EverAfter closes $13M to help companies ride off into the sunset with their customers

EverAfter secured $13 million in seed funding to continue developing its no-code customer-facing tool that streamlines onboarding and retention and enables business-to-business clients to embed personalized customer portals within any product.

The Tel Aviv-based company was founded in 2020 by Noa Danon and Tal Shemesh. CEO Danon, who comes from a project management background, said they saw a disconnect between the user and product experience.

The company’s name, EverAfter, comes from the concept that in SaaS companies, someone has to be in charge of the “EverAfter,” with customers, even as the relationship changes, Danon told TechCrunch.

Via its no-code platform, customer success teams are able to build a website in weeks using drop-and-drag widgets like training materials, timelines, task management and meeting summaries, and then configure what each user sees. Then there is a snippet of code that is embedded into the product.

EverAfter also integrates with existing customer relationship management, project management and service ticket tools, while also updating Salesforce and HubSpot directly through an interface.

“It’s like the customer owns a piece of real estate inside the product,” Danon said.

TLV Partners and Vertex Ventures co-led the round and were joined by angel investors Benny Shneider, Zohar Gilon and Amit Gilon.

Yanai Oron, general partner at Vertex Ventures, said he is seeing best-in-breed companies try to solve customer churn or improve the relationship process on their own and failing, which speaks to the complexity of the problem.

Startups in this space are coming online and raising money, but with EverAfter, they are differentiating themselves by not only putting a dashboard on their product, but launching with the capabilities to manage thousands of customers using the product, he added.

“I’ve been tracking the customer success space over the past few years, and it is a growing field with the least sophisticated tools,” Oron said. “During COVID, companies realized it was easier to retain customers rather than get new ones. We are all used to more self-service and wanting to get the answer ourselves, and customers are the same. Companies also started to be more at ease in letting customers develop things on their own and leave R&D departments to do other things.”

Clients include Taboola, AppsFlyer and Verbit, with Verbit reporting its company’s customer success managers save 10 hours a week managing ongoing customer communication by using EverAfter, Danon added. This comes as CallMiner reports that unplanned customer churn costs companies $35.3 billion in the U.S. alone.

EverAfter offers both customer success and partner management software and clients can choose a high-touch service or kits and templates for self-service.

The new funding will enable the company to focus on integration and expansion into additional use cases. Since being founded, EverAfter has grown to 20 employees and 30 customers. The founders also want to utilize the data they are collecting on what works and doesn’t work for each customer.

“There are so many interesting things that happen between companies and customers, from onboarding to business reviews, and we are going to expand on those,” Danon said. “We want to be the first thing companies put inside their product to figure out the relationship between customers and customer success teams and managers.”

 


By Christine Hall

Trade promotion management startup Cresicor raises $5.6M to keep tabs on customer spend

Cresicor, a consumer packaged goods trade management platform startup, raised $5.6 million in seed funding to further develop its tools for more accurate data and analytics.

The company, based remotely, focuses on small to midsize CPG companies, providing them with an automated way to manage their trade promotion, a process co-founder and CEO Alexander Whatley said is done primarily manually using spreadsheets.

Here’s what happens in a trade promotion: When a company wants to run a discount on one of their slower-selling items, the company has to spend money to do this — to have displays set up in a store or have that item on a certain shelf. If it works, more people will buy the item at the lower price point. Essentially, a trade promotion is the process of spending money to get more money in the future, Whatley told TechCrunch.

Figuring out all of the trade promotions is a complicated process, Whatley explained. Companies receive data feeds on the promotions from several different places, revenue data from retailers, accounting source data to show how many units were shipped and then maybe data directly from retailers. All of that has to be matched against the promotion.

“No API is bringing this data back to brands, so our software helps to automate and track these manual processes so companies can do analytics to see how the promotions are doing,” he added. “It also helps the finance team understand expenses, including which are valid and those that are not.”

What certain companies spend on trade promotions can represent their second-largest cost behind manufacturing, and companies often end up reinvesting between 20% and 30% of their revenue into trade promotions, Whatley said. This is a big market, representing untapped growth, especially with U.S. CPG sales topping $720 billion in 2020.

“You can see how messy the whole industry is, which is why we have a bright future and huge TAM,” he added. “With this new funding, we can target other parts of the P&L like supply chain and salaries. We also provide analytics for their strategy and where they should be spending it — which store, on which supply. By allocating resources the right way, companies typically see a 10% boost in sales as a result.”

Whatley started the company in 2017 with his brother, Daniel, Stuart Kennedy and Nikki McNeil while a Harvard undergrad. Since raising the funding back in February, the company has grown 2.5x in revenue, while employee headcount grew 4x over the past 12 months to 20.

