List of Data Analysis Startup Companies Everyone Should Know
August 16th, 2016
Data analysis is one of the main IT trends of the last few years and the list of data analytics companies rapidly increases. Big data and its analysis are both fast-developing fields and they are not limited by the companies driven by information technologies. Other businesses can (and should!) profit from interpreting their data as well.
There are new opportunities for doing business, new horizons beyond the existing and traditional data storage like local CRM and ERP systems. Today’s data might contain anything from social media messages and clients’ travel information to Internet of Things data and many other.
Back in November of 2015, IDC research company published a report where they forecasted the cumulative growth (CAGR) for big data technology & services to be 23.1% from 2014 to 2019. Annual expenses in this segment will reach $48.9 billion by 2019.
The growing demand for solutions and services for big data analysis leads to the emergence of a large number of suppliers and numerous entrepreneurs are thinking about starting an analytics company. Data collection, storage, analysis, and transformation of raw results into real business profit is the focus of many of today’s popular big data analysis startups.
Check out the best companies for data analytics (according to our opinion as well as the investors' trust).
The startup was founded in September 2014 and was able to attract $30.9 million in investments. The founders were originally LinkedIn employees.
The main product of Confluent is a solution that allows to process user data quickly in real time and respond to their demands. In short, Confluent is focused on producing tools that make it easier to work with Apache Kafka messaging analytics system, which was developed by the team that later created Confluent.
Apache Kafka is a distributed messaging system for the software components communication. In conjunction with Confluent, it solves the streaming data management problem. Open-source Kafka can process data in real time with high bandwidth and low latency while Confluent product works as a fault-tolerant messaging system that can collect data from different sources, for example, user activities logs, data from measurement instruments or stock prices dynamic change. Within the first six months of 2015, the number of Confluent downloads has grown by 400%.
The startup analyzes HR data and provides advice on personnel management. According to the startup founders, human resources are one of the most valuable assets in business and its correct use will help the business to achieve success. Visier developers promise their product can estimate with a fairly high degree of accuracy which of the employees will leave the company within three months, which specialists will be needed by the business within three years, and who of existing staff is ready for promotion.
Operating on the market since 2011, H2O.ai startup has attracted over $33 million in investments. The startup offers an open-source machine learning platform that works with Hadoop and Spark. Working with H2O.ai is possible via a web interface or programming environment, including Java, Scala, Python, and JSON. H2O.ai supports database and common file types, including Microsoft Excel, R-Studio, and Tableau.
The startup was founded in 2013 and its goal was to solve the data analysis problem using available business intelligence tools without relying on special analytical software.
AtScale software allows you to work with data in Hadoop clusters using the familiar tools like Microsoft Excel and Tableau. In addition, AtScale Intelligence platform has offered its clients a service for hybrid queries - the ability to make requests directly in Hadoop, using queries for the MDX or SQL data.
BlueTalon’s mission is to ensure the safety of big data. One of the BlueTalon’s products is a Virtual Database service, which allows you to restrict access to information for various groups of users, regardless of where the information you want to protect is located. BlueTalon’s products can work with relational database management systems as well as with NoSQL, support Hadoop, and work on Microsoft Azure and Amazon Web Services.
An analog of AppStore, Algorithmia is a marketplace for algorithms. Created in 2013, the startup allows developers to sell their algorithms to interested people. In 2015, Algorithmia received an investment of $2.4 million. There are over 2,000 algorithms in the database at the moment and the user community is over 16,000 people. The site offers users to order an algorithm, buy one, and sell your own algorithm to other people. In addition, the authors can specify the size of royalties for the use of their product.
The startup was founded in 2014 and can boast an investment of $28 million. It’s main product Cazena is a cloud platform for big data management.
At the same time, they became the founders of the three new solutions like “data lake as a service,” “data warehouse as a service,” and “sandbox as a service.” The solutions allow you to store data, search it, send requests, archive the data, and in “sandbox as a service” solution you can also test out new algorithms and tools for working with big data. Cazena uses data encryption, can optimize the workload and work with infrastructure that consists of various technologies (Hadoop, MPP SQL, Spark and so on).
RJMetrics offers two data analytics instruments that help companies to analyze traffic and user behavior on their websites. These solutions are called RJMetrics CloudBI and RJMetrics Pipeline. The products are able to combine e-commerce projects’ data from different platforms and sources (for example, a visit to the site, mobile app, and Facebook page) and offer concrete ways to improve the conversion or amplify certain parameters of the business performance.
Wavefront is a real-time data monitoring system that is able to predict and prevent downtown in hardware and software, instantly identifying and diagnosing problems in their work. Wavefront product works with SaaS-products and allows to spot problems that are not seen by other analytical and monitoring systems as well as build a notification system for the errors found.
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