Got Scary Data? Halloween data quality tip sheet. take your data from bone-chilling to bona fide with these 7 steps

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Sharon Sutton
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1 Got Scary Data? Halloween data quality tip sheet take your data from bone-chilling to bona fide with these 7 steps

Step 1: Promote a Pumpkin King The first step towards data quality nirvana is to assign ownership 4 Strengths of a Data-QUality Leader A data quality owner should have the right background and

2 Step 1: Promote a Pumpkin King The first step towards data quality nirvana is to assign ownership 4 Strengths of a Data-QUality Leader A data quality owner should have the right background and authority to influence organizational practices and beliefs. Strong Technical Background Data Orientation Seniority Understand Business Value of Data Competency with the technology and familiarity with the platforms each digital property is built upon is a must. A data-oriented steward understands the effects of breakage, knows where to look for problems and will choose effective corrective actions. A person with context, influence and trust improves the odds you ll have a better marketing technology ROI. Your pupkin king must understand and create dialogue about the requirements, purposes and methods of data collection.

Step 2: Gather Your Goblins Define and deploy a data governance strategy Data collection and governance best practices revolve around three key processes: 1.

3 Step 2: Gather Your Goblins Define and deploy a data governance strategy Data collection and governance best practices revolve around three key processes: 1. New Tags Create clear, consistent business processes for data collection, integration with development and content deployment cycles. 2. Verify Tags Confirm variables are properly populating and code functions as expected under all conditions. Ensure data collection is as consistent as possible across browsers, devices, geographies and does not hurt the user experience. 3. Conduct Audits Deploy a process to confirm that tag deployments are maintained over time. Websites have enough moving parts and enough people touching the code that tagging problems are common and frequent.

4 Step 3: Skeletons in Your Data? Watch for duplicate tags on your site One of the lesser known problems in the web analytics world data inflation is exactly what it sounds like: digital data that is reported inaccurately high. When more than one instance of a tag fires on a page, data inflation occurs. Each time a tag fires, an action is counted, and when a tag fires more than once, the action is being counted more than once. For example, if two Adobe Analytics tags exist on the same page, a single visitor can be inaccurately recorded as two visitors. The causes of data inflation are common and occur often. Tags can be coded to multiple places on the skeleton of a page, transposed from other pages or duplicated in some other way. Data inflation also happens when new versions of tags are deployed, but the old tags are not removed. Inflation can affect web analytics data such as traffic counts, sales attribution data advertising, data such as clicks and impressions and a variety of other important metrics. And as this inflated data gets passed into other systems throughout your MarTech stack, the effects of the inflation are magnified.

Step 4: Things That Go Bump in the Night Be aware that website tags are bound to break One of the most dreaded conversations a digital analytics professional has to face begins when an executive with

5 Step 4: Things That Go Bump in the Night Be aware that website tags are bound to break One of the most dreaded conversations a digital analytics professional has to face begins when an executive with a printed report in his or her hand asks, Why don t these numbers look right? Have you ever found yourself in this web analytics death spiral? In a split second, you must decide if you are going to defend your data and risk your reputation (possibly even your job) or decide to backpedal saying that you will have to recheck your data. It s a lose-lose situation. When it comes to web tags and implementations, things break and go bump in your reports. Take the appropriate measures to continually review and verify your web analytics efforts, and follow best data governance practices to help work towards greater data quality. Want to know more? Check out this blog post: Website Tags are Bound to Break

6 Step 5: Got Ghosts? Prevent data leakage Ghosts are so scary because you might not even realize they are present, haunting your space, you just know something isn t quite right You won t see flickering lights or hear a poltergeist plodding down the hallway in your analytics architecture, but that doesn t mean data leakage ghosts aren t lurking around your site. In the whitepaper Data Quality and the Digital World, Eric Peterson warns that companies have started to leak data through the tag-based data collectors deployed across their digital properties. Deploying data collection systems without a clear plan for maintaining accuracy, validity and security of data collected is undoubtedly a poor practice. Given the relative ease with which they can be added to a website, combined with the fragmented approach companies take to digital measurement, analysis and optimization, it should be no surprise companies have started to leak data. - Eric Peterson CEO, Web Analytics Demystified Consider These Five scary Scenarios: An employee leaves the company to work for a direct competitor but maintains access to traffic and revenue data through unknown deployment of analytical tools. An agency deploys tools with questionable Personally Identifiable Information (PII) collection and handling practices. A Tag Management Solution (TMS) is deployed in an effort to consolidate the chain of authority for controlling site tags, but that doesn t prevent other groups from circumventing this process and deploying tags outside of the TMS to meet their own needs. A third-party vendor deploys tracking that sells data to other third-parties, which potentially exposes your data to direct competitors. A policy bans particular technologies on all sites, yet their tags continue to appear.