Costanoa Ventures led the investment and was joined by Torch Capital and a group of angel investors including Fivestars CTO Matt Doka and Hu’s Kitchen CEO Mark Ramadan.

John Cowgill, partner at Costanoa, said though Cresicor raised a seed round, the company was already acquiring brands and capital before releasing a product and grew to almost a Series A company without any outside capital, saying it “blew me away.”

Cresicor is the “perfect example” of a company that Costanoa would get excited about — a vertical software company using data or machine learning to augment a pain point, Cowgill added.

“The CPG industry is in the middle of a rapid change where we see all of these emerging, digital native and mission-driven brands rapidly eating share from incumbents,” he added. “For the next generation of brands to compete, they have to win in trade promotion management. Cresicor’s opportunity to go beyond trade is significant. It is just a starting point to build a company that is the core enabler of great brands.”

The new funding will be used mainly to hire more talent in the areas of engineering and customer success so the company can hit its next benchmarks, Alexander Whatley said. He also intends to use the funding to acquire new brands and on software development. Cresicor boasts a list of customers including Perfect Snacks, Oatly and Hint Water.

The retail industry is valued at $5.5 trillion, and one-fifth of it is CPG, Whatley said. As a result, he has his eye on going after other verticals within CPG, like electronics and pet food, and then expanding into other areas.

“We are also going to work with enterprise companies — we see an opportunity to work with companies like P&G and General Mills, and we also want to build an ecosystem around trade promotion and launch into other profit and loss areas,” Whatley said.


By Christine Hall

Insight Partners leads $30M round into Metabase, developing enterprise business intelligence tools

Open-source business intelligence company Metabase announced Thursday a $30 million Series B round led by Insight Partners.

Existing investors Expa and NEA joined in on the round, which gives the San Francisco-based company a total of $42.5 million in funding since it was founded in 2015. Metabase previously raised $8 million in Series A funding back in 2019, led by NEA.

Metabase was developed within venture studio Expa and spun out as an easy way for people to interact with data sets, co-founder and CEO Sameer Al-Sakran told TechCrunch.

“When someone wants access to data, they may not know what to measure or how to use it, all they know is they have the data,” Al-Sakran said. “We provide a self-service access layer where they can ask a question, Metabase scans the data and they can use the results to build models, create a dashboard and even slice the data in ways they choose without having an analyst build out the database.”

He notes that not much has changed in the business intelligence realm since Tableau came out more than 15 years ago, and that computers can do more for the end user, particularly to understand what the user is going to do. Increasingly, open source is the way software and information wants to be consumed, especially for the person that just wants to pull the data themselves, he added.

George Mathew, managing director of Insight Partners, believes we are seeing the third generation of business intelligence tools emerging following centralized enterprise architectures like SAP, then self-service tools like Tableau and Looker and now companies like Metabase that can get users to discovery and insights quickly.

“The third generation is here and they are leading the charge to insights and value,” Mathew added. “In addition, the world has moved to the cloud, and BI tools need to move there, too. This generation of open source is a better and greater example of all three of those.”

To date, Metabase has been downloaded 98 million times and used by more than 30,000 companies across 200 countries. The company pursued another round of funding after building out a commercial offering, Metabase Enterprise, that is doing well, Al-Sakran said.

The new funding round enables the company to build out a sales team and continue with product development on both Metabase Enterprise and Metabase Cloud. Due to Metabase often being someone’s first business intelligence tool, he is also doubling down on resources to help educate customers on how to ask questions and learn from their data.

“Open source has changed from floppy disks to projects on the cloud, and we think end users have the right to see what they are running,” Al-Sakran said. “We are continuing to create new features and improve performance and overall experience in efforts to create the BI system of the future.

 


By Christine Hall

Tellius announces $8M Series A to build ML-fueled business data query tool

Getting actionable business information into the hands of users who need it has always been a challenge. If you have to wait for experts to help you find the answers, chances are you’re going to be too late. Enter Tellius, an early stage startup building a solution to help business users find the information they need when they need it.

Today the company announced an $8 million Series A led by Sands Capital Ventures with participation from Grotech. Today’s investment brings the total raised to $17 million, according to the company.

CEO and founder Ajay Khanna says the company is attempting to marry two technologies that have traditionally lived in silos: business intelligence and artificial intelligence. He believes that bringing them together can lead to greater wisdom and help close the insight gap.

“Tellius is an AI-driven decision intelligence platform, and what we do is we combine machine learning — AI-driven automation — with a Google-like natural language interface, so combining the left brain and the right brain to enable business teams to get insights on the data,” Khanna told me.

