No-code business intelligence service y42 raises $2.9M seed round

Berlin-based y42 (formerly known as Datos Intelligence), a data warehouse-centric business intelligence service that promises to give businesses access to an enterprise-level data stack that’s as simple to use as a spreadsheet, today announced that it has raised a $2.9 million seed funding round led by La Famiglia VC. Additional investors include the co-founders of Foodspring, Personio and Petlab.

The service, which was founded in 2020, integrates with over 100 data sources, covering all the standard B2B SaaS tools from Airtable to Shopify and Zendesk, as well as database services like Google’s BigQuery. Users can then transform and visualize this data, orchestrate their data pipelines and trigger automated workflows based on this data (think sending Slack notifications when revenue drops or emailing customers based on your own custom criteria).

Like similar startups, y42 extends the idea data warehouse, which was traditionally used for analytics, and helps businesses operationalize this data. At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines.

y42 founder and CEO Hung Dang

y42 founder and CEO Hung Dang.

“We’re taking the best of breed open-source software. What we really want to accomplish is to create a tool that is so easy to understand and that enables everyone to work with their data effectively,” Y42 founder and CEO Hung Dang told me. “We’re extremely UX obsessed and I would describe us as no-code/low-code BI tool — but with the power of an enterprise-level data stack and the simplicity of Google Sheets.”

Before y42, Vietnam-born Dang co-founded a major events company that operated in over 10 countries and made millions in revenue (but with very thin margins), all while finishing up his studies with a focus on business analytics. And that in turn led him to also found a second company that focused on B2B data analytics.

Image Credits: y42

Even while building his events company, he noted, he was always very product- and data-driven. “I was implementing data pipelines to collect customer feedback and merge it with operational data — and it was really a big pain at that time,” he said. “I was using tools like Tableau and Alteryx, and it was really hard to glue them together — and they were quite expensive. So out of that frustration, I decided to develop an internal tool that was actually quite usable and in 2016, I decided to turn it into an actual company. ”

He then sold this company to a major publicly listed German company. An NDA prevents him from talking about the details of this transaction, but maybe you can draw some conclusions from the fact that he spent time at Eventim before founding y42.

Given his background, it’s maybe no surprise that y42’s focus is on making life easier for data engineers and, at the same time, putting the power of these platforms in the hands of business analysts. Dang noted that y42 typically provides some consulting work when it onboards new clients, but that’s mostly to give them a head start. Given the no-code/low-code nature of the product, most analysts are able to get started pretty quickly  — and for more complex queries, customers can opt to drop down from the graphical interface to y42’s low-code level and write queries in the service’s SQL dialect.

The service itself runs on Google Cloud and the 25-people team manages about 50,000 jobs per day for its clients. the company’s customers include the likes of LifeMD, Petlab and Everdrop.

Until raising this round, Dang self-funded the company and had also raised some money from angel investors. But La Famiglia felt like the right fit for y42, especially due to its focus on connecting startups with more traditional enterprise companies.

“When we first saw the product demo, it struck us how on top of analytical excellence, a lot of product development has gone into the y42 platform,” said Judith Dada, General Partner at LaFamiglia VC. “More and more work with data today means that data silos within organizations multiply, resulting in chaos or incorrect data. y42 is a powerful single source of truth for data experts and non-data experts alike. As former data scientists and analysts, we wish that we had y42 capabilities back then.”

Dang tells me he could have raised more but decided that he didn’t want to dilute the team’s stake too much at this point. “It’s a small round, but this round forces us to set up the right structure. For the series, A, which we plan to be towards the end of this year, we’re talking about a dimension which is 10x,” he told me.

#alteryx, #analytics, #berlin, #big-data, #business-intelligence, #business-software, #ceo, #cloud, #data, #data-analysis, #data-management, #data-warehouse, #enterprise, #general-partner, #information-technology, #judith-dada, #recent-funding, #shopify, #sql, #startups, #vietnam

0

Noogata raises $12M seed round for its no-code enterprise AI platform

Noogata, a startup that offers a no-code AI solution for enterprises, today announced that it has raised a $12 million seed round led by Team8, with participation from Skylake Capital. The company, which was founded in 2019 and counts Colgate and PepsiCo among its customers, currently focuses on e-commerce, retail and financial services, but it notes that it will use the new funding to power its product development and expand into new industries.

The company’s platform offers a collection of what are essentially pre-built AI building blocks that enterprises can then connect to third-party tools like their data warehouse, Salesforce, Stripe and other data sources. An e-commerce retailer could use this to optimize its pricing, for example, thanks to recommendations from the Noogata platform, while a brick-and-mortar retailer could use it to plan which assortment to allocate to a given location.

Image Credits: Noogata

“We believe data teams are at the epicenter of digital transformation and that to drive impact, they need to be able to unlock the value of data. They need access to relevant, continuous and explainable insights and predictions that are reliable and up-to-date,” said Noogata co-founder and CEO Assaf Egozi. “Noogata unlocks the value of data by providing contextual, business-focused blocks that integrate seamlessly into enterprise data environments to generate actionable insights, predictions and recommendations. This empowers users to go far beyond traditional business intelligence by leveraging AI in their self-serve analytics as well as in their data solutions.”

Image Credits: Noogata

We’ve obviously seen a plethora of startups in this space lately. The proliferation of data — and the advent of data warehousing — means that most businesses now have the fuel to create machine learning-based predictions. What’s often lacking, though, is the talent. There’s still a shortage of data scientists and developers who can build these models from scratch, so it’s no surprise that we’re seeing more startups that are creating no-code/low-code services in this space. The well-funded Abacus.ai, for example, targets about the same market as Noogata.

“Noogata is perfectly positioned to address the significant market need for a best-in-class, no-code data analytics platform to drive decision-making,” writes Team8 managing partner Yuval Shachar. “The innovative platform replaces the need for internal build, which is complex and costly, or the use of out-of-the-box vendor solutions which are limited. The company’s ability to unlock the value of data through AI is a game-changer. Add to that a stellar founding team, and there is no doubt in my mind that Noogata will be enormously successful.”

