Prophecy, a low-code data engineering platform, raised $25M in Series A funding this week. The round was led by Insight Partners, with other participants from existing investors Berkeley SkyDeck and SignalFire, and new investor Dig Ventures. The funding will go towards building out the platform, as well as investing in the go-to-market side. Prophecy seeks to standardize data refinement for use at scale, making the process more predictable and visible.
On January 18, Vectice, a data science knowledge capture company, announced it had raised a $12.6M series A round. The round was co-led by Crosslink Capital and Sorenson Ventures. Additional participants included Global Founders Capital (GFC), Silicon Valley Bank, and Spider Capital. Vectice will use the funds to further expand its team, and to onboard select accounts into their Beta program. Vectice automatically captures the assets that data science teams generate throughout a project, and generates documentation throughout the project lifecycle.
Also this week, Diversio, a diversity, equality, and inclusion platform, raised $6.5M in series A funding. Participants included Chandaria Family Holdings, First Round Capital, and Golden Ventures. Plans for the funding include expanding the sales and client success teams, accelerating product development, and amplifying marketing efforts. Diversio combines analytics, AI, and subject matter expertise to understand where DEI efforts at organizations are getting derailed, and offer action plans for setting and meeting DEI goals.
On January 18, DataRobot released its State of AI Bias Report, assessing how AI bias can impact organizations, along with ways to mitigate said bias. Common challenges organizations face include the inability to understand the reasons for a specific AI decision, or the correlation between inputs and outputs, along with the difficulty of developing trustworthy algorithms and determining what data is used to train a given model. All of these challenges have led to some combination of lost revenue, customers, and employees, along with legal fees and reputation damage to the company; organizations are seeking guidance to avoid these issues.
On Wednesday, January 26, Domino Data Lab will host a free one-hour virtual event: “Unleashing Exceptional Performance,” focusing on data science. Featured speakers include surgeon and author Dr. Atul Gawande, and Janssen Research and Development’s Chief Data Science Officer and Global Head of Strategy and Operations Dr. Najat Khan. There will be two sessions to accommodate various timezones, one at 1300 GMT and one at 11 am PT/2 pm ET. To register for the event, please visit the Domino event registration site.
The hottest online game of January 2022 is Wordle, a deceptively addictive online game where one tries to guess a five-letter word starting from scratch. Perhaps you’ve started seeing a lot of posts that look like this:
OK, that said, the rules of this game are fairly simple: you have six chances to guess the word of the day. This game, created by software developer Josh Wardle, was adorably created as a game for his partner to enjoy. But its simplicity has made it a welcome online escape in the New Year. The website isn’t trying to sell you anything. It isn’t designed to “go viral.” All it does is ask you to guess a word.
But for those who have played the game, the question quickly comes up on how to play this game better. Are there quantitative tricks that can be used to make our Wordle attempts more efficient? How do we avoid that stressful sixth try where the attempt is “do or die?”
For the purposes of this blog, we will not be going directly into any direct Wordle sources because what fun would that be?
Here’s a few tips for Wordle based on some basic analytic data problem solving strategies.
Step 1: identify the relevant universe of data
One way to model an initial guess is to think about the distribution of letters in the English language. Any fan of the popular game show “Wheel of Fortune” has learned to identify R, S, T, L, N, and E as frequently used letters. But how common are those letters?
One analysis of the Oxford English Dictionary done by Lexico.com shows that the relative frequency of letters in the English language is as follows:
Letter
Frequency
Letter
Frequency
A
8.50%
N
6.65%
B
2.07%
O
7.16%
C
4.54%
P
3.17%
D
3.38%
Q
0.20%
E
11.16%
R
7.58%
F
1.81%
S
5.74%
G
2.47%
T
6.95%
H
3.00%
U
3.63%
I
7.54%
V
1.01%
J
0.20%
W
1.29%
K
1.10%
X
0.29%
L
5.49%
Y
1.78%
M
3.01%
Z
0.27%
This is probably a good enough starting point. Or is it?
Step 2: Augment or improve data, if possible
Stanford GraphBase has a repository of 5757 five letter words used as a starting point for analysis. We know this isn’t exactly the Wordle word bank, as the New York Times wrote an article describing how Wardle and his partner Palak Shah whittled down the word bank to a 2,500 word pool. We can use this to come up with a more specific distribution of letters. So, how does that differ?
