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Hightouch raises $2.1M to help businesses get more value from their data warehouses
Hightouch, a SaaS service that helps businesses sync their customer data across sales and marketing tools, is coming out of stealth and announcing a $2.1 million seed round. The round was led by Afore Capital and Slack Fund, with a number of angel investors also participating.
At its core, Hightouch, which participated in Y Combinator’s Summer 2019 batch, aims to solve the customer data integration problems that many businesses today face.
During their time at Segment, Hightouch co-founders Tejas Manohar and Josh Curl witnessed the rise of data warehouses like Snowflake, Google’s BigQuery and Amazon Redshift — that’s where a lot of Segment data ends up, after all. As businesses adopt data warehouses, they now have a central repository for all of their customer data. Typically, though, this information is then only used for analytics purposes. Together with former Bessemer Ventures investor Kashish Gupta, the team decided to see how they could innovate on top of this trend and help businesses activate all of this information.
HighTouch co-founders Kashish Gupta, Josh Curl and Tejas Manohar.
“What we found is that, with all the customer data inside of the data warehouse, it doesn’t make sense for it to just be used for analytics purposes — it also makes sense for these operational purposes like serving different business teams with the data they need to run things like marketing campaigns — or in product personalization,” Manohar told me. “That’s the angle that we’ve taken with Hightouch. It stems from us seeing the explosive growth of the data warehouse space, both in terms of technology advancements as well as like accessibility and adoption. […] Our goal is to be seen as the company that makes the warehouse not just for analytics but for these operational use cases.”
It helps that all of the big data warehousing platforms have standardized on SQL as their query language — and because the warehousing services have already solved the problem of ingesting all of this data, Hightouch doesn’t have to worry about this part of the tech stack either. And as Curl added, Snowflake and its competitors never quite went beyond serving the analytics use case either.
Image Credits: Hightouch
As for the product itself, Hightouch lets users create SQL queries and then send that data to different destinations — maybe a CRM system like Salesforce or a marketing platform like Marketo — after transforming it to the format that the destination platform expects.
Expert users can write their own SQL queries for this, but the team also built a graphical interface to help non-developers create their own queries. The core audience, though, is data teams — and they, too, will likely see value in the graphical user interface because it will speed up their workflows as well. “We want to empower the business user to access whatever models and aggregation the data user has done in the warehouse,” Gupta explained.
The company is agnostic to how and where its users want to operationalize their data, but the most common use cases right now focus on B2C companies, where marketing teams often use the data, as well as sales teams at B2B companies.
Image Credits: Hightouch
“It feels like there’s an emerging category here of tooling that’s being built on top of a data warehouse natively, rather than being a standard SaaS tool where it is its own data store and then you manage a secondary data store,” Curl said. “We have a class of things here that connect to a data warehouse and make use of that data for operational purposes. There’s no industry term for that yet, but we really believe that that’s the future of where data engineering is going. It’s about building off this centralized platform like Snowflake, BigQuery and things like that.”
“Warehouse-native,” Manohar suggested as a potential name here. We’ll see if it sticks.
Hightouch originally raised its round after its participation in the Y Combinator demo day but decided not to disclose it until it felt like it had found the right product/market fit. Current customers include the likes of Retool, Proof, Stream and Abacus, in addition to a number of significantly larger companies the team isn’t able to name publicly.
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SingleStore, formerly MemSQL, raises $80M to integrate and leverage companies’ disparate data silos
While the enterprise world likes to talk about “big data”, that term belies the real state of how data exists for many organizations: the truth of the matter is that it’s often very fragmented, living in different places and on different systems, making the concept of analysing and using it in a single, effective way a huge challenge.
Today, one of the big up-and-coming startups that has built a platform to get around that predicament is announcing a significant round of funding, a sign of the demand for its services and its success so far in executing on that.
SingleStore, which provides a SQL-based platform to help enterprises manage, parse and use data that lives in silos across multiple cloud and on-premise environments — a key piece of work needed to run applications in risk, fraud prevention, customer user experience, real-time reporting and real-time insights, fast dashboards, data warehouse augmentation, modernization for data warehouses and data architectures and faster insights — has picked up $80 million in funding, a Series E round that brings in new strategic investors alongside its existing list of backers.
The round is being led by Insight Partners, with new backers Dell Technologies Capital, Hercules Capital; and previous backers Accel, Anchorage, Glynn Capital, GV (formerly Google Ventures) and Rev IV also participating.
Alongside the investment, SingleStore is formally announcing a new partnership with analytics powerhouse SAS. I say “formally” because they two have been working together already and it’s resulted in “tremendous uptake,” CEO Raj Verma said in an interview over email.
Verma added that the round came out of inbound interest, not its own fundraising efforts, and as such, it brings the total amount of cash it has on hand to $140 million. The gives the startup money to play with not only to invest in hiring, R&D and business development, but potentially also M&A, given that the market right now seems to be in a period of consolidation.
Verma said the valuation is a “significant upround” compared to its Series D in 2018 but didn’t disclose the figure. PitchBook notes that at the time it was valued at $270 million post-money.
