BIG DATA MANAGEMENT

Daasity Raised $15M in Series A Headed by VMG Catalyst to Make Smarter, Strategic Decisions With Data

Daasity | January 18, 2022

Data Analytics
Daasity, a consumer product analytics, and data platform, said that it had raised $15 million in Series A funding headed by existing lead investor VMG Catalyst. The money will make data more accessible and allow firms to augment their marketing channels with more tailored customer data.

“We're proud that leading investors in the consumer product brand space continue to partner and back us,” said Dan LeBlanc, CEO of Daasity. “Our 300% growth in under a year proves how scattered data is holding brands back from making urgent, critical business decisions. This capital will accelerate our vision of making actionable data accessible to all e-commerce brands.”

“We are thrilled to continue our partnership with Dan and the Daasity team as they enter their next phase of growth,” says Brooke Kiley, Partner at VMG Catalyst. “We consistently see how difficult it is for brands to act on their data, yet there is a greater need than ever. It is vital to utilize data to succeed in today’s competitive e-commerce environment. Daasity makes data accessible to all brands, empowering them with actionable insights and the ability to enrich their marketing channels with essential customer data.”

By centralizing actionable sales (Shopify, Amazon), marketing (Facebook, Google), and inventory data, Daasity reveals insights for over 1,600 consumer goods firms. Thanks to Daasity's customizable eCommerce data platform, team members, leadership, and analysts from firms like Manscaped, Vuori, Tula Skincare, Kopari, American Giant, and Caraway Home are now data-driven.

“Having our data at our fingertips has been key in allowing us to accelerate our growth strategy,” says Josh Knopman, Director of Growth & Digital Product at Caraway Home. “Daasity is a core component of our marketing stack. The ability to pull in data from dozens of sources and track daily performance at both a business and channel level gives our team the confidence and understanding to place smart bets faster. Analyzing all our customer data in one place allows us to better segment and personalize our marketing.”

Follow-on investments from Exeter Capital, VMG Catalyst, Mooring Ventures, Cove Fund, 1855 Capital, Okapi Venture Capital, and Serra Ventures fully funded the oversubscribed $15 million raise.

Spotlight

Analytics-generated insights are increasingly driving successful decision-making for communication service providers (CSPs). From network enablers to business support systems, there are opportunities to utilize insights gathered from careful analysis of network data. Big data is at the core of this opportunity. But the traditional technology view of big data is not enough. Rather than focusing on big data technology, CSPs should start from the business value they want to create and apply extensive telecom competence to understand how the relevant insights can be extracted from raw data before applying big data techniques as needed.


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BIG DATA MANAGEMENT

Aryballe Launches New Advanced Analytics Toolset

Aryballe | August 09, 2022

Aryballe, the pioneer in digital olfaction, today announced the launch of its Advanced Analytics toolset. Available exclusively in the cloud, Advanced Analytics is the first product in the global Aryballe SaaS platform, and is designed to support and provide remote access to data acquired using Aryballe NeOse Advance and Amplifier. Digital olfaction is catching the attention of manufacturers across an abundance of industries - from automotive to perfume and fragrance to consumer-packaged goods. It is increasingly becoming a tool for companies to ensure quality and consistency of their products and differentiate themselves from the competition. Through the use of tools such as Aryballe’s NeOse Advance with Advanced Analytics, users will gain improved sensor data analysis, which allows them to go beyond standard intensity and discrimination metrics. They will have access to higher-level metrics, such as chemical composition estimation via advanced machine learning, and advanced dataset filtering and outlier detection. Analyte zone selector is also available for customizing the window of sensor responses to analyze, which gives users more flexibility for complex samples. “It’s clearer than ever before that when it comes to optimizing odor and taste within the food industry, data analytics is power,” said Florian Viton, Vice President, R&D Management Executive at CJ CheilJedang. “Aryballe’s advanced analytics capabilities enable us to analyze digital odor data better, helping us enhance our digital transformation efforts and provide our consumers with the highest quality products.” Engineered to assist in chemical and olfactory analysis laboratories and product development, Aryballe’s Advanced Analytics solution provides users with a robust digital olfaction technology evaluation. It can be applied to three core scenarios: Improved assessment of measured samples for database building Enhanced flexibility for discriminating product samples Advanced metrics around limit of detection for intensity-based decision making “At Aryballe we are helping companies realize the power of the senses and introducing them to digital olfaction as a way to improve their manufacturing and quality control processes. “The introduction of Advanced Analytics will empower users to more quickly and better understand the data available to them and therefore apply it to important business decisions.” Sam Guilaumé, Aryballe CEO About Aryballe Based in Grenoble, France and founded in 2014, Aryballe combines biochemistry, advanced optics and machine learning to mimic the human sense of smell. The company’s premier product offering, NeOse Advance, uses silicon photonics technology to detect, record and recognize odor data, which powers improved decision making for R&D, quality control, manufacturing and end-user experiences. Aryballe Suite, the company’s cloud-enabled software, enables customers to intuitively access and customize analysis of odors based on their unique needs. With operations in France, South Korea and the USA, Aryballe works with global leaders in automotive, food manufacturing and flavor & fragrances.