Surprisingly, there’s enough of a difference that we need to decide on which option to use. We know that a lot of plural worlds end in s, for instance, which is reflected in the Stanford data. If I were doing this for work, I would look at all of the s-ending words and determine which of those were plural, then cleanse that data since I assume Wordle does not have duplicate plurals. But since Wordle is not a mission-critical project, I’ll stick with using the Stanford data as it has a number of other useful insights.
Step 3: Identify the probable outcomes
So, what are the chances that a specific letter will show up in each word? Wordle isn’t just about the combination of potential letters that can be translated into words. In a theoretical sense, there are 26^5 potential combinations of words that exist or 11,881,376 words. But in reality, we know that AAAAA and ZZZZZ are not words.
Here’s a quick breakdown of how often each letter shows up in each position in the Stanford five-letter data along with a few highlights of letter positions that stand out as being especially common or especially rare.
The 30.64% of words ending in “s” are overwhelmingly plural nouns or singular verbs which leads to the big question of whether one believes that “s-ending” words are in Wordle or not. If they are, this chart works well. If not, we can use the Oxford estimate instead, which will give us less granular information.
1 – (1-[probability])^5
But with the Stanford data, we can do one better and look both at the possibility of each letter in each position as well as to get an idea of the overall odds that a letter might be used by looking at
To figure out the chances that a letter will be used. And we come to the following table and chart.
I highlighted the three letters most likely to show up. I didn’t show off the next tier only because I was trying to highlight what stood out most. In general, I try to highlight the top 10% of data that stands out just because I assume that more than that means that nothing really stands out. My big caveat here is that I’m not a visual person and have always loved data tables more than any type of visualization, but I realize that is not common.
Step 4: Adjust analysis based on updated conditions
As we gain a better understanding of our Wordle environment, the game provides clues on which letters are associated with the word in question. Letters that are in the word of the day but are not in the right position are highlighted in yellow. Based on the probabilities we have, we can now adjust our assumptions. For instance, let’s look at the letter “a”
If we are looking at a word that has the letter “a”, but we know it is not in the first position, we know now we’ve cut down the percentage of words we’re thinking of by about 10%. We can also see that if that “a” isn’t in the second position, it’s probably in the third position.
Step 5: Provide results that will lead to making a decision
Based on the numbers, we can now guess that there’s a 50% chance that “a” is in the second position as 16% of five-letter words have an “a” out of the 31.57% of words that have an “a” but not in the first position. That is just one small example of the level of detail that can be made based on the numbers. But if I am providing this information with the goal of helping with guidance, I am probably not going to provide these tables as a starting point. Rather, I would start by providing guidance on what action to take. The starting point would likely be something like:
The letters used more than 20% of the time in five-letter words are the vowels a, e, i, and o and the consonants l, n, r, s, & t, much as one would expect from watching Wheel of Fortune. Top words to start with based on this criteria include “arise,” “laser,” and “rates.”
In contrast, if one wishes to make the game more challenging, one should start with words that are unlikely to provide an initial advantage. Words such as “fuzzy” and “jumpy” are relatively poor starting points from a statistical perspective.
Conclusion
First, this common approach to data definitely showed me a lot about Wordle that I wouldn’t have known otherwise. I hope this approach helps you both in thinking about your own Wordle approach and to further explore the process of Wordle and other data. And it all started with some basic steps:
So, having done all this analysis, how much do analytics help the Wordle experience? One of the things that I find most amazing about the process of playing Wordle is how our brains approximate the calculations made here from a pattern recognition perspective that reflects our use of language. Much as our brain is effectively solving the parallax formula every time we catch a ball thrown in the air, our brains also intuitively make many of these probabilistic estimates based on our vocabulary every time we play a game of Wordle.
I think that analytic approaches like this help to demonstrate the types of “hidden” calculations that often are involved in the “gut reactions” that people make in their decision-making. Gut reactions and analytic reactions have often been portrayed as binary opposites in the business world, but gut reactions can also be the amalgamation of intelligence, knowledge, past experiences, and intuitive feelings all combined to provide a decision that can be superior or more innovative in comparison to pure analytic decisions. Analytics are an important part of all decision-making, but it is important not to discount the human component of judgment in the decision-making process.