The idea is to let the machine learning teams and the business analysts continue to do their thing, but provide an application where business users can put all of that to work. “We believe that to go from data to decisions, you need to know not only what happened, but why things change and how you can improve your company,” he said.

The product takes aim at three employee groups. The first is the business user, who can simply query the data with a natural language question to get results. The second is a data analyst, who can get more granular by choosing a specific model to base the query on, and finally a data scientist who can enhance the query with Python or Spark code.

It connects to various data sources including Salesforce and Google Analytics, data lakes like Snowflake, csv files to take advantage of Excel data or cloud storage tools like Amazon S3. It comes in two versions: one that the customer can connect to the cloud infrastructure provider of choice, and one which they run as a service and manage for the customers.

Khanna says that as companies struggled to change the way they do business in during the pandemic, they needed the kind of insights his company provides and business grew 300% last year as a result.

The startup launched in 2016 after Khanna sold a previous company, which allowed him to bootstrap while in stealth. They spent a couple of years building the product and brought the first version of Tellius to market in Q3 2018. That’s when they took a $7.5 million seed round.


By Ron Miller

Dataminr raises $475M on a $4.1B valuation for real-time insights based on 100k sources of public data

Significant funding news today for one of the startups making a business out of tapping huge, noisy troves of publicly available data across social media, news sites, undisclosed filings and more. Dataminr, which ingests information from a mix of 100,000 public data sources, and then based on that provides customers real-time insights into ongoing events and new developments, has closed on $475 million in new funding. Dataminr has confirmed that this Series F values the company at $4.1 billion as it gears up for an IPO in 2023.

This Series F is coming from a mix of investors including Eldridge (a firm that owns the LA Dodgers but also makes a bunch of other sports, media, tech and other investments), Valor Equity Partners (the firm behind Tesla and many tech startups), MSD Capital (Michael Dell’s fund), Reinvent Capital (Mark Pincus and Reid Hoffman’s firm), ArrowMark Partners, IVP, Eden Global and investment funds managed by Morgan Stanley Tactical Value, among others.

To put its valuation into some context, the New York-based company last raised money in 2018 at a $1.6 billion valuation. And with this latest round, it has now raised over $1 billion in outside funding, based on PitchBook data. This latest round has been in the works for a while and was rumored last week at a lower valuation than what Dataminr ultimately got.

The funding is coming at a critical moment, both for the company and for the world at large.

In terms of the company, Dataminr has been seeing a huge surge of business.

Ted Bailey, the founder and CEO, said in an interview that it will be using the money to continue growing its business in existing areas: adding more corporate customers, expanding in international sales and expanding its AI platform as it gears up for an IPO, most likely in 2023. In addition to being used journalists and newsrooms, NGOs and other public organizations, its corporate business today, Bailey said, includes half of the Fortune 50 and a number of large public sector organizations. Over the last year that large enterprise segment of its customers doubled in revenue growth.

“Whether it’s for physical safety, reputation risk or crisis management, or business intelligence or cybersecurity, we’re providing critical insights on a daily basis,” he said. “All of the events of the recent year have created a sense of urgency, and demand has really surged.”

Activity on the many platforms that Dataminr taps to ingest information has been on the rise for years, but it has grown exponentially in the last year especially as more people spend more time at home and online and away from physically interacting with each other: that means more data for Dataminr to crawl, but also, quite possibly, more at stake for all of us as a result: there is so much more out there than before, and as a result so much more to be gleaned out of that information.

That also means that the wider context of Dataminr’s growth is not quite so clear cut.

The company’s data tools have indeed usefully helped first responders react in crisis situations, feeding them data faster than even their own channels might do; and it provides a number of useful, market-impacting insights to businesses.

But Dataminr’s role in helping its customers — which include policing forces — connect the dots on certain issues has not always been seen as a positive. One controversial accusation made last year was that Dataminr data was being used by police for racial profiling. In years past, it has been barred by specific partners like Twitter from sharing data with intelligence agencies. Twitter used to be a 5% shareholder in the company. Bailey confirmed to me that it no longer is but remains a key partner for data. I’ve contacted Twitter to see if I can get more detail on this and will update the story if and when I learn more. Twitter made $509 million in revenues from services like data licensing in 2020, up by about $45 million on the year before.

In defense of Dataminr, Bailey that the negative spins on what it does result from “misperceptions,” since it can’t track people or do anything proactive. “We deliver alerts on events and it’s [about] a time advantage,” he said, likening it to the Associated Press, but “just earlier.”

“The product can’t be used for surveillance,” Bailey added. “It is prohibited.”