#analytics, #artificial-intelligence, #big-data, #business, #business-intelligence, #computing, #data-warehouse, #e-commerce, #enterprise, #machine-learning, #noogata, #recent-funding, #salesforce, #startups, #stripe, #team8

0

Big data VC OpenOcean hits $111.5M for third fund, appoints Ekaterina Almasque to GP

OpenOcean, a European VC which has tended to specialise in big data-oriented startups and deep tech, has reach the €92 million ($111.5 million) mark for its third main venture fund, and is aiming for a final close of €130 million by mid-way this year. LPs in the new fund include the European Investment Fund (EIF), Tesi, pension funds, major family offices and Oxford University’s Corpus Christi College.

Ekaterina Almasque — who has already led investments in IQM (superconducting quantum machines) and Sunrise.io (multi-cloud hyper-converged infrastructure) and is leading the London team and operations for the firm — has been appointed as general partner. Before joining, Almasque was a managing director at Samsung Catalyst Fund in Europe, led investments in Graphcore’s processor for Artificial Intelligence, Mapillary’s layer for rapid mapping and AIMotive’s autonomous driving stack.

The enormous wealth of data in the modern world means the next generation of software is being built at the infrastructure. Thus, the fund said it would invest primarily at the Series A level with initial investments of €3 million to €5 million, across OpenOcean’s principle areas of artificial intelligence, application-driven data infrastructure, intelligent automation and open source.

OpenOcean’s team includes Michael “Monty” Widenius, the “spiritual father” of MariaDB, and one of the original developers of MySQL, the predecessor to MariaDB; Tom Henriksson, who invested in MySQL and MariaDB; as well as Ralf Wahlsten and Patrik Backman.

Tom Henriksson, general partner at OpenOcean, commented: “Ekaterina… brings an immense amount of expertise to the team and exemplifies the way we want to support our founders. Fund 2020 is an important step for OpenOcean, with prestigious LPs trusting our approach and our knowledge, and believing in our ability to identify the very best data solutions and infrastructure technologies in Europe.”

Almasque said: “The next five years will be critical for digital infrastructure, as breakthrough technologies are currently being constrained by the capabilities of the stack. Enabling this next level of infrastructure innovation is crucial to realising digitisation projects across the economy and will determine what the internet of the future looks like. We’re excited by the potential of world-leading businesses being built across Europe and are looking forward to supporting the next generation of software leaders.”

Speaking to TechCrunch she added: “It’s very rare to find such a VC so deep in the stack which also invested in one of the first unicorns in Europe and really built the open source ecosystem globally. So for me, this was absolutely an interesting team to join. And what OpenOcean was doing since inception in 2011 was very unique among pioneering ecosystems, such as big data analytics… and it remains very pioneering, pushing the frontiers in artificial intelligence and now quantum computing. This is what really attracts me, and I think there is a very, very big future.”

In an interview Henriksson told me: “What we are seeing is that our economy is shifting more and more towards the digital, data-driven economy. It started with few industries, but now we see a larger shift, including new industries like healthcare, like manufacturing.”

Asked about the effects of the pandemic on the sector, he said: “Obviously we see a lot of startups who are plugging into things like the UiPath platform. This is very relevant for the pandemic. Because the companies that had started automating strongly before the pandemic hit… they’ve actually accelerated and they find benefits for their teams and organisations and actually the people are happier because they have better automation technologies in place. The ones that didn’t start before [the pandemic hit] they’re a little behind now.”

#aria, #artificial-intelligence, #big-data, #computing, #data-management, #databases, #drupal, #europe, #european-investment-fund, #infrastructure, #london, #manufacturing, #mapillary, #mariadb, #mysql, #openocean, #tc, #venture-capital, #wordpress

0

An argument against cloud-based applications

In the last decade we’ve seen massive changes in how we consume and interact with our world. The Yellow Pages is a concept that has to be meticulously explained with an impertinent scoff at our own age. We live within our smartphones, within our apps.

While we thrive with the information of the world at our fingertips, we casually throw away any semblance of privacy in exchange for the convenience of this world.

This line we straddle has been drawn with recklessness and calculation by big tech companies over the years as we’ve come to terms with what app manufacturers, large technology companies, and app stores demand of us.

Our private data into the cloud

According to Symantec, 89% of our Android apps and 39% of our iOS apps require access to private information. This risky use sends our data to cloud servers, to both amplify the performance of the application (think about the data needed for fitness apps) and store data for advertising demographics.

While large data companies would argue that data is not held for long, or not used in a nefarious manner, when we use the apps on our phones, we create an undeniable data trail. Companies generally keep data on the move, and servers around the world are constantly keeping data flowing, further away from its source.

Once we accept the terms and conditions we rarely read, our private data is no longer such. It is in the cloud, a term which has eluded concrete understanding throughout the years.

A distinction between cloud-based apps and cloud computing must be addressed. Cloud computing at an enterprise level, while argued against ad nauseam over the years, is generally considered to be a secure and cost-effective option for many businesses.

Even back in 2010, Microsoft said 70% of its team was working on things that were cloud-based or cloud-inspired, and the company projected that number would rise to 90% within a year. That was before we started relying on the cloud to store our most personal, private data.

Cloudy with a chance of confusion

To add complexity to this issue, there are literally apps to protect your privacy from other apps on your smart phone. Tearing more meat off the privacy bone, these apps themselves require a level of access that would generally raise eyebrows if it were any other category of app.

Consider the scenario where you use a key to encrypt data, but then you need to encrypt that key to make it safe. Ultimately, you end up with the most important keys not being encrypted. There is no win-win here. There is only finding a middle ground of contentment in which your apps find as much purchase in your private data as your doctor finds in your medical history.