And as far as Wordle goes, I think it is fun to try the optimized version of Wordle a few times to see how it contrasts with your standard process. On the flip side, this data also provides guidance on how to make Wordle harder by using words that are less likely to be helpful. But ultimately, Wordle is a way for you to have fun and analytics is best used to help you have more fun and not to just turn Wordle into an engineering exercise. Happy word building and good luck!
On January 13, 2022, Observable raised a $35.6 million Series round led by Menlo Ventures with participation from existing investors Sequoia Capital and Acrew Capital. This round increases the total amount raised by Observable to $46.1 million. Observable is interesting to the enterprise analytics community because it provides a platform to help data users to collaborate throughout the data workflow of data discovery, analysis, and visualization.
Traditionally, data discovery, contextualization, analytics, and visualization can potentially be supported by different solutions within an organization. This complexity is multiplied by the variety of data sources and platforms that have to be supported and the number of people who need to be involved at each stage which leads to an unwieldy number of handoffs, the potential issue of using the wrong tool for the wrong job, and an extended development process that results from the inability for multiple people to simultaneously work on creating a better version of the truth. Observable provides a single solution to help data users to connect, analyze, and display data along with a library of data visualizations that help provide guidance on potentially new ways to present data.
From a business perspective, one of the biggest challenges of business intelligence and analytics has traditionally been the inability to engage relevant stakeholders to share and contextualize data for business decisions. The 2020s are going to be a decade of consolidation for analytics where enterprises have to make thousands of data sources available and contextualized. Businesses have to bridge the gaps between business intelligence and artificial intelligence, which are mainly associated with the human aspects of data: departmental and vertical context, categorization, decision intelligence, and merging business logic with analytic workflows.
This is where the opportunity lies for Observable in allowing the smartest people across all aspects of the business to translate, annotate, and augment a breadth of data sources into directional and contextualized decisions while using the head start of visualizations and analytic processes that have been shared by a community of over five million users. And then by allowing users to share these insights across all relevant applications and websites, these insights can drive decisions in all relevant places by bringing insights to the users.
Observable goes to market with a freemium model that allows companies to try out Observable for free and then to add editors at tiers of $12/user/month and $40/user/month (pricing as of January 13, 2022). This level of pricing makes Observable relatively easy to try out.
Amalgam Insights currently recommends Observable for enterprises and organizations with three or more data analysts, data scientists, and developers who are collaboratively working on complex data workflows that lead to production-grade visualization. Although it can be more generally used for building analytic workflows collaboratively, Observable provides one of the most seamless and connected collaborative experiences for creating and managing complex visualizations that Amalgam Insights has seen.
Yesterday, January 6, Alteryx announced that it has acquired Trifacta for $400M in a cash offer. Trifacta and Alteryx have historically been viewed as competitors, but Trifacta’s greater depth of capability re data engineering and cleansing complements Alteryx’ strengths in analytic workflows.
Today, January 7, Meta debuted Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised framework for understanding speech that combines video input from lip movements and audio input from speech, both as raw unlabeled data. The goal is to improve accuracy even in environments where audio input may be compromised, such as from loud background noise.
On Thursday, January 6, Qlik announced that it had confidentially submitted its draft regulation statement related to a proposed IPO. The expected IPO comes over five years after private equity investment firm Thoma Bravo purchased Qlik and took them private.
On Wednesday, January 5, Fractal, an AI and advanced analytics provider, announced that TPG, a global asset firm, will be investing $360M in Fractal. Puneet Bhatia and Vivek Mohan of TPG will join Fractal’s board of directors as part of the deal.
WEKA, a data storage platform, announced on Tuesday, January 4, that they have raised $73M in a Series C funding round, raising total funding to $140M. The oversubscribed round was led by Hitachi Ventures. Other participants include Cisco, Hewlett Packard Enterprise, Ibex Investors, Key 1 Capital, Micron, MoreTech Ventures, and NVIDIA. The funding will go towards accelerating go-to-market activities, operations, and engineering.