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BIG DATA MANAGEMENT

Datafold Launches Open Source data-diff to Compare Tables of Any Size Across Databases

Datafold | June 23, 2022

Datafold, a data reliability company, today announced data-diff, a new open source cross-database diffing package. This new product is an open source extension to Datafold’s original Data Diff tool for comparing data sets. Open source data-diff validates the consistency of data across databases using high-performance algorithms. In the modern data stack, companies extract data from sources, load that data into a warehouse, and transform that data so that it can be used for analysis, activation, or data science use cases. Datafold has been focused on automated testing during the transformation step with Data Diff, ensuring that any change made to a data model does not break a dashboard or cause a predictive algorithm to have the wrong data. With the launch of open source data-diff, Datafold can now help with the extract and load part of the process. Open source data-diff verifies that the data that has been loaded matches the source of that data where it was extracted. All parts of the data stack need testing for data engineers to create reliable data products, and Datafold now gives them coverage throughout the extract, load, transform (ELT) process. “data-diff fulfills a need that wasn’t previously being met. “Every data-savvy business today replicates data between databases in some way, for example, to integrate all available data in a warehouse or data lake to leverage it for analytics and machine learning. Replicating data at scale is a complex and often error-prone process, and although multiple vendors and open source tools provide replication solutions, there was no tooling to validate the correctness of such replication. As a result, engineering teams resorted to manual one-off checks and tedious investigations of discrepancies, and data consumers couldn’t fully trust the data replicated from other systems. Gleb Mezhanskiy, Datafold founder and CEO Mezhanskiy continued, “data-diff solves this problem elegantly by providing an easy way to validate consistency of data sets across databases at scale. It relies on state-of-the art algorithms to achieve incredible speed: e.g., comparing one-billion-row data sets across different databases takes less than five minutes on a regular laptop. And, as an open source tool, it can be easily embedded into existing workflows and systems.” Answering an Important Need Today’s organizations are using data replication to consolidate information from multiple sources into data warehouses or data lakes for analytics. They’re integrating operational systems with real-time data pipelines, consolidating data for search, and migrating data from legacy systems to modern databases. Thanks to amazing tools like Fivetran, Airbyte and Stitch, it’s easier than ever to sync data across multiple systems and applications. Most data synchronization scenarios call for 100% guaranteed data integrity, yet the practical reality is that in any interconnected system, records are sometimes lost due to dropped packets, general replication issues, or configuration errors. To ensure data integrity, it’s necessary to perform validation checks using a data diff tool. Datafold’s approach constitutes a significant step forward for developers and data analysts who wish to compare multiple databases rapidly and efficiently, without building a makeshift diff tool themselves. Currently, data engineers use multiple comparison methods, ranging from simple row counts to comprehensive row-level analysis. The former is fast but not comprehensive, whereas the latter approach is slow but guarantees complete validation. Open source data-diff is fast and provides complete validation. Open Source data-diff for Building and Managing Data Quality Available today, data-diff uses checksums to verify 100% consistency between two different data sources quickly and efficiently. This method allows for a row-level comparison of 100 million records to be done in just a few seconds, without sacrificing the granularity of the resulting comparison. Datafold has released data-diff under the MIT license. Currently, the software includes connectors for Postgres, MySQL, Snowflake, BigQuery, Redshift, Presto and Oracle. Datafold plans to invite contributors to build connectors for additional data sources and for specific business applications. About Datafold Datafold is a data reliability platform that helps data teams deliver reliable data products faster. It has a unique ability to identify, prioritize and investigate data quality issues proactively before they affect production. Founded in 2020 by veteran data engineers, Datafold has raised $22 million from investors including NEA, Amplify Partners, and YCombinator. Customers include Thumbtack, Patreon, Truebill, Faire, and Dutchie.

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BIG DATA MANAGEMENT

Katipult Launches Enterprise-Grade Data Integration Capabilities to its DealFlow Platform

