separate true issues from false positives.
- Remediate issues in the data, at root cause, and in relevant processes to correct data quality issues and improve data collection quality on a go-forward basis.
Leveraging the capabilities in Google Cloud and in consultation with clients, Napkyn developed dNOC (Data Network Operations Centre) , a secure, scalable, privacy safe, data quality monitoring service geared toward major brands. dNOC monitors selected key measures of Data Quality in first-party data such as privacy compliance, financial normalization, measurement infrastructure, AdTech tools, analytics instrumentation. Etc.
dNOC can be used to assess Google Analytics data for compliance with privacy requirements and to identify and address PII leakage within the collected data. The impact of PII leaks in historical data can have dramatic consequences for a brand’s ability to generate insights and make decisions. However, by monitoring PII leaks Napkyn can improve the quality of their clients Google Analytics data assets by:
- Providing greater certainty on the current and historical quality of their collected data
- Improving organizational productivity by integrating data quality monitoring into existing processes and team workflows.
- Boosting compliance with existing corporate privacy policies and relevant regulations.
- Increasing their ability to depend on this first-party data to drive decision making.
dNOC PII Leak Monitoring Impacts
There are several key questions brands need to ask when evaluating the health of their first-party data asset:
- Where is all your first party data coming from?
- Do you trust in the quality of that data?
- Is someone monitoring it 24/7?
- Do you trust the environment you’ve got to give you a timely heads up?
- What is the impact of the decisions you make if you don’t trust your data 24/7?
To learn more about the capabilities of dNOC, including examples of how Napkyn is helping two leading brands answer these questions to significantly improve their data quality, and turn their Marketing First-Party Data from liability to asset, click here to download the presentation slides.