Best Practice Guide
The ultimate step-by-step guide for analytic content creators.
Data Preparation and Enrichment
The success of any dashboard or analytical project is the trust and confidence that your end-users will have in the data. Trust and confidence are built when:
1. The data quality is high
2. The data is usable
3. The data is timely
4. The breadth of the data enables business questions to be answered
5. The data is secure
In this section, we explore the best practices that will enable you to build trust and confidence in your data and enable you to confidently create dashboards and deliver self-service reporting to your end-users.
There are seven key causes of data quality issues that every business needs to consider, monitor continually and plan for when preparing data for analysis and managing it on an ongoing basis.
Data enrichment is one of the key processes by which you can add more value to your data. It refines, improves and enhances your data set with the addition of new attributes. For example, using an address post code/ZIP field, you can take simple address data and enrich it by adding socio economic demographic data, such as average income, household size and population attributes. By enriching data in this way, you can get a better understanding of your customer base, and potential target customers.
Getting your meta-data layer right is a key step to enabling business user self-service, generating automated analysis and speeding up the creation of all of your content. Spending a little extra time on creating a great meta-data layer is highly recommended, as it will pay huge dividends later on. Here, we cover best practices, and why it's important.