Finally, Wednesday, January 5, Databricks announced that it had appointed Naveen Zutshi as their new Chief Information Officer. Zutshi joins Databricks from Palo Alto Networks, where he was the CIO for six years, expanding Palo Alto Networks into new security categories and scaling up at speed. Prior to that, Zutshi was the SVP of Technology at Gap Inc, overseeing global infrastructure, ops, and security for the retailer.
On November 10, IBM revealed new natural language processing enhancements planned for IBM Watson Discovery. Business users will be able to train Watson Discovery to surface insights more quickly on a corpus of industry-specific documents without needing traditional data science skills. Specific capability enhancements include pre-trained document structure understanding, automatic text pattern detection, and a custom entity extractor feature that will help identify industry-specific words and phrases with specific contexts. The announced enhancements are forthcoming, though IBM did not announce a target release date.
On November 11, Informatica debuted their Cloud Data Marketplace. The Cloud Data Marketplace will allow Informatica business users to “shop” for both datasets and AI and analytics models, surfacing existing assets to encourage reuse of more-vetted resources rather than duplicating efforts by re-gathering data or building a model from scratch. Informatica Cloud Data Marketplace is available today with consumption-based pricing on Informatica’s Intelligent Data Management Cloud.
On November 9, at Tableau Conference 2021, Tableau announced a host of innovations for the Tableau platform and ecosystem, focused on bringing analytic capabilities to the workflows and environments workers already use. Highlights include Model Builder, a new feature in Tableau Business Science that allows Tableau users to build predictive models using Einstein Discovery; and Scenario Planning, another new Tableau Business Science feature to compare scenarios and “what-ifs,” supported by Einstein AI.
On November 9, Domino Data Lab announced a fully-managed offering with solutions partner Tata Consultancy Services that allows Domino customers to run high-performance computing and data science workloads on NVIDIA DGX systems, hosted in the TCS Enterprise Cloud. This marks the next step in a deepening relationship between Domino and NVIDIA, with the Domino integration into the NVIDIA AI Enterprise suite on the horizon.
On November 8, H2O.ai closed $100M in Series E funding. The round was led by customer Commonwealth Bank of Australia, with participation by existing investors Crane Venture Partners and Goldman Sachs Asset Management and new investor Pivot Investment Partners. The funding will be used to scale up partnerships, sales, marketing, and customer success at a global level.
On August 16, Cloudera launched Cloudera DataFlow for the Public Cloud to better manage customer data flows. When too many data flows are deployed into a single cluster, performance often falters, yet choosing larger infrastructure footprints “just in case” is expensive. Cloudera DataFlow was created to automate and manage complex cloud-native data flow operations, automatically scale up and down said streaming data flows more efficiently, and cut customers’ cloud costs. Cloudera DataFlow is generally available on AWS now.
On August 17, SnapLogic announced its August 2021 product release, introducing no-code SnapLogic Flows for business users, ELT support to Databricks’ Delta Lake, and zero downtime upgrades, along with updating its API lifecycle and development portal. SnapLogic Flows will enable business users to construct data flows and apps to integrate into popular business software such as Salesforce without needing to know how to code, while allowing IT to provide guiderails and requirements to oversee said apps. New features in SnapLogic API lifecycle management include the abilities to maintain, improve, unpublish, deprecate, and retire APIs, ensuring that older versions aren’t used in error.
On August 17, Apollo GraphQL announced a $130M Series D funding round. Insight Partners led the round, with participation from existing funders Andreessen Horowitz, Matrix Partners, and Trinity Ventures, and new investor Next47. The funding will be used on continuing R+D of open source graph technology to make app development faster and more accessible.
On August 17, Monte Carlo, a data reliability company, announced a $60M Series C funding round, led by ICONIQ Growth. Salesforce Ventures, along with existing investors Accel, GGV Capital, and Redpoint Ventures, all participated. Monte Carlo will use the funds to expand its product offerings, support more use cases, and open up to new markets.
Adapdix, an edge AI/ML platform, announced the acquisition of Edge Intelligence, a data management platform, on August 16. Edge Intelligence will improve Adapdix’ existing EdgeOps Data Mesh with better data management capabilities, and allow Adapdix to expand its existing offerings in edge automation.
