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Peak.AI raises $21 million to drive enterprise AI adoption

February 17, 2021 Big Data

Peak.AI, a startup developing AI solutions for enterprise customers, today announced that it closed a $ 21 million series B round. The funds, which bring Peak’s total raised to date to $ 43 million, will drive the company’s R&D and commercial expansion in the U.S. and India, according to CEO Richard Potter.

The global enterprise AI market size was valued at $ 4.68 billion in 2018 and is projected to reach $ 53.06 billion by 2026, according to Allied Market Research. But the corporate sector’s adoption curve hasn’t been as steep as some had predicted despite the promise of AI. A survey of publicly traded U.S. retailers’ earnings calls found that only 9 of about 50 companies had started to discuss an AI strategy, and a separate study from Genesys shows that 68% of workers aren’t yet using tools that leverage AI.

Peak aims to simplify the implementation of AI systems with a subscription-based software-as-a-service (SaaS) offering that spans infrastructure, data processing, workflow, and applications. Its customers — brands like Pepsi and Marshalls — supply their data, which Peak’s platform ingests through built-in connectors to accomplish things like optimizing supply and demand and supporting fulfillment processes, courtesy of a library of configurable AI engines.

Once AI engines go live, their predictive and prescriptive outputs can be exposed through APIs or explored, visualized, and exported with Peak’s Data Studio. The platform can handle datasets of virtually any size running on Amazon Web Services, and it serves models in an always-on fashion so that they self-improve over time. Peak also screens all ingested data through an algorithm to identify and anonymize any personally identifiable information.

Peak’s team optionally works with customers to define objectives, quantify opportunities using a sample of data, and scope out a business case for sign-off and launch. The company can take care of deployment and onboarding as well as operationalizing, and it can configure a solution to an individual user’s needs.

There’s no shortage of managed AI development platforms with venture backing. H2O recently raised $ 72.5 million to further develop its platform that runs on bare metal or atop existing clusters and supports a range of statistical models and algorithms. Cnvrg.io — which recently launched a free community tier — has raised $ 8 million to date for its end-to-end AI model tracking and monitoring suite.

But Peak, which claims that revenues doubled over the past 12 months thanks to customer wins in Europe, the U.S., the Middle East, and Asia, asserts that its platform is more performant. The company says it has helped customers achieve a 5% increase in total company revenues, a doubling of return on advertising spend, a 12% reduction in inventory holdings, and a 5% reduction in supply chain costs.

“It’s becoming impossible to run a business without AI. Modern businesses are complex and operate in an ever-changing world,” Potter said in a statement. “Our software empowers day-to-day decision makers across businesses, and we’re proud to be working with household names such as PrettyLittleThing, KFC, and PepsiCo, and other industry leaders like Marshalls and Speedy Hire. We’re delighted to have secured this new funding in an oversubscribed round.”

Oxx led Peak’s latest fundraising round with participation from existing investors MMC Ventures and Praetura Ventures and new investor Arete. The company, which was founded in December 2014 by Potter, David Leitch, and Atul Sharma, has additional offices in Jaipur and Edinburgh and plans to hire 130 employees in the coming year.

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IBM’s Arin Bhowmick explains why AI trust is hard to achieve in the enterprise

February 16, 2021 Big Data

While appreciation of the potential impact AI can have on business processes has been building for some time, progress has not nearly been as quick as many initial forecasts led many organizations to expect.

Arin Bhowmick, chief design officer for IBM, explained to VentureBeat what needs to be done to achieve the level of AI explainability that will be required to take AI to the next level in the enterprise.

This interview has been edited for clarity and brevity.

VentureBeat: It seems a lot of organizations are still not trustful of AI. Do you think that’s improving?

Arin Bhowmick: I do think it’s improved or is getting better. But we still have a long way to go. We haven’t historically been able to bake in trust and fairness and explainable AI into the products and experiences. From an IBM standpoint, we are trying to create reliable technology that can augment [but] not really replace human decision-making. We feel that trust is essential to the adoption. It allows organizations to understand and explain recommendations and outcomes.

