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SparkCognition, which develops AI solutions for a range of industries, nabs $123M



As a result of pandemic headwinds and the general trend toward automation, the industrial sector is increasingly piloting AI technologies across different lines of business. According to a Deloitte survey on AI adoption in manufacturing, 93% of companies believe that AI will be a pivotal technology to drive growth and innovation in the future. Illustrating the transformation, a McKinsey report found that 15% of manufacturing companies now use AI to optimize key areas of production such as yield, energy, or throughput optimization, up from 9% in 2018.

But stumbling blocks stand in the way of successful AI deployment in industrial applications. For example, Chinese companies responding to the above-mentioned Deloitte poll said that 91% of their AI projects failed to meet expectations either in terms of their benefits or time invested. Among the biggest obstacles cited were infrastructure limitations, poor data collection practices and quality, a lack of engineering experience, and excessively large project scale and complexity.

AI-focused consultancies have emerged recently to assist industrial as well as oil and gas, renewables, financial services, transportation, and government organizations in implementing AI technologies. Fractal Analytics, Tata Consultancy Services, Wipro, Tredence, LatentView, and Mu Sigma occupy a growing category of “AI-as-a-service” companies that work with enterprises to develop AI solutions customized for their organizations. So does SparkCognition, an Austin, Texas-based firm that uses AI to analyze, optimize, and learn from customers’ data to predict future outcomes, optimize processes, and ostensibly prevent cyberattacks.

In a show of the market’s strength, SparkCognition today announced that it raised $123 million in series D funding at a $1.4 billion valuation led by March Capital, Doha Venture Capital, B. Riley Venture Capital, AEI Horizon X, Temasek, Alan Howard, and Peter Löscher, bringing the company’s total raised to $300 million. The financing follows a record year of growth for SparkCognition, with revenue increasing 90% year over year and booking climbing by five times.

Developing AI solutions

SparkCognition was founded in 2013 by Amir Husain. Husain previously launched Kurion, which created branded web portals for companies including Barnes and Noble, Dun & Bradstreet, and financial services institutions. He then led development of desktop computing products at ClearCube before joining virtualization services company VDIworks as CTO.

“[T]he pandemic has heightened our customers’ understanding of the value AI can deliver. In the face of supply chain uncertainties, fluctuating demand for resources like oil and gas, and increased remote work, it is never more critical to have advanced analytics predicting failures and cyberattacks before they occur, flagging operational inefficiencies, and identifying opportunities for increased production. This ultimately impacts our customers’ bottom line, helping them see business growth even in uncertain times,” Husain told VentureBeat via email.

SparkCognition provides a range of services developed to overcome particular data science hurdles in organizations. For instance, the company’s Darwin tool abstracts away many of the steps in developing and maintaining AI models, including data preparation and cleansing. Husain claims that Darwin can uncover problems like missing data while suggesting solutions to problems in an AI training dataset, such as malformed or missing data. Darwin can also ostensibly deliver “explainable” model results that spotlight important aspects of a dataset, he says.

An example of the dashboards that SparkCognition creates

On the cybersecurity side, SparkCognition offers DeepArmor, which leverages AI to attempt to mitigate executable-based cyberattacks. Meanwhile, the company’s DeepNLP service automates workflows of unstructured data to simplify tasks like information retrieval, document classification, and analytics. SparkCognition’s SparkPredict and Ensemble are AI-powered asset management and predictive maintenance platforms, built to detect suboptimal production yields and equipment failures proactively. Rounding out the product portfolio is Maana, which aims to encode organizations’ institutional knowledge, and an AI-powered market trading platform called Orca.

“Enterprises are faced with data overload, and 90% of data available is unstructured, which traditionally requires an extraordinary amount of manual effort to sort through and extract insights … [B]ut echnologies like the solutions we offer use machine learning and natural language processing to expedite that process significantly,” Husain said. “We take an end-to-end approach, leveraging technology like artificial intelligence, machine learning, deep learning, natural language processing, and knowledge representation. We deliver these solutions in a user-friendly interface that quickly and clearly provides insights and alerts when a process or asset needs attention.”