The cloud is not tangible, nor is it something we as givers of the data can access. Each company has its own cloud servers, each one collecting similar data. But we have to consider why we give up this data. What are we getting in return? We are given access to applications that perhaps make our lives easier or better, but essentially are a service. It’s this service end of the transaction that must be altered.

App developers have to find a method of service delivery that does not require storage of personal data. There are two sides to this. The first is creating algorithms that can function on a local basis, rather than centralized and mixed with other data sets. The second is a shift in the general attitude of the industry, one in which free services are provided for the cost of your personal data (which ultimately is used to foster marketing opportunities).

Of course, asking this of any big data company that thrives on its data collection and marketing process is untenable. So the change has to come from new companies, willing to risk offering cloud privacy while still providing a service worth paying for. Because it wouldn’t be free. It cannot be free, as free is what got us into this situation in the first place.

Clearing the clouds of future privacy

What we can do right now is at least take a stance of personal vigilance. While there is some personal data that we cannot stem the flow of onto cloud servers around the world, we can at least limit the use of frivolous apps that collect too much data. For instance, games should never need access to our contacts, to our camera and so on. Everything within our phone is connected, it’s why Facebook seems to know everything about us, down to what’s in our bank account.

This sharing takes place on our phone and at the cloud level, and is something we need to consider when accepting the terms on a new app. When we sign into apps with our social accounts, we are just assisting the further collection of our data.

The cloud isn’t some omnipotent enemy here, but it is the excuse and tool that allows the mass collection of our personal data.

The future is likely one in which devices and apps finally become self-sufficient and localized, enabling users to maintain control of their data. The way we access apps and data in the cloud will change as well, as we’ll demand a functional process that forces a methodology change in service provisions. The cloud will be relegated to public data storage, leaving our private data on our devices where it belongs. We have to collectively push for this change, lest we lose whatever semblance of privacy in our data we have left.

#big-data, #cloud, #cloud-computing, #cloud-infrastructure, #cloud-storage, #column, #opinion, #privacy, #security

0

Archaeology is going digital to harness the power of Big Data

Archaeology is catching up with the digital humanities movement with the creation of large online databases, combining data collected from satellite-, airborne-, and UAV-mounted sensors with historical information.

Enlarge / Archaeology is catching up with the digital humanities movement with the creation of large online databases, combining data collected from satellite-, airborne-, and UAV-mounted sensors with historical information. (credit: Brown University)

There’s rarely time to write about every cool science-y story that comes our way. So this year, we’re once again running a special Twelve Days of Christmas series of posts, highlighting one science story that fell through the cracks in 2020, each day from December 25 through January 5. Today: archaeologists are using drones and satellite imagery, among other tools, to build large online datasets with an eye toward harnessing the power of big data for their research.

Archaeology is finally catching up with the so-called “digital humanities,” as evidenced by a February special edition of the Journal of Field Archaeology, devoted entirely to discussing the myriad ways in which large-scale datasets and associated analytics are transforming the field. The papers included in the edition were originally presented during a special session at a 2019 meeting of the Society for American Archaeology. The data sets might be a bit smaller than those normally associated with Big Data, but this new “digital data gaze” is nonetheless having a profound impact on archaeological research.

As we’ve reported previously, more and more archives are being digitized within the humanities, and scholars have been applying various analytical tools to those rich datasets, such as Google N-gram, Bookworm, and WordNet. Close reading of selected sources—the traditional method of the scholars in the humanities—gives a deep but narrow view. Quantitative computational analysis can combine that close reading with a broader, more generalized bird’s-eye approach that can reveal hidden patterns or trends that otherwise might have escaped notice. The nature of the data archives and digital tools are a bit different in archaeology, but the concept is the same: combine the traditional “pick and trowel” detailed field work on the ground with more of a sweeping, big-picture, birds-eye view, in hopes of gleaning hidden insights.

Read 26 remaining paragraphs | Comments

#12-days-of-christmas, #archaeology, #big-data, #digital-archaeology, #digital-humanities, #gaming-culture, #incan-empire, #science

0

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 founders

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.

#afore-capital, #analytics, #articles, #big-data, #business-intelligence, #cloud, #computing, #data-management, #data-warehouse, #developer, #enterprise, #information, #personalization, #recent-funding, #slack-fund, #software-as-a-service, #startups, #tc

0

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.”

#big-data, #enterprise, #memsql, #singlestore

0

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 paint 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 roadmap already. With this first release today, the focus is on mapping a company’s data estate.

Image Credits: Microsoft

“Next [on the roadmap] 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 Azure Synapse, Microsoft’s next-generation data warehousing and analytics service, into general availability. 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

#analytics, #big-data, #business-intelligence, #cloud, #computing, #data, #data-management, #data-protection, #developer, #enterprise, #general-data-protection-regulation, #information, #microsoft, #rohan-kumar, #tc

0

Drive predictable B2B revenue growth with insights from big data and CDPs

As the world reopens and revenue teams are unleashed to meet growth targets, many B2B sellers and marketers are wondering how they can best prioritize prospect accounts. Everyone ultimately wants to achieve predictable revenue growth, but in uncertain times — and with shrinking budgets — it can feel like a pipe dream.

Slimmer budgets likely mean you’ll need more accurate targeting and higher win rates. The good news is your revenue team is likely already gathering tons of prospect data to help you improve account targeting, so it’s time to put that data to work with artificial intelligence. Using big data and four essential AI-based models, you can understand what your prospects want and successfully predict revenue opportunities.

Big data and CDPs are first steps to capturing account insights

Capturing and processing big data is essential in order to know everything about prospects and best position your solution. Accurately targeting your campaigns and buyer journeys necessitates more data than ever before.

Marketers today rely on customer data platforms (CDPs) to handle this slew of information from disparate sources. CDPs let us mash together and clean up data to get a single source of normalized data. We can then use AI to extract meaningful insights and trends to drive revenue planning.