What we are essentially trying to do is akin to a nutritional label. We’re looking to have a similar kind of transparency in AI systems. There is still some hesitation in adoption of AI because of a lack of trust. Roughly 80-85% of some of the professionals that took part in an IBM survey from different organizations said their organization has been pretty negatively impacted by problems such as bias, especially in the data. I would say 80% or more agree that consumers are more likely to choose services from a company that offers transparency and an ethical framework for how its AI models are built.

VentureBeat: As an AI model runs, it can generate different results as the algorithms learn more about the data. How much does that lack of consistency impact trust?

Bhowmick: The AI model used to do the prediction is as good as the data. It’s not just models. It’s about what it does and the insight it provides at that point in time that develops trust. Does it tell the user why the recommendation is made or is significant, how it came up with the recommendations, and how confident it is? AI tends to be a black box. The trick around developing trust is to unravel the black box.

VentureBeat: How do we achieve that level of AI explainability?

Bhowmick: It’s hard. Sometimes it’s hard to even judge the root cause of a prediction and insight. It depends on how the model was constructed. Explainability is also hard because when it is provided to the end user, it’s full of technical mumbo jumbo. It’s not in the voice and tone that the user actually understands.

Sometimes explainability is also a little bit about the “why,” rather than the “what.” Giving an example of explainability in the context of the tasks that the user is doing is really, really hard. Unless the developers who are creating these AI-based [and] infused systems actually follow the business process, the context is not going to be there.

VentureBeat: How do we even measure this?

Bhowmick: There is a fairness score and a bias score. There is a concept of model accuracy. Most tools that are available do not provide a realistic score of the element of bias. Obviously, the higher the bias, the worse your model is. It’s pretty clear to us that a lot of the source of the bias happens to be in the data and the assumptions that are used to create the model.

What we tried to do is we baked in a little bit of bias detection and explainability into the tooling itself. It will look at the profile of the data and match it against other items and other AI models. We’ll be able to tell you that what you’re trying to produce already has built-in bias, and here’s what you can do to fix it.

VentureBeat: That then becomes part of the user experience?

Bhowmick: Yes, and that’s very, very important. Whatever bias feeds into the system has huge ramifications. We are creating ethical design practices across the company. We have developed specific design thinking exercises and workshops. We run workshops to make sure that we are considering ethics at the very beginning of our business process planning and design cycle. We’re also using AI to improve AI. If we can build in sort of bias and explainable AI checkpoints along the way, inherently we will scale better. That’s sort of the game plan here.

VentureBeat: Will every application going have an AI model embedded within it?

Bhowmick: It’s not about the application, it’s about whether there are things within that application that AI can help with. If the answer is yes, most applications will have infused AI in them. It will be unlikely that applications will not have AI.

VentureBeat: Will most organizations embed AI engines in their applications or simply involve external AI capabilities via an application programming interface (API)?

Bhowmick: Both will be true. I think the API would be good for people who are getting started. But as the level of AI maturity increases, there will be more information that is specific to a problem statement that is specific to an audience. For that, they will likely have to build custom AI models. They might leverage APIs and other tooling, but to have an application that really understands the user and really gets at the crux of the problem, I think it’s important that it’s built in-house.

VentureBeat: Overall, what’s your best AI advice to organizations?

Bhowmick: I still find that our level of awareness of what is AI and what it can do, and how it can help us, is not high. When we talk to customers, all of them want to go into AI. But when you ask them what are the use cases, they sometimes are not able to articulate that.

I think adoption is somewhat lagging because of people’s understanding and acceptance of AI. But there’s enough information on AI principles to read up on. As you develop an understanding, then look into tooling. It really comes down to awareness.

I think we’re in the hype cycle. Some industries are ahead, but if I could give one piece of advice to everyone, it would be don’t force-fit AI. Make sure you design AI in your system in a way that makes sense for the problem you’re trying to solve.

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How to Choose Between Today’s Top Enterprise Master Data Management Solutions

February 3, 2021 TIBCO Spotfire

Reading Time: 2 minutes

Every day we’re faced with different choices that we have to make. Some are relatively easy, such as what kind of milk you want in your coffee or what music to listen to for motivation while working. Others can be more complex and require deeper research to understand your options and avoid regretting your decisions.

