| I had a read through some of the comments here and it seems to me that the predominant stance is that reports are only useful for understanding the what and the why is addressed through more focused, thorough analysis. Also, most comments mention self-serve analytics are silly because stakeholders often don't know what they're looking for or they rather have a summary. I understand this, but I disagree with some of this or have trouble understanding how this can be applied in practice: - Reports can absolutely be built in a way that is flexible enough to enable knowledge discovery. If instead of creating a chart that plots Conversion Rate over time, instead create a chart that plots a Primary Metric against a Primary Dimension and use parameters to allow users to choose what the Primary Metric and Primary Dimension are. This drastically reduces the maintenance costs of reports because you don't need to create more charts, rather you just need to make new data available. - This design strategy can be expanded to Secondary Metrics, Secondary Segments and Splits to enable comparison between segments. This is a big step towards finding out the why - If you're a big business with both a team of BI developers and a team of Data Analysts I can imagine you'll have plenty of resources to conduct more thorough analysis whenever they are needed. But if you're a startup, you probably have a few Analytics Engineers doing both BI development and analysis. How do you enable them to do both if stakeholders most often don't know what they need? You have to be efficient and I don't think that means having these few Analytics Engineers holding stakeholders hands through a series of discussions to figure out what the hell do they even need... - Why would you not want everyone in the business to be able to discover new things in the data? Why only allow data analysts to do that? If you provide a platform that enables data exploration in a guided way to avoid wrong use/interpretation of data, isn't it best to open it up to everyone? More people looking into data = more hypotheses = higher probability that at least one of them will be proven and very impactful. - I think there are different types of data work: setting up data architecture to collect and transform data into a format that enables easy analysis; building solutions for monitoring KPIs (the what); building solutions for understanding the drivers of KPI fluctuations (the why); advanced analytics to support decision making (the actions). My opinion is that the real value is in the last point. Whatever we can do to serve the other needs with minimal effort, we should do |