6 steps on how to get setup with your first data reporting project

Throwing analysts at a problem might be a short term solution to solving your reporting needs but does not build a process for repeatable analysis.

I’ve used these 6 steps to be able to set up reporting in the right way over and over again. It doesn’t matter if you are a startup or a 1000 person company this 6 step process can be used for all of your data reporting use cases.

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Figure out which project you want to add visibility to

Popular projects for companies starting out include – visibility into go to market strategy, how customers are using your product, different segments of users based on behavior.
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What questions do you need to answer?

Once you have your project, you can work backwards and figure out what are the questions you need to answer on a daily basis in order to add the right visibility to your project. For example – As a marketer I want to be able to see the lead source for every sale. Do this for every persona inside your company.
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What are the data sources you will need?

Once you have the questions you need to answer you can figure out what data sources are going to be needed in order to answer these questions. If you are not already collecting this data at this point you can figure out the right way of tracking it. Starting with manual tracking in google spreadsheets for data sources you are not tracking is a quick way to get started
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Ingest your data into a central data warehouse

The average startup has 10-12 different data sources. We need to build an automated pipeline to ingest all of this data into a data warehouse. Most companies waste time building & maintaining their own pipelines. This should be avoided.
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Figure out your data model

Your raw data is structured for your different source systems. We want to design a data model that is designed to answer the business questions we listed in step 2. During this step we are able to join the data from various different data sources and also clean and validate the data for reporting purposes.
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Implement a self – service BI layer.

Once we have the data modeled correctly in the warehouse, we want to set up the right BI tool to allow business users to slice and dice this data. Setting up the data for self service allows users to answer their own questions without having to wait for an analyst to run a report for them.

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