Of course, in the ongoing debate about surveillance, it’s more about how Dataminr’s customers might ultimately use the data that they get through Dataminr’s tools, so the criticism is more about what it might enable rather than what it does directly.

Despite some of those persistent questions about the ethics of AI and other tools and how they are implemented by end users, backers are bullish on the opportunities for Dataminr to continue growing.

Eden Global Partners served as strategic partner for the Series F capital round.


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By Ingrid Lunden

TigerGraph raises $105M Series C for its enterprise graph database

TigerGraph, a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by Tiger Global and brings the company’s total funding to over $170 million.

“TigerGraph is leading the paradigm shift in connecting and analyzing data via scalable and native graph technology with pre-connected entities versus the traditional way of joining large tables with rows and columns,” said TigerGraph found and CEO, Yu Xu. “This funding will allow us to expand our offering and bring it to many more markets, enabling more customers to realize the benefits of graph analytics and AI.”

Current TigerGraph customers include the likes of Amgen, Citrix, Intuit, Jaguar Land Rover and UnitedHealth Group. Using a SQL-like query language (GSQL), these customers can use the company’s services to store and quickly query their graph databases. At the core of its offerings is the TigerGraphDB database and analytics platform, but the company also offers a hosted service, TigerGraph Cloud, with pay-as-you-go pricing, hosted either on AWS or Azure. With GraphStudio, the company also offers a graphical UI for creating data models and visually analyzing them.

The promise for the company’s database services is that they can scale to tens of terabytes of data with billions of edges. Its customers use the technology for a wide variety of use cases, including fraud detection, customer 360, IoT, AI, and machine learning.

Like so many other companies in this space, TigerGraph is facing some tailwind thanks to the fact that many enterprises have accelerated their digital transformation projects during the pandemic.

“Over the last 12 months with the COVID-19 pandemic, companies have embraced digital transformation at a faster pace driving an urgent need to find new insights about their customers, products, services, and suppliers,” the company explains in today’s announcement. “Graph technology connects these domains from the relational databases, offering the opportunity to shrink development cycles for data preparation, improve data quality, identify new insights such as similarity patterns to deliver the next best action recommendation.”


By Frederic Lardinois

AWS adds natural language search service for business intelligence from its data sets

When Amazon Web Services launched QuickSight, its business intelligence service, back in 2016 the company wanted to provide product information and customer information for business users — not just developers.

At the time, the natural language processing technologies available weren’t robust enough to give customers the tools to search databases effectively using queries in plain speech.

Now, as those technologies have matured, Amazon is coming back with a significant upgrade called QuickSight Q, which allows users to just ask a simple question and get the answers they need, according to Andy Jassy’s keynote at AWS re:Invent.

“We will provide natural language to provide what we think the key learning is,” said Jassy. “I don’t like that our users have to know which databases to access or where data is stored. I want them to be able to type into a search bar and get the answer to a natural language question.

That’s what QuickSight Q aims to do. It’s a direct challenge to a number of business intelligence startups and another instance of the way machine learning and natural language processing are changing business processes across multiple industries.

“The way Q works. Type in a question in natural language [like]… ‘Give me the trailing twelve month sales of product X?’… You get an answer in seconds. You don’t have to know tables or have to know data stores.”

It’s a compelling use case and gets at the way AWS is integrating machine learning to provide more no-code services to customers. “Customers didn’t hire us to do machine learning,” Jassy said. “They hired us to answer the questions.”


By Jonathan Shieber

Databricks launches SQL Analytics

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a ‘lake house’ to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databrick’s co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But ‘lake house’ caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, Chief Product Officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience that Databricks users are familiar with. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.


By Frederic Lardinois

Mozart Data lands $4M seed to provide out-of-the-box data stack

Mozart Data founders Peter Fishman and Dan Silberman have been friends for over 20 years, working at various startups, and even launching a hot sauce company together along the way. As technologists, they saw companies building a data stack over and over. They decided to provide one for them and Mozart Data was born.

The company graduated from the Y Combinator Summer 2020 cohort in August and announced a $4 million seed round today led by Craft Ventures and Array Ventures with participation from Coelius Capital, Jigsaw VC, Signia VC, Taurus VC and various angel investors.

In spite of the detour into hot sauce, the two founders were mostly involved in data over the years and they formed strong opinions about what a data stack should look like. “We wanted to bring the same stack that we’ve been building at all these different startups, and make it available more broadly,” Fishman told TechCrunch.

They see a modern data stack as one that has different databases, SaaS tools and data sources. They pull it together, process it and make it ready for whatever business intelligence tool you use. “We do all of the parts before the BI tool. So we extract and load the data. We manage a data warehouse for you under the hood in Snowflake, and we provide a layer for you to do transformations,” he said.

