VSC Labs – our venture builder arm regularly studies how start-ups in big data & analytics got funded: financing structures, basis for valuation, business modelling, team formation and quality, strategies for scalability, and more (see below).
We also proactively identify start-ups in Asia (starting with Singapore, Malaysia, Indonesia and the Philippines) and assess their ability to adopt similar approaches.
Once possible, we gradually start injecting the right dosage of talents, projects, exposures, intellectual property assets (IPA) and linkages into specially selected start-ups – giving them the boost in both valuation and competencies.
For the month of January 2016, through personal network and relationships, we have met/will be meeting up to 12 VCs/investors in Singapore to showcase a few promising ventures at the preliminary stage, with at least half of whom we met, have indicated their strong interests to invest.
We’d love to share the experience and what it takes to be ready by inviting you to attend our roundtable soon.
Do leave your contacts by writing in to firstname.lastname@example.org. Our team will be in touch with you to explain the next steps.
8 CASE STUDIES FOR DISCUSSION
*all in USD$
Case Study #1: Fast-growing analytics company
Funding: $44.6 million
A single place that collects all customer data that can then be used by many other apps like analytics, marketing automation and databases.
Case Study #2: Self-service big data for business people
Funding: $48 million
Offers self-service big-data analytics software that makes it easy for data scientists to let business people ask questions and do research.
Case Study #3: The inventors and keepers of Spark
Funding: $47 million
Provides commercial support for a popular big data open source project called Apache Spark. Spark crunches through vast amounts of data very fast.
Case study #4: : turn data into charts no matter where its stored
Funding: $22.2 million
Companies have collected vast amounts of data stored in low-cost systems like Hadoop or open-source databases. Lets them easily turn that data into charts, graphs and answers.
Case study #5: Bringing big data to human resources
Funding: $46.5 million
Analyses HR data to help companies analysing their human resources efforts.
Case Study #6: big data from the oceans
Funding: $15.8 million
Built a big data platform that captures data from maritime sources. It can track a single ship and shipment to analysing worldwide trade patterns.
Case Study #7: letting smart computers analyse your job ads
Funding: $9.5 million
Uses machine learning to show how your job listings and candidate emails will perform before you’ve even posted them.
Case Study #8: instant, real-time analytics
Funding: $2.37 million
Analytics on data and transactions as they occur.
What’s important to note is that according to a McKinsey study,
Knowledge work automation. “Advances in software, especially machine-learning techniques such as deep learning and neural networks, are key enablers of knowledge work automation. These techniques give computers to ability to draw conclusions from patterns they discern within massive data sets. … Moreover, these machines can ‘learn’ more and get smarter as they go along; the more they process big data, the more refined their algorithms become.”