If your data doesn’t have a COAT, there could be a range of bad or costly decisions made which could affect the business performance, financial situation, risk jobs, or even the fate of the company. You wouldn’t go out in freezing temperatures without the appropriate coat and you shouldn’t work with data or make business decisions without the same level of protection – accurate data.
And, just like with coats, there are different levels of quality data services out there. If you buy a cheap jacket, it might not be waterproof or protect you from the elements, it won’t last much longer than one season and you’ll need to buy another the next time winter rolls around again. It’s the same with data – if you don’t invest in good quality service you will end up paying twice as much, if not more, in the long run to fix the earlier mistakes. Don’t be left out in the cold.
So, what is COAT?
Consistent – Generally data is used by many people or teams, which can lead to multiple classifications of one product. For example, one person might put DHL as a ‘courier’, while another might log it as ‘logistics’ or ‘warehousing’. A taxi might be classified generically as ‘travel’ when it should be classed as ‘Travel > Road Transport > Taxis’ and a project cost should be assigned to the same budget or GL code, not several. Or it could even be a simple as units of measurement. One person may use ‘Litre’, another ‘Ltr’ and another ‘L’ – but these should all be one format. This means everything can be reported accurately, you get a true picture of what’s going on and better business decisions can be made.
Organised – Data is only useful if it’s organised. Think of a messy closet, you’re looking for your favourite top but can’t find it as everything has been thrown in there. And, much like your closest, you can organise your data in different ways, depending on what you want to get out of it and that will produce different reports/analytics. You may want to assign data to employees, teams, departments, functions or internal categories, as well as time periods such as months and quarters, or year groups like P1, P2 etc… So, for example, when you need the information on the accounts that Sharon in Finance is working on, or the sales teams’ performance for the quarter – you can pull that information quickly.
Accurate – This can mean different things to different people. At its most basic level, accurate data is correct. In more detail, this could be no duplicate information; correct invoice descriptions; correct classifications; no missing product codes; standard units of measure (e.g. ltr, l, litres); no currency issues; correctly spelled vendors; fully classified data; or the right data in the right columns.
So, what does this mean? It means greater visibility across your business in several areas, allowing better decisions, as well as time and cost savings and increased profits.
Trustworthy – This is critical. Business decisions around jobs, staffing, budgets, cost savings and more are all based on data. Data is used by everyone from the bottom to the top of an organisation. You have to be able to trust that what you’re looking at is the right information, and you need it to be accurate in order for your teams to use the data in their daily jobs.
If they don’t trust the data, then they might not use the fancy new expensive software you’ve just spent tens of thousands of pounds installing. Or the new AI you’ve installed may not produce the right results because it’s learning from dirty data.
Like a good coat, data is an investment – not a cost. By making sure it has its COAT on, you’re saving time, money and avoiding future problems. And also like any coat, it needs to be maintained. You need to continually ensure your data is consistent, organised, accurate and trustworthy to get the most out of it.
About the Author:
With nearly a decade of experience fixing your dirty data, Susan Walsh is The Classification Guru. She brings clarity and accuracy to data and procurement; helps teams work more effectively and efficiently; and cuts through the jargon to address the issues of dirty data and its consequences in an entertaining and engaging way. Susan is a specialist in data classification, supplier normalisation, taxonomy development, and data cleansing and can help your business find cost savings through spend and time management – supporting better, more informed business decisions.
You can contact her on email@example.com.