The service is aimed mostly at technical people who know some SQL like data analysts, data scientists and sales and marketing operations. They founded the company earlier this year with their own money, and joined Y Combinator in June. Today, they have about a dozen customers and six employees. They expect to add 10-12 more in the next year.

Fishman says they have mostly hired from their networks, but have begun looking outward as they make their next hires with a goal of building a diverse company. In fact, they have made offers to several diverse candidates, who didn’t ultimately take the job, but he believes if you start looking at the top of the funnel, you will get good results. “I think if you spend a lot of energy in terms of top of funnel recruiting, you end up getting a good, diverse set at the bottom,” he said.

The company has been able to start from scratch in the midst of a pandemic and add employees and customers because the founders had a good network to pitch the product to, but they understand that moving forward they will have to move outside of that. They plan to use their experience as users to drive their message.

“I think talking about some of the whys and the rationale is our strategy for adding value to customers […], it’s about basically how would we set up a data stack if we were at this type of startup,” he said.


By Ron Miller

Explo snags $2.3M seed to help build customer-facing BI dashboards

Explo, a member of the Y Combinator Winter 2020 class, which is helping customers build customer-facing business intelligence dashboards, announced a $2.3 million seed round today. Investors included Amplo VC, Soma Capital and Y Combinator along with several individual investors.

The company originally was looking at a way to simplify getting data ready for models or other applications, but as the founders spoke to customers, they saw a big need for a simple way to build dashboards backed by that data and quickly pivoted.

Company CEO and co-founder Gary Lin says the company was able to leverage the core infrastructure, data engineering and production that it had built while at Y Combinator, but the new service they have created is much different from the original idea.

“In terms of the UI and the output, we had to build out the ability for our end users to create dashboards, for them to embed the dashboards and for them to customize the styles on these dashboards, so that it looks and feels as though it was part of their own product,” Lin explained.

While the founders had been working on the original idea since last year, they didn’t actually make the pivot until September. They made the change because they were hearing this was really what customers needed more than the tool they had been building while at Y Combinator. In fact, Chen says that their YC mentors and investors have been highly supportive of the switch.

The company is just getting started with the four original co-founders — Lin, COO Andrew Chen, CTO Rohan Varma and product designer Carly Stanisic — but the plan is to use this money to beef up the engineering team with three to five new hires.

With a diverse founding team, the company wants to continue looking at diversity as it builds the company. “One of the biggest reasons that we think diversity is important is that it allows us to have a bigger perspective and a grander perspective on things. And honestly, it’s in environments where I have personally […] been involved where we’ve actually been able to create the best ideas was by having a larger perspective. And so we definitely are going to be as inclusive as possible and are definitely thinking about that as we hire,” Lin said.

As the company has grown up during the pandemic, the founding core is used to working remotely and the goal moving forward is to be a distributed company. “We will be a remote distributed company so we’re hiring people no matter where they are, which actually makes it a lot easier from a hiring perspective because we’re able to reach a much more diverse and large pool of applicants,” Lin said.

They are in the process of thinking about how they can build a culture as they bring in distributed employees. “I think the way that we’ve started to see it is that working distributed is not a reduced experience, but just a different one and we are thinking about different things like how e organize new people when they on board, and maybe we can meet up as a team and have a retreat where we are located in the same place [when travel allows],” he said.

For now, they will remain remote as they take their first half dozen customers and begin to build the company with the new investment.


By Ron Miller

MachEye raises $4.6M for its business intelligence platform

We’ve seen our fair share of business intelligence (BI) platforms that aim to make data analysis accessible to everybody in a company. Most of them are still fairly complicated, no matter what their marketing copy says. MachEye, which is launching its AI-powered BI platform today, is offering a new twist on this genre. In addition to its official launch, the company also today announced a previously unreported $4.6 seed funding round led by Canaan Partners with participation from WestWave Capital.

MachEye is not just what its founder and CEO Ramesh Panuganty calls a “low-prep, no-prep” BI platform, but it uses natural language processing to allow anybody to query data using natural language — and it can then automatically generate interactive data stories on the fly that put the answer into context. That’s quite a different approach from its more dashboard-centric competition.

“I have seen the business intelligence problems in the past,” Panuganty said. “And I saw that Traditional BI, even though it has existed for 30 or 40 years, had this paradigm of ‘what you ask is what you get.’ So the business user asks for something, either in an email, on the phone or in person, and then he gets an answer to that question back. That essentially has these challenges of being dependent on the experts and there is a time that is lost to get the answers — and then there’s a lack of exploratory capabilities for the business user. and the bigger problem is that they don’t know what they don’t know.”

