At Phase 5, one of the most common issues clients bring up is that they are awash in customer data, but struggle to gain relevant and ongoing insights from it. While they may be collecting a 360o view of their customers’ needs and experiences through multiple sources and channels (e.g. surveys, behavioral data, voice of the customer information, social media, call center notes etc.), most clients just don’t know where to start in making sense of it all (for more on taking a 360o view of your customers, see my colleague Doug Church’s recent post).
To that end, we encourage undertaking a “data audit.” In short, a data audit is a structured process that allows a 10,000-foot assessment of an organization’s data and its collection processes, with recommendations for charting a path that integrates with the organization’s strategy and business goals. A data audit brings together relevant stakeholders to:
- Inventory data
- Make connections between business problems and the data
- Identify immediate opportunities to address key business problems using that data
- Chart a path forward
Throughout the rest of this post, we’ll show you how to conduct a data audit in your organization.
Step 1: Identify the right people
Gaining a 360o view of customers requires input from many sources. Therefore, having the right people in the room is critical for a data audit. This will vary by organization, however, as a rule of thumb, we recommend bringing together representatives from market research/insights, data analytics, and at least two substantive business areas (i.e. product development, channel strategy, marketing, etc.). With regard to insights and analytics, we recommend bringing in those with the broadest knowledge of data availability and current research initiatives. Substantive experts, on the other hand, should be able to identify and prioritize issues that are of high importance to large segments of the company.
Step 2: Identify the key business problems
Once you’ve got the right people in the room, identify a handful of key business problems that data can be used to solve. These may come directly from the substantive experts or they may be common requests made to the insights team. Regardless of how you identify them, the problems on which you focus should be prioritized based on the following criteria:
- The problem’s longevity: Will this issue be relevant in 6 months? A year? If it is a short-term problem, it may be irrelevant by the time you are able to execute your data strategy.
- The reach of the problem: How many people/departments/customers does this problem impact? Is it critical to the mission of the company? In general, prioritize problems with the farthest reach.
Step 3: Inventory your data
The next step is figuring what data is at your disposal. Although knowing what data is available is key, it’s only the first step. You also need to know the quality of that data and how it can be used. Here are some questions that can help guide this process:
- Where is the data housed?
- What is the quality of the data?
- At what level is the data measured? (i.e. person, group, geographic, time period, etc.)
- What can the data be linked to?
- How and when is it collected?
Going into step 4 with these questions answered will ensure that you don’t start down a research path that gets held up by data-related issues.
Step 4: Make connections
Once you’ve inventoried your data, as a team, identify the data that is relevant to your key business problems. Although this seems straightforward, it may not be. Doing this effectively requires both the collective intelligence of the data audit team and a healthy dose of creativity. To this last point, we recommend focusing on the key elements of your business problems and identifying as many data points that measure those elements as possible. For instance, if your company is trying to understand when to target prospects with an ad, you’ll want to identify data that measures time. This might include anything from the time of day/week/month/year someone visits your website to perhaps your clients’ birthdays. The further outside the box you can think, the better.
Step 5: Identify opportunities and barriers
This step brings the previous steps together by identifying questions that can be answered using your data. When doing this, it’s important to identify “quick wins” (insights that can be derived immediately) and long-term goals (opportunities that will take additional work/data to realize). For something to be a quick win, the quality of the data must be good, linkages between data should be strong (i.e. a high percentage of cases should be able to be linked), and key stakeholders must be able to access the data. For long-term goals, it’s important to identify current data barriers and develop plans for remedying these.
Step 6: Chart a path forward
Once you’ve identified your short- and long-term goals, you’re ready to develop a plan for execution . This should include making sure that relevant stakeholders are on board. For instance, if data are housed in different departments, you’ll need to get buy-in from the people who authorize data usage in those departments. To this end, identifying some shared benefits between stakeholders may make the process easier. You’ll also need to ensure that adequate resources exist within the organization to complete the projects you’ve identified. Again, this may require drawing from multiple departments, so shared goals are key. If you do not have adequate resources, you may consider hiring an outside firm to complete the work. Finally, developing timelines for completion will make sure that everyone is working towards the same goals and that projects stay on track.
Ultimately, businesses that successfully adopt a customer-centric strategy draw insights from across the customer journey. Many organizations think that collecting and reporting data will be enough. However, for the efforts to be successful, there must a coordinated approach that takes a company’s current data capabilities into account and casts a vision for the future.
Robert Vagi, PhD, is Phase 5’s lead data scientist. Drawing from his background in both quantitative research and education, Rob is passionate about helping clients use data to tackle their most challenging business problems. Throughout his career, he has helped clients in both private and public sectors make the most of their data. This work has received both local and national media attention. Rob is based in Minneapolis, MN.