The word audit sounds stressful. But data audits bring real benefits to your business.
Data security is a major concern for businesses of all sizes; you don’t have to look very far to find high-profile breaches affecting companies, organizations, and even cities. Legal compliance is another major consideration, particularly after passage of the California Consumer Privacy Act (CCPA) and the European Union’s General Data Protection Regulation (GDPR). Data storage? Just another real and major concern for businesses of every shape and size.
Data audits aren’t only important because of key business concerns, though. According to Gartner, more than 40% of all data and analytics projects will relate to some aspect of customer experience (CX) by 2021 (full article available to Gartner clients).
Because business decisions are increasingly driven by analytics, it’s critical that teams have full confidence in their data.
An audit to improve your confidence in and the quality of your business data isn’t just about accuracy. The audit could uncover silos, access issues, or areas where a greater depth or breadth of collection would be beneficial.
In this intro to data auditing, we’ll cover the ABCs and 1-2-3s of a successful audit process.
The 1-2-3s of data auditing
Your data-quality audit should result in stronger business analytics. To make that happen, follow these three steps.
1. Bring in relevant stakeholders
It’s very possible you have relevant customer experience or marketing data living in different departments. Your sales team may have important personal and purchase history information, IT may have website experience and performance information, and marketing may have customer satisfaction scores (CSAT) and digital campaign click-through rates.
Depending on organizational structure and information architecture, data across departments, teams, or individuals may be siloed. Before you can begin to evaluate data quality, you need to make sure you’re not missing major players or components.
What to do at your business: Set up a meeting with team leads or send out a company survey to determine who is using CX-relevant data. Use the meeting or survey results to find key stakeholders who should be involved in your data audit process. These stakeholders should be able to tell you what data they use, how they use it, where it’s stored, and how frequently it’s updated.
2. Map out where your data is and how it’s stored
Now that you know who is using your data and how, take a close look at your information infrastructure and processes. Does some data live only in one place, while other data lives across multiple channels or platforms? If data stored in multiple places needs an update, is it changed simultaneously or is there a lag for one channel?
Is some data siloed in an inaccessible place (such as a personal Excel file or email) when other team members may need it? Is some data unified within your CRM solution or customer data platform, but not all relevant information?
What to do at your business: Actually map out what type of information you have where, and who needs access to it. Note the frequency with which information is collected or updated. By the end of this step, you should have a comprehensive understanding of your company’s own information architecture.
3. Take a deep look at what data you have and evaluate quality
Even if departments share information effectively and your data is stored and updated in an accessible, timely way, you could still have data quality issues. That’s why your final step is looking at your data through the lens of three core data values: accuracy, breadth, and consistency—the ABCs.
We’ll explain how to approach each of these values in the section below, but it’s worth asking yourself at the outset what your priorities are, should you reveal issues that need to be addressed for all three.
Completing the first two steps may have already provided insight on priorities. Meetings with stakeholders may turn up complaints of inaccuracies, or you may have identified consistency issues when mapping out where and how your data is stored. Such insight can give clarity on where to focus extra attention during the evaluation step.
What to do at your business: Closely evaluate your data for accuracy, breadth, and consistency. Keep an eagle eye out for recurring errors, formatting or structuring issues, gaps, or methodological problems. By the end of this step, you should have a deep understanding of what’s going on with your data.
The ABCs of Data Auditing
Now that you know who uses your data, how they use it, and how/where it’s stored, you can focus on data quality. For data-quality evaluations, use accuracy, breadth, and consistency as core values to weigh your data against.
By accuracy, we mean information that is factually correct. Despite your best efforts, inaccuracies can creep into your data in all sorts of ways.
For example, there may be bots swarming your website, creating a trail of misleading web traffic data that could ultimately throw off your performance evaluations. In this situation, you could use a robots.txt file to block bots to improve your traffic analytics accuracy.
Human error is a common source of inaccuracies. Finding ways to automate certain tasks is an easy way to cut down on human error. Data management or data integration software can flag potential errors and identify outliers in fields that should have a standard format. For small businesses with tight budget considerations, there are a number of freemium options in Capterra’s directory.
By breadth, we mean information that includes your desired scope and represents your population of interest. Keep in mind that this isn’t just a question of breadth in data collection. You can have sufficient breadth for your scope of interest alongside silos preventing breadth of information access across your organization.
Ask yourself (and other stakeholders) if your data truly represents your customer base and their interactions with your company. Is there a segment of your customer base that’s missing from your data collection process, such as infrequently captured in-store purchases? Working with stakeholders is critical to finding and solving issues of data breadth.
Performing a gap analysis can illuminate breadth issues, so be sure to look at your unfulfilled CX objectives to determine whether you’re missing data points needed to fulfill your objectives.
By consistency, we mean information that adheres to specific formats and methodologies, without gaps or deviations. If there are anomalies in data formatting, some information could be lost when filtering for analysis. If there are inconsistencies in methodology, your overall analysis could be flawed.
On a granular level, consistency can simply mean making sure everyone is using the same format for coding or storing information (e.g., consistent U.S. state formats). On a macro level, it means taking a close look at your methodology to ensure it has remained consistent for each data point, both in process and frequency.
A customer data platform can help enforce data format standardization, as well as unify disparate data sources. Even small changes—such as making associates use drop-down value selections within your CRM (as opposed to typing out free-text values)—can improve both consistency and accuracy.
Opportunities for automating your data audits
There are multiple software solutions that can help you with data accuracy, breadth, and consistency.
Depending on your goals, you may want help creating automated workflows that audit, flag, or share data in standardized formats. Depending on your specific needs, data quality, data management, or data integration software could be an the ideal solution for your business.
This post is one in a series about customer experience data, and we’ll continue talking about customer data platforms in the coming weeks! Stay tuned to learn how to find valuable contextual data, and how to select the right customer data platform for your personalization strategy