August 6, 2015
This blog is part of our “How to Be a Better Pricing Strategist” series, in which we asked top thought leaders and industry experts in the pricing field: “What is the best advice you could give a fellow pricing strategist?” Read all the responses here.
The quality of your pricing analytics can only be as good as the quality of your source data. Good data is essential to effective analyses and competent pricing decisions.
But, this does not mean that you should wait until you have a perfect dataset before you begin putting that information to use. Big data and perfect data are not synonymous, so do not become paralyzed in a search for a utopian dataset. Rather, keep a watchful eye for anomalies, and when one arises, take the time to investigate if that abnormality is valid or not.
You cannot chase every rabbit down every hole. But, when you do uncover and correct one segment of bad data, also check the immediate vicinity – items in the same product group, items from the same manufacturing location or vendor and items that consist of identical or similar components. You may be able to quickly discover and correct other issues with less research and exploration than was required for that initial finding.
Much like gardening, managing and improving the quality of your data is a constant, ongoing process. Just because you go through and pull all the weeds in the spring, does not mean that your garden will remain weed-free all year long. The vigilance of continually identifying, removing and correcting flawed data should help you recognize if you have recurring sources of faulty information, potentially allowing the root causes to be remedied at their origins rather than pursuing and fixing the effects of those sources of bad data.
To read more expert tips and advice, take a look at our Pricing Best Practices SlideBook here.