As Equifax prepares to pay out as much as $US700 million in compensation for its spectacular 2017 security breach, and cosmetics retailer Sephora apologises for a leak of Asia Pacific customer data, now is a good time to consider the advantages of data ownership.
Peace of mind around security isn’t the only advantage, there are a whole host of other good business reasons as well.
At Poplin we see ownership as a first step on the road to organisational change, a change that can begin to fulfil some of the huge potential many companies know their data holds but find difficult to unlock.
Still, given recent developments, the risk of a breach isn’t a bad place to start.
On Monday the United States Federal Trade Commission announced credit reporting agency Equifax had agreed to pay out consumers affected by its 2017 hacking breach that compromised the financial information of nearly 148 million consumers.
Worldwide, the implementation of comprehensive laws like the European Union’s General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), together with amendments to Australia’s own Privacy Act, are increasing responsibility and accountability.
At the same time, consumers are wielding greater control over the data they generate.
What does full data ownership mean?
Companies must know exactly where data is stored and be able to access it if they want to stand any chance of complying with these growing demands.
Some tools such as Google Analytics can be a good starting point for small firms and startups. They provide the basic functionality needed to begin piecing together a broad-brush picture of customer behaviour.
The disadvantage is users don’t get full ownership; access to the raw data isn’t part of the package and remains with the platform provider.
First party analytics solutions, like Snowplow, give organisations complete data ownership. Our clients – Catch Group, Finder, The Iconic – are enjoying the security and operational benefits available when you control your business data.
Security considerations aside, limitations inherent in third party data arrangements begin to become apparent as a company grows.
The ability to answer more complex questions – the relative value of different referral channels or change goals historically, say – simply isn’t there. It’s like the difference between trying to understand the world via an image recorded on a 3-megapixel camera versus the human eye.
Once companies grow beyond a certain size, they need that high definition picture. They also need to start thinking about potential future applications for their data, such as powering data science, AI and cross-cloud applications.
And it’s around this time they begin to look for something more sophisticated.
For many, this will come down to a decision about which platform to invest in, or perhaps whether to build something bespoke.
At Poplin we recognise those technical decisions are important – technology is at the core of what we do – but our focus isn’t so much on the tools as to how they’re used.
That’s because data itself isn’t a panacea. You need to be able to use it and make it accessible, otherwise it just sits there. Data alone is passive.
Delivering on the promise of big data
A global survey from McKinsey Digital shows companies have high expectations of data analytics but not all of them are achieving results. What’s interesting is the problem doesn’t seem to originate with the tools themselves nor with strategy so much as the way both are applied.
High performing companies attribute a lot of their success to senior leadership involvement, while low-performing companies cite problems with organisational structure and lack of management buy-in.
Platforms promise a lot of things but they can’t hope to solve problems like these because they exist on a human level rather than a technological one.
Mobilising data properly takes a lot of the time and emotion out of decision-making.
It becomes possible to build a hypothesis and test it against real evidence. In short, business starts to become more like a science than an art, albeit one informed by creative thinking. Following five steps the DMAIC method is an effective process to determine the right data based decisions.
- Define the problem
- Measure the consequences caused by the problem
- Analyse the causes of the problem
- Improve by implementing and testing solutions
- Control the problem with the right solution
That’s not to say gut feel and experience aren’t critical. Industry veterans can provide vital context to an otherwise sterile data set and both inputs are needed to get the best results.
Data as a competitive advantage
Ultimately, decision making is a human process and designing structures to support that is the key part of our work at Poplin.
We help our clients build a culture in which different teams begin to use data to address the various challenges and opportunities they face.
Working with Catch Group we helped the online retailer gain an advantage in the competitive eCommerce market. We built a complete data system giving Catch ownership of their data so they can define, measure and judge the success of any project they undertake. (Read more about this project here).
Pretty soon, they can see for themselves what’s testable and what isn’t, what kind of data they need and how to get it.
Once those structures are in place, they need to be maintained, monitored and, where necessary, corrected.
It’s not a one-off process but an iterative one. And it all starts with data ownership.
Over the coming months, we will continue to explore Data Ownership including topics around centralising data sources, data governance and more.
Read more:
To understand more about how Snowplow compares to other analytics vendors and Snowplow Analytics view on Data Ownership