Panuganty’s background includes time at Sun Microsystems and Bell Labs, working on their operating systems before becoming an entrepreneur. He build three companies over the last 12 years or so. The first was a cloud management platform, Cloud365, which was acquired by Cognizant. The second was analytics company Drastin, which got acquired by Splunk in 2017, and the third was the AI-driven educational platform SelectQ, which Thinker acquired this April. He also holds 15 patents related to machine learning, analytics and natural language processing.

Given that track record, it’s probably no surprise why VCs wanted to invest in his new startup, too. Panuganty tells me that when he met with Canaan Partners, he wasn’t really looking for an investment. He had already talked to the team while building SelectQ, but Canaan never got to make an investment because the company got acquired before it needed to raise more funding. But after an informal meeting that ended up lasting most of the day, he received an offer the next morning.

Image Credits: MachEye

MachEye’s approach is definitely unique. “Generating audio-visuals on enterprise data, we are probably the only company that does it,” Panuganty said. But it’s important to note that it also offers all of the usual trappings of a BI service. If you really want dashboards, you can build those, and developers can use the company’s APIs to use their data elsewhere, too. The service can pull in data from most of the standard databases and data warehousing services, including AWS Redshift, Azure Synapse, Google BigQuery, Snowflake and Oracle. The company promises that it only takes 30 minutes from connecting a data source to being able to ask questions about that data.

Interestingly, MachEye’s pricing plan is per seat and doesn’t limit how much data you can query. There’s a free plan, but without the natural search and query capabilities, an $18/month/user plan that adds those capabilities and additional search features, but it takes the enterprise plan to get the audio narrations and other advanced features. The team is able to use this pricing model because it is able to quickly spin up the container infrastructure to answer a query and then immediately shut it down again — all within about two minutes.


By Frederic Lardinois

Data virtualization service Varada raises $12M

Varada, a Tel Aviv-based startup that focuses on making it easier for businesses to query data across services, today announced that it has raised a $12 million Series A round led by Israeli early-stage fund MizMaa Ventures, with participation by Gefen Capital.

“If you look at the storage aspect for big data, there’s always innovation, but we can put a lot of data in one place,” Varada CEO and co-founder Eran Vanounou told me. “But translating data into insight? It’s so hard. It’s costly. It’s slow. It’s complicated.”

That’s a lesson he learned during his time as CTO of LivePerson, which he described as a classic big data company. And just like at LivePerson, where the team had to reinvent the wheel to solve its data problems, again and again, every company — and not just the large enterprises — now struggles with managing their data and getting insights out of it, Vanounou argued.

Image Credits: Varada

The rest of the founding team, David Krakov, Roman Vainbrand and Tal Ben-Moshe, already had a lot of experience in dealing with these problems, too, with Ben-Moshe having served at the Chief Software Architect of Dell EMC’s XtremIO flash array unit, for example. They built the system for indexing big data that’s at the core of Varada’s platform (with the open-source Presto SQL query engine being one of the other cornerstones).

Image Credits: Varada

Essentially, Varada embraces the idea of data lakes and enriches that with its indexing capabilities. And those indexing capabilities is where Varada’s smarts can be found. As Vanounou explained, the company is using a machine learning system to understand when users tend to run certain workloads and then caches the data ahead of time, making the system far faster than its competitors.

“If you think about big organizations and think about the workloads and the queries, what happens during the morning time is different from evening time. What happened yesterday is not what happened today. What happened on a rainy day is not what happened on a shiny day. […] We listen to what’s going on and we optimize. We leverage the indexing technology. We index what is needed when it is needed.”

That helps speed up queries, but it also means less data has to be replicated, which also brings down the cost. AÅs Mizmaa’s Aaron Applebaum noted, since Varada is not a SaaS solution, the buyers still get all of the discounts from their cloud providers, too.

In addition, the system can allocate resources intelligently to that different users can tap into different amounts of bandwidth. You can tell it to give customers more bandwidth than your financial analysts, for example.

“Data is growing like crazy: in volume, in scale, in complexity, in who requires it and what the business intelligence uses are, what the API uses are,” Applebaum said when I asked him why he decided to invest. “And compute is getting slightly cheaper, but not really, and storage is getting cheaper. So if you can make the trade-off to store more stuff, and access things more intelligently, more quickly, more agile — that was the basis of our thesis, as long as you can do it without compromising performance.”

Varada, with its team of experienced executives, architects and engineers, ticked a lot of the company’s boxes in this regard, but he also noted that unlike some other Israeli startups, the team understood that it had to listen to customers and understand their needs, too.

