To paraphrase Jeff Bezos, we think trying to build reporting infrastructure like this is “undifferentiated heavy lifting” – it’s a complex undertaking that is not connected to your core capabilities, and thus, is always best outsourced.

However, if you’re determined to try and build this yourself, this guide will be a good starting point.

Platform configuration

You will need to begin with a review of your current data collection practices. This involves reviewing the set-up of Google Analytics / Google Tag Manager or a similar web analytics platform, as well as your digital advertising, e-commerce, CRM and payment platforms. These platforms must be correctly configured so that your data can be joined to create a single view of the customer.

Data collection

You must build and maintain custom API connectors to target and retrieve the data you need to create a single view of the customer. This includes advertising platforms (eg. Google Ads, Bing, Facebook, Adroll etc.), web-analytics platforms (eg. Google Analytics, Google Tag Manager) e-commerce platforms (eg. Shopify, Magento), payment platforms (eg. Stripe, Paypal) and CRM platforms (eg. Salesforce, Hubspot).

Data cleaning, formatting and joining

Once the data has been accessed, you must devise a process of cleaning, formatting and joining your data to ensure a connection is made between all of your advertising touch points, web analytics, and payment / CRM / e-commerce data. This complex step is the foundation for your analytics, data visualisations and data modelling.

Data visualisation

Once a clean data hub has been created, you will need to layer-on your analytics and dashboard insights.

Data automation

World-class analytics are not single-shot. They should be designed to be consumed on a daily basis, thus, once your solution has been implemented with the correct data schema, it must be automated to ensure the insights continue to flow beyond the initial implementation.

Data maintenance

APIs regularly update, and data structures change; data edge also cause additional complications. Thus, your API connectors require ongoing maintenance. Your entire analytics infrastructure relies on clean, correctly formatted data flowing from your API connectors, so this aspect should not be overlooked.