{"id":1673,"date":"2024-03-04T18:21:57","date_gmt":"2024-03-04T18:21:57","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1673"},"modified":"2024-03-20T18:26:35","modified_gmt":"2024-03-20T18:26:35","slug":"get-your-coat-youve-pulled-clean-data","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/get-your-coat-youve-pulled-clean-data\/","title":{"rendered":"Get Your COAT, You\u2019ve Pulled (clean data)"},"content":{"rendered":"
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If your data doesn\u2019t 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\u2019t go out in freezing temperatures without the appropriate coat, and you shouldn\u2019t work with data or make business decisions without the same level of protection \u2013 accurate data.<\/p>\n

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\u2019t last much longer than one season, and you\u2019ll need to buy another the next time winter rolls around again. It\u2019s the same with data \u2013 if you don\u2019t 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\u2019t be left out in the cold.<\/p>\n

So, what is COAT?<\/strong><\/h3>\n

Consistent\u00a0<\/strong>\u2013 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 \u2018courier,\u2019 while another might log it as \u2018logistics\u2019 or \u2018warehousing.\u2019 A taxi might be classified generically as \u2018travel\u2019 when it should be classed as \u2018Travel > Road Transport > Taxis,\u2019 and a project cost should be assigned to the same budget or GL code, not several. It could even be as simple as units of measurement. One person may use \u2018Litre,\u2019 another \u2018Ltr,\u2019 and another \u2018L\u2019 \u2013 but these should all be one format. This means everything can be reported accurately, you get an accurate picture of what\u2019s going on, and better business decisions can be made.<\/p>\n

Organised<\/strong> \u2013 Data is only useful if it\u2019s organised. Think of a messy closet; you\u2019re looking for your favourite top but can\u2019t 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, internal categories, and 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\u2019 performance for the quarter \u2013 you can pull that information quickly.<\/p>\n

Accurate\u00a0<\/strong>\u2013 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.<\/p>\n

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.<\/p>\n

Trustworthy\u00a0<\/strong>\u2013 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\u2019re 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.<\/p>\n

If they don\u2019t trust the data, then they might not use the fancy new expensive software you\u2019ve just spent tens of thousands of pounds installing. Or the new AI you\u2019ve installed may not produce the right results because it\u2019s learning from dirty data.<\/p>\n

Like a good coat, data is an investment \u2013 not a cost. By making sure it has its COAT on, you\u2019re saving time and 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<\/p>\n

So, get your COAT \u2013 you\u2019ve pulled clean data!<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

If your data doesn\u2019t 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. 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