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Data accuracy is important but sometimes the bigger problems to solve are discovery, advanced filtering, and readability. I've played around with Mattermark a l

Data accuracy is important but sometimes the bigger problems to solve are discovery, advanced filtering, and readability. I've played around with Mattermark a little bit and I think it does a fantastic job in solving the later problems. From a hacker's perspective you can do curl/wget, awk, etc to aggregate and filter data from various sources yourself. But to make the same tooling available to a wider audience is what gets you to the market. Dropbox is a solid example, for instance, that made rsync super simple to use for anyone.

dmritard96 12y ago
agreed, we did some number crunching against crunchbase to look for the right investor profiles, making that into a usable product is much more involved
7Figures2Commas 12y ago
> Data accuracy is important but sometimes the bigger problems to solve are discovery, advanced filtering, and readability. Discovery, advanced filtering and visualization are increasingly solved problems. Open source solutions like Elasticsearch Kibana[1] make it incredibly easy to analyze and visualize large amounts of data, and to do it better than many paid services. For high-value commercial use cases, like those in financial services, data accuracy and completeness is all-important because your ability to identify the best opportunities and make good decisions is almost always proportional to your knowledge of the market. Take a seemingly simple venture capital use case: I want to identify pre-Series A fintech companies in California and New York founded in the past 3 years that have raised between $100,000 and $1 million and last raised funds 4-8 months ago. If funding data and corporate information is incomplete or inaccurate, and/or funding events have not been properly categorized, the list of companies surfaced will likely exclude companies that meet the criteria, and include companies that don't meet the criteria. For many use cases, it doesn't take many false positives or false negatives to render a data set effectively useless. [1] http://www.elasticsearch.org/overview/kibana http://www.elasticsearch.org/overview/kibana

dmritard96 12y ago

agreed, we did some number crunching against crunchbase to look for the right investor profiles, making that into a usable product is much more involved

7Figures2Commas 12y ago

> Data accuracy is important but sometimes the bigger problems to solve are discovery, advanced filtering, and readability. Discovery, advanced filtering and visualization are increasingly solved problems. Open source solutions like Elasticsearch Kibana[1] make it incredibly easy to analyze and visualize large amounts of data, and to do it better than many paid services. For high-value commercial use cases, like those in financial services, data accuracy and completeness is all-important because your ability to identify the best opportunities and make good decisions is almost always proportional to your knowledge of the market. Take a seemingly simple venture capital use case: I want to identify pre-Series A fintech companies in California and New York founded in the past 3 years that have raised between $100,000 and $1 million and last raised funds 4-8 months ago. If funding data and corporate information is incomplete or inaccurate, and/or funding events have not been properly categorized, the list of companies surfaced will likely exclude companies that meet the criteria, and include companies that don't meet the criteria. For many use cases, it doesn't take many false positives or false negatives to render a data set effectively useless. [1] http://www.elasticsearch.org/overview/kibana http://www.elasticsearch.org/overview/kibana