Why Data Quality Matters at Every Level of Your Organization

As a CEO and former CMO (and especially as a mum), I’ve spent a lot of time crafting and telling stories that make an impact. In marketing, our job is to take the mountains of data we collect and craft a compelling story around it. In doing so, we drive our strategy and prove the value of our campaigns and the results we produce. Leveraging the right data to tell the most impactful story wins budget and drives programs.

But it’s not always easy to take data and turn it into a meaningful story that will create the right kind of impact. CEOs and the rest of the executive team must take into account how the data influences revenue, because at the end of the day, data drives decisions. Data impacts the way you move forward with different strategies and gains executive buy-in to continue those strategies.

Simply taking the numbers from your team without asking how the data was compiled and sourced is a big mistake. Use inaccurate data and the story becomes misleading, causing you to make faulty marketing decisions and invest in the wrong campaigns. Even the best data maintenance leaves room for errors, given the sheer volume of information we deal with every day. That’s okay – what matters is distinguishing between what’s accurate and what’s faulty, subjective or unhelpful.

Here’s what good data quality looks like:

  • The data is complete. Customer names should have phone numbers and email addresses attached to them. Company names need to be complete and correct titles should be included. Existing buyers are associated with their buying history.
  • Each data record is relevant and unique. Duplicate data wastes time and money. You need to establish thoughtful business rules for sourcing and combining data from multiple sources in order to create a single record for each unique entity.
  • Your data is accurate and current. If old data is mixing with more recent updates, and you’re not sure which is which, you’ve set up your team to work from a distorted picture. Good data includes details from the last engagement date.
  • Consistent patterns and terminology keep your data manageable. With multiple systems feeding your data, it’s easy to collect phone numbers, last touch date, birthdates, titles and more in a variety of formats. This can lead to duplicate data sets or correct information discarded in favor of incorrect data.

With big data showing no signs of slowing down, developing a smart data cleansing program is mandatory. So how do you keep your data in top condition?

Pay attention to the big picture.

Marketing is the only department to see how the numbers from every customer engagement touchpoint, campaign and angle fit into one cohesive customer experience story. Research and develop benchmarks for the industry and for each methodology and source so you can put metrics in context to detect trends. You can then share this with other departments across your organization so everyone has a complete picture.

Also important: interpreting data for each pipeline stage so you can diagnose any problems in sales and marketing alignment. For instance, if you have low sales accepted leads, you may need to reexamine your lead qualification criteria for better quality. If you have good sales accepted leads but low sales qualified leads and high lost rates, you may have a noncompetitive solution or a sales team with poor selling skills.

Dive into your sales and marketing tech ecosystem.

By understanding how data flows across your marketing automation and CRM systems, as well as any reporting tools and compliance processes, you’ll spot possible channel conflicts and sources of incomplete or conflicting data. For instance, maybe your vertical classifications seem all over the map and are not aligned across systems. Perhaps your data capture cookies are scattering data instead of directing information to the right records.

By understanding your data and systems infrastructure, you can tighten up your database and the reporting results for better insights.

Develop an efficient integration system.

You can almost guarantee that different databases will have slightly different sets of the same information, creating an abundance of errors and duplicates. Is this easy to resolve manually? Not if you have a sizable data trove fed by a significant number of systems. Instead, use good data quality tools to identify your data and organize it in a consistent, modern way.

You’ll need a master data management (MDM) tool to build a single master record (sometimes called a “golden record”) that gives a composite view of your data. You can then build a data subset for your marketing needs and have the right rules for synchronizing your data across teams and systems. If your CRM system has different core contact information than your marketing automation system, sales and marketing alignment will be at risk.

Tap third-party sources.

Trying to build an effective approach on partial data can be as futile as having no data at all. Since you’re unlikely to collect a complete and comprehensive data set on every single prospect, consider supplementing what you do have by acquiring high-quality data from third-party sources. Predictive and intent analytics can provide a wealth of helpful data to prioritize both accounts and contacts for the right outreach.

Set your data system up for success.

Data will always be an important piece of your strategy. Your data will continue to grow as your organization does. Ensure you keep the systems you’re using clear and organized so they are ready to scale with you. Data has a ripple effect that can be positive and negative, so be conscious of how you manage it moving forward.

-Morag Lucey

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