That single source of truth also lets marketers dive into the ocean of accounts and segment them by similar attributes. You can break them down into industry, location, buying stage, intent, engagement — any combination of factors. When it’s time to introduce prospects to your cadence, you’ll have segment-specific insights to guide your campaigns.

AI realizes data-based insights

You might find that your data ocean is much deeper than you expected. While transforming all that data into a single source to drive actionable insights, you’ll also need the right resources and solutions to convert raw data into highly targeted prospect outreach.

This is where AI shines. AI and machine learning enable revenue teams to analyze data for historical and behavioral patterns, pluck out the most relevant intent data, and predict what will move prospects through the buyer journey.

#artificial-intelligence, #big-data, #column, #customer-data-platform, #customer-relationship-management, #ecommerce, #finance, #machine-learning, #marketing, #online-advertising, #product-marketing, #targeted-advertising, #tc

0

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.

 

#big-data, #business-intelligence, #cloud, #computing, #cto, #data, #data-management, #dell-emc, #developer, #enterprise, #flash, #information, #israel, #liveperson, #mizmaa-ventures, #recent-funding, #sql, #startups, #tc, #tel-aviv

0

Will automation eliminate data science positions?

“Will automation eliminate data science positions?”

This is a question I’m asked at almost every conference I attend, and it usually comes from someone from one of two groups with a vested interest in the answer: The first is current or aspiring practitioners who are wondering about their future employment prospects. The second consists of executives and managers who are just starting on their data science journey.

They have often just heard that Target can determine whether a customer is pregnant from her shopping patterns and are hoping for similarly powerful tools for their data. And they have heard the latest automated-AI vendor pitch that promises to deliver what Target did (and more!) without data scientists. We argue that automation and better data science tooling will not eliminate or even reduce data science positions (including use cases like the Target story). It creates more of them!

Here’s why.

Understanding the business problem is the biggest challenge

The most important question in data science is not which machine learning algorithm to choose or even how to clean your data. It is the questions you need to ask before even one line of code is written: What data do you choose and what questions do you choose to ask of that data?

What is missing (or wishfully assumed) from the popular imagination is the ingenuity, creativity and business understanding that goes into those tasks. Why do we care if our customers are pregnant? Target’s data scientists had built upon substantial earlier work to understand why this was a lucrative customer demographic primed to switch retailers. Which datasets are available and how can we pose scientifically testable questions of those datasets?

Target’s data science team happened to have baby registry data tied to purchasing history and knew how to tie that to customer spending. How do we measure success? Formulating nontechnical requirements into technical questions that can be answered with data is amongst the most challenging data science tasks — and probably the hardest to do well. Without experienced humans to formulate these questions, we would not be able to even start on the journey of data science.

Making your assumptions

After formulating a data science question, data scientists need to outline their assumptions. This often manifests itself in the form of data munging, data cleaning and feature engineering. Real-world data are notoriously dirty and many assumptions have to be made to bridge the gap between the data we have and the business or policy questions we are seeking to address. These assumptions are also highly dependent on real-world knowledge and business context.

In the Target example, data scientists had to make assumptions about proxy variables for pregnancy, realistic time frame of their analyses and appropriate control groups for accurate comparison. They almost certainly had to make realistic assumptions that allowed them to throw out extraneous data and correctly normalize features. All of this work depends critically on human judgment. Removing the human from the loop can be dangerous as we have seen with the recent spate of bias-in-machine-learning incidents. It is perhaps no coincidence that many of them revolve around deep learning algorithms that make some of the strongest claims to do away with feature engineering.

So while parts of core machine learning are automated (in fact, we even teach some of the ways to automate those workflows), the data munging, data cleaning and feature engineering (which comprises 90% of the real work in data science) cannot be safely automated away.

A historical analogy

There is a clear precedent in history to suggest data science will not be automated away. There is another field where highly trained humans are crafting code to make computers perform amazing feats. These humans are paid a significant premium over others who are not trained in this field and (perhaps not surprisingly) there are education programs specializing in training this skill. The resulting economic pressure to automate this field is equally, if not more, intense. This field is software engineering.

Indeed, as software engineering has become easier, the demand for programmers has only grown. This paradox — that automation increases productivity, driving down prices and ultimately driving up demand is not new — we’ve seen it again and again in fields ranging from software engineering to financial analysis to accounting. Data science is no exception and automation will likely drive up demand for this skillset, not down.

#artificial-intelligence, #automation, #big-data, #business-intelligence, #column, #cybernetics, #developer, #ecommerce, #emerging-technologies, #machine-learning, #science, #software-engineering, #tc

0

Secret Service buys location data that would otherwise need a warrant

Stock photo of hands using smartphones against white background.

Enlarge / Dozens of apps on your phone know where you are, whether you’re home, at a doctor’s appointment, at the airport, or sitting still in a blank white room to pose artfully for a photo shoot. (credit: JGI | Tom Grill | Getty Images)

An increasing number of law enforcement agencies, including the US Secret Service, are simply buying their way into data that would ordinarily require a warrant, a new report has found, and at least one US senator wants to put a stop to it.

The Secret Service paid about $2 million in 2017-2018 to a firm called Babel Street to use its service Locate X, according to a document (PDF) Vice Motherboard obtained. The contract outlines what kind of content, training, and customer support Babel Street is required to provide to the Secret Service.

Locate X provides location data harvested and collated from a wide variety of other apps, tech site Protocol reported earlier this year. Users can “draw a digital fence around an address or area, pinpoint mobile devices that were within that area, and see where else those devices have traveled” in the past several months, Protocol explained.

Read 8 remaining paragraphs | Comments

#big-data, #data-privacy, #location-data, #policy, #privacy, #surveillance, #the-feds, #united-states-secret-service

0

Mode raises $33M to supercharge its analytics platform for data scientists

Data science is the name of the game these days for companies that want to improve their decision making by tapping the information they are already amassing in their apps and other systems. And today, a startup called Mode Analytics, which has built a platform incorporating machine learning, business intelligence and big data analytics to help data scientists fulfil that task, is announcing $33 million in funding to continue making its platform ever more sophisticated.

