Advance/Newhouse Acquires 1010data For $500M

Advance/Newhouse has decided to acquire big data analytics company 1010data for $500M. 1010data facilitates big data discovery and data sharing by means of a spreadsheet-like interface. The platform boasts predictive analytics, reporting and visualization as well as solutions for data sharing and monetization. The 1010data management team will lead the company but the new capital will be used to accelerate product development and expand sales operations. The 1010 platform gained initial traction in the financial services industry but has subsequently expanded to a customer roster of over 750 companies that also include retail, gaming, telecommunication and manufacturing. 1010data’s acquisition by Advance/Newhouse illustrates the vitality of market interest in big data discovery and data visualization solutions. Advance/Newhouse solutions is the parent company of Conde Nast magazines and Bright House Networks.


DataHero Finalizes $6.1M In Series A Funding For Its Data Visualization Platform

DataHero today announced the finalization of $6.1M in Series A funding led by existing investor Foundry Group. The funding raise will be used to scale the company’s operations in anticipation of rapid growth and customer demand for its cloud business intelligence solution. As told to Cloud Computing Today in a phone interview with DataHero founder Chris Neumann, DataHero gives organizations the capability to rapidly visualize data in structured and semi-structured form by means of a cloud-based platform that accepts the intake of data in csv, Excel or integrations with select third party data feeds. For example, customers can load one or more Excel files into DataHero’s business intelligence platform to visualize, transform and perform drill-downs on data. The DataHero platform features connectors to a range of third party platforms that include Marketo, HubSpot, Salesforce, Zendesk, Google Analytics and cloud storage platforms such as Box, Dropbox and Google Drive. In comparison to other data visualization platforms, DataHero focuses on non-technical end users that need a streamlined and simplified path toward visualizing the larger significance of data of interest. Today, DataHero also revealed the appointment of serial entrepreneur Ed Miller as CEO. Miller has led a number of tech startups and his appointment underscores both DataHero’s historical growth as well as the urgency of its plans to gear up for the next phase of its evolution. Expect DataHero to continue expanding the roster of platforms with which it integrates and deepening its analytic capabilities as it continues to deliver on its sweet spot of visualizing and presenting data to end users in ways that empower business stakeholders to make more convincing cases for their business decisions using data presented in compelling and palatable forms.

ExtraHop And Sumo Logic Collaborate To Deliver IT Insights That Combine Wire And Log Data

Wire data analytics leader ExtraHop and machine data analytics vendor Sumo Logic recently announced a partnership whereby ExtraHop’s wire data will complement machine data aggregated by Sumo Logic’s cloud platform. The partnership brings together ExtraHop’s leadership in wire data analytics and Sumo Logic’s recognized machine data analytics platform to create a unified framework for event detection and management. As a result of the collaboration, ExtraHop’s Open Data Stream delivers real-time, streaming feeds of wire data to Sumo Logic’s platform for aggregating and analyzing machine data. Meanwhile, Sumo Logic customers enjoy access to a more comprehensive universe of data about an IT infrastructure and its constituent set of applications and networking topology. ExtraHop’s real-time wire data enhances Sumo Logic’s cloud-based machine data platform with L2-L7 wire data as illustrated below:

The ExtraHop dashboard depicted above elaborates the ability of the ExtraHop platform to analyze wire data that contains insights regarding application performance, security and infrastructure availability. The Sumo Logic dashboard shows the integration of ExtraHop’s wire data into its platform and its corresponding user interface. ExtraHop’s partnership with SumoLogic delivers real-time data feeds to Sumo Logic’s cloud platform that are ingested into Sumo Logic’s cloud platform for the purpose of delivering actionable business intelligence about the health of IT infrastructures based on the aggregation of log and wire data. The graphics differentially illustrate how ExtraHop’s wire data enriches Sumo Logic’s aggregation of machine data by providing it with an additional dataset that Sumo Logic’s cloud platform can integrate into its massive, multi-tenant unstructured cloud database built on Amazon Web Services to deliver advanced analytics and data visualization regarding the detection of infrastructure and application related events.

Mark Musselman, Vice President, Strategic Alliances at Sumo Logic, remarked on the significance of the partnership between ExtraHop and Sumo Logic as follows:

Adding ExtraHop data as a new source into the Sumo Logic service for proactive analysis against other feeds enables IT teams to gain deeper performance, security and business insights from across IT infrastructure. Sumo Logic’s cloud-native architecture means the service serves an aggregation point for diverse data sources. The result is an IT team that acts on timely information from within their infrastructure – even information they did not know to ask for. A critical piece to the puzzle lies in Sumo Logic’s Anomaly Detection, a proprietary capability that delivers insight from patterns in data and insights beyond what IT teams themselves know to query.

Here, Musselman comments on the way in which ExtraHop’s data facilitates “deeper performance, security and business insights” by serving as an additional data source that enables advanced analytics about enterprise IT architectures. The integrated data repository marked by the confluence of ExtraHop wire data and Sumo Logic log data leverages Sumo Logic’s proprietary advanced analytics and machine learning technology to deliver notifications about events of interest within the infrastructure while iteratively refining those same alerts in correspondence with the actions initiated by the recipients of those same notifications. In all, the partnership between ExtraHop and Sumo Logic underscores the significance of wire data for analytics related to machine data analytics and the internet of things while concurrently enriching the capabilities of Sumo Logic’s cloud-based log management and analytics platform. With ExtraHop’s real-time wire data now streaming into the Sumo Logic platform, the case for a Sumo Logic IPO grows stronger while ExtraHop similarly benefits from enumerating the value of its wire data aggregation and analytics technology.