Barriers ahead

Three-hundred-employee SparkCognition positioned itself for growth last year, acquiring three companies and expanding into the financial services, maritime and renewable energy markets. According to Husain, SparkCognition — which has 65 customers — helped a major power generation company spot an anomaly that “enabled critical event detection a month in advance,” helping that company to avoid costly repairs. It also worked with a beverage manufacturer to address water waste and leaks, Husain said — reducing consumption of the resources throughout the manufacturing plant.

“This additional capital will enable us to deepen our subject matter expertise, enhance our patented portfolio, and accelerate the diversity of problems we solve for customers, maximizing their return on investment,” Husain continued.

But surveys show that organizations struggle to derive value from their AI deployments — representing an existential threat to SparkCognition’s business. For example, a 2018 report from 451 Research found that the majority of AI early adopters have failed to define key performance indicators around their AI and machine learning initiatives and encountered technical limitations while operationalizing data.

In more sobering metrics, 76% of organizations in a 2020 PricewaterhouseCoopers-sponsored survey reported barely breaking even with their investments in AI capabilities. Despite the fact that 80% of executives said they believed AI will fundamentally change their business, only 6% had AI initiatives scaled across the enterprise.

“Organizations are relying on existing talent and processes more oriented to software development than to the dynamic nature of AI. Many may underestimate the effort and investment they need in order to see returns,” a piece in the Harvard Business Review reads. “And many organizations may lack the governance structures to monitor AI effectively.”

Still, Husain believes that SparkCognition is primed to make a change once the proceeds from the series D are put toward planned marketing, sales, and R&D efforts. To his point, the broader AI market shows signs of accelerating, not slowing, with 39% of large companies planning to invest in AI services as of 2020.

Verified Market Research predicts that the global market for AI will reach $641.30 billion by 2028.

“We’re seeing quite a few challenges facing our customers across industry. These include aging and failing assets, climate change and net-zero initiatives, emerging cyberthreats to IT … infrastructure, an aging workforce and consequential skill gaps, and data overload,” Husain added. “To address these challenges, we encourage businesses across all of our key industries to invest in AI solutions that allow customers to gather insights to prevent unexpected downtime, maximize asset performance, and ensure worker safety, all while avoiding zero-day cyberattacks on essential IT … infrastructure.”

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Cyber Monday shopping expected to set record but annual growth has slowed | Adobe



Cyber Monday shopping sales hit at least $6.3 billion through part of the day in the U.S. today, according to the latest online shopping data from Adobe Analytics.

It’s not unusual for Cyber Monday and Black Friday online shopping results to break records, but it this economic climate it’s encouraging to see it happen. Still, growth has slowed from 2021 and 2020 holiday seasons.

Consumers spent $6.3 billion up through 3:00 pm Pacific time for Cyber Monday. Adobe expects that when the final tally is in, consumers will spend between $11.2 billion and $11.6 billion for the day, making Cyber Monday the biggest online shopping day of the year (and of all time).

Today, the top 15 hot sellers (not in ranked order) have included Legos, Hatchimals, Disney Encanto, Pokémon cards, Bluey, Dyson products, strollers, Apple Watches, drones, and digital cameras. Gaming consoles also remain popular, along with games including Mario Party, FIFA 23, Madden 23 and Call of Duty: Modern Warfare II.

Over the past weekend, the top sellers were included Hot Wheels, Cocomelon, Bluey, Disney Encanto, L.O.L. Surprise dolls, Roblox, and Fortnite in the toys category. Nintendo Switch, Xbox Series X and PlayStation 5 remain the top selling gaming consoles, with popular games including FIFA 23, God of War Ragnarök, Call of Duty: Modern Warfare II, Madden 23, and NBA 2k23. Other hot sellers included Apple iPads, Apple MacBooks, digital cameras, Roku devices, drones, gift cards and Instapots.