“In Israel, you have a history — and it’s become less and less the case — but historically, there’s a joke that it’s ‘ready, fire, aim.’ You build a technology, you’ve got this beautiful thing and you’re like, ‘alright, we did it,’ but without listening to the needs of the customer,” he explained.

The Varada team is not afraid to compare itself to Snowflake, which at least at first glance seems to make similar promises. Vananou praised the company for opening up the data warehousing market and proving that people are willing to pay for good analytics. But he argues that Varada’s approach is fundamentally different.

“We embrace the data lake. So if you are Mr. Customer, your data is your data. We’re not going to take it, move it, copy it. This is your single source of truth,” he said. And in addition, the data can stay in the company’s virtual private cloud. He also argues that Varada isn’t so much focused on the business users but the technologists inside a company.

 


By Frederic Lardinois

As the pandemic creates supply chain chaos, Craft raises $10M to apply some intelligence

During the COVID-19 pandemic, supply chains have suddenly become hot. Who knew that would ever happen? The race to secure PPE, ventilators and minor things like food was and still is an enormous issue. But perhaps, predictably, the world of “supply chain software” could use some updating. Most of the platforms are deployed “empty” and require the client to populate them with their own data, or “bring their own data.” The UIs can be outdated and still have to be juggled with manual and offline workflows. So startups working in this space are now attracting some timely attention.

Thus, Craft, the enterprise intelligence company, today announces it has closed a $10 million Series A financing round to build what it characterizes as a “supply chain intelligence platform.” With the new funding, Craft will expand its offices in San Francisco, London and Minsk, and grow remote teams across engineering, sales, marketing and operations in North America and Europe.

It competes with some large incumbents, such as Dun & Bradstreet, Bureau van Dijk and Thomson Reuters . These are traditional data providers focused primarily on providing financial data about public companies, rather than real-time data from data sources such as operating metrics, human capital and risk metrics.

The idea is to allow companies to monitor and optimize their supply chain and enterprise systems. The financing was led by High Alpha Capital, alongside Greycroft. Craft also has some high-flying angel investors, including Sam Palmisano, chairman of the Center for Global Enterprise and former CEO and chairman of IBM; Jim Moffatt, former CEO of Deloitte Consulting; Frederic Kerrest, executive vice chairman, COO and co-founder of Okta; and Uncork Capital, which previously led Craft’s seed financing. High Alpha partner Kristian Andersen is joining Craft’s board of directors.

The problem Craft is attacking is a lack of visibility into complex global supply chains. For obvious reasons, COVID-19 disrupted global supply chains, which tended to reveal a lot of risks, structural weaknesses across industries and a lack of intelligence about how it’s all holding together. Craft’s solution is a proprietary data platform, API and portal that integrates into existing enterprise workflows.

While many business intelligence products require clients to bring their own data, Craft’s data platform comes pre-deployed with data from thousands of financial and alternative sources, such as 300+ data points that are refreshed using both Machine Learning and human validation. Its open-to-the-web company profiles appear in 50 million search results, for instance.

Ilya Levtov, co-founder and CEO of Craft, said in a statement: “Today, we are focused on providing powerful tracking and visibility to enterprise supply chains, while our ultimate vision is to build the intelligence layer of the enterprise technology stack.”

Kristian Andersen, partner with High Alpha commented: “We have a deep conviction that supply chain management remains an underinvested and under-innovated category in enterprise software.”

In the first half of 2020, Craft claims its revenues have grown nearly threefold, with Fortune 100 companies, government and military agencies, and SMEs among its clients.


By Mike Butcher

Microsoft launches Azure Synapse Link to help enterprises get faster insights from their data

At its Build developer conference, Microsoft today announced Azure Synapse Link, a new enterprise service that allows businesses to analyze their data faster and more efficiently, using an approach that’s generally called ‘hybrid transaction/analytical processing’ (HTAP). That’s a mouthful, it essentially enables enterprises to use the same database system for analytical and transactional workloads on a single system. Traditionally, enterprises had to make some tradeoffs between either building a single system for both that was often highly over-provisioned or to maintain separate systems for transactional and analytics workloads.

Last year, at its Ignite conference, Microsoft announced Azure Synapse Analytics, an analytics service that combines analytics and data warehousing to create what the company calls “the next evolution of Azure SQL Data Warehouse.” Synapse Analytics brings together data from Microsoft’s services and those from its partners and makes it easier to analyze.

“One of the key things, as we work with our customers on their digital transformation journey, there is an aspect of being data-driven, of being insights-driven as a culture, and a key part of that really is that once you decide there is some amount of information or insights that you need, how quickly are you able to get to that? For us, time to insight and a secondary element, which is the cost it takes, the effort it takes to build these pipelines and maintain them with an end-to-end analytics solution, was a key metric we have been observing for multiple years from our largest enterprise customers,” said Rohan Kumar, Microsoft’s corporate VP for Azure Data.

