Rethinking data principles - Phil Jones, Enterprise Data Governance Manager, Marks & Spencer
Image courtesy of Kings Church International, www.Unsplash.com
Many years ago, I bumped into an Enterprise Data Architect, Gordon, in-between sessions in an MDM Conference in London. Over coffee and cookies, Gordon shared his frustrations on how data was managed in his organisation. I asked him to talk through some of his data architecture principles so that I could get a feel of whether they made sense … which he did, and they were impressive and well thought-through. I may have even borrowed a few for my own use. “So, what’s the problem?”, I asked. “No-one takes any notice of them”, he replied dejectedly. “They might as well not exist”.
Implementing principles based on the 4E approach
It has been said that “a principle is not a principle until it costs you something[1]”: unless principles are acted upon, and enforceable, they are toothless. For the policing of the public health regulations to help reduce the spread of the coronavirus (Covid-19), the Metropolitan Police came up with an effective mechanism to do this. Their 4Es model was recently explained by Cressida Dick[2]:
“Throughout the pandemic we have adopted the approach of the 4Es: invented in London and adopted nationally. We engaged with people at the time; we explained the restrictions; we encouraged people to adhere to them and, as a last resort … but only as a last resort … we moved to enforcement”
We are trialling the implementation of data governance principles and policies based on this 4Es approach: to engage with our data producers and consumers, explain the data governance principles and why they are required, and encourage them to adopt and abide by them: to direct their actions and behaviours on how they manage and use data. In those instances where people do not follow the principles, we have means in place to enforce them via the formalised roles and decision-making groups as defined in our Data Governance Operating Model.
How to make the principles more engaging
An example of a core data governance principle might be to fix data quality issues at source and not where the issue manifested itself. This might be a clear statement for a data governance geek, but potentially less so to others: they are entitled to ask “why?”. Scott Taylor[3] evangelises the need to create a compelling narrative for your stakeholders: to bring your data story to life, make it more impactful, and ensure that the core message is memorable.
Plumbing analogous to the “Fix at Source” Data Governance Principle
So, to replay the “fix at source” principle using Scott’s approach, we can try out a plumbing analogy: if a drain is overflowing it is best to start off by understanding the plumbing system and then apply this knowledge to isolate the problem (e.g., a dripping tap) and fix the root cause (fit a new washer) rather than to mistakenly fix the consequence of the overflow (dig a bigger drain).
A Food Supply Chain analogous to Information Lifecycle Management
I favour the POSMAD information lifecycle that I first came across in Danette McGilvray’s book[1]: the activities to Plan for, Obtain, Store & Share, Maintain, Apply, and Dispose of data. Working in a major UK retailer, any analogy about Food is likely to resonate with my commercial colleagues. So, when I talk to colleagues about the need to manage data across its lifecycle, I refer to the established practices that we already have in place in terms of how we manage our Food products.
|
POSMAD
|
POSMAD applied to Data
|
POSMAD applied to Food
|
|
Plan
|
Prepare for the data resource: standards and definitions; data architectures; data structures, etc.
|
Planning for the sourcing of new raw ingredients: food standards, sourcing standards, supplier standards, etc. Plus, considerations of how the raw ingredient will be used in the preparation of meal products
|
|
Obtain
|
Acquire the data resource
|
Acquire the raw product from the farmer
|
|
Store and Share
|
Data are stored and made available for use, and shared through such means as networks or data warehouses
|
Appropriate storage for the raw ingredients throughout the supply chain, all the way through to how it is stored in our stores and after its purchase
|
|
Maintain
|
Ensure that the resource continues to work properly
|
Maintain the steady flow of raw materials through the supply chain to support the continued production of the product, and the availability and freshness of the product in our stores for our customers
|
|
Apply
|
Use the data to accomplish your goals
|
Use the raw ingredients in the production of great products for our customers
|
|
Dispose
|
Discard the data (archive or delete) when it is no longer of use or required
|
Best before dates, and our store procedures to check that products past their shelf life are removed from display and disposed of
|
Extending this analogy further allows us to position how data is an asset to our organisation in the same way as our raw products are our assets, plus emphasising the fact that we are already governing stuff in our organisation. Data Governance might be complex, but it is not a new concept.
The Highway Code analogous to Governance Principles
We have used the UK Highway Code as an analogy to our Data Governance principles, policies, and standards. The Highway Code provides the “governance and rules of the road” to ensure that all road users – pedestrians, cyclist, and motorists – have a shared and consistent way of using the same shared resource – roads and pavements – without colliding with one another. Playing out these themes with data in mind: the equivalent Data Governance principles and policies are the “governance of data” to ensure that all data roles – definers, producers, and consumers – have a shared and consistent way of using the same shared resource – data – without causing data “collisions”.
Keeping the principles alive
The Highway Code also helps to position the fact that Principles and Policies are a living document. You might be aware that the Highway Code is being updated on 29th January: the main changes are around the thinking that “those who do the greatest harm have a higher level of responsibility”. We need to ensure that we periodically check that our governance principles and policies are keeping track of … or are ahead of … legislation and how our customers and the wider society view our use of their data. As a closing thought do you have a data governance principle in place that is the equivalent to the medical profession’s Hippocratic Oath: “First, do no harm”? How do you govern the ethical use of data?
[1] Executing Data Quality Projects, Danette McGilvray
[1] Bill Bernbach, American advertising executive and founder of DDB, link
[2] Cressida Dick, Met Police Chief
[3] Telling your Data Story, Scott Taylor, link
[4] Executing Data Quality Projects, Danette McGilvray
