Last year, I had the opportunity to talk to the upper echelons of the finance organisation of one of the UK’s leading supermarket chains…about data quality. I’m bored already: thankfully they only had to endure (a strict) 4 minutes. If you are still reading, this is how it went...
Data quality: data governance.
Do you first need to actively govern the data that feed your reporting, planning and analysis platforms, or should you fix what is wrong when it reaches you and you identify issues?
Chicken: egg.
When you are eating at a restaurant you implicitly trust that the food you are being served is, not just tasty and satisfying, but uncontaminated. The farms on which the chickens were reared are governed by a strict set of government regulations, the kitchen in which the egg is poached has got a 5 star rating, but what if the waiter has just “paid a visit” and has forgotten to wash his hands before touching your plate? (Completely necessarily of course: he was under massive pressure from management to get your food to you promptly). We tend to have faith that this is not the case and most of the time this would go unnoticed anyway, only realising if we are the unlucky guest who has an unfortunate reaction to this bacterial transfer.
All of the expensive checks and measures have gone to waste and you are left with an unhappy customer, because of one sloppy waiter, or maybe because of one performance indicator that has unintentionally promoted bad practice. To be fully confident you need to know “Where does my egg come from, who touches it on its journey to me?”
Where does my data come from, who touches it on the journey to me?
Parts of this journey will be documented, there will be pockets of excellent governance and management practice to ensure adequate data quality for particular areas, the supply chain components of product on-boarding are usually good examples. But are these pockets ensuring that the data is adequate for finance’s needs?
Mapping & understanding the data journey is the first part of challenge. Asking the person or system providing you with data, “Where did you get this data from and what, exactly, have you done with it whilst it has been in your custody?”, then getting them to ask the same to their “data suppliers” and repeating until you get to the true sources of all the data is not a straight forward task.
Mapping the journey out can be very revealing. The tube map here depicts the convoluted journey taken by the data elements supporting just one simple regulatory key figure for a logistics client. This Clients often know that they have issues with the accuracy of data but cannot pinpoint where the data is open to being touched by dirty hands and in danger of material deviations from the truth. Where are the invisible copy and pasting between spreadsheets, how open is the process to “Could you just rework that spreadsheet for the board meeting that is in 5 minutes?”
Once this data journey is made visible you are able to start justifying investment in bolstering governance and putting in data quality checks at points of high risk. A portfolio of pragmatic interventions to measure and reduce the material impact of data issues could include: spot fixes; process monitoring; activity controlling; process automation; system implementation; system retirement; spreadsheet eradication; refocusing existing initiatives; or extending the reach of existing good practice.
You can call this Data Governance or Data Quality Management, you can call the practitioners Data Stewards, you can call them Chickens, or Eggs. The important things are that we
- Identify and utilise the good practices already within the business (maybe outside of finance)
- Incrementally deliver improved trust in data by executing a portfolio of initiatives.
All whilst moving closer to being able to stamp a “Lion Quality Mark” on your financial reports, planning and analytics.
At least this held their attention for 4 minutes, and they started relating it to their specific challenges and worries about the unknowns in their data journeys. It got them thinking about the importance of looking after their data through a combination of proactive controls and reactive monitoring - not just in the systems & processes that they are accountable for but all the way along the data supply chain. It got them thinking of ways of simplifying the journey by consolidating steps and the reducing opportunities for contamination.
Whatever your journey, however good your map, however often you wash your hands: good cluck!
