Most businesses make use of statistics to see what customers are doing, what customers might do and what's working for customers versus what isn't. But if you only ever see the statistical data at a glance and they aren't adding up, it could be that the data you're relying on is misleading you.
Data isn't always as reliable as we need it to be.
Here's how to know when business data or statistics are misleading – and why they could be throwing you off.
Spotting Lying Data
If the statistical data or reports you're seeing aren't matching up with what the rest of your business says in terms of returns or customers, it's likely that the data or statistics you have been relying on aren't accurate.
Start by breaking down the broader views of statistics into smaller parts. Here, you're more likely to see flaws – for example, viewing the past four weeks as opposed to the past six months of data.
Sometimes you might need the services of a data analyst who can tell you where your statistics went skew.
Sometimes Data is Influenced
Data can be influenced, sometimes by competitors. The use of spam, spam bots and fake reviews can make your data go awry, and while things might look right at a glance, all it takes is a closer look to see that outside influences outside your control have been driving your statistical data into the ground.
Companies can take protective measures against this, including hiring cybersecurity experts or locking their social media pages where the problem of fake data becomes particularly bad.
Sometimes Data Doesn't Give the Whole Picture
There's one thing that can be more damaging to your business than incorrect data, and that's partial data. Where data doesn't give you the entire picture or forgets to including a single crucial factor in the equation, it risks skewing all of your statistical data – and it can make all of it unreliable.
Where data looks like it might be lying, take a closer look at any possible factors that you might have missed the first time and recalculate for the best results.
Sometimes Data Samples Are Too Small
Smaller samples of statistical data are sometimes used to see larger amounts at a glance. For example, surveying ten people and using the same statistics to apply to a hundred of them. This is a common quicker way to get to a statistical answer, but where you're hoping to have a more reliable picture, a larger amount of statistical data (usually over a larger amount of time with a larger sample size) is needed in order to achieve any reliable statistics that you can use.
Always Use Reliable Business Intelligence Software
As a last resort, if you've checked out everything else and your statistics still aren't adding up, the problem could be the software used to quantify your statistics. Always make use of the most reliable business intelligence software that you can find. It's becoming a more exact science every day, and software can now even use AI in order to tell you where possible statistical flaws or shortfalls might lie.