On August 19, Databricks announced that they had appointed Fermín Serna as the company’s new Chief Security Officer. Serna is coming over from Citrix, where he was the Chief Information Security Officer; before this, Serna was the Head of Product Security at Google. At Databricks, Serna will lead the network, platform and user security programs, as well as governance and compliance efforts.
On August 19, TigerGraph, a graph analytics platform, announced that they had hired Dr. Jay Yu as Vice President of Product Innovation, and as GM at the San Diego Innovation Center for TigerGraph. Dr. Yu comes to TigerGraph from 18 years at Intuit, where he led the Financial Knowledge Graph project and encouraged graph technology adoption in large commercial cases. TigerGraph also announced the Graph + AI Summit for this fall on two dates, October 5 in San Francisco and October 19th in New York. Both hybrid events will be livestreamed to virtual attendees, as well as including in-person attendance.
On August 9, Snorkel AI, a programmatic data labeling platform, snagged an $85M Series C round at a $1B valuation. Addition and various BlackRock funds and accounts led the round, with participation from previous investors Greylock, GV, Lightspeed Venture Partners, Nepenthe Capital, and Walden. The funding will go towards scaling Snorkel AI’s engineering team and growing its go-to-market team for global sales.
On August 11, Mindtech announced updates to Chameleon, their synthetic image creation and curation platform for training visual AI systems. Data scientists and machine learning engineers will be able to create the exact annotated images they need to train their visual AI models. Key new features and enhancements include Simulator, which uses real-world behavior modeling to create synthetic data sets, and Curation Manager, which performs visual analysis of synthetic and real datasets to identify diversity and bias. Chameleon 21.1 is available for immediate licensing.
On August 10, Oracle announced MySQL Autopilot, a new component of Oracle’s MySQL HeatWave service. Autopilot automates HeatWave, a MySQL query acceleration engine in the Oracle cloud, by building machine learning models to help it learn how to perform optimally. Oracle also debuted MySQL Scale-out Data Management at the same time to improve the performance of reloading data into HeatWave by 100x.
On August 11, Talend announced updates to Talend Data Fabric, its data integration and governance platform. Key innovations include native integration with Databricks 7.3 and AWS EMR 6.2 on Apache Spark 3 to enable faster advanced analytics at scale, private connectivity between Talend and AWS or Azure to support HIPAA and PCI compliance, and adding read/write capabilities to a campaign directly from a data pipeline.
On August 12, Qlik acquired NodeGraph, a metadata management platform. NodeGraph’s interactive data lineage function will contribute to Qlik’s “explainable BI” capabilities, while the governance aspects will enhance the Qlik data fabric, and NodeGraph’s impact analysis capabilities will expand Qlik’s SaaS offerings.
On August 12, DataRobot welcomed three new appointments to their C-Suite. Jay Schuren moves up as DataRobot’s first Chief Data Science Officer, having come over in 2017 with the Nutonian acquisition. Sirisha Kadamalakalva joined DataRobot as their first Chief Strategy Officer from Bank of America, where she was the Managing Director and Global Head of AI/ML, Analytics, and CRM Software Investment Banking. Steve Jenner came over from Zscaler, where he was the Vice President of Worldwide Sales Engineering.
On August 5, Dataiku announced that they had raised $400M in a Series E round of funding. Tiger Global led the round, with participation from existing investors Battery Ventures, CapitalG, Dawn Capital, FirstMark Capital, ICONIQ Growth, and Snowflake Ventures, as well as new investors Eurazeo, Insight Partners, and Lightrock.
On August 3, Ahana announced a $20M series A funding round. Third Point Ventures led the round, with participation from GV, Leslie Ventures, and Lux Capital. In addition, Robert Schwartz, a Managing Partner at Third Point Ventures, will join Ahana’s Board of Directors. With the funding, Ahana will accelerate engineering and contributions to the open source data lake analytics Presto project, and expand its go-to-market team.
On August 4, Spell launched the eponymous Spell, an MLOps platform for deep learning. The goal is to reduce the cost of operationalizing complex deep learning models that use natural language processing, machine vision, voice recognition, and other similarly complex models dependent on deep learning.
On August 3, Exadel announced improvements to CompreFace, their open source facial recognition application. The updates include additional services and plugins such as face detection and verification, age and gender detection, support for the facial recognition library InsightFace, an improved user experience, added scalability and GPU support, and JavaScript and Python SDKs to make integration easier.
