First Published: 31 May, 2022
Written by Sharon Abbott, Head of Data Governance, ANZ.
Member of the Marketing Association’s Data Special Interest Group.
A few months ago, I was fortunate to be part of on online event, the MA’s Marketing Law Online Series. I’ve previously attended the Marketing Law Conferences that are consistently one of the most popular on the MA calendar. And I was rapt to be part of the recent line-up.
My presentation wasn’t really about Marketing Law – although there are certainly a great many legal considerations. What I spoke about and will write about here is the growth in business models that involve sharing of data between 2 or more organisations. And I labelled this ‘a checklist approach’ – offering up a few ideas with a large dose of humility given the calibre of other presenters, and of my audience.
Obviously data sharing and partnerships are not new. But there are many new aspects to consider given the wider environment where customer expectations and technological possibilities can act in direct tension with laws that are designed to protect those very customers.
Given that I’m not a lawyer, this is where a check-list can come in handy. The commentary going something like this: ‘well I don’t have a template for this, but if I were to do it, then here’s what I would be thinking about…the questions I would ask… the people I would want in support … etc.”
I currently work for ANZ bank. My role as Head of Data Governance is one that many people don’t understand and that many organisations don’t feel they need, and most people will politely stifle a yawn before skipping to a topic that appears way more exciting. So I have a weird combination of educating and selling to do when I talk about the day job (please read next section – don’t yawn and skip it).
I started my career in business development, then marketing, then wound up in insights and research, and customer experience and then back in Marketing – for several organisations ranging from FMCG to Education, Telco to Energy, and finally Banking. I realised that all of what I had done had a massive piece about data and that this is at the heart of what I know and am passionate about.
The truth is that many companies may not need a role like my one at ANZ Bank, but I can certainly advocate for the brand, marketing, CX, data, digital and insights teams within your business to build competency and a culture that can face into the subject areas that I am privileged to look after.
Google can provide you with lots of clever academic definitions of what data governance is and does. But when I am presenting to those who don’t encounter this term often, I use this:
Data Governance is here to guard against, clean up and sort out Dumb data, Dirty data and Disasters with data.
I coined the ‘3 D’s’ a few years ago trying to get cut through and a few laughs on an internal presentation, and I’ve found that it kind of works.
Dumb data is data that has no value or is not treated as valuable. Organisations that have dumb data may have data that isn’t even labelled for use (or re-use). The data may be unclassified for appropriate access, permissions, uses – and therefore un-protected from compromise. It’s also dumb (and expensive) to collect or keep data that you shouldn’t have or don’t need, and not to keep it long enough for your obligations under law.
Smart data has metadata – that is, information about the data that you are collecting, holding, storing, using and destroying. Metadata is the foundation of your data management capability and totally rocks!
Dirty data is the data governance concept that everyone gets – it’s about data quality. But what people don’t get is how difficult it can be to fix-up the f*-ups.
Fixing dirty data once it’s in your business is tough – there may be issues with consent and permission, there may be risks around data matching, updating records and even data compromise. But there are also significant risks in using data when it’s of poor quality. Therefore, the most effective treatment is preventing dirty data from ever entering your organisation.
Bad data is most often the result of bad processes and of free-form entry that allows for error by a customer or a front-line staff member. To clean up our data, we need to go back to the point of entry into the business and close out the chance for us mere mortals to get it wrong. Field validation, verification, drop down menus, pre-populated forms and other smarts can help with this.
The third D relates to Disasters with Data, and I use this as a catch-all for anything related to Privacy, Ethics and Data-protection risks. I’m fascinated by the expectations we have as consumers – the immediate value we want from our apps and how quickly we click ‘I agree’ to any organisation doing whatever it wants with our precious information, holding it goodness-knows-where on the planet.
If you take one thing away from this download on data governance, take away the idea that deliberate, empathetic decisions around data on behalf of customers are part of the Brand story. I believe that good organisations think deeply about what customers need to know, should have the option to agree to (or not), and the impacts of the decisions made around data use.
A set of Ethical Principles are also important: what are the lines that our business will never cross? And when it comes to using data, ‘just because we can, doesn’t mean we should…’
Back in the early days, I remember using purchased lists for old-school direct marketing. I remember the data was often from a flimsy cohort, with dodgy permissions and even dodgier accuracy - the quality and return rates could be really terrible. Thankfully sophistication and marketing practice has come a long way!
And I was thinking about this as I was thinking about partnerships in the new era. Actually, the risks are actually not so vastly different – perhaps what has changed the most are the consequences.
Choosing a bad partner, or a bad partnership strategy could have massive impacts on reputation. Not to mention impacts to data capability if we’re talking about bringing dirty, dodgy data into the business – or sending it out.
And it’s not surprising that many organisations are thinking about this now. Banks like the one I work for are looking to a future where Open Banking will put more control and optionality in the hands of customers. In the near term, this might simply include making payments work better, and comparisons easier across banking products. But longer term it will open up opportunities for new and better services from both within and outside of the bank and making these available through a single sign-on portal or platform.
And this opening up might start with Open Banking, but it will eventually become Open Data more broadly as other industry sectors, products and services are enabled and added to the mix.
The trick to making this work seamlessly for customers is the ability for data to pass seamlessly from one party to another in order to deliver each part of the proposition.
And that will require each one of the three D’s to be operating and ‘inter-operating’ in absolute harmony across all players: common definitions and classifications, explicit consent, controls to ensure quality data capture and an agreement on shared ethical-use foundations will be the hall-marks of the most successful data ecosystems.
It will matter less about the size of the organisations participating, and more about the data governance culture and data management disciplines that are upheld in seeking to build customer value.
When I started writing this, I was tempted just to put the presentation notes. And then I looked at the slides from the MA Marketing Law Online Series and decided our readers could make a choice to extract what you find most helpful from either this article, or the powerpoint slides via the link below.
And please let the Marketing Association know your thoughts on future presentation sessions, articles, or training that you may find useful. You can send your suggestions to contactus@marketing.org.nz
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