Selecting a master data management solution often falls into the second category, but it doesn’t have to be that way. Data and analytics leaders can use the 2021 Gartner Magic Quadrant for Master Data Management Solutions report to help them navigate the complexities of choice in enterprise MDM solutions.

More on the Magic Quadrant report

In the Magic Quadrant for Master Data Management Solutions report, Gartner, Inc. evaluates the strengths and cautions of MDM solution providers that it considers most significant in the marketplace, and provides readers with a graph (the Magic Quadrant), plotting the vendors based on their ability to execute and their completeness of vision.

Why TIBCO was named a Leader

TIBCO has been recognized as a Leader in the 2021 Gartner Magic Quadrant for Master Data Management Solutions, making this the 5th time. We believe TIBCO should be your MDM solution of choice because it provides all the capabilities you need across the entire data management lifecycle. Breaking down data silos, TIBCO addresses every type of data—metadata, reference data, master data, transactional, and streaming data—to meet any business need at scale.

Here are four reasons to choose TIBCO for all your data management needs:

Model-driven: Implement with your data, workflows, and security models
Multidomain: Manage any domain of data and their hierarchies
All-in-one: A single solution to manage, govern, and consume shared data assets
Designed for business teams: Encourage adoption and collaborative with self-service capabilities

TIBCO should be your MDM solution of choice because it provides all the capabilities you need across the entire data management lifecycle. Breaking down data silos, TIBCO addresses every type of data to meet any business need at scale. Click To Tweet

To help make your choice easy, read this complimentary copy of the 2021 Gartner Magic Quadrant for Master Data Management.

Gartner Magic Quadrant for Master Data Management Solutions, 27 January 2021, Simon Walker, Sally Parker, Malcolm Hawker, Divya Radhakrishnan, Alan Dayley

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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How the pandemic is accelerating enterprise open source adoption

January 27, 2021 Big Data

Enterprises increasingly shifted to open source software solutions in 2020 to meet their remote organizational needs and address new market demands for quality and speed. COVID-19 challenged not only the economy, but also enterprises’ existing frameworks for how, when, and at what volume people use information technology.

Last year, major players like LinkedIn and Spotify open-sourced tools they developed — from Java machine learning libraries to audio file processing ecosystems — for non-proprietary IT team members like data scientists and software engineers to use. Seed-level startups like Eradani and RudderStack that built their products on open source thrived despite launching only a few months before the pandemic began to escalate.

Most companies don’t publicize their internal tooling and infrastructure strategy transformations, but GitHub’s data suggests these have much to do with open source solutions. GitHub says 72% of Fortune 50 companies used GitHub Enterprise, which runs GitHub services on their local networks, between Q4 2019 and Q3 2020. GitHub also found over 40% YOY growth in open source project creation per any active user between late April 2019 and late April 2020. This first spiked with the pandemic in early March, when countries began to close schools, ban visitors, and initiate lockdowns en masse.

In an interview with VentureBeat, GitHub VP Mario Rodriguez said, “Open source project creation just kind of shoots up” after March. He added, “2020 is interesting because everything from a technology perspective got accelerated, and you were trying to do more and more.”

According to Rodriguez, “2020 also opened up a new set of talent from a software development perspective.”

Companies were previously limited by their geography and could only hire within certain cities, like San Francisco or New York. But the transition to remote work has begun to change that.

“Now, you do not have those restrictions … and the majority of software developers out there use open source today. And so you bring it into your enterprise now at an accelerated rate, which allows you to learn and continue to evolve your practices on how you develop software and how you use open source,” he said.

The pandemic disrupted existing tech trends and likely helped amplify the growing movement toward open source software solutions as enterprises’ distributed, remote workforces needed to use more custom applications internally, to innovate their IT quickly with the help of reusable code, and to retain developers by using the tools they prefer.