Most recently, for example, the company has started to introduce tools (including SQL and Python tutorials) for less technical users, specifically those in product teams, so that they can structure queries that data scientists can subsequently execute faster and with more complete responses — important for the many follow up questions that arise when a business intelligence process has been run. Mode claims that its tools can help produce answers to data queries in minutes.

This Series D is being led by SaaS specialist investor H.I.G. Growth Partners, with previous investors Valor Equity Partners, Foundation Capital, REV Venture Partners, and Switch Ventures all participating. Valor led Mode’s Series C in February 2019, while Foundation and REV respectively led its A and B rounds.

Mode is not disclosing its valuation, but co-founder and CEO Derek Steer confirmed in an interview that it was “absolutely” an up-round.

For some context, PitchBook notes that last year its valuation was $106 million. The company now has a customer list that it says covers 52% of the Forbes 500, including Anheuser Busch, Zillow, Lyft, Bloomberg, Capital One, VMWare, and Conde Nast. It says that to date it has processed 830 million query runs and 170 million notebook cell runs for 300,000 users. (Pricing is based on a freemium model, with a free “Studio” tier and Business and Enterprise tiers priced based on size and use.)

Mode has been around since 2013, when it was co-founded by Steer, Benn Stancil (Mode’s current president) and Josh Ferguson (initially the CTO and now chief architect).

Steer said the impetus for the startup came out of gaps in the market that the three had found through years of experience at other companies.

Specifically, when all three were working together at Yammer (they were early employees and stayed on after the Microsoft acquisition), they were part of a larger team building custom data analytics tools for Yammer. At the time, Steer said Yammer was paying $1 million per year to subscribe to Vertica (acquired by HP in 2011) to run it.

They saw an opportunity to build a platform that could provide similar kinds of tools — encompassing things like SQL Editors, Notebooks, and reporting tools and dashboards — to a wider set of users.

“We and other companies like Facebook and Google were building analytics internally,” Steer recalled, “and we knew that the world wanted to work more like these tech companies. That’s why we started Mode.”

All the same, he added, “people were not clearly exactly about what a data scientist even was.”

Indeed, Mode’s growth so far has mirrored that of the rise of data science overall, as the discipline of data science, and the business case for employing data scientists to help figure out what is “going on” beyond the day to day, getting answers by tapping all the data that’s being amassed in the process of just doing business. That means Mode’s addressable market has also been growing.

But even if the trove of potential buyers of Mode’s products has been growing, so has the opportunity overall. There has been a big swing in data science and big data analytics in the last several years, with a number of tech companies building tools to help those who are less technical “become data scientists” by introducing more intuitive interfaces like drag-and-drop features and natural language queries.

They include the likes of Sisense (which has been growing its analytics power with acquisitions like Periscope Data), Eigen (focusing on specific verticals like financial and legal queries), Looker (acquired by Google) and Tableau (acquired by Salesforce).

Mode’s approach up to now has been closer to that of another competitor, Alteryx, focusing on building tools that are still aimed primary at helping data scientists themselves. You have any number of database tools on the market today, Steer noted, “Snowflake, Redshift, BigQuery, Databricks, take your pick.” The key now is in providing tools to those using those databases to do their work faster and better.

That pitch and the success of how it executes on it is what has given the company success both with customers and investors.

“Mode goes beyond traditional Business Intelligence by making data faster, more flexible and more customized,” said Scott Hilleboe, MD, H.I.G. Growth Partners, in a statement. “The Mode data platform speeds up answers to complex business problems and makes the process more collaborative, so that everyone can build on the work of data analysts. We believe the company’s innovations in data analytics uniquely position it to take the lead in the Decision Science marketplace.”

Steer said that fundraising was planned long before the coronavirus outbreak to start in February, which meant that it was timed as badly as it could have been. Mode still raised what it wanted to in a couple of months — “a good raise by any standard,” he noted — even if it’s likely that the valuation suffered a bit in the process. “Pitching while the stock market is tanking was terrifying and not something I would repeat,” he added.

Given how many acquisitions there have been in this space, Steer confirmed that Mode too has been approached a number of times, but it’s staying put for now. (And no, he wouldn’t tell me who has been knocking, except to say that it’s large companies for whom analytics is an “adjacency” to bigger businesses, which is to say, the very large tech companies have approached Mode.)

“The reason we haven’t considered any acquisition offers is because there is just so much room,” Steer said. “I feel like this market is just getting started, and I would only consider an exit if I felt like we were handicapped by being on our own. But I think we have a lot more growing to do.”

#analytics, #apps, #big-data, #data-science, #enterprise, #mode-analytics, #recent-funding, #startups, #tc

0

Quantexa raises $64.7M to bring big data intelligence to risk analysis and investigations

The wider field of cyber security — not just defending networks, but identifying fraudulent activity — has seen a big boost in activity in the last few months, and that’s no surprise. The global health pandemic has led to more interactions and transactions moving online, and the contractions we’re feeling across the economy and society have led some to take more desperate and illegal actions, using digital challenges to do it.

Today, a UK company called Quantexa — which has built a machine learning platform branded “Contextual Decision Intelligence” (CDI) that analyses disparate data points to get better insight into nefarious activity, as well as to (more productively) build better profiles of a company’s entire customer base — is raising a growth round of funding to address that opportunity.

The London-based startup has picked up $64.7 million, a Series C the it will be using to continue building out both its tools and the use cases for applying them, as well as expanding geographically, specifically in North America, Asia-Pacific and more European territories.

The mission, said Vishal Marria, Quantexa’s founder and CEO, is to “connect the dots to make better business decisions.”