Black Friday online shopping sales were $9.12 billion, up 2.3% from a year ago, and Thanksgiving itself came in at $5.29 billion, up 2.9% from a year ago. Those were above Adobe’s projections. Last year, consumers spent $10.7 billion on Cyber Monday.

Strong consumer spend has been driven by net-new demand, and not just higher prices. The Adobe Digital Price Index, which tracks online prices across 18 product categories (complements the Bureau of Labor Statistics’ Consumer Price Index, which also includes prices for offline only products and services like gasoline and rent) shows that prices online have been nearly flat in recent months (down 0.7% YoY in October 2022).

Adobe Analytics says Cyber Monday will set a record.

Adobe’s numbers are not adjusted for inflation, but if online inflation were factored in, there would still be growth in underlying consumer demand, the company said.

On a category basis, toys were a major growth driver in the days leading up to Cyber Monday, with online sales up 452% over the average day in October 2022. Appliances (up 305%) and baby/toddler products (up 289%) also saw strong demand, in addition to electronics (up 276%) and apparel (up 258%).

Shoppers will find record discounts today for computers (peaking at 27% off listed price). Deals will also be found in nearly all categories tracked, including apparel (19%), toys (33%), electronics (25%), sporting goods (16%), televisions (15%), and furniture (11%). Those looking to buy an appliance should consider waiting until Thursday (December 1), when discounts are set to peak at 18% on average.

Weekend spending remained strong

Consumers spent over a Black Friday’s worth of ecommerce over the weekend at $9.55 billion, up 4.4% YoY ($4.59 billion on November 26, up 2.6% YoY / $4.96 billion on November, up 6.1% YoY). Season-to-date (November 1 to November 27), consumers have spent a total of $96.42 billion online, up 2.1% YoY.

And while the big days (Thanksgiving Day, Black Friday) have reached new heights, consumers spent at record levels all season. Since November 1, shoppers spent over $2 billion every single day, with 19 days above $3 billion in online spend. Broad, early discounts were the main drivers for the shift in consumer spending.

“Shoppers have seen massive discounts this past week, which is the exact opposite situation from last season when supply chain constraints kept prices elevated,” said Vivek Pandya, lead analyst at Adobe Digital Insights, in a statement. “While discounting will have an impact on margins for retailers, it is also driving a level of demand that can help brands build long-term loyalty and net some short-term gains.”

Additional Adobe Analytics Insights

Over the weekend, online sales of toys were up 383% (compared to average daily sales for the category in October 2022), with baby toys seeing strong demand (up 252%). Other categories that surged over the weekend include jewelry (up 230%), sporting goods (up 239%), and apparel (up 217%).

With online spending hitting new records and inflation impacting consumers, flexible payments have become a big story this season. In the last week (November 21 to November 27), “buy now, pay later” orders have risen 68% and revenue has increased 72%, when compared to the week prior.

Over the weekend, smartphones drove over half of online sales for the first time (52%, up from 48% last year). Adobe expects mobile shopping to dip on Cyber Monday however, based on historical trends. Many people are back at work and using laptops, which will be the preferred device for shopping online.

Forecast for Cyber Week

Adobe expects Cyber Week (the five days from Thanksgiving Day through Cyber Monday) to generate $34.8 billion in online spend, up 2.8% YoY, and represent 16.3% share of the full November through December holiday season.

Cyber Monday is expected to remain the season’s and year’s biggest online shopping day, bringing in between $11.2 billion and $11.6 billion. Black Friday generated a record $9.12 billion in online spend, up 2.3% YoY, while Thanksgiving brought $5.29 billion in online spend, up 2.9% YoY.

Adobe analyzes direct consumer transactions online. The analysis covers over one trillion visits to U.S. retail sites, 100 million SKUs, and 18 product categories.

