8 Amazing Big Data Companies You Should Know, But Probably Don’t
The Big Data industry is changing weekly and unless you pay very close attention to who’s who in the maelstrom of emerging big data innovators, these upstarts might have slipped under your radar. Expect to hear more about some of them, and the technologies, services and applications they are developing, in the near future. Of course in the fast currents they are trying to swim in, others may sink without a trace!
Crowdflower calls itself a “data enrichment platform” and uses the power of crowdsourcing to sift through its customer’s datasets in search of the insights they are looking for.
Their network of 5 million data scientists work in a similar way to Kaggle. However instead of concentrating on solving big, one-off problems for giants like Netflix or Google, they can be put to work solving more everyday data dilemmas for companies of any size.
Flatiron is helping the fight against cancer with its OncologyCloud, They are aiming to build the biggest database of medical information relating to cancer and make it available to doctors, patients, teachers and researchers through the cloud.
It works on the principle that aggregated data is currently only collected from a very small sample of cancer patients – the 4% which take part in registered clinical trials. The wealth of data from the other 96% which could hold vital clues to causes and treatments often remains locked away in medical records and doctors’ notes.
Flatiron hopes by aggregating this information through its data capture technology then professionals around the world will have a powerful, data-driven tool at their disposal which will lead to new treatments and greater survival rates.
LendUp has built a reputation for itself as a “responsible” alternative to payday loans companies, which traditionally charge exorbitant interest rates for short-term loans, backed up with high charges and aggressive debt collection practices if customers miss payments.
Their credit-scoring is built around a big data analysis of the lending landscape, meaning they can more accurately assess the risk of every application, and offer fairer rates of interest. They offer a way for individuals with legitimate need to access short-term credit at interest rates comparable to a credit card or bank loan.
Infobright styles itself as a “data analytics platform for the internet of things”.
Their algorithms and database architecture are designed to help companies who are generating large amounts of machine-driven data – for example from sensors attached to manufacturing equipment in an industrial environment. These datasets can grow very large, very quickly and this service is designed to keep it all manageable.
After marketing, detecting fraud is one of the most popular uses that companies of all sizes are finding for big data, particularly if they rely on online transactions for their revenue.
Feedzai claims it can reduce the fraudulent transactions that your company handles by up to 80%, blocking them if its predictive algorithms find that the transaction fits a profile which is too risky.
Reducing fraud across the board means increases in profits for retailers which can be passed on to consumers in the form of lower prices, hopefully saving us all a bit of money!
Data curation is about managing the data flow coming into a company and ensuring that the right data is being collected to match the job at hand. Tamr has developed services to automate the identifying, cleaning and preparing the necessary information for analysis.
Part of their premise is that data scientists need a clear, concise representation of the data they are studying in order to be able to draw reliable conclusions, so Tamr offers an interface for drawing all of your data sources together. It is also designed to work with a high level of automation – making many of the decisions about what data to include, and how to present it, automatically through machine learning algorithms. However, it will “reach out” to human experts on your team – asking the most appropriate person for help when it needs to make a decision above its pay grade.
Losing customers (churn) is a good way for a business to fail. So Appuri lets a business put data algorithms to work to track the whole customer life cycle – the contact and interaction each customer has from the moment they start buying your products or using your services, to the moment they stop.
The idea is that this will throw up insights into why your customers stop using your service, meaning you can put changes in place to stop it happening. If you know a build-up of events is taking place that can be expected to lead to high churn rates, you can launch a counter-attack, with a campaign designed to win back departing customers.
Gnip is designed to help businesses make sense, and gain insights from, the ever-growing mountains of data being broadcast over social networks.
The public is getting more and more comfortable with the idea of sharing details of their lives (such as what they buy) over Twitter, Facebook, Instagram and many other services. This offers a way to analyze your own network and hopefully gain valuable insights into how you can help them solve their problems or fill their needs.
As always, I hope you enjoyed my post. I’d love to hear from you about any other companies you would add to this list or indeed any other comment you might have on the topic. For more, please check out my other posts in The Big Data Guru column and feel free to connect with me via Twitter, LinkedIn, Facebook, Slideshare and The Advanced Performance Institute.
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