Embracing the challenge

Knowing where to start and setting achievable data goals

Sometimes it may seem that people don’t care about data as much you do (but we love and appreciate them anyway!). As you embark on your quest to convince management and database users to value data with the same fervour that you do, never underestimate the power of infectious enthusiasm. Whether you’re framing data clean-up as the solution to a problem or a vision for the future, the key is to motivate and encourage staff to embrace their role in contributing to the clean-up and enhancement of your organisation’s data repository.

It can be tempting to start conversations and reports by highlighting the breadth of the work ahead. While you might be able to generate some engagement with people by mutually complaining about how bad the situation is, this method isn’t a long term motivator. Fear and anger might drive action in the short term, but if we focus on how much work we need to do, we risk depressing motivation and enthusiasm for the project.

If you’re really excited about the project, be positive and tell people why. Bring them along with you on the journey. At the same time, have realistic expectations of yourself as you embrace the challenges ahead.



If you’re a database geek, the idea of pristine, perfectly formatted data is a thing of beauty. You can’t help but imagine how easy and efficient everyone’s job would be if only the entire database was filled with detailed, neatly entered data in the appropriate case and pluralisation and no fields that read “???”. It also baffles you that everyone else can’t see how delightful a standards-based data utopia would be.

In reality, no database will ever be 100% clean (but we can dream!). Try viewing clean-up on a spectrum where the goal is constantly improving the relevance, usability and accessibility of your data. Clean-up will always be an ongoing task as an organisation’s needs change. If you haven’t already, take a moment to wave goodbye to the idea that one day you’ll have perfect data.

Why is letting go of this perception important? Releasing yourself from unrealistic goals helps you concentrate on identifying the best data management goals for your organisation. It shifts the focus to meeting an organisation’s needs and not on creating cleanliness for cleanliness sake. Clean-up isn’t necessarily about creating the perfect database; it’s about making sure the data serves the priorities of all the audiences that engage with your organisation’s information now and in the future.


Sometimes the more we investigate, the easier it is to feel like our own database is the biggest mess of them all and that we are trailing behind our peers. While the scope of clean-up projects may be mighty, give yourself a break. Deep down we all know that every organisation has issues with data quality. Axiell has over 3000 customers and there is not one that doesn’t want to improve the quality of their data. Your data will likely always be one or two steps behind what you need in the moment, but if you measure your success based on the improvements you’ve made and the wins you’ve had so far, you’ll always find yourself making progress.


To measure progress towards data clean-up goals accurately, it helps to establish the definition of what “clean” looks like. There is no one size fits all approach to data clean-up. At one site, or even for one department, clean may mean that 25 core fields are complete and approved; at another it may only be a title and description. Regardless of the criteria for what constitutes a clean record, having a clear baseline is critical to understanding how close we are to our goals and keeping on track.

The Museum of London has implemented a tiered approach to ranking the status of a record. Quality is distinguished at two levels: core and extended. Records are updated by various staff members and undergo a quality assurance (QA) process. Since the assessment is recorded in the database, the documentation team can provide metrics on their progress at any time.

In the long run, you’ll probably save a lot of time and energy if you begin your project knowing what needs to happen to a data element to pronounce it “clean”. Standards should be clear, easy to communicate and measurable. Above all, they should be documented and readily accessible to all system users. Establishing expectations and providing reference materials keeps everyone on the same page and minimises the risk of departments going rogue. It also reduces the risk of undermining the quality of another user’s data due to a lack of understanding about how a field should be used.


Your data clean-up plans will have the biggest impact when they are tailored to the goals and priorities of your organisation and each of its departments. Look at your organisation’s five year plan – what are the top five priorities? How will these priorities affect who accesses data, via what channels and for what purposes? Based on the current status of your data and current input methodologies, how can you break down barriers and use data to propel the achievement of these goals?

Start with deciding a vision of where your data needs to be in five years to meet the goals of the organisation, however pie in the sky that sounds right now, then work backwards to identify the biggest needs for change.

Once you have a strong vision of what clean data is, consider making a bucket list of all the updates you would ideally apply. Review this list and prioritise which processes should be set into motion immediately and those that can potentially wait until a later date. For each clean-up task, determine if it can be broken down further into a series of three to five phases. Approaching each clean-up milestone in this way allows you to focus on smaller, more manageable chunks of activity at a time. It also makes it easier to communicate your current priorities to others.

Now that you have a plan, you can approach management. Once they jump on the data clean-up bandwagon, as funding and resources become available (it can happen!), steadily power through your action items, working your way through in stages to achieve your end goals.

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