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Snowflake 101: 5 ways to build a secure data cloud 



Today, Snowflake is the favorite for all things data. The company started as a simple data warehouse platform a decade ago but has since evolved into an all-encompassing data cloud supporting a wide range of workloads, including that of a data lake

More than 6,000 enterprises currently trust Snowflake to handle their data workloads and produce insights and applications for business growth. They jointly have more than 250 petabytes of data on the data cloud, with more than 515 million data workloads running each day.

Now, when the scale is this big, cybersecurity concerns are bound to come across. Snowflake recognizes this and offers scalable security and access control features that ensure the highest levels of security for not only accounts and users but also the data they store. However, organizations can miss out on certain basics, leaving data clouds partially secure. 

Here are some quick tips to fill these gaps and build a secure enterprise data cloud.


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1. Make your connection secure

First of all, all organizations using Snowflake, regardless of size, should focus on using secured networks and SSL/TLS protocols to prevent network-level threats. According to Matt Vogt, VP for global solution architecture at Immuta, a good way to start would be connecting to Snowflake over a private IP address using cloud service providers’ private connectivity such as AWS PrivateLink or Azure Private Link. This will create private VPC endpoints that allow direct, secure connectivity between your AWS/Azure VPCs and the Snowflake VPC without traversing the public Internet. In addition to this, network access controls, such as IP filtering, can also be used for third-party integrations, further strengthening security.

2. Protect source data

While Snowflake offers multiple layers of protection – like time travel and fail-safe – for data that has already been ingested, these tools cannot help if the source data itself is missing, corrupted or compromised (like malicious encrypted for ransom) in any way. This kind of issue, as Clumio’s VP of product Chadd Kenney suggests, can only be addressed by adopting measures to protect the data when it is resident in an object storage repository such as Amazon S3 – before ingest. Further, to protect against logical deletes, it is advisable to maintain continuous, immutable, and preferably air-gapped backups that are instantly recoverable into Snowpipe.

3. Consider SCIM with multi-factor authentication

Enterprises should use SCIM (system for cross-domain identity management) to help facilitate automated provisioning and management of user identities and groups (i.e. roles used for authorizing access to objects like tables, views, and functions) in Snowflake. This makes user data more secure and simplifies the user experience by reducing the role of local system accounts. Plus, by using SCIM where possible, enterprises will also get the option to configure SCIM providers to synchronize users and roles with active directory users and groups.

On top of this, enterprises also should use multi-factor authentication to set up an additional layer of security. Depending on the interface used, such as client applications using drivers, Snowflake UI, or Snowpipe, the platform can support multiple authentication methods, including username/password, OAuth, keypair, external browser, federated authentication using SAML and Okta native authentication. If there’s support for multiple methods, the company recommends giving top preference to OAuth (either snowflake OAuth or external OAuth) followed by external browser authentication and Okta native authentication and key pair authentication.

4. Column-level access control

Organizations should use Snowflake’s dynamic data masking and external tokenization capabilities to restrict certain users’ access to sensitive information in certain columns. For instance, dynamic data masking, which can dynamically obfuscate column data based on who’s querying it, can be used to restrict the visibility of columns based on the user’s country, like a U.S. employee can only view the U.S. order data, while French employees can only view order data from France.

Both features are pretty effective, but they use masking policies to work. To make the most of it, organizations should first determine whether they want to centralize masking policy management or decentralize it to individual database-owning teams, depending on their needs. Plus, they would also have to use invoker_role() in policy conditions to enable unauthorized users to view aggregate data on protected columns while keeping individual data hidden.

5. Implement a unified audit model

Finally, organizations should not forget to implement a unified audit model to ensure transparency of the policies being implemented. This will help them actively monitor policy changes, like who created what policy that granted user X or group Y access to certain data, and is as critical as monitoring query and data access patterns. 

