11 tips on data management by a shared services veteran

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Editor Coda
Jun 10, 2015

‘Master data management’. Never have I heard these three words so passionately voiced than during my recent interview with seasoned shared services leader Dan Foley. Now heading up the Mouchel shared services operation, Dan was deeply involved in the build and running of shared services centers for Diageo, ITV, Marks and Spencer, and Premier Foods. Considering this history, you can confidently say that when he talks about what’s what in shared services, people listen.

During the interview, my central question to him was: “What is the one thing you wish you’d known at the beginning?”

His response was clear: the importance of master data. Here we examine Dan’s tips on data management that all shared services leaders should know.

Tip 1 – Plan ahead properly for system change

This is particularly true when you are moving from one system to another or upgrading a system. When undergoing a system change, there is an intense focus on getting the system live, rather than getting the content right. Dan says this is a fine example of where a change program is easily harmed because people have underestimated the importance of planning ahead for how the data management workstream will work. Bad data planning is seen too often when moving from a legacy system to an ERP. Many legacy systems function by operating through workarounds, forcing data though old, unintuitive, unintelligent avenues. When moving to an ERP, all these flows can and must change, and must be supported in planning.

Tip 2 – Don’t give your data program to a junior person

It’s vital to ensure that the data, data archiving and data cleansing elements are in a safe pair of hands and being managed by someone that is trustworthy. It’s very typical to hear of shared services projects where junior members of the workforce are tasked with running important data programs. The lack of a senior member of the team on the program can be very costly for the business. The result can be business critical – a vendor being loaded incorrectly can lead to your supply chain grinding to a halt, or production lines stopping because recipes for products were loaded incorrectly. You may have great people, and great systems, but this counts for very little when the operation is brought to its knees because of bad data.

Tip 3 – Don’t put data off

According to Dan, data is the thing that shared services leaders are forever ‘getting around to’. This lack of prioritization can prove costly. Especially when starting up a center, there is a very strong possibility you won’t be able to operate if you haven’t channelled focus into data management. You can have people ready, processes ready, and systems ready, but if you can’t pay a supplier because their data wasn’t migrated, the shared services will be poor in delivery. According to Dan, data is the life blood of systems, and needs to be respected.

Tip 4 - Know what good looks like

Data has a huge impact on the success of shared services transformations. It’s key to bring in an experienced data management and maintenance professional to manage the workstream – a senior professional who has done this before. This is someone who knows what good looks like. Often inexperienced people will look at data and rate it as green, but a seasoned data maintenance person would look at the same data and only see red… flashing read. Use Dan’s catchphrase to ask if the data is green or gangrene.

Tip 5 - Build engagement

Spell out to the business what happens to it if data is managed badly. All too often the business is unaware, and they need to be educated, Dan says. Make sure you build awareness across the business on the importance of respecting data, and what excellent data management handling looks like, so they can support the investment in the solution.

Tip 6 - Think about where you situate your data managers

Physically situate as many people involved in the datastream in one area. For example if you are managing data cleansing across Europe, and you are co-ordinating the program from Manchester, it might prove challenging to see that the person in the Bratislava shared services center is not being pulled off the program by other distractions deemed ‘more important’. Data has a poor reputation when it comes to its business-criticality. People don’t get it, so to avoid your team being distracted by other seemingly important projects, Dan recommends bringing that person into Manchester for two months to complete the job there properly.

Tip 7 - Once clean, set up a structure to keep it clean

Examine your data cleansing routines, and have a strong data archiving policy. This is where you cannot afford to be relaxed about the structure that keeps your data clean. For example, postcodes: how the format of these is captured by your shared services will be one factor that determines how effective the center will be. I know! Who would have thought the space in the postcode would be so important. It is. And this is a good example where the devil is in the detail. Use the intelligence of the system to write your routines to check the data, cleanse the data if needed, and place the data back where it was found.

Tip 8 - Hibernate old data

See that the data in your system is live data. This makes the data management handling more focused and straight forward. If you have tens of thousands of supplier records that represent vendors not used in 3 years, hibernate them in a different location, away from the live data. You can always bring them back into the live system when needed.

Tip 9 - Train your keyers well

See there are no shortcuts or work arounds when it comes to data integrity. Train your keyers to such a degree that you are confident they will enter data in a way that ensures that the high standard required by the center is met.

Tip 10 - Get data ownership right

Data needs to be owned. Rarely will there be one person across the whole company that owns data, but this is an area worth re-examining. If one person were to own the data, it might be the CIO or the COO.

Typically it’s owned by the business owner within that area – i.e. the Head of Procurement would own vendor data. However, because vendor data is often managed by the shared services, it’s vital, says Dan, that the Head of Shared Services and the Head of Procurement work together to determine how the data will best be managed.

Tip 11 - Measure data quality

How do you measure the cleanliness of master data? This can be a challenge, but one obvious way, according to Dan, is based on the percentage of errors when processing. If your error rate is low, you will likely have solid data. Choose the processing error rate KPI that can tell you how good your data is, and use this as a chief metric for your data management quality.

As a closing note, perhaps warning, Dan has spent 16 years in shared services, and he sees master data management as the critical success factor that unfortunately gets left behind. He concludes on this subject: “it’s a failure factor – if you don’t get the data right, you’re setting yourself up to fail.”

With thanks to Dan Foley, Shared Services Director, Mouchel

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