Alteryx made an additional partnership announcement at the end of last week around their efforts to address analytics automation; given the context of the automation acquisitions announced this week, it’s worth mentioning as part of a larger trend. In addition to the BluePrism and PwC announcements, Alteryx has become an Elite Technology Partner in the Snowflake ecosystem, building on the deeper relationship announced in June and reflecting a growing number of Alteryx-Snowflake users working on automated analytics projects.
On August 2, Salesforce announced their intent to acquire Servicetrace, an RPA provider, and to integrate it into their MuleSoft data integration platform. In particular, Servicetrace’s capabilities will enhance Salesforce’s Einstein Automate, empowering additional workflow automation for Salesforce customers.
On August 5, ServiceNow announced their intent to acquire Swarm64, a database performance company. Once Swarm64 has been integrated into ServiceNow’s solutions, customers will be able to query their data sources more quickly as part of the workflows they build with ServiceNow’s Now Platform, in addition to working with larger datasets.
On July 27, DataRobot announced a $300M Series G funding round. Repeat investors Altimeter Capital and Tiger Global led the round, along with participation from new investors Counterpoint Global (Morgan Stanley), Franklin Templeton, ServiceNow Ventures, and Sutter Hill Ventures. The funding will be used for further development of DataRobot’s Augmented Intelligence platform, as well as expanding its go-to-market team.
Also on July 27, Kili Technology revealed that it had raised a $25M Series A funding round. Balderton Capital led the round. The company is looking to accelerate hiring with the funding.
On July 29, SAS announced that it intended to be ready for an IPO by 2024. Key steps SAS will take over the next couple of years include refining its financial reporting structure, optimizing certain operational processes, and further developing its AI and analytics capabilities.
Alteryx revealed news about two partnerships this week. On July 28, Alteryx announced a new partnership with Blue Prism, an intelligent automation company, along with a bi-directional integration between the two platforms. Blue Prism developers will be able to include Alteryx analytic processes within RPA-driven processes, while Alteryx analysts will be able to trigger Blue Prism digital workers from within their Alteryx workflows.
On July 29, Alteryx announced that it was expanding its relationship with PwC. The original partnership, announced in February 2020, focused on upskilling and digital transformation efforts in US companies; these efforts are now being expanded globally.
On July 29, Atos, a digital transformation company, announced that it was acquiring Visual BI, a cloud data analytics company. By acquiring Visual BI, Atos hopes to address its customers’ increasing need for cloud-based BI and analytics.
On July 29, Domino Data Lab debuted the Domino Partner Network, connecting technology and services partners across four categories: Tools & Data, Infrastructure, Solutions, and Implementation & Consulting. Inaugural members include Accenture, AWS, DataArt, Dell Technologies, MathWorks, NetApp, NVIDIA, and Snowflake, among others.
SAP had two partner announcements this week as well. On July 28, SAP announced that it intends to expand the availability of SAP finance and data management solutions on IBM Cloud for Financial Services, which have built-in security and compliance controls to address financial regulations around risk and data. The goal is to accelerate adoption of IBM Cloud within the financial services industry.
On July 29, SAP and Google Cloud announced that they would be expanding their strategic partnership. Google Cloud will now be a strategic partner for RISE with SAP, SAP’s digital transformation offering. Similar to SAP’s IBM partnership, the goal is to accelerate cloud adoption and business process migration even further.
On July 29, AtScale introduced AI-Link. AtScale allows BI teams to consume live cloud data with preferred apps; data scientists can now access the same data with Python via AI-Link to put into augmented analytics and machine learning models. AI-Link is available today as an add-on to AtScale.
On July 27, EZOPS announced that they are incorporating Snowflake support. Snowflake customers will be able to use EZOPS’ AI models to analyze data stream patterns for anomalies, and then escalate said anomalies within EZOPS Workflow for further triage and analysis.
On July 27, Trifacta debuted pre-built cloud data engineering templates to further enable what they’re calling “self-service data management.” The templates will allow Trifacta users to tweak pre-configured data engineering workflows to suit their needs, then quickly deploy them. Some examples of the available templates include transforming data to a target schema using a mapping table, identifying sentiment keywords and calculating NPS, and importing data from Google Cloud Storage to BigQuery.