Trifacta’s Big Data Transformation Platform Leverages Machine Learning To Streamline Data Analysis

Hot on the heels of its $12M Series B funding in December, Trifacta recently announced the general availability of the Trifacta Data Transformation Platform. Based on its innovative Predictive Interaction™ technology, the Trifacta Data Transformation Platform uses visualization and machine learning to streamline and enrich user-level interactions with Big Data such as the type experienced by data scientists and business analysts. Trifacta’s Predictive Interaction technology features three components: (1) visualization of big data that empowers analysts to specify trends, values or analytics of interest; (2) interaction whereby the analyst responds to the data visualizations; and (3) prediction of the data transformations suggested by user interactions, with corresponding visualizations of the data transformation. The platform’s machine learning capability iteratively responds to user behavior to generate analytics of increasing value and interest. As a result, users can swiftly proceed from a raw, unprocessed archive of big data to incisive analytics and visualizations without the pre-processing, data cleansing and data transformation steps that are typically necessary to obtain deeper insights into about the data in question. The Trifacta Data Transformation Platform enables business analysts without scripting experience to derive nuanced insights about big data and additionally amplifies analyst productivity by means of its unique visualization and machine learning technology platform. Trifacta Customers include Lockheed Martin and Accretive Health, both of which remarked on the way in which the Trifacta Data Transformation Platform accelerates the data analysis lifecycle and streamlines user workflows. Trifacta’s technology is unique in the Big Data industry because of its focus on streamlining and enhancing the end user of big data analysis. Given the ubiquity of data visualization in the industry, much of the platform’s ability to differentiate itself will hinge on the sophistication of its predictive modeling and machine learning capabilities.

Treasure Data Partners With Tableau Software For Its Big Data Acquisition, Storage And Analytics Platform

Today, Treasure Data partnered with Tableau Software to enhance the ability of its customers to visualize and produce analytics on data stored within its platform. As a result of the partnership, “Tableau technology [will be] directly integrated into the Treasure Data Service,” meaning Treasure Data customers can easily take advantage of Tableau’s renowned data analytics and business intelligence capabilities. Tableau, meanwhile, stands to benefit from contributing to Treasure Data’s cloud-based, managed service for big data acquisition and analysis. Like Amazon Kinesis, Treasure Data is designed to accommodate the collection of data from sensors, internet feeds, machine logs and machine data from the internet of things. Given the managed nature of the offering, Treasure Data handles data management functionality such as the scaling of the database, fail-over, load balancing and replication. Subsequent to loading, customers can analyze and visualize their data in minutes as illustrated below:

As the graphic indicates, the Treasure Data service features two modalities of acquisition: deployment of a code-based “Treasure Agent” designed to collect real-time streaming data, or a bulk import from databases or applications by means of a parallel loading process. Customers have the option of filtering incoming data or storing it raw, within Treasure Data’s “proprietary database technology as well as Hadoop components.” Once data has been successfully loaded, customers can analyze the data using Tableau’s integrated technology, Treasure Viewer, SQL queries. Metric Insights or Excel.

Today’s news of Treasure Data’s partnership with Tableau builds upon a recent announcement regarding the company’s market traction as evinced by a quarterly growth rate of 50% for the addition of new customers, alongside a 90% quarterly growth rate for the amount of data stored within its platform. Treasure Data’s continued success will hinge on its ability to convince customers to opt for its fully managed big data acquisition and analytics platform as opposed to integrating a business intelligence platform on top of a Hadoop deployment. That said, Treasure Data’s technology for facilitating the acquisition of streaming data has few counterparts in the industry, until, of course, competitors emerge and multiply within the big data landscape.

Trifacta Finalizes $12M In Series B Funding For Data Transformation Platform

Trifacta today announced the finalization of $12M in Series B funding in a round led by Greylock Partners and Accel Partners. Trifacta, a data transformation provider, delivers a solution that helps customers standardize and transform data into a form that optimizes the degree of actionable business intelligence that can be derived from that data. More importantly, the solution features a user experience that enables analysts and data scientists to leverage machine learning to transform data as desired while minimizing the writing of scripts and the execution of complex operations on datasets. The Trifacta platform strives to deliver a highly intuitive user interface for transforming data that enables business analysts to engage data in highly sophisticated ways while concurrently improving the productivity of data scientists. As such, the solution tackles two, often overlooked problems in the data analytics space, namely, transformation of data into a useable form, and improving the productivity of analysts engaged in the work of that transformation. Trifacta’s data visualization functionality and algorithmic, machine learning-based mapping of user interactions with data works to understand, recommend and optimize workflows for interacting with data. In an interview with Cloud Computing Today, Trifacta CEO Joe Hellerstein noted that the solution is currently in public Beta amongst customers from a wide range of verticals, all of whom face the same problem of transforming their data into a form that facilitates development of nuanced, actionable analytics and visualizations. Today’s funding raise brings the total capital raised by Trifacta to $16.3M.