When I last spoke with the startup in May of this year — when it announced a debt facility of $50 million — it was not called SingleStore; it was MemSQL. The company rebranded at the end of October to the new name, but Verma said that the change was a long time in the planning.
“The name change is one of the first conversations I had when I got here,” he said about when he joined the company in 2019 (he’s been there for about 16 months). “The [former] name didn’t exactly flow off the tongue and we found that it no longer suited us, we found ourselves in a tiny shoebox of an offering, in saying our name is MemSQL we were telling our prospects to think of us as in-memory and SQL. SQL we didn’t have a problem with but we had outgrown in-memory years ago. That was really only 5% of our current revenues.”
He also mentioned the hang up many have with in-memory database implementations: they tend to be expensive. “So this implied high TCO, which couldn’t have been further from the truth,” he said. “Typically we are ⅕-⅛ the cost of what a competitive product would be to implement. We were doing ourselves a disservice with prospects and buyers.”
The company liked the name SingleStore because it is based a conceptual idea of its proprietary technology. “We wanted a name that could be a verb. Down the road we hope that when someone asks large enterprises what they do with their data, they will say that they ‘SingleStore It!’ That is the vision. The north star is that we can do all types of data without workload segmentation,” he said.
That effort is being done at a time when there is more competition than ever before in the space. Others also providing tools to manage and run analytics and other work on big data sets include Amazon, Microsoft, Snowflake, PostgreSQL, MySQL and more.
SingleStore is not disclosing any metrics on its growth at the moment but says it has thousands of enterprise customers. Some of the more recent names it’s disclosed include GE, IEX Cloud, Go Guardian, Palo Alto Networks, EOG Resources, SiriusXM + Pandora, with partners including Infosys, HCL and NextGen.
“As industry after industry reinvents itself using software, there will be accelerating market demand for predictive applications that can only be powered by fast, scalable, cloud-native database systems like SingleStore’s,” said Lonne Jaffe, managing director at Insight Partners, in a statement. “Insight Partners has spent the past 25 years helping transformational software companies rapidly scale-up, and we’re looking forward to working with Raj and his management team as they bring SingleStore’s highly differentiated technology to customers and partners across the world.”
“Across industries, SAS is running some of the most demanding and sophisticated machine learning workloads in the world to help organizations make the best decisions. SAS continues to innovate in AI and advanced analytics, and we partner with companies like SingleStore that share our curiosity about how data and analytics can help organizations reimagine their businesses and change the world,” said Oliver Schabenberger, COO and CTO at SAS, added. “Our engineering teams are integrating SingleStore’s scalable SQL-based database platform with the massively parallel analytics engine SAS Viya. We are excited to work with SingleStore to improve performance, reduce cost, and enable our customers to be at the forefront of analytics and decisioning.”
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Microsoft launches Azure Purview, its new data governance service
As businesses gather, store and analyze an ever-increasing amount of data, tools for helping them discover, catalog, track and manage how that data is shared are also becoming increasingly important. With Azure Purview, Microsoft is launching a new data governance service into public preview today that brings together all of these capabilities in a new data catalog with discovery and data governance features.
As Rohan Kumar, Microsoft’s corporate VP for Azure Data, told me, this has become a major pain point for enterprises. While they may be very excited about getting started with data-heavy technologies like predictive analytics, those companies’ data and privacy-focused executives are very concerned to make sure that the way the data is used is compliant or that the company has received the right permissions to use its customers’ data, for example.
In addition, companies also want to make sure that they can trust their data and know who has access to it and who made changes to it.
“[Purview] is a unified data governance platform which automates the discovery of data, cataloging of data, mapping of data, lineage tracking — with the intention of giving our customers a very good understanding of the breadth of the data estate that exists to begin with, and also to ensure that all these regulations that are there for compliance, like GDPR, CCPA, etc, are managed across an entire data estate in ways which enable you to make sure that they don’t violate any regulation,” Kumar explained.
At the core of Purview is its catalog that can pull in data from the usual suspects, like Azure’s various data and storage services, but also third-party data stores, including Amazon’s S3 storage service and on-premises SQL Server. Over time, the company will add support for more data sources.
Kumar described this process as a “multi-semester investment,” so the capabilities the company is rolling out today are only a small part of what’s on the overall road map already. With this first release today, the focus is on mapping a company’s data estate.
Image Credits: Microsoft
“Next [on the road map] is more of the governance policies,” Kumar said. “Imagine if you want to set things like ‘if there’s any PII data across any of my data stores, only this group of users has access to it.’ Today, setting up something like that is extremely complex and most likely you’ll get it wrong. That’ll be as simple as setting a policy inside of Purview.”
In addition to launching Purview, the Azure team also today launched into general availability Azure Synapse, Microsoft’s next-generation data warehousing and analytics service. The idea behind Synapse is to give enterprises — and their engineers and data scientists — a single platform that brings together data integration, warehousing and big data analytics.
“With Synapse, we have this one product that gives a completely no-code experience for data engineers, as an example, to build out these [data] pipelines and collaborate very seamlessly with the data scientists who are building out machine learning models, or the business analysts who build out reports for things like Power BI.”
