Taking on a data management initiative can be a scary proposition.  However, the business benefits are there.  Rich, accurate data is the fuel for multiple groups: Sales leaders want better territory planning, Marketers want lead to account mapping, and Ops wants automation to help the business scale.  The price of NOT having a strong data management practice is pretty high. Poor data quality costs organizations an average of $15 million per year!  The scary part is that realizing these benefits without putting a stranglehold on the very departments you are trying to help is a fine line to walk.  This blog post will try and help navigate through some common data management pitfalls, and get a process up and running that makes Operations shine like the tech wizards they really are.

The Data Situation

Larger enterprises sometimes move forward with a Master Data Management (MDM) approach. This requires a higher level of discipline for controlling, transforming and standardizing data from disparate systems, and may be overkill for most companies with data management needs.

A newer player (or idea) in the data management game are Customer Data Platforms (CDPs).  Forrester recently released a report covering CDPs, although I prefer this Hubspot blog post. The concept is focused on understanding and identifying a customer everywhere they interact with a company.  CDP is in its early days which means it’s hard to predict how well any vendor will be able to live up to the broad vision of CDP. 

Find the “Why”

This brings me to one of two central points of this blog post: When it comes to your data management initiative, focus on the needs of the business.  What, specifically, are you going to improve?  Why are these the most pressing needs?

Need more defined account information for better territory assignment?  Great!  How are territories assigned?  By location, industry, and company size?  How are you sourcing that data today?  How are account hierarchies strung together? (You might not want different sales people working branches of the same company.) 

Need to identify and retire old leads and contacts? OK! Do you have a way to validate email deliverability?  What’s the plan for duplicate leads and contacts?

It’s worthwhile to do some “discovery” here with your vendor.  New data elements might be available that can improve the business.  For example, with technographic data you might be able to better target potential new customers with the specific technologies you’re going after. 

Once all the “asks” are in, it’s time to prioritize.  The discovery work you did earlier can pay off big here, and put you in a position to help drive the conversations.  Here is also where you’ll get buy-in and support from the stakeholders who will help the initiative be successful.  Technology is part of the solution, with people and process rounding out the solution for a successful data management initiative.

This brings me to the second point:  Designing a good process will be instrumental in the success of the data management initiative. 

Let’s talk about the process, because there are a few pointers I picked up from a few of our customers who have robust data management practices.

Understanding “who gets to do what” helps put bookends on how the process will flow.  In this context, “who” can also mean tools for automation.  For example, will the sales team be responsible for updating the accounts they own?  They should have a tool that can help them do that easily, since the focus of sales should be selling.  Is marketing going to double check leads to make sure they get matched to accounts?  Who gets to add new accounts to the CRM, and how often are updates needed?  A solid audit of the tools in place can even uncover some more needs so you can automate further.

Nail, then Scale

Let’s move on to the roll out.  Since there is automation built into data management and we are operating on thousands, even millions of data bits, of course it makes sense to run a pilot in sandbox.  The best vendors offer an implementation team that assists with this.  Even when it’s time for production, it’s best practice to go live on a subset or single cohort before the process is broadly deployed.  This is the time to showcase early lessons that did not go to plan, as well as early successes with the business stakeholders. Pilots can also be used to calculate ROI on your data management initiative.  If Marketing retires 20,000 old leads, they might see 14% savings on their Marketing Automation renewal, as the cost of Marketing Automation is often based on the amount of data in the system.

Naturally, improving data quality and measuring the results of those improvements is not a one-time activity. Like any exercise, you get better with time and can do more with the same amount of effort as you build expertise.  And since you’ve established a prioritized list of business needs, once the immediate ones are met, it’s possible to take on the second round of priorities.