Step 6: Count Your Candy Audit your data collection once, twice, three times Website content is constantly added, changed and retired and this creates tracking challenges that must be addressed by

7 Step 6: Count Your Candy Audit your data collection once, twice, three times Website content is constantly added, changed and retired and this creates tracking challenges that must be addressed by performing regular and comprehensive data audits. New deployments are an occurance that should always be accompanied by an audit. You must manage these changes while still being productive in your other duties. A THREEFOLD AUDITING STRATEGY IS PRESCRIBED FOR ALL NEW CONTENT 1. Test frequently during the development process Audit multiple times during any large-scale deployment effort. This instills resources throughout your company with data collection best practices. Share the results with developers and project managers when appropriate. 2. Test often during quality assurance and testing phases Closely monitor the data being passed to your analytical systems after your development project has passed into your QA environment. 3. Test immediately following deployment to confirm data is being collected as planned Confirm data collection has successfully migrated from the development and staging environments to the production environment. When problems are identified in this phase, act quickly to correct them. Assign resources to post-deployment efforts tasked with ensuring data collection is correct. THE PAY-OFF The deployment process is not considered complete until all aspects of the code are deemed functional in a production environment including data collection code. Commitment to data accuracy and utility within the broader enterprise means asterisks next to numbers are seldom seen.

8 Step 7: Beware! Keep Out! Danger! Caution! Deploy a Data Quality Assurance Platform and be alerted to problems before they become data disasters Of course, all of the above steps crucial as they may be to fixing your scary data are also labor intensive, expensive, time-consuming and still prone to human error when your web analytics team tries to monitor it all without automated technology. A data quality assurance platform automatically and independently validates your marketin technology to help you keep a pulse on all of the metrics that matter most to your business model. DATA QUALITY MATTERS Web Analytics, Advertising, Testing tools, DMPs and TMSes are only as good as the data they process. Data Quality Assurance confirms that the source of this We have 40 or 50 developers dedicated to the website and we roll code twice a week. {Before ObservePoint s Data Quality Assurance Platform}, we didn t have an easy process to validate page tagging on such a frequent basis. I needed to hand check new or altered pages with each roll-out, which could easily lead to missed errors in the tags. Tim Munsell, Lead Web Analyst at DaveRamsey.com data the tags on your website, in your video and in your apps are deployed correctly and completely. MEASURE AND IMPROVE DATA QUALITY Compared to the cost of traditional Quality Assurance processes and the risk associated with bad digital data, you can t afford to not deploy Data Quality Assurance. IMPROVE ROI IN ALL DIGITAL Consistent, correct and compliant data improves the ROI of your entire digital marketing technology stack.

9 Want to see if you have any ghosts or goblins lurking in your analytics data? Using your company URL, and without deploying a tag, we ll initiate a custom, complimentary audit on your website and guide you through our findings, alerting you to: Missing Tags Duplicate Tags Slow-Loading Pages Third-Party Code Broken Links Data Leaks JavaScript Errors Click here to Get started now

ESTABLISHING THE PILLARS. of a Successful Data Governance Program

ESTABLISHING THE PILLARS. of a Successful Data Governance Program ESTABLISHING THE PILLARS of a Successful Data Governance Program 1 TABLE OF CONTENTS DATA GOVERNANCE IN THE ENTERPRISE 3 TAG AUDITING 4 GOVERNANCE DOCUMENTATION 6 COMPLIANCE TESTING 8 DATA GOVERNANCE BOARD