Kyndryl, the new separate company of IBM’s Managed Infrastructure Services, has appointed David Wyshner as the company’s CFO. Wyshner was most recently the CFO of XPO Logistics; prior to that, Wyshner led the separation of Wyndham Worldwide into three separate companies during his time as CFO there.
On July 26, Informatica announced that Betsy Rafael has joined the Informatica board of directors, as well as the chair of the audit committee. Most recently, Rafael was the Chief Transformation Officer at GoDaddy. Preceding her stint at GoDaddy, Rafael served as Vice President and Corporate Controller and Principal Accounting Executive at Apple, and Vice President, Corporate Finance at Cisco.
On July 21, at Subservice Live, Dremio debuted Dremio Cloud, a cloud-native SQL-based data “lakehouse” service. The service marries various aspects of data lakes and data warehouses into a SQL lakehouse, enabling high-performance SQL workloads in the cloud and expediting the process of getting started. Dremio Cloud is now available in the AWS Marketplace.
On July 22, Google Cloud announced Healthcare Data Engine, now in private preview. Healthcare Data Engine integrates healthcare and life sciences data from multiple sources such as medical records, claims, clinical trials, and research data, enabling a more longitudinal view of patient health along with advanced analytics and AI in a secure environment. With the introduction of Amazon HealthLake last week, it’s clear that expanding healthcare and life sciences analytics capabilities continue to be a top priority among data services providers.
Dipping a toenail into the waters outside their usual large established organization customer base, Palantir announced the launch of Foundry for Builders, providing access to the Palantir Foundry platform for startups under a fully-managed subscription model. Foundry for Builders is starting off with limited availability; the initial group of startups provided access are all connected to Palantir alumni, with the hope of expanding to other early-stage “hypergrowth” companies down the road.
On July 20, Redpoint announced In Situ, a service that provides data quality and identity resolution. In Situ uses Redpoint’s data management technology to supply identity resolution and data integration services in real time within an organization’s virtual private cloud, without needing to transfer said private data across the internet.
On July 21, Sisense debuted the Sisense Extense Framework, a way to deliver interactive analytics experiences within popular business applications. Initially supported apps include Slack, Salesforce, Google Sheets, Google Slides, and Google Chrome, now available on the Sisense Marketplace. The Sisense Extense Framework will be released more broadly later this year to partners looking to build similar “infusion” apps.
On June 20, at Vertica Unify 2021, Vertica announced the Vertica 11 Analytics Platform. Key improvements include broader deployment support, strengthened security, increased analytical performance, and enhanced machine learning capabilities.
On July 19, Cube Dev announced that they had raised $15.5M in Series A funding. Decibel led this round, with participation from Bain Capital Ventures, Betaworks and Eniac Ventures. The funding will be used to scale go-to-market activities and accelerate R+D on its first commercial product. Cube Dev also brought aboard Jonathan E. Cowperthwait of npm as Head of Marketing and Jordan Philips of Dashbase as Head of Revenue Operations to support their commercial expansion.
Julia Computing announced the completion of a $24M Series A funding round on July 19. Dorilton Ventures led the round, with participation from Menlo Ventures, General Catalyst, and HighSage Ventures. Julia Computing will use the funding to further develop JuliaHub, its secure, high-performance cloud platform for scientific and technical modeling, and to grow the Julia ecosystem overall. Bob Muglia, the former CEO of Snowflake, joined the Julia Computing board on the same day.
Lucata, a platform to scale and accelerate graph analytics, AI, and machine learning capabilities, announced July 19 that it had raised $11.9M in Series B funding. Notre Dame, Middleburg Capital Development, Blu Ventures Inc., Hunt Holdings, Maulick Capital, and Varian Capital all participated in the round. The funding will fuel an “aggressive” go-to-market strategy.
On July 18, Zoom announced that it had entered into a definitive agreement to acquire Five9, a cloud contact center service provider, for $14.7B in stock. In welcoming Five9 to the Zoom platform, Zoom expects to build a better “customer engagement platform,” complementary with its Zoom Phone offering. Later in the week, Zoom also announced the launch of Zoom Apps and Zoom Events, further enhancing the collaboration capabilities of the primary Zoom video communications suite.