The startup built its business on the back of doing work for major banks and others in the financial services sector, and Marria added that the plan will be to continue enhancing tools for that vertical while also expanding into two growing opportunities: working with insurance and government/public sector organizations.

The backers in this round speak to how Quantexa positions itself in the market, and the traction it’s seen to date for its business. It’s being led by Evolution Equity Partners — a VC that specialises in innovative cybersecurity startups — with participation also from previous backers Dawn Capital, AlbionVC, HSBC and Accenture, as well as new backers ABN AMRO Ventures. HSBC, Accenture and ABN AMRO are all strategic investors working directly with the startup in their businesses.

Altogether, Quantexa has “thousands of users” across 70+ countries, it said, with additional large enterprises including Standard Chartered, OFX and Dunn & Bradstreet.

The company has now raised some $90 million to date, and reliable sources close to the company tell us that the valuation is “well north” of $250 million — which to me sounds like it’s between $250 million and $300 million.

Marria said in an interview that he initially got the idea for Quantexa — which I believe may be a creative portmanteau of “quantum” and “context” — when he was working as an executive director at Ernst & Young and saw “many challenges with investigations” in the financial services industry.

“Is this a money launderer?” is the basic question that investigators aim to answer, but they were going about it, “using just a sliver of information,” he said. “I thought to myself, this is bonkers. There must be a better way.”

That better way, as built by Quantexa, is to solve it in the classic approach of tapping big data and building AI algorithms that help, in Marria’s words, connect the dots.

As an example, typically, an investigation needs to do significantly more than just track the activity of one individual or one shell company, and you need to seek out the most unlikely connections between a number of actions in order to build up an accurate picture. When you think about it, trying to identify, track, shut down and catch a large money launderer (a typical use case for Quantexa’s software) is a classic big data problem.

While there is a lot of attention these days on data protection and security breaches that leak sensitive customer information, Quantexa’s approach, Marria said, is to sell software, not ingest proprietary data into its engine to provide insights. He said that these days deployments typically either are done on premises or within private clouds, rather than using public cloud infrastructure, and that when Quantexa provides data to complement its customers’ data, it comes from publicly available sources (for example Companies House filings in the UK).

There are a number of companies offering services in the same general area as Quantexa. They include those that present themselves more as business intelligence platforms that help detect fraud (such as Looker) through to those that are secretive and present themselves as AI businesses working behind the scenes for enterprises and governments to solve tough challenges, such as Palantir, through to others focusing specifically on some of the use cases for the technology, such as ComplyAdvantage and its focus on financial fraud detection.

Marria says that it has a few key differentiators from these. First is how its software works at scale: “It comes back to entity resolution that [calculations] can be done in real time and at batch,” he said. “And this is a platform, software that is easily deployed and configured at a much lower total cost of ownership. It is tech and that’s quite important in the current climate.”

And that is what has resonated with investors.

“Quantexa’s proprietary platform heralds a new generation of decision intelligence technology that uses a single contextual view of customers to profoundly improve operational decision making and overcome big data challenges,” said Richard Seewald, founding and managing partner of Evolution, in a statement. “Its impressive rapid growth, renowned client base and potential to build further value across so many sectors make Quantexa a fantastic partner whose team I look forward to working with.” Seewald is joining the board with this round.

#artificial-intelligence, #big-data, #enterprise, #fraud, #funding, #quantexa, #recent-funding, #security, #startups, #tc

0

Secretive data startup Palantir has confidentially filed for an IPO

Secretive surveillance startup Palantir said late Monday it has confidentially filed paperwork with the U.S. Securities and Exchange Commission to go public. 

Its statement for the secretive, government-friendly big data operation, co-founded by Peter Thiel, said little more. “The public listing is expected to take place after the SEC completes its review process, subject to market and other conditions.” 

Palantir did not say when it plans to go public nor did it provide other information such as how many shares it would potentially sell or the share price range for the IPO. Confidential IPO filings allow companies to bypass the traditional IPO filing mechanisms that give insights into their inner workings such as financial figures and potential risks. Instead, Palantir can explore the early stages of setting itself up for a public listing without the public scrutiny that comes with the process. The strategy has been used by companies such as Spotify, Slack and Uber. However, a confidential filing doesn’t always translate to an IPO. 

A Palantir spokesperson, when reached, declined to comment further.

Palantir is one of the more secretive firms in Silicon Valley, a provider of big data and analytics technologies, including to the U.S. government and intelligence community. Much of that work has drawn controversies from privacy and civil liberties activists. For example, investigations show that the company’s data mining software was used to create profiles of immigrants and consequently aid deportation efforts by the ICE.

As the coronavirus pandemic spread throughout the world, Palantir pitched its technology to bring big data to tracking efforts. 

Last week, Palantir filed its first Form D in four years indicating that it is raising $961 million. According to the filing, $550 million has already been raised and capital commitments for the remaining allotment have been secured. 

With today’s news, the cash raise looks complementary to the company’s ambitions to go public. One report estimates that the company’s valuation hovers at $26 billion

Palantir’s filing is another example of how the IPO market is heating up yet again, despite the freeze COVID-19 put on so many companies. Last week, insurance provider Lemonade debuted on the public market to warm waters. Accolade, a healthcare benefits company, similarly is sold more shares than expected. 

#big-data, #ipo, #palantir, #security, #tc

0

SEC filing indicates big data provider Palantir is raising $961M, $550M of it already secured

Palantir, the secretive big data and analytics provider that works with governments and other public and private organizations to power national security, health and a variety of other services, has reportedly been eyeing up a public listing this autumn. But in the meantime it’s also continuing to push ahead in the private markets.

The company has filed a Form D indicating that it is in the process of raising nearly $1 billion — $961,099,010, to be exact — with $549,727,437 of that already sold, and a further $411,371,573 remaining to be raised.

The filing appears to confirm a report from back in September 2019 that the company was seeking to raise between $1 billion and $3 billion, its first fundraising in four years. That report noted Palantir was targeting a $26 billion valuation, up from $20 billion four years ago. A Reuters article from June put its valuation on secondary market trades at between $10 billion and $14 billion.

