So for example, the author saw that supply chain team had difficulty managing the complexity and scale of their analysis in large part due to the scalability of their spreadsheet solution. I would have pushed them to use Airtable which is basically a more scalable spreadsheet. By choosing the data pipeline route, the people who understand how to improve the supply chain model and the history of decisions that went into it, as well as previous missteps, now have limited ability to experiment with improving it. In my experience, every rewrite of a system has something lost in translation which makes me think that in the authors example that the life of the analysts got better but may have made the quality of supply chain model worse.
In the long run, there is plenty of useful logistics software that should do everything they want but the most important thing is to empower the people with domain expertise in the data to be as close to the solution as possible. Better decisions are often a result of better information/experience than better analysis. Unfortunately I haven’t studied these vendors well enough to make any suggestions though I believe that the solutions are well defined enough to write textbooks on them, which suggests to me that existing software and I would mostly implement similar methodologies.
On the marketing and product analytics tools, I think 80% of the problems boil down to measuring conversion rates and the comparing those rates across different contexts to select for the contexts which improves those rates.
Another user mentioned heap, which is great product if you know you don’t know what contextual data is meaningful but you suspect that it’s partially in how they interact with other parts of your website. Personally I’d use heap judiciously since I suspect there will be limitations to how useful the historical data will be in the future and collecting everything is expensive. One limitation is that site interactions are only part of the potentially important context. Another limitation is that startups change rapidly, so their historical data often depreciates in terms providing insight into their current problems. For an extreme example, I’m sure zoom’s conversion data before and during pandemic look completely different. But even a small tweak to google’s search algorithm could totally change what type of customer finds your site.
Personally I’d advocate talking to customers, potential customers, and other stake holders to understand what is important and measure that. Most companies, currently do the opposite where they take a lot of measurements and then try to figure out what’s important. The first approach can probably be done in google analytics. The second I might try and use Amplitude which is I what imagine a tool like heap will eventually try to evolve into.
The hardest person to help with data in the organization is the CEO because really they use data as form sales tool and reporting. The closest I have seen a tool to doing this in a way the CEO could mostly self service is Sisu data. Though it’s the CEO so it’s probably reasonable to hire some help anyway.
Lastly data warehouses were the gold standard in the early 2010s but Presto is better fit these days for companies whose data is distributed across many different places.