Katipult | July 06, 2022

Katipult Technology Corp. , a leading Fintech provider of software for powering the exchange of capital in equity and debt markets, announced today that its private placements platform, DealFlow, has been upgraded with the addition of a new enterprise-grade data integration module – DealFlow: DataHub. This module enables users to securely link their backend systems with the DealFlow platform, allowing them to directly populate subscription documents with the latest information from their systems of record. "We're very excited to announce the launch of the DealFlow: DataHub module. Our experience working with investment banks and broker dealers showed us that being able to seamlessly interface with their legacy systems of record is critical for helping them accelerate the pace of digital transformation. DealFlow:DataHub further amplifies the efficiency-boosting capabilities of DealFlow by removing yet another manual step in the private placements process. Not only is scalability improved, but there are also positive knock-on effects on compliance as data integrity and continuity are preserved." Gord Breese, Katipult CEO DealFlow:'s DataHub extracts large volumes of data from the commonly used systems of record in the industry, such as ISM or Dataphile. The data is then streamlined and used to populate the intelligent digital subscription documents that are core to the DealFlow platform. With the addition of DealFlow: DataHub, customers will no longer need to manually input or update the data that will populate the subscription documents. Further, DataHub will also enable single sign-on to the DealFlow platform, allowing users to sign on with their standard enterprise credentials. Katipult's goal with DealFlow is to help institutions unlock the full potential of private placements by streamlining as many processes as possible. DealFlow: DataHub represents yet another step forward in that direction. About Katipult Katipult is a provider of industry leading and award-winning software infrastructure for powering the exchange of capital in equity and debt markets. Our cloud-based platform and solutions digitize investment workflow by eliminating transaction redundancy, strengthening compliance, delighting investors, and accelerating deal flow. Katipult provides unparalleled adaptability for regulatory compliance, asset structure, business model, and localization requirements.

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BIG DATA MANAGEMENT

Nullafi Announces New Products, People, and Patents to Ensure End Users See Only the Data They Need to See

Nullafi | May 31, 2022

Nullafi, a fast-growing provider of data security software, today announced the general availability of Nullafi Shield, an agentless, Zero Trust data security solution that sits between applications and endpoints, tying in with existing network technology in order to redact data across applications. Nullafi intelligently recognizes and obfuscates sensitive data in transit before it gets to the user's device, no matter where it originates, what field it's in, or how it's labeled. Nullafi was co-founded by Robert Yoskowitz, CEO, and Elder Santos, CTO. Cameron Smith, formerly of Gigamon, has joined as VP of Product, and Walter Specht has joined as VP of Channel Sales. The company is supported by a world-class board of advisors, including Chase Cunningham, Chief Strategy Officer, Ericom; Simon Moffatt, Founder and Analyst, The Cyber Hut; Jason Stutt, SVP Global Sales, Virsec; Steven Hua, VP of Marketing, BetterCloud; and Becca Chambers, Senior Vice President Global Brand and Communications, Corel. "The sudden shift to remote work and the unprecedented increase in the number of business applications used daily has created unfettered and unmanaged access to sensitive data in plain text. Simply put, companies have completely lost control over who can see what data, and where — it's a mess. If you can't answer the basic question of who is allowed to see what types of data while they're doing their jobs, regardless of the applications they are using, that's a big problem. "It's the problem we built our solution to address in a new way. We wanted to bring a solution to market that was agnostic for applications, data sources, and data types. A solution that could protect employee and third-party access and could be set up and maintained quickly and easily. I couldn't be prouder of the incredible innovations that the Nullafi team has created to accomplish this goal." Robert Yoskowitz, Co-founder and CEO of Nullafi Because Nullafi Shield operates at the network level, it can work with any application, any data, anytime, anywhere, with no application integrations or endpoint deployments needed. With less than a 15-minute setup, customers can detect and redact sensitive data to solve data privacy, security, and access challenges. And because the software runs in clients' own environments, there's no need to worry about latency, third-party data risk, or downtime. In recognition of its innovation, Nullafi has been awarded eight patents from the United States Patent & Trademark Office (USPTO). These include inventions for desensitizing dynamic data, methods of continuously monitoring data integrity, as well as inventions that cover containing and nullifying the impact of data loss events. "No vendor had an intelligent, Zero Trust approach to the problem of data security until I saw Nullafi," said Chase Cunningham, a former Forrester Research analyst who is an advisor to Nullafi and Chief Strategy Officer of Ericom. "Cybersecurity is ultimately about data protection. Data is what hackers steal; it's what brings down companies. Nullafi offers the approach to the problem, removing the risk from data compromises across the kill chain." About Nullafi Nullafi is a fast-growing provider of data security software that helps customers quickly, easily, and comprehensively detect and redact sensitive data, automate policy enforcement, and eliminate risks such as data leakage, inadvertent access, and improper downloading — all while allowing business to continue without interruption. With Nullafi, users see only the data they need to see, giving organizations unprecedented control. The company serves primarily mid-market companies, technology resellers, and application developers in North America and is privately funded by angel investors. With rave reviews from analysts, multiple patents granted, and key partnerships already established, Nullafi is well-positioned to transform data security as we know it.

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Spotlight

Analytics-generated insights are increasingly driving successful decision-making for communication service providers (CSPs). From network enablers to business support systems, there are opportunities to utilize insights gathered from careful analysis of network data. Big data is at the core of this opportunity. But the traditional technology view of big data is not enough. Rather than focusing on big data technology, CSPs should start from the business value they want to create and apply extensive telecom competence to understand how the relevant insights can be extracted from raw data before applying big data techniques as needed.

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