If it's hard for you than it's hard for your customers and they have a reason to pay for your product.
After I left a job where I developed a neural search engine for patents (years before BERT) I talked with many of the vendors in the enterprise search and what I found was that few of them did systematic work to improve the relevance of their results [1] and few of them tried to sell their product based on the quality of the results.
What they all promoted was ease of integration with hundreds of data sources, security, privacy, scale, rapid sync, etc. Looking at the way these got sold, I'd say that all of that is the core work and the actual search engine is an afterthought.
Yeh I tend to agree. Real value comes from carefully curating the data and applying smart optimizations, which is something few companies focus on. But I also get the sense that a lot of energy ends up being spent elsewhere - on integration, infrastructure, lots of fragmented OS libraries, etc at the expense of iteration speed and relevance-focused experimentation.
I was frustrated with enterprise search vendors and their customers because they didn't see it my way. Here are some ways of thinking about it.
Most cynically, enterprise software is bought by different people than those who use it. The buyers have a list of items to check and the fastest way to get eliminated is to not have an integration for a data source they have so vendors will put up a comprehensive list of them on their web site. The buyers will never test the relevance of the results against their data, though the users will feel it every day, unless the search engine is so bad that they just don't use it. (Common!)
On the other hand, if the integration doesn't work, you get recall of 0% no matter how smart and well tuned your search engine is.
I think a lot of founders and data scientists believe in a variant of the Pareto principle which comes down to "I want to do the 20% of the work that gets me 80% of the way there". The trouble is that a minimum viable product has to be viable, and you have to get to 100% of that minimum or you are always going to be a bridesmaid and never a bride.
The awful truth about data science, relevance, ML and all that is that data is dirty and takes a huge amount of work to wrangle. If you want "iteration speed and relevance-focused experimentation" you have to make investments in product, people and process to run more cycles in less calendar time. Look up my profile and ping me if you want to hear war stories.
I'd say this is normal? There may be some solutions popping up, but I haven't been drinking straight from X.com AI/ML firehose lately so I don't know of one unisolution at the moment.
If it's hard for you than it's hard for your customers and they have a reason to pay for your product.
After I left a job where I developed a neural search engine for patents (years before BERT) I talked with many of the vendors in the enterprise search and what I found was that few of them did systematic work to improve the relevance of their results [1] and few of them tried to sell their product based on the quality of the results.
What they all promoted was ease of integration with hundreds of data sources, security, privacy, scale, rapid sync, etc. Looking at the way these got sold, I'd say that all of that is the core work and the actual search engine is an afterthought.
[1] See https://trec.nist.gov/
Yeh I tend to agree. Real value comes from carefully curating the data and applying smart optimizations, which is something few companies focus on. But I also get the sense that a lot of energy ends up being spent elsewhere - on integration, infrastructure, lots of fragmented OS libraries, etc at the expense of iteration speed and relevance-focused experimentation.
I was frustrated with enterprise search vendors and their customers because they didn't see it my way. Here are some ways of thinking about it.
Most cynically, enterprise software is bought by different people than those who use it. The buyers have a list of items to check and the fastest way to get eliminated is to not have an integration for a data source they have so vendors will put up a comprehensive list of them on their web site. The buyers will never test the relevance of the results against their data, though the users will feel it every day, unless the search engine is so bad that they just don't use it. (Common!)
On the other hand, if the integration doesn't work, you get recall of 0% no matter how smart and well tuned your search engine is.
I think a lot of founders and data scientists believe in a variant of the Pareto principle which comes down to "I want to do the 20% of the work that gets me 80% of the way there". The trouble is that a minimum viable product has to be viable, and you have to get to 100% of that minimum or you are always going to be a bridesmaid and never a bride.
The awful truth about data science, relevance, ML and all that is that data is dirty and takes a huge amount of work to wrangle. If you want "iteration speed and relevance-focused experimentation" you have to make investments in product, people and process to run more cycles in less calendar time. Look up my profile and ping me if you want to hear war stories.
I'd say this is normal? There may be some solutions popping up, but I haven't been drinking straight from X.com AI/ML firehose lately so I don't know of one unisolution at the moment.