Synapse Link takes the work Microsoft did on Synaps Analytics a step further by removing the barriers between Azure’s operational databases and Synapse Analytics, so enterprises can immediately get value from the data in those databases without going through a data warehouse first.

“What we are announcing with Synapse Link is the next major step in the same vision that we had around reducing the time to insight,” explained Kumar. “And in this particular case, a long-standing barrier that exists today between operational databases and analytics systems is these complex ETL (extract, transform, load) pipelines that need to be set up just so you can do basic operational reporting or where, in a very transactionally consistent way, you need to move data from your operational system to the analytics system, because you don’t want impact the performance of the operational system in any way because that’s typically dealing with, depending on the system, millions of transactions per second.”

ETL pipelines, Kumar argued, are typically expensive and hard to build and maintain, yet enterprises are now building new apps — and maybe even line of business mobile apps — where any action that consumers take and that is registered in the operational database is immediately available for predictive analytics, for example.

From the user perspective, enabling this only takes a single click to link the two, while it removes the need for managing additional data pipelines or database resources. That, Kumar said, was always the main goal for Synapse Link. “With a single click, you should be able to enable real-time analytics on you operational data in ways that don’t have any impact on your operational systems, so you’re not using the compute part of your operational system to do the query, you actually have to transform the data into a columnar format, which is more adaptable for analytics, and that’s really what we achieved with Synapse Link.”

Because traditional HTAP systems on-premises typically share their compute resources with the operational database, those systems never quite took off, Kumar argued. In the cloud, with Synapse Link, though, that impact doesn’t exist because you’re dealing with two separate systems. Now, once a transaction gets committed to the operational database, the Synapse Link system transforms the data into a columnar format that is more optimized for the analytics system — and it does so in real time.

For now, Synapse Link is only available in conjunction with Microsoft’s Cosmos DB database. As Kumar told me, that’s because that’s where the company saw the highest demand for this kind of service, but you can expect the company to add support for available in Azure SQL, Azure Database for PostgreSQL and Azure Database for MySQL in the future.


By Frederic Lardinois

Fishtown Analytics raises $12.9M Series A for its open-source analytics engineering tool

Philadelphia-based Fishtown Analytics, the company behind the popular open-source data engineering tool dbt, today announced that it has raised a $12.9 million Series A round led by Andreessen Horowitz, with the firm’s general partner Martin Casada joining the company’s board.

“I wrote this blog post in early 2016, essentially saying that analysts needed to work in a fundamentally different way,” Fishtown founder and CEO Tristan Handy told me, when I asked him about how the product came to be. “They needed to work in a way that much more closely mirrored the way the software engineers work and software engineers have been figuring this shit out for years and data analysts are still like sending each other Microsoft Excel docs over email.”

The dbt open-source project forms the basis of this. It allows anyone who can write SQL queries to transform data and then load it into their preferred analytics tools. As such, it sits in-between data warehouses and the tools that load data into them on one end, and specialized analytics tools on the other.

As Casada noted when I talked to him about the investment, data warehouses have now made it affordable for businesses to store all of their data before it is transformed. So what was traditionally “extract, transform, load” (ETL) has now become “extract, load, transform” (ELT). Andreessen Horowitz is already invested in Fivetran, which helps businesses move their data into their warehouses, so it makes sense for the firm to also tackle the other side of this business.

“Dbt is, as far as we can tell, the leading community for transformation and it’s a company we’ve been tracking for at least a year,” Casada said. He also argued that data analysts — unlike data scientists — are not really catered to as a group.

Before this round, Fishtown hadn’t raised a lot of money, even though it has been around for a few years now, except for a small SAFE round from Amplify.

But Handy argued that the company needed this time to prove that it was on to something and build a community. That community now consists of more than 1,700 companies that use the dbt project in some form and over 5,000 people in the dbt Slack community. Fishtown also now has over 250 dbt Cloud customers and the company signed up a number of big enterprise clients earlier this year. With that, the company needed to raise money to expand and also better service its current list of customers.

“We live in Philadelpha. The cost of living is low here and none of us really care to make a quadro-billion dollars, but we do want to answer the question of how do we best serve the community,” Handy said. “And for the first time, in the early part of the year, we were like, holy shit, we can’t keep up with all of the stuff that people need from us.”

The company plans to expand the team from 25 to 50 employees in 2020 and with those, the team plans to improve and expand the product, especially its IDE for data analysts, which Handy admitted could use a bit more polish.


By Frederic Lardinois