The bigger story of that Reuters report was that Palantir confirmed two fundraises from strategic investors that both work with the company: $500 million in funding from Japanese insurance company Sompo Holdings, and $50 million from Fujitsu. Together, it seems like these might account for $550 million already sold on the Form D.

It’s not clear if this fundraise would essentially mean a delay to a public listing, or if it would complement it.

To date Palantir has raised $3.3 billion in funding, according to PitchBook data, with no less than 108 investors on its cap table. But if you dig into the PitchBook data (some of which is behind a paywall) it also seems that Palantir has raised a number of other rounds of undisclosed amounts. Confusingly (but probably apt for a company famous for being secretive) some of that might also be part of this Form D amount.

We have reached out to Palantir to ask about the Form D and will update this post as we learn more.

While Palantir was last valued at $20 billion when it last raised money four years ago, there are some data points that point to a bigger valuation today.

In April, according to a Bloomberg report, the company briefed investors with documents showing that it expects to make $1 billion in revenues this year, up 38% on 2019, and breaking even in the first time since being founded 16 years ago by Peter Thiel, Nathan Gettings, Joe Lonsdale, Stephen Cohen, and current CEO, Alex Karp.

(The Bloomberg report didn’t explain why Palantir was briefing investors, whether for a potential public listing, or for the fundraise we’re reporting on here, or something else.)

On top of that, the company has been in the news a lot around the global novel coronavirus pandemic. Specifically, it’s been winning business, in the form of projects in major markets like the UK (where it’s part of a consortium of companies working with the NHS on a COVID-19 data trove) and the US (where it’s been working on a COVID-19 tracker for the federal government and a project with the CDC), and possibly others. Those projects will presumably need a lot of upfront capital to set up and run, possibly one reason raising money now.

#big-data, #enterprise, #palantir

0

Google Sheets will soon be able to autocomplete data for you

Google today announced a couple of updates to Google Sheets that will make building spreadsheets and analyzing data in them a little bit easier.

The most interesting feature here, surely, is the upcoming launch of Smart Fill. You can think of it as Smart Compose, the feature that automatically tries to finish your sentences in Gmail, but for spreadsheets. The idea here is that Smart Fill, which will launch later this year, can autocomplete your data for you.

“Say you have a column of full names, but you want to split it into two columns (first and last name, for example),” Google explains in today’s announcement. “As you start typing first names into a column, Sheets will automatically detect the pattern, generate the corresponding formula, and then autocomplete the rest of the column for you.”

 

That’s a nifty feature, though it’s worth noting that Microsoft has made some major strides in bringing a lot of ML-based features to Excel, too, which can now automatically create new columns based on its understanding of what your spreadsheet is about, for example. It just extended the number of these AI-driven data types to well over 100 at its Build developer conference. The use case here is a bit different, but both companies are using similar techniques to make building spreadsheets easier.

One feature that’s nice about how Google built this is that it doesn’t so much auto-magically fill a column but that it builds a formula to fill it, giving you quite a bit of flexibility to then manipulate that data as needed.

The second new feature that will be coming in the near future is Smart Cleanup, which, as the name implies, can help you clean up your data by finding duplicate rows and formatting issues. The tool will suggest changes, which users can then accept or ignore.

The company also today announced the general availability of Connected Sheets, a feature that connects a BigQuery data warehouse with Sheets so that you can analyze petabytes of data in sheets without having to know SQL or really any programming language. This feature aims to democratize access to big data analytics by giving anybody in a company who knows how to use a spreadsheet the ability to analyze that data and create charts based on it.

Connected Sheets is now available to G Suite Enterprise, G Suite Enterprise for Education and G Suite Enterprise Essentials users.

#ai, #artificial-intelligence, #big-data, #google, #google-sheets, #machine-learning, #spreadsheets

0

Indonesian startup Delman raises $1.6 million to help companies clean up data

Delman, a Jakarta-based data management startup, has raised $1.6 million in seed funding. The round was led by Intudo Ventures, with participation from Prasetia Dwidharma Ventures and Qlue Performa Indonesia, and will be used to establish a research and development center and hire software engineers and data scientists.

Delman was founded in 2018 by chief executive officer Surya Halim, chief product officer Raymond Christopher and chief technology officer Theo Budiyanto, who were classmates at the University of California, Berkeley. After graduation, they worked at tech companies in Silicon Valley, including Google and Splunk, before deciding to focus on the Indonesian market.

Originally launched as an end-to-end big data analytics provider, Delman shifted its focus to data preparation and management after talking to clients in Indonesia, said Halim. Many companies said they had budgeted for expensive data analytics solution, but then realized their data was not ready for analysis because it was spread across multiple formats. Delman’s mission is to make it easier for data engineers and scientists to do their jobs by cleaning up and preparing data.

Halim says many large companies in Indonesia typically spend up to $200,000 to clean and warehouse data, but Delman gives them a more cost-efficient and faster alternative.

“We have the capability to do analytics and data visualization for clients, but there are so many established companies that already do that, which is why we shifted our business model to something more niche and needed,” said Halim. “It also enables us to open our door to partner with everyone doing data analytics services.”

While newer companies and startups have cleaner datasets, Halim said many older Indonesian companies, especially ones with branches in multiple cities, often have large amounts of data spread across pen-and-paper ledgers, Excel spreadsheets and other software. The data may also have code, keywords and typos that need to be corrected.

“It’s easier for a new company, because everything is already standardized,” Halim said, “But if a company that was established in the 1970s wants to unify previous generations of data to integrate it into their system and keep notes on what customer behavior is like in order to compete with up-and-coming companies, then they need to have a data-driven policy.”

