The Neglected Step-Child of Analytics: Data Integrity

The Scenario

You have invested many months and hundreds of thousands of dollars on hiring the right talent, marketing the analytics program internally, partnering with agencies, redesigning websites, launching mobile apps, and configuring an analytics platform. Yet, you still cannot identify which content is being consumed, which channels are driving conversion, or how to determine the value your team brings to the company’s bottom line. You sit behind your desk, staring at a page views report showing you a trend line over the past twelve months, with a couple of spikes, but you have no way of knowing why, what happened, or what to do next.

Have you failed? Has this all been a colossal waste of time, money, and resources? Where did the project go wrong? Was it the team? The agency? The design? The app? After many conversations with your team and the business users, you determine it’s the data collection tool, so you switch.

You repeat the cycle, only to end up exactly where you started, with the same problems. Perhaps in this round, it takes less time to implement a new platform, because you have leveraged some lessons learned from your past mistakes, but that’s not much of a consolation to the Executive team, who have now invested close to seven figures in a bid to realize the promise of data-driven decision making. You are once again blankly staring at a dashboard that adds no value, no insight, and you have no direction.

The Truth

Your challenges with irrelevant reports have nothing to do with the analytics tool. The main problem is that you neglected data standards, integrity, and governance. You disrespected your data by not spending the time needed to fully review, document, and communicate the importance of accurate data collection.

Every time you heard of a new tool that claimed to do something new and revolutionary, you just had to have it, and you ultimately lost sight of the original intent of the system: to drive valuable insights. So you are back where you started, with a bunch of meaningless trend reports.

Now What?

The first step is accepting that it is probably not the tool that is causing you pain. There are thousands of businesses around the world using that same tool, and many of them are gaining invaluable insights from it. That is because they’ve invested in establishing a foundation for data integrity. If you do not do this before the first server packet is sent to your production repository, you’re going to end up having to start over.

The Approach

Thanks to the onset of Tag Management Systems (TMS), it is now a lot easier to start over. If you are still collecting data the old way, I strongly encourage you to migrate to a TMS . In short, to start over means to create a new data repository to collect the fresh, standardized and consistent data that maps to your initial core set of KPIs.

The added bonus is that by establishing a robust data layer, it’s so much easier to switch analytics platforms in the future, so you don’t have to be bound to the same provider.

I believe in taking an iterative approach; continuously building on the phase before:

The first phase is to simply get the system up and running with an initial set of core KPIs. Nothing more than traffic activity along with a few best practice tweaks.
After reviewing the data in a test environment, and making additional data adjustments, launch to a production reporting repository.
Monitor the data and reporting functionality for a week. Keep tweaking it until every data point matches back to the core KPIs and meets the reporting expectations.

Of course, while you are working through the testing phase, your teams are focused on the next phase of data collection. It could be conversion events, enhanced behavioral data, marketing channels, a shopping cart, or a product catalog. What really matters here, is that in each subsequent phase, you are reviewing the quality of the data being passed, documenting it, and communicating the standards. Standards on when the data is collected, where it is collected, and how it is being collected are all critical factors to the success of your program.

Benefits of a Phased Approach

Allows time for your dev teams to become comfortable with the tagging process
Provides time to get your business users trained on the capabilities
Delivers reports to the business users sooner, rather than waiting for each data point
Identifies data issues quickly and efficiently
Creates a loop of continuous improvement
Strengthens processes with each iteration

Embracing Your Data

Neglected data should not lead to relaunching data collection tools every year. Stop making analytics so hard; make a commitment to your data: to nurture it, to respect it, to correct it, to understand it, to grow it and eventually learn from it. Start fresh, reset expectations, take smaller steps, communicate frequently, document the standards, and take your analytics program to the next level.

Need Help?

We can help you optimize the way in which you utilize your analysts and get them refocused on what matters to your bottom line. We can also offer technical assistance by showing you the best methodologies on pulling data, creating dashboards, and getting the right info to the right decision makers’ hands. Contact us to speak with a data analytics experts.

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