Guide to Data Assurance:
Why Quality Assurance of Data Matters

We live in the age of big data and analytics. It’s a time when your company is probably collecting vast quantities of information about your customers so that you can better understand them and ultimately make smarter business decisions.

Being able to collect the right data can give you a considerable competitive advantage. That’s because you can analyze that data to learn more about your prospects and customers and ultimately improve how you go to market. And it really works. In fact, according to McKinsey research, companies that make extensive use of customer analytics see a 126 percent profit improvement over their competitors.

The reality is that data collection is a complex, brittle process that relies on lots of dependencies to work.

Not surprisingly, a lot of data comes from the Internet. If all goes well, that data will not only be accurate, but also flow into a range of tools that underpin your reporting, help you derive important insights, facilitate personalization, and enable you to allocate your marketing spend more effectively.

Unfortunately, however, things don’t always go according to plan.

The reality is that data collection is a complex, brittle process that relies on lots of dependencies to work. If anything goes wrong, it can lead to an array of problems including but not limited to:

• Important data not being collected as a result of missing tags, tagging errors, or other complications.
• Only collecting part of the data that you need or, worse yet, data that’s inaccurate.
• Collecting personally identifiable information that you shouldn’t be, which can quickly lead to data privacy concerns for your customers.
• Data loss when you unwittingly hand over your data to third parties as a result of piggyback tagging.
• Inconsistencies and other issues, which require you to spend extra time cleaning the data before you can analyze it.

Any of these issues can lead to serious problems that cost significant amounts of time and money to fix. Imagine, for example, collecting inaccurate data without being aware of the fact. If you’re using that data to inform business decisions, you’ll make the wrong calls. As a result, you’ll not only be at risk of allocating precious budget dollars to the wrong places, but also of hampering your overall chances for success as you go to market. For companies that rely on data to personalise their customer experiences or to determine how they spend marketing budgets, the cost of having poor data is particularly dire.

While data collection may seem as easy as creating a few tags and waiting for the information to start pouring in, in reality it’s a complicated process. Things can and do go wrong when executing the tags that makes data collection possible. Likewise when it comes to transmitting data across long stretches of network to an ever-growing list of different types of devices, each with its own quirks. That’s especially true when those devices are running other apps in the background that can throw things off, making accurate data collection virtually impossible.

And it’s not just that. There are three other big factors that are probably impeding your company’s ability to collect accurate data:

• The ever-changing parts of the Internet you can control.

  Websites change every day and yours is no exception. Every time that you add new features and functionality or add, remove, or duplicate your tracking tags you risk disrupting your data collection Even seemingly insignificant changes like launching a new campaign or publishing a piece of content can interfere with data collection with serious consequences for your business.

• The proliferation of analytics tools.

  The number of analytics tools that look at the data that tags collect has grown considerably since they first arrived on the scene in the early 2000s. When tag management systems came into play several years later, they helped to bring order to the chaos that had ensued. But it was a double-edged sword. In addition to bringing order, they also paved the way for companies to deploy more tags more rapidly, often resulting in errors.

• The relentless need for speed.

  With the ongoing adoption of Agile practices, development is happening much faster than ever before. Most companies have the same expectations when it comes to the speed and agility of their tag management. And while that’s a challenge at face value, the good news is that just as developers have put an automatic testing framework in place to preserve the quality of their code, you can use automated testing to ensure accurate tag management. Importantly, however, that testing needs to happen on a regular and ongoing basis.

The bottom line is that change is a constant. As a result, your ability to consistently collect the accurate data you need to run your business may very well be in jeopardy.

Many companies invest significant time and effort into figuring out their data measurement strategy and how it fits in with their overall business plans. They devote resources to understanding what metrics matter most to them and how they will capture those metrics. That work ultimately informs how they design a solution and implement an analytics tool.

Strategies need to be much more fluid so that they’re able to keep up with the rapid pace of change.

Unfortunately, that effort can often go to waste if their data collection isn’t working properly. As a result, they not only find themselves in a situation where the value of their investment is lost, but where they’re also making bad business decisions. As we’ll see later in this guide, given the evolving nature of things, these strategies need to be much more fluid so that they’re able to keep up with the rapid pace of change.

Of equal importance is the fact that companies need to pay much greater attention to the integrity of their data collection. The potential for flawed data is very real and can have significant implications for any business, resulting in poor decisions and a loss of data confidence.

Data assurance is the big challenge facing everyone in digital analytics right now. In the sections that follow, we’ll take a closer look at data assurance and how you can use it to establish the checks and balances needed to ensure that your data collection runs smoothly and accurately.