The least reliable data management software in mining
Does any software work harder than the Excel spreadsheet? While it might be considered rudimentary software, at best, the humble spreadsheet is in widespread use. Every single day, all over the world, at every natural resource company, mission-critical data is stored in spreadsheets.
That’s a problem.
Here’s why.
Risks with using spreadsheets for geoscientific information management
Geoscientific information, including environmental data, is used in multiple ways for functions like reporting, forecasting and business calculations. It’s no secret spreadsheets are prone to human error and become outdated very quickly. To prevent inaccuracies, people develop their own spreadsheets and build virtual walls around them to limit data corruption. Before long, data becomes siloed throughout an organisation. Trust in data exists in pockets. There’s no consensus about accuracy – or anything related to the data. Sharing of data is limited and nothing like a single source of truth exists anywhere in the organisation.
It’s a mess. Everyone may have the best intentions when using spreadsheets for data management but the technology lets them down in multiple ways.
Sharing spreadsheets is difficult.
Sharing and updating a single master spreadsheet of all your geoscientific information across an entire team quickly becomes a logistical nightmare. Attaching spreadsheets to an email is a common way to do this, which puts your whole organisation at risk of cybercriminals. It also means the most current version of your geological data could be sitting unopened in someone’s inbox.
There’s no audit trail.
It can be difficult, if not impossible, to know who updated the spreadsheet last and whether the information was accurate and current. Historical data is easily lost.
Multiple versions of the same data quickly appear.
Everyone might start working from the same master spreadsheet but it doesn’t take long before someone forgets to upload changes or overwrites someone else’s updates. People start keeping their own copies to ensure their work is preserved and those splinter ‘master’ copies propagate quickly.
Reporting is hard.
If you’re pulling data from multiple spreadsheets, and need multiple people to provide you with access to their data, it can take hours or even days to produce a single report. And then you have no real guarantee that data from different sources is accurate or contemporary. Answering a simple question like, “Are we compliant?” can be a week-long task.
Scaling is next to impossible.
You may be able to find a way to manage data in one office, or for one commodity, but once you start adding more sites, more companies, or more commodities, it’s unlikely you’ll be able to manage your data in any kind of cohesive manner.
Limited validation.
You cannot apply business rules to your data in a spreadsheet. Excel does provide some validation for data entry but it’s closer in function to a spellchecker than any real business intelligence. It’s easy to corrupt your original observations and measurements – or enter incorrect obligations for your environmental data.
These are only some of the more high-level errors that occur when using spreadsheets for various forms of geoscientific information management. But here’s the real problem: humans are behind the success or failure of spreadsheets.
Research shows spreadsheet errors are unavoidable.
A swath of research concludes three things:
- Errors are rare on a per-cell basis. This provides a false sense of confidence. In large programs – like those found in mining data management applications – at least one incorrect bottom-line value is likely to occur.
- Errors in spreadsheets are difficult to detect and correct.
- Spreadsheet developers and businesses tend to be wildly overconfident in the accuracy of their spreadsheets.
No-one can say why people are so confident about the precision of their own data, especially when they’re rightly suspicious of everyone else’s. That’s true even of people they respect as colleagues. A paper published in Computer Science called “What We Don’t Know About Spreadsheet Errors Today: The Facts, Why We Don’t Believe Them, and What We Need to Do” shows:
- Humans cannot be error free no matter how hard we try.
- Our intuition about errors and how to reduce them is based on appallingly bad knowledge.
The paper concludes that the only way to reduce errors is to rigorously test your spreadsheets and implement a methodology used by software developers for code inspection.
If you have to go to that much trouble, isn’t it time to evaluate the risks of using Excel and consider using a quality and purpose-built information management solution for your most critical business systems? Let’s face it, when you’re dealing with mining or environmental data, it doesn’t get more business critical.
What an alternative data management solution looks like
Investing in mature information management software solutions like GIM Suite or EnviroSys gives you immediate advantages over spreadsheet systems.
- You have a single source of data
- There’s widespread trust in the data across your organisation
- Reporting is quick and efficient
- You can apply business rules at data capture
- You know when you’re compliant and when you’re not
- You can easily manage data from multiple sources
Maybe the best reason to invest in leading data management software for the natural resources industries is to release your staff from fiddling with data and get them back to their trained profession.
Using information management software that’s been purposely developed to manage geological data or environmental data is a good place to start. You can dispense with the spreadsheet and be assured you won’t end up eating humble pie due to poor data management practices.
Where to find reliable geoscientific data management software
Find out more about acQuire’s software solutions that provide a single source of truth for a variety of geoscientific information.
Contact our team to discuss your information management requirements when managing the earth’s natural resources.
