Are you getting the most out of your data?

Repeatability

If you create reports that do not appear to show a consistent story over time then it is difficult to trust the information. How can you compare like-for-like sales if you have not taken into consideration school or bank holidays, for example? Customer data analytics relies on history of change in the relationship with that person.

So, can your customer analysis model recognise when there is a change of postal address or someone’s surname changes after a marriage / divorce? When does that ability to differentiate between customers really matter to your business? Insight generation is valuable when it can show you who are your valuable customers and which of your products provide you with the highest profit margin.

Timeliness

What happens if you don’t get the reports that you need from your data as soon as you need them? This is an important question for you to address before you embark on a project to implement a BI and analytics infrastructure. If your resourcing requirements are being driven by two week old data this could be costly. If you do not have up to date product availability then you could be discounting a popular line unnecessarily. Actionable data is what drives value from a data model. So getting customer data analytics to the right team at the right time is essential.

Accuracy

If the underlying data in your reporting is not complete or accurate then it can undermine what you are trying to achieve and may even lead to incorrect assumptions and actions. It is worth investing in data quality. Much of the data quality processes can be automated to ensure that the data that is in your warehouse is accurate from the point of ingestion. Showing the business value of data is only possible if the people throughout the organisation believe the integrity of the data reporting.

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