Speed: Accelerating technology with a remote, distributed workforce

Internet traffic skyrocketed by over 30% in March, especially to platforms for online learning and telecommuting. Microsoft Teams users set a new record for 2.7 billion meeting minutes in one day, and Microsoft, along with Netflix and YouTube, temporarily reduced video streaming quality and download speeds to cut back on bandwidth consumption.

These changes highlighted consumer demands for new digital communication tools and challenged enterprise IT teams to create and manage them — quickly. And enterprise IT teams, now distributed and in some cases fully remote, had to organize their own work around new kinds of applications and develop them in days or weeks, as opposed to months or years.

“From an IT perspective, you have to accelerate the [number] of apps that you are creating for your internal use as an enterprise,” Rodriguez said. “So that has actually allowed the enterprises to say, ‘You know what, if we are going to have these constraints, maybe we should start to research and figure out a way to empower more use of open source internally, as well.’ … You’re trying to go faster.”

While some enterprise frameworks and custom logic for business applications remain fully proprietary, integrating open source code can be a faster way to develop most software. Developers can import the existing work of thousands of people with a few inputs, making it easier to pull new applications together. For example, companies expedite their training and development of machine learning models with Google’s TensorFlow. Now that information is more democratized with open source software, some enterprise leaders suggest it’s hard to compete without it.

“I’m a member of the CTO forum, which is a group of 150 CTOs around the globe that talks a lot about tech,” Pinterest head of engineering Jeremy King said in a recent interview with VentureBeat. “And for sure, lifecycles have sped up.”

King described how companies used to try out maybe three or four vendors with an open source technology stack at a time for six months. Afterward, the company might figure out which vendor performed best and negotiate a rate.

“All that has gone away,” King said. He said if a company knows that a given open technology works, they’ll make a prototype and they’ll have changed the cycle by the next week.

“People are more rapidly adopting [open source], and I think it’s just because the tolerance for failure and making mistakes and moving quick has gone up,” he said. He explained that this is also true when it comes to “dealing with the consequences of moving fast in production, which, a year ago was unheard of.”

Elephant Ventures CEO Arthur Shectman also commented on the pandemic’s disruption of enterprise IT in an interview with VentureBeat. “The market kind of collapsed around people. In that moment of high volatility or prices and market stress, you reach for data,” Shectman said. “You’re like, ‘These decisions are going to have profound impacts … the net impact a week from now is huge.’”

Elephant Ventures is a digital transformation consultancy that helps corporations like Pfizer and American Express build their engineering capabilities. Shectman said he applies a technology readiness framework to his approach, finding ways for clients to create increments of business value with ETL technologies, API tooling patterns, and other strategies for deploying applications and workflows.

According to Shectman, “People were clamoring for data, and they were clamoring to transform their kind of data ecosystems, very rapidly.” He said that in the past few months, the conversation went from planning out three years of digital transformation retool to looking for immediate answers with a return on investment in 90 days. Shectman noted that waiting to purchase proprietary software could cost his enterprise clients $ 3 million in some instances.

Shectman said proprietary software’s cost and speed of deployment became greater roadblocks for his clients during 2020. “I felt like there was a lot more willingness to instantly adopt open source technologies and start applying them without any additional kind of software purchase cost to get a rapid ROI cycle project,” Shectman added. “Over the last year, if you could demonstrate that you knew how to implement it, you had a pattern that generated success in critical dimensions.”

Volume: Innovating with code reuse and existing tools

Developers can build applications at greater volumes by reusing open source code instead of starting from scratch. Enterprise IT teams can also integrate open source tools into their existing workflows to manage their data with improved precision. This control is increasingly important, given the pandemic’s overall shift toward digitalization, which increases the amount of data itself.

“The number of apps that get created right now are at an all-time high. And then the number of those apps that [use] open source are also at an all-time high. It’s probably because of code reuse and ability to just go from zero to 60% or 70% of what you need to create very, very quickly,” Rodriguez said.

These current trends in open source software are a continuation of many enterprises’ strategies. “I think we use a lot of open source technologies, largely due to the fact that our scale often prevents us from using a commercial product,” King said.

