August 15, 2019      3 min read

We are living in a big data world mainly attributed to how business is vastly conducted with the use of computers and technology. These modern-day processes and operations give us the ability to keep digital records of large data sets that can then be analysed for trends sometimes offering powerful and valuable insights.

On the flip side, bad data is becoming more of a widespread problem due to the sheer amount of useless data that we’re treating as good data, or when confidence in the data you rely on is low — these all have devastating consequences on your operations.

We’ve put together a list of four key warning signs of bad data that can signal something is wrong. These tell-tale signs of bad data are useful for inventory managers and business owners alike looking to provide their business with the right data to make the best decisions.

Having incorrect or insufficient records

Failing to keep on top of inventory stock movement can be a sign that you’re using bad data.

For businesses just starting to grow, they often underestimate the importance of inventory management, thinking their output isn’t large enough to warrant it. This results in costly mistakes that stifle business growth, leaving them wishing they’d invested in an inventory management software earlier!

For more established businesses, a sign that bad data is being used is when there may not be enough time, resources or qualified staff to keep on top of data. In the same way, if research, records and analytics are inadequate, insufficient and incomplete, your business can end up not measuring what you think it measures, leading to decisions based on inaccurate and poor data.

Questionable reliability

This questions the reliability of data and relates specifically to the tools or people giving or receiving data. A good litmus test here is to ask yourself: “Does the question/tool measure what we say it measures?”

Learn more about metrics you should be measuring

Having limited variations or range of data

When using a tool to measure data, does this actually reveal and produce the variation and range that you expect to find in reality? For example, if an inventory system shows the exact units and doesn’t merely round inventory stock up to the nearest 10 units or ignores smaller quantities altogether, then this depicts the true range of measurements and the data you rely on for decision making will be better. The compounding effect of missing information can make data ranges quite inaccurate.

Lacking validity

Does what you’re measuring really matter? It really is in a business’ interest to benefit out of what it can effectively measure data for and not all data is worth using or measuring. Questioning if this data is useful to predict things in the real world in a relevant and meaningful way, will determine whether or not the data is actually valid.

These situations are classic warning signs of bad data — if any resonate, then you could easily be using bad data without realising it. In order to maximise the value of data and analytics in your organisation to get true insights into your business, you need the right tools and processes in place. Data is often imperfect and incomplete, but with smarter data processes you can be well on your way to becoming a fully good data-driven business with positive consequences for inventory management, sales and profitability.

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