To view account usage patterns, use system-defined, read-only shared database named SNOWFLAKE. It has a schema named ACCOUNT_USAGE containing views that provide access to one year of audit logs.

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WhatsApp rolls out new ‘Message Yourself’ feature globally • TechCrunch



To get a roundup of TechCrunch’s biggest and most important stories delivered to your inbox every day at 3 p.m. PDT, subscribe here.

We’re joining the Cyber Monday fun with 25% off annual subscriptions to TechCrunch+ content and analysis starting today until Wednesday, November 30. Plus, today only, get 50% off tickets to discover the vast unknown and attend TechCrunch Sessions: Space in Los Angeles!

Okay, we haven’t done a newsletter since Wednesday, and while the U.S. team was chillin’ like villains, the rest of the team was hard at work, so here’s some of the highlights from the last half-week of TechCrunchy goodness! — Christine and Haje

The TechCrunch Top 3

  • Talking to yourself just went digital: Instead of having that internal monologue stay in your head, now you can play out all of your thoughts to yourself in WhatsApp, Jagmeet writes. The messaging platform began rolling out an easier way to talk to yourself today after completing beta testing.
  • Great Wall of porn: That’s how Rita and Catherine describe the bot surge in China that is making it difficult to get any legitimate Twitter search results when trying to find out something about Chinese cities. Why, you ask? Rita writes that “the surge in such bot content coincides with an unprecedented wave of (COVID) protests that have swept across major Chinese cities and universities over the weekend.”
  • Your calendar, only more productive: Get ready for your calendar to be more than just a place to record things you have to do that day. Romain writes about Amie, a startup that grabbed $7 million to link your unscheduled to-do list with your calendar. The app also enables users to be social with coworkers.

Startups and VC

Dubai-based mass transit and shared mobility services provider SWVL has carried out its second round of layoffs, affecting 50% of its remaining headcount, Tage reports. The news is coming six months after SWVL laid off 32% (over 400 employees) of its workforce in a “portfolio optimization program” effort geared toward achieving positive cash flow next year.

There’s a couple of new funds in town, too! Harri reports that Early Light Ventures plots a second, $15 million fund for software ‘underdogs,’ while Mike writes that BackingMinds raises a new €50 million fund to fund normally overlooked entrepreneurs. He also writes about Pact, an all-women led VC for mission-driven startups, backed by Anne Hathaway.

And we have five more for you:

Lessons for raising $10M without giving up a board seat

Blackboard showing soccer strategy

Image Credits: Ihor Reshetniak (opens in a new window) / Getty Images

Over the last two years, intelligent calendar platform raised $10 million “using a more incremental approach,” writes co-founder Henry Shapiro.

“We’ve done all this without giving up a single board seat, and Reclaim employees continue to own over two-thirds of the company’s equity,” rejecting conventional wisdom that founders should “raise as much as you can as fast as you can.”

In a TC+ post, Shapiro reviews the process they used to identify follow-on investors, shares the email template used to pitch the SAFE, and explains why “a larger cap table means more founder control.”

Three more from the TC+ team:

TechCrunch+ is our membership program that helps founders and startup teams get ahead of the pack. You can sign up here. Use code “DC” for a 15% discount on an annual subscription!

Big Tech Inc.

Amazon’s recent cost-cutting measures seem to be affecting more than just its delivery business. Manish writes that the company is shutting down its wholesale distribution business, called Amazon Distribution, in India. Amazon had started this unit to help neighborhood stores secure inventory. The company didn’t say why it was closing this particular business down, but Manish notes that this is the third such Amazon unit to be shuttered in India.

Meanwhile, Natasha L reports that Meta has gotten itself into trouble again with the European Union’s General Data Protection Regulation (aka, the agency that regulates data protection). Facebook’s parent company is being hit with $275 million in penalties for what the agency said was breaches in data protection that resulted in some 530 million users’ personal information being leaked.

Now enjoy six more:

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