Delman is industry-agnostic and its clients range from large corporations and consulting firms to government agencies. Its customers have included PWC and Qlue. Halim said that the startup plans to expand into other Southeast Asian markets and expects that as COVID-19 changes the way people work, companies will want to invest more heavily in their IT infrastructure and make their databases easier to access outside of a central location.

In a press statement, Intudo Ventures founding partner Eddy Chan said, “By combining a highly localized approach with global technical expertise, Delman is providing Indonesian businesses with Indonesian-developed big data solutions, ultimately leading to better outcomes for end-users. Since meeting the Delman founding team in Silicon Valley in 2017, we have witnessed their growth as a management team, and are excited to continue to support them in their entrepreneurial journey ahead.”

#asia, #big-data, #data-analytics, #data-management, #delman, #fundings-exits, #indonesia, #southeast-asia, #startups, #tc

0

Data dashboard startup Count raises $2.4M from LocalGlobe, with Global Founders Capital

Early-stage companies often have trouble dealing with the amount of data that can run through the organization, especially as it grows. Large sums are spent on data software, dislocated data, dealing with data pipelines. All of which involve data warehousing, cleaning tools and a visualization platform.

Count is a startup that is an attempt to create an all-in-one data platform to deal with this problem, providing early-stage teams with tools to build data pipelines more cheaply.

It’s also coming out of stealth mode and announcing a $2.4m fund-raise led by LocalGlobe, with participation from Global Founders Capital . Its angel investors include Charlie Songhurst, the former head of corporate strategy at Microsoft .

The company was founded in 2016 by former management consultant Oliver Hughes and Imperial College physicist Oliver Pike, who identified that companies weren’t able to make data-driven decisions because of the complexity of standard data software and the technical and design constraints accepted by the industry. 

In a statement, Hughes described the problem they are addressing: “The teams making the most progress were having to invest hundreds of thousands of dollars a year, across separate solutions, to help them get their data under control and it was taking them up to 12-18 months to purchase and implement it all. So many startups get locked into long term contracts with tools that are no longer suitable for them. Count has a simple pay-as-you-go model so teams can start using the platform for free and only pay more as their team and data grow.”

Remus Brett, Partner at LocalGlobe, said: “Most people know that data is incredibly important but the ability to take it and tell stories with it still remains difficult. Now more than ever, we see the value in being able to process and analyze data at speed, to help us make critical decisions. Count makes it possible for even very early stage companies to begin making decisions based on analysis of their data.”

Edd Read, CTO at Tiney.co which uses count said: “Count has given us a way to pull all our data together and build reports for the whole team,” said. “Notebooks are a powerful way for us to share insights in context and give the team the ability to query data without having to learn SQL.” 

Count competes with a number of different solutions including Data warehouses such as Snowflake; Data cleaning tools like DBT; and analytics platforms like Looker.

#big-data, #business-intelligence, #computing, #cto, #data-management, #data-warehouse, #europe, #global-founders-capital, #information, #information-technology, #looker, #microsoft, #solutions, #tc, #tiney

0

Collibra nabs another $112.5M at a $2.3B valuation for its big data management platform

GDPR and other data protection and privacy regulations — as well as a significant (and growing) number of data breaches and exposées of companies’ privacy policies — have put a spotlight on not just the vast troves of data that businesses and other organizations hold on us, but also how they handle it. Today, one of the companies helping them cope with that data in a better and legal way is announcing a huge round of funding to continue that work. Collibra, which provides tools to manage, warehouse, store and analyse data troves, is today announcing that it has raised $112.5 million in funding, at a post-money valuation of $2.3 billion.

The funding — a Series F, from the looks of it — represents a big bump for the startup, which last year raised $100 million at a valuation of just over $1 billion. This latest round was co-led by ICONIQ Capital, Index Ventures, and Durable Capital Partners LIP, with previous investors CapitalG (Google’s growth fund), Battery Ventures, and Dawn Capital also participating.

Collibra was originally a spin-out from Vrije Universiteit in Brussels, Belgium, and today it works with some 450 enterprises and other large organizations. Customers include Adobe, Verizon (which owns TechCrunch), insurers AXA, and a number of healthcare providers. Its products cover a range of services focused around company data, including tools to help customers comply with local data protection policies and store it securely, and tools (and plug-ins) to run analytics and more.

These are all features and products that have long had a place in enterprise big data IT, but they have become increasingly more used and in-demand both as data policies have expanded, as security has become more of an issue, and as the prospects of what can be discovered through big data analytics have become more advanced.

With that growth, many companies have realised that they are not in a position to use and store their data in the best possible way, and that is where companies like Collibra step in.

“Most large organizations are in data chaos,” Felix Van de Maele, co-founder and CEO, previously told us. “We help them understand what data they have, where they store it and [understand] whether they are allowed to use it.”

As you would expect with a big IT trend, Collibra is not the only company chasing this opportunity. Competitors include Informatica, IBM, Talend, and Egnyte, among a number of others, but the market position of Collibra, and its advanced technology, is what has continued to impress investors.

“Durable Capital Partners invests in innovative companies that have significant potential to shape growing industries and build larger companies,” said Henry Ellenbogen, founder and chief investment officer for Durable Capital Partners LP, in a statement (Ellenbogen is formerly an investment manager a T. Rowe Price, and this is his first investment in Collibra under Durable). “We believe Collibra is a leader in the Data Intelligence category, a space that could have a tremendous impact on global business operations and a space that we expect will continue to grow as data becomes an increasingly critical asset.”

“We have a high degree of conviction in Collibra and the importance of the company’s mission to help organizations benefit from their data,” added Matt Jacobson, general partner at ICONIQ Capital and Collibra board member, in his own statement. “There is an increasing urgency for enterprises to harness their data for strategic business decisions. Collibra empowers organizations to use their data to make critical business decisions, especially in uncertain business environments.”

#big-data, #collibra, #data-management, #data-protection, #enterprise, #europe, #funding, #gdpr, #privacy, #recent-funding, #saas, #startups, #tc

0