Among Microsoft’s marquee customers for the service, which Kumar described as one of the fastest-growing Azure services right now, are FedEx, Walgreens, Myntra and P&G.
“The insights we gain from continuous analysis help us optimize our network,” said Sriram Krishnasamy, senior vice president, strategic programs at FedEx Services. “So as FedEx moves critical high-value shipments across the globe, we can often predict whether that delivery will be disrupted by weather or traffic and remediate that disruption by routing the delivery from another location.”
Image Credits: Microsoft
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Jitsu nabs $2M seed to build open-source data integration platform
Jitsu, a graduate of the Y Combinator Summer 2020 cohort, is developing an open-source data integration platform that helps developers send data to a data warehouse. Today, the startup announced a $2 million seed investment.
Costanoa Ventures led the round with participation from Y Combintaor, The House Fund and SignalFire.
In addition to the open-source version of the software, the company has developed a hosted version that companies can pay to use, which shares the same name as the company. Peter Wysinski, Jitsu’s co-founder and CEO, says a good way to think about his company is an open-source Segment, the customer data integration company that was recently sold to Twilio for $3.2 billion.
But, he says, it goes beyond what Segment provides by allowing you to move all kinds of data, whether customer data, connected device data or other types. “If you look at the space in general, companies want more granularity. So let’s say for example, a couple years ago you wanted to sync just your transactions from QuickBooks to your data warehouse, now you want to capture every single sale at the point of sale. What Jitsu lets you do is capture essentially all of those events, all of those streams, and send them to your data warehouse,” Wysinski explained.
Among the data warehouses it currently supports are Amazon Redshift, Google BigQuery, PostGres and Snowflake.
The founders built the open-source project called EventNative to help solve problems they themselves were having moving data around at their previous jobs. After putting the open-source version on GitHub a few months ago, they quickly attained 1,000 stars, proving that they had delivered something that solved a common problem for data teams. They then built the hosted version, Jitsu, which went live a couple of weeks ago.
For now, the company is just the two co-founders, Wysinski and CTO Vladimir Klimontovich and couple of contract engineers, but they intend to do some preliminary hiring over the next year to grow the company, most likely adding engineers. As they begin to build out the startup, Wysinski says that being open source will help drive diversity and inclusion in their hiring.
“The goal is essentially to go after that open-source community and hire people from anywhere because engineers aren’t just […] one color or one race, they’re everywhere, and being open source, and especially being in a remote world, makes it so, so much simpler [to build a diverse workforce], and a lot of companies I feel are going down that road,” he said.
He says along that line, the plan is to be a fully remote company, even after the pandemic ends, as they hire from anywhere. The goal is to have quarterly offsite meetings to check in with employees, but do the majority of the work remotely.
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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 Databricks’ 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 with which Databricks users are familiar. 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.
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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.
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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.
Explo 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 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 we 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.
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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. As MizMaa’s Aaron Applbaum 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 so 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.
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“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,” Applbaum 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.
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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.
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IoT and data science will boost foodtech in the post-pandemic era
Sunny Dhillon is an early-stage investor at Signia Ventures in San Francisco where he invests in retail tech, e-commerce infrastructure and logistics, alongside consumer and enterprise software startups.
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Even as e-grocery usage has skyrocketed in our coronavirus-catalyzed world, brick-and-mortar grocery stores have soldiered on. While strict in-store safety guidelines may gradually ease up, the shopping experience will still be low-touch and socially distanced for the foreseeable future.
This begs the question: With even greater challenges than pre-pandemic, how can grocers ensure their stores continue to operate profitably?
Just as micro-fulfillment centers (MFCs), dark stores and other fulfillment solutions have been helping e-grocers optimize profitability, a variety of old and new technologies can help brick-and-mortar stores remain relevant and continue churning out cash.
Today, we present three “must-dos” for post-pandemic retail grocers: rely on the data, rely on the biology and rely on the hardware.
Rely on the data
Image Credits: Pixabay/Pexels (opens in a new window)
The hallmark of shopping in a store is the consistent availability and wide selection of fresh items — often more so than online. But as the number of in-store customers continues to fluctuate, planning inventory and minimizing waste has become ever more so a challenge for grocery store managers. Grocers on average throw out more than 12% of their on-shelf produce, which eats into already razor-thin margins.
While e-grocers are automating and optimizing their fulfillment operations, brick-and-mortar grocers can automate and optimize their inventory planning mechanisms. To do this, they must leverage their existing troves of customer, business and external data to glean valuable insights for store managers.
Eden Technologies of Walmart is a pioneering example. Spun out of a company hackathon project, the internal tool has been deployed at over 43 distribution centers nationwide and promises to save Walmart over $2 billion in the coming years. For instance, if a batch of produce intended for a store hundreds of miles away is deemed soon-to-ripen, the tool can help divert it to the nearest store instead, using FDA standards and over 1 million images to drive its analysis.
Similarly, ventures such as Afresh Technologies and Shelf Engine have built platforms to leverage years of historical customer and sales data, as well as seasonality and other external factors, to help store managers determine how much to order and when. The results have been nothing but positive — Shelf Engine customers have increased gross margins by over 25% and Afresh customers have reduced food waste by up to 45%.
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