Muddy Boots, Clean Data

Best Practices

Our boots might be muddy but we like to keep our data clean. Through the years we have developed best practices and tricks that we use regularly to keep your data clean and useful. Your equipment master database is central to what you do and it’s only as useful as the data it contains. Our customers have added to that experience and we have compiled all that knowledge and made it available to you so you can be confident your information is correct.

Maintaining Data is like tending a garden

If you have ever tried to grow a garden you know that planting is just the start and just planting the seeds will not ensure you have fresh veggies to eat or pretty flowers to enjoy, it’s the tending that makes the difference. Daily watering, frequent weeding and maintaining a good fence to keep the deer and rabbits away is what will keep your garden healthy and thriving.

Your data in muddy boots is no different. Implementation may seem like the whole process, but it’s regular weeding, care and protection that will ensure you harvest the rewards.


Take the time to review your data at regular intervals and don’t take it personally when you find mistakes – that’s the goal of this process.


  • Break it into small chunks – perhaps fields or areas – and focus on one chunk at a time

  • Use tools like Petro Ninja and the monthly public data load to make sure your internal data matches the public data.

  • Remember this is a marathon, not a sprint. Some days it’ll feel like the bad data is winning – but that will all be forgotten when you can find the answers you need.


Don’t enter data carelessly. It might take you 20 minutes to fix now, but it could take you 5 hours to undo the mess down the road.


  • Excel is your friend. Use functions like the duplicate finder and match to avoid fat finger mistakes.

  • Don’t be afraid to ask for help. The Muddy Boots Team has lots of experience ensuring your data is sparkly clean. Whether you are looking for advice or maybe a helping hand we are happy to help.

  • TRAINING, TRAINING, TRAINING. It cannot be emphasized enough, anyone who will be accessing the system should receive training. It will prevent frustration, encourage usage and create a whole group of people who rely on the information – which means they care about quality! More people with access to more clean data who are using it to make informed decisions – that’s a win-win-win situation.


Good protection does not necessarily mean keeping people out, but it’s about giving people the appropriate levels of access and ensuring each level receives the right training.


  • Know who will ultimately be responsible for the data – ensure this individual has the support and training they need to make this happen.

  • Make one person responsible for the quality of the data. This doesn’t mean they have to do it all, but it will make sure nothing slips through the cracks.

  • Use the roles access in Muddy Boots to fine tune access. If someone’s role changes make sure their access changes with it.

  • Set up processes to help communicate changes that are easy to follow and well dispersed throughout the organization.

  • Make people accountable. Every change is tracked in, ensure people know that quality is important.

Have a story or tip of your own you’d like to share? Pass it along to, we would love to hear it.