DataRPM, based in Fairfax, Va., announced Tuesday that it has closed a $5.1 million Series A funding round.
Led by InterWest Partners and joined by CIT GAP Funds, the round will be used to accelerate DataRPM’s global go-to-market strategy.“Our mission is to simplify business intelligence, said Sundeep Sanghavi, co-founder and CEO of DataRPM. “We provide insights, actions, and results from of the massive amount of data with a machine first, removing the cumbersome manual lift. Pairing our computational search platform with machine learning and natural language interface, greatly simplifies the process of gaining insights from data.”
Shankar Rao, CTO of Transaction Network Services, said, “We have used several BI tools in the past and with each we spent significant amounts of money and time on setup. We had trouble scaling and adapting to business changes. The solutions were also quite technical and required extra analyst involvement, distancing our business users from the data they needed. DataRPM came in and in less than 30 days provided us with an end-to-end BI solution with a significant cost of ownership reduction.”
According to DataRPM, the company changes the way individuals work with data, making analytics more accessible and user-friendly by solving the two main barriers to the adoption of data analysis — time consuming data modeling and usability. The DataRPM business intelligence (BI) platform removes those barriers, automating the data modeling process and employing a natural language question-and-answer interface to simplify data analysis and visualization, according to the company.
“By lowering the cost of ownership and emphasizing usability, DataRPM is making business intelligence a no-brainer,” said Khaled Nasr, partner at InterWest. “Until now, BI solutions and Big Data have largely ignored the data modeling process. DataRPM uses sophisticated algorithms to automate what is otherwise a heavy manual lift. Their combination of affordability and ease-of-use creates the opportunity for companies of all sizes to get meaningful insights from their data.”
