We hear a lot of talk about how, why and when organisations collect data from individuals. Rightly so. There are laws to comply with, and good practice to implement. No company, surely, wants to be put through the wringer for failing on either ground.
But what about when customers leave an organisation, or when a product or service is discontinued? Customers, products and services all generate and consume data, and one would hope that organisations hoping to be ‘data-enabled’ or ‘data-driven’ ensure this data is well maintained while it is useful to the company, and personal data particularly, is managed in line with the requirements of the law. Well maintained data has the best chance of maximising its intrinsic value and conversely, it is less able to deliver those insights in direct proportion to how poorly maintained it is.
Both onboarding new data and managing it well while it is in use are topics which receive a lot of attention. However, we hear much less about the third and final stage of the data lifecycle – offboarding.
Customers aren’t loyal, they leave. Products and services aren’t sold forever. When these things happen, what is done with data that relates to them? In his book “Ends. Why we overlook endings for humans, products, services and digital. And why we shouldn’t” Joe McLeod confronts the challenge of designing for the “end of life experience”, and we can apply his thoughts to data.
The problem with data hoarding
Evaluating data’s uniqueness, quality, usage and risk of loss is important in understanding whether data should be offboarded. Metrics might tell you how often particular data has been used, however that is generally only part of the story – and perhaps not the most important part. A piece of data could be used only very infrequently, but provide valuable insights that aren’t available through other routes. Weighing up ‘frequency of use’ with ‘value when used’ is something to be done on a business by business basis, and it is something well worth spending some time and effort on.
What often follows from the notion that a single piece of data might be infrequently used but of great value, is referred to by Caroline Carruthers and Peter Jackson in their book “The Chief Data Officer’s Playbook” as ‘data hoarding’ – the retention of data just in case it might be useful some time. But in fact, data hoarding can be the manifestation of a lack of strategy and understanding. It can be a sign that a firm has no data strategy or vision beyond acquisition, that it doesn’t understand its data enough to be able to identify what is – or what might be – useful, and that, in the worst cases, it doesn’t even understand what business it is in.
Even if we ignore regulatory considerations (which, of course, we can’t), there is a solid business case to actively consider whether or not to retain data, and when and how to offboard it. Just to put a real-world value onto this idea, it is worth noting that IBM has calculated that poor quality data costs the US economy $3.1 trillion a year.
The way to redress the balance between data hoarding and retaining data that really does have potential to be useful is to address a few straightforward questions: What product or service does the data support? Does the data support ongoing or ended relationships? If the data doesn’t support a product, service or current relationship, what business purpose or objective is served by keeping it and do we have permission to keep it?
Closure is an opportunity
There might seem to be a paradox in saying that offboarding data creates an opportunity. But in the glass half full world of business, that’s exactly what it is. When a customer leaves a company, opportunities are created. In “Ends”, MacLeod promotes the case that closure creates an opportunity. DAMA’s DM-BOK cursorily mentions data can be purged when it is no longer useful, however understanding the full lifecycle of that data will clarify what “useful” means. In the case of customers, an organisation can ask the customer for their reasons for leaving and use that data to help formulate customer retention policies. And it can wish the departing customer well, looking forward to a time when, perhaps, the customer will again need the kind of services it offers, and be minded, because of a positive departure experience (and good service throughout, of course), to return.
The same approach can be applied if a company discontinues a product or service. If that’s been provided by a contractor, then the contractor equivalent of an ‘exit interview’ can deliver genuinely helpful insights, and generate data it’s beneficial to retain for future use. In addition, the data relating to the product or service itself can be archived (where it can be analysed for future planning exercises, but won’t impinge on the utility of data about live services), or it can be deleted depending on company policy and legal requirements.
There are, of course, exceptional cases like blockchain which requires the indefinite retention of data in its datastore (a distributed ledger), for the purposes of integrity, transparency and decentralisation.
Begin with the end in mind
The second of Stephen R Covey’s 7 Habit of Highly Effective People is to begin with the end in mind, and “Ends” gives us an approach to do just that. In the context of letting go of data, this means understanding that offboarding is a legitimate and important part of data’s lifecycle. To get this understanding, organisations need to develop a framework that will allow them to see what good data looks like so they can determine when its utility is ending.
In the case of blockchains, thinking about ends becomes critical in considering what is needed to balance the integrity, transparency and decentralisation requirements of any blockchain solution and understanding what data should be kept or discarded.
Taking into consideration the offboarding and end-of-life experience of customers, products and services has profound impacts on data and data management. Being mindful of those endings will translate into a clear data strategy that is aware data has a limited useful life, and understanding that there will come a point when the data needs to be let go should get any firm on the right road to understanding how to manage data offboarding. The result will be less data for sure. But that data will be better quality, and will provide more useful insights.
Markus Buhmann, Data Management and Architecture Leader, JDX
Image Credit: Flickr / janneke staaks