It's common to deal with messy or scarce data
Data shame - the instinct to avoid disorganized or sparsely populated data sources - often hinders Customer Intelligence programs by leading people to not even attempt data unification. “We’ll do it later,” they tell themselves, “When we’ve gotten a handle on our data.”
Here are a few common concerns that we help our own clients fix when it comes to disorganized, inconsistent or sparsely populated data, so that they can get meaningful insights about customers’ health. involve.ai's Data Science Consultant Vikash recently posted to the Customer Intelligence Community about his experiences and insights from supporting customers through the process of cleaning up and fleshing out data if you'd like to learn more from a first-person experience.
Bring disparate data into a unified view
One of the biggest sources of data discomfort is its scattered nature. Financial data may live in one system, while contact data is locked in another, and product usage data is tracked separately. Rather than avoid data-driven programming altogether, try integrating your data sources together to allow for unified, easy access and cross-functional visibility. Involve.ai’s data mapping tool is one of many options that can help your business accomplish this, but even a monthly, manual process would start to have an impact!
Decide what you want from your data and address only the messy or sparse data that matters.
Maybe your Salesforce setup requires users to enter 342 validations for each lead, or your Training team collects phone numbers and T-shirt sizes for every learner. Are all those metrics truly necessary? Determine what you want to analyze, and set up your systems and teams to collect specifically the data that will help you achieve that. Then, take the next step and clearly define how you want those different metrics laid out and calculated in your unified view. We have an article on how to do that too!
Create a consistent common identifier across your tech stack
If you’re early in your data journey, establish a unique identifier for each customer that you use across every system that measures them. If you’re with a big company that relies on several instances of each data source, offers multiple product lines, and serves acquired customers, this is far easier said than done. Using a fuzzy-matching algorithm can help you develop that red thread across platforms; while imperfect, it will begin to create the consistency that allows for slicing and dicing.
A little lift for a ton of downstream impact
Don’t get us wrong. All of these initiatives take time and resources. Determining what you want from your data and reconfiguring your systems to capture it, finding and aggregating your data sources, and ensuring alignment across the entire tech stack is not a simple endeavor. But it sets the foundation for immense value by giving your organization a clean, robust set of metrics to mine for correlations, insights, and data-driven decision making that empowers your team, delivers value to customers, and boosts your bottom line.
Involve.ai’s Data Science Consulting team and our no-code aggregation tool support these and other ways of handling missing and messy data and we would love to show you how.
Don’t let the state of your data stop you from pursuing Customer Intelligence!