> Data accuracy is important but sometimes the bigger problems to solve are discovery, advanced filtering, and readability. Discovery, advanced filtering and v

> 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