Digital Transformation & Effective Data Management

If you’re one of the many businesses thinking about, or part way through your digital transformation, there’s one key area to make sure is a big part of the objectives: data management. 

Unfortunately, for many businesses taking on a transformation project, data is assumed to be suitable or not considered upfront. In our experience, this assumption can lead to a lot of problems for the business, but specifically marketing.

Businesses that have siloed teams and technologies (typically large corporations) run a risk that individuals or teams are managing their own data, simply because the one provided by the business isn’t sufficient. Hands up if you’ve ever heard a marketer or salesperson talk about their own data file/spreadsheet of information which could (or should) be on the CRM? 

On the other end of the scale, businesses that have seen rapid growth or transformation haven’t had the time to consider foundations and you end up with multiple different ways of managing or updating the same data. Both examples cause issues and both are related to a lack of proper data management practices. Throw in a transformation project and it’s not hard to see how this can become complex and nightmarish.

Whilst data management is a wider business issue that shouldn’t be owned by marketing, good or bad data can really impact your day to day and it’s imperative that it’s a consideration. Here are three data focuses that can ensure stronger marketing after your transformation:

Master data

A master data set is the “single source of truth” for your company data. Every technology that you have should take its data from here, if any data is updated then it should start or marry back to the master data. You’ll find that in businesses with old sets of data, there will need to be a big consideration about how they’re all linked and the hierarchy within them. This means thinking about:

• Unique identifiers to match data to the same individual - this could be an email address or specifically defined customer ID but that identifier needs to be present across all data sets to allow seamless matching.
  

• The hierarchy - how will you split between the different layers of data within your business? This can be simple or complex but think about if you need to separate accounts from contacts, head office data from branches, or even intermediaries from end customers. What takes priority and how do you roll up?
  

Cleansing and appending

We alluded to this when talking about the master data, but the premise of this is that there should be a defined and clear process of how data is cleaned, updated and enhanced. If you spot that someone is no longer working at a business, or they’ve changed their job titles, those changes should be applied to all data across all technologies, not just in one place. There’s also the consideration of whether the business will allow anyone to update data or if those requests need to be managed by a specific team and/or locked down to a specific type of data. For example, a marketer updating a contact's communication preferences makes sense, but their billing address not so much.

Data connectivity

Again, another piece we’ve already mentioned but it’s worth calling it out specifically. How your data is connected across the business is crucial. Even though it’s rarely a priority for a business to think end to end (typically projects or tech are brought in to fix one individual problem), not having your data flow through and connected across all the stages of a customer’s life cycle will be problematic. From marketing to customer onboarding to account management to finance, having an idea of how this connects will give you a wealth of data that you can utilise within your campaigns. Who wouldn’t want to know when a customer’s renewal period is approaching, the product upsell opportunities, or product feedback and innovation requests?

Data management isn’t just marketing’s responsibility, it’s the business’s. But that doesn’t mean marketing doesn’t see a benefit from getting it right. If you’re investing significant time and resources into a long term digital transformation project then make sure there’s a strategy and plan for how data is managed across all teams and technology. Bringing in new technology or processes won’t fix broken data so make sure it’s considered at the start of a project.

Want to chat about what this could look like for you? Get in touch.