As someone who has spent 13 years in the weeds of data, I witnessed the rise of the "data-driven" trend first hand. Before starting and selling my first data startup, I spent time as a statistical analyst building sales forecasting models in R, a software engineer creating data transformation jobs, and a product manager running A/B tests and analyzing user behaviors. What all these roles had in common was that they gave me an understanding that the context of data -- what it represents, how it was generated, when it was updated last, and the ways it could be joined with other datasets -- is essential to maximizing the data's potential and driving successful outcomes. However, accessing and understanding the context of data is quite difficult. This is because the context of data is often tribal knowledge, meaning it lives only in the brains of the engineers or analysts who have worked with it recently.

In the past year or two, many companies have shared their data discovery platforms (the latest being Facebook's Nemo). Based on this list, we now know of more than 10 implementations. I haven't been paying much attention to these developments in data discovery and wanted to catch up. By the end of this, we'll learn about the key features that solve 80% of data discoverability problems. We'll also see how the platforms compare on these features, and take a closer look at open source solutions available.

Explorium, a data discovery platform for machine learning models, received a couple of unannounced funding rounds over the last year -- a $3.6 million seed round last September and a $15.5 million Series A round in March. Today, it made both of these rounds public. The seed round was led by Emerge with participation of F2 Capital. The Series A was led by Zeev Ventures with participation from the seed investors. The total raised is $19.1 million.

In this special guest feature, Pallab Deb, Vice President and Global Head, Analytics at Wipro Limited, outlines how you can gain benefit from using your gut business instincts by combining them with extreme data insights. Pallab Deb in his current role heads the Wipro's Analytics service line which delivers state of the art analytics solutions to a global clientele. With his extensive experience in Information Technology, Pallab has not only held multiple leadership roles in Connected Enterprise Services (CES) service line of Wipro, client engagements & strategic alliances but also has led sales teams on complex consulting, system integration and outsourcing deals in North America and Europe in High Tech, Manufacturing, Retail & Consumer Goods, Life Sciences and Utilities industries delivering on aggressive sales growth targets for seven consecutive years. Organizations are tantalizingly close to a vast amount of data that can prove meaningful to business. There is a prodigious amount of text, visual and audio information flowing across media reports, company filings, government records, surveys, research documents, social media, messaging applications, blogs, email, IVR, machine logs, contracts, ERP, POS, CRM, MES, IoT, etc. Somewhere, within that blur of rapidly flowing real-time data, is the insight that could change your business.