As marketers, we’re accumulating more data, more assets, and more engagement history from more systems. It’s getting costlier and more difficult to migrate all that data, some of which may itself have been migrated from an even older database. As a result, many of us continue to operate in the same marketing databases for longer and longer periods of time.
Even as you’re reading this, your data is changing. Good, fresh data is coming in. But at the same time, existing data is decaying and becoming unusable to the tune of 25-30% of your stored data over the next 12 months. Not only are you paying to store that decayed data, but if you use it, the consequences can be very costly. MIT Sloan Management Review estimates that bad data even causes a loss of 15-25% of revenue.
But wait, there’s more! With the advent of GDPR in 2018, CCPA this year, and more privacy directives on the horizon, over-retaining data has the potential to result in additional costs. In fact, last year a Danish firm was fined over a quarter of a million US dollars under the GDPR — simply for storing data, but not using it. A German firm was fined almost USD 16M for not having any retention policy in place at all.
Generally speaking, marketers are data hoarders. As ownership of the marketing automation platform and other MarTech transferred from IT to Marketing, we didn’t necessarily adopt the same rigor about managing that data as our predecessors. We rely on the systems themselves to prevent us from using bad data, such as opt-outs. When we don’t like the data we already have, we simply buy more. We keep what we have “just in case” we might need it later.
But few of us have really considered many of the key tenets of a data governance policy. Bo-ring!
Well, given the increased regulatory attention and enforcement and the obvious risks of over-retention from a security perspective, initiating a data retention policy should be a key priority for 2020.
But wait, I’m not in IT!
Data management has typically been considered the purview of the IT team, so this likely isn’t something that your marketing team has thought about, much less ever encountered.
That’s probably why an Adestra survey of marketers reported that that almost half of respondents found “Data management” to be difficult, with only 28% feeling they were effective at it. Compounding marketers’ lack of knowledge about data management is the fact that we now have so many data sources available to us, often siloed, and this landscape is frequently changing.
Making matters worse, a McKinsey Global Survey reported that organizations have challenges finding and retaining not only data management talent, but also more active CEO involvement and sponsorship for these initiatives.
Protecting your organization against these costs, however, requires a data governance strategy, so it’s time to charge ahead into unfamiliar —and sometimes uncomfortable — territory.
Don’t let perfect be the enemy of good – start small by making incremental improvements toward a holistic data governance strategy. Many of our clients initiated the development of a data retention strategy because they simply needed to remove contacts in their marketing automation platform, where that is frequently the unit of cost in their contract. Defining what constitutes a record to be archived or deleted for this exercise can become the foundation of a larger policy.
9 questions for an effective data retention policy
We talk a lot about data democratization, where “everyone” owns the data, but often no one has guidance on what that entails. So, first and foremost, ensure you have the right stakeholders involved from across the business, which may include not just Marketing and Sales, but also IT and Finance.
Then, begin your journey to good data governance by working together to answer the following questions:
1. What are our goals for this policy?
Ensure your goals are measurable.
Examples:
- Keeping below a certain contact band
- Maintaining plenty of free space for expansion
- Cutting down on clutter
2. Which categories of data will our policy cover?
Examples:
- Profile data
- Engagement data
- Contractual/financial data
- Emails/landing pages/other assets
3. Where is the data covered by the policy stored?
A. What are the data retention policies of our vendors and how do they overlay with ours?
B. Do we have “islands of data” that have been removed from the primary database, such as exported lists or reports, that need to be considered?
C. Which categories of data pose the biggest risk, and therefore need to be made a priority and managed more proactively?
4. With which global legal requirements do we need to comply?
This will depend on the categories of data you store.
Examples:
- HIPAA
- SOX
- COPPA
- GDPR
- CCPA
5. What is our definition of a record that should be archived or deleted?
Examples of factors to consider:
- Created Date versus Last Modified/Used/Engaged Date
- Duplicate records
- Internally created test or externally provided junk records
- Other criteria, such as form or email spam
6. Do we archive the entire record, or only pieces of data within it? How and where do we archive it? How do we delete it?
7. Do we have the right tools to find data that doesn’t conform to our policy?
8. Who will review the data to identify what needs to be archived or deleted? What training is required?
9. Who will be responsible for enforcing the retention policy? How will these stewards be held accountable?
Once you’ve answered these questions, it’s time to stress-test your proposed data retention policy. Invite key stakeholders together for a brainstorming session to explore how well it holds up for expected or past use cases, which might include:
- A Data Subject Access Request (DSAR)
- A data subject withdrawing consent to processing
- A product liability lawsuit
- A customer contract ending
There’s no such thing as “set it and forget it”
Over time, your data retention policy will likely need iteration. Data, data sources, the people involved, and the required skillsets will change. As people move or change jobs, your data will continue to decay. As marketers, we need to change our past strategy of over-retention to treat data as a crucial asset of our business.
If you suspect (or know) that you are over-retaining data but don’t have the full scope of the impact, a DemandGen Data Audit can provide key insights and priorities to address. You’re not alone: 80% of companies do not have a sophisticated approach to data quality, according to Experian’s 2019 global data management research. Understanding your data is the first step in the journey to a governance strategy.
And if you’re ready to improve the quality of your data but don’t have the needed resources, our DataMD managed service offers the technology, data analytics skills, and subject matter expertise to give you a jump on continuing that journey — and help you formulate the answers needed to develop your organization’s data retention policy.
Gaea Connary, Consultant at DemandGen, focuses on helping organizations strengthen their lead management processes, lead scoring, nurturing strategy, and reporting and analysis to get the best return on their technology investment and meet their marketing objectives.
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