“Pinterest is a 10-year-old company, and we have billions of pins … and that’s not always something you can just buy off the shelf. And so whether it’s logging data, understanding images, searching — all these technologies didn’t exist when we started; they’re getting better and better over time. Even the off-the-shelf cloud providers are getting better. But we’ve made Pinterest very unique to what we’re looking for as a result of us using a lot of open source technology,” he added.

Other enterprises have tailored their tech stacks to maximize productivity and output with open source solutions more recently. Even maintaining the same products requires additional speed of deployment when competing in a volatile COVID-19 era market.

McKinsey reports that North American businesses accelerated their share of digitized product offerings by 20% between March and October 2020. Open source software might play a role in expediting this process if the bulk of digitized products’ code can be built from an existing repository’s code.

“For a long time, the digital economy and the computer revolution were just driven by Moore’s law, with your chips getting better and faster at this crazy growth rate,” Shectman said as he traced his client’s technology solutions from the early 2000s to late 2020. “Now, after you go through the early iterations of your product, you are able to do a certain amount of things with commercially available software, [but] you’re gonna have typically an easier time unwrapping and fixing or customizing the open source stuff.”

According to Shectman, understanding and acting upon data has become key to enterprises. “I think it’s the scale of data production. I don’t think anybody really had a great sense of how rapidly data would proliferate in the world.”

The market’s space for tools, particularly open source technologies that sieve and curate data, has widened as a result. In the past 30 years — and mostly in the last 10 — over 200 companies with open source technology at their core have raised over $ 10 billion in capital.

Startups such as data warehousing platform RudderStack, which provides an open source-based alternative to Segment, have recently capitalized on this change. RudderStack was launched in late 2019, and its 2020 growth mirrors the growth patterns of other early-stage startups, like Prisma and Streamlit, which contribute to GitHub community code and base their business strategies in open source software.

RudderStack marketing lead Gavin Johnson said in an interview with VentureBeat, “Being open source makes it easy to deploy RudderStack in your own environment. … Anyone can look through the code and figure out what is being done with their customer data in-product, something you can’t do with closed-source products.” According to Johnson, RudderStack’s open source software reduces the number of tools enterprise engineering teams would have to build for sending and collecting data and allows the teams to modify the platform with custom integrations. He also suggested end-user enterprise IT teams tend to save more money with these alternatives to proprietary platforms.

Novelty: Energizing IT teams and long-term growth

The Linux Foundation reports that in 2020, 93% of hiring managers found it difficult to recruit employees with open source software skills and 37% of them wanted to hire more skilled IT professionals, despite the pandemic’s economic impact.

Rodriguez told VentureBeat that in 2020, “We didn’t see companies shrink in developers. We actually saw them expand in developers.” He believes this growth has to do with enterprises’ goals that now, more than ever, revolve around IT capabilities.

“You’re trying to create all this software, which means that you need more developers in order to do it right. And if you’re trying to actually have more, [you need] to not only have the best tools for your developers but have the best practices as well.” He said that is another reason he thinks open source accelerated significantly in 2020.

The open source community organizes conferences, groups, and companies around making code more accessible. Developers contribute to repositories like Open Source for Good and develop personal projects with open source code.

Johnson said that data engineers and data scientists work a lot in open source. He said, “RudderStack’s mission is to help make engineering and data teams be heroes of their organizations. So we needed to build our product in a way they like, which means open source.”

Some enterprises are turning to open source tools to bring in skilled technical employees. King said open source attracts and retains engineers. “And the people who are the best in the world want to publish, and they want to work on things that are well known, and they want to contribute back. And so it is definitely a good return on investment as well,” he said.

In addition to using open source for internal transformations, enterprises have been contributing open source tools to the community. Open source contributions can improve enterprises’ visibility and relevance, and this business strategy is important now that most, if not all, of their public operations are digital. RedHat brings open source technologies to enterprises that need them and has since it was founded in 1993. But in 2020, end-user companies like Facebook, LinkedIn, Spotify, and Uber have also begun to open-source their own tools for the public.

“The open source movement is pretty well established and reasonably mature at this point, so it’s not a surprise for any large corporation,” Shectman said. “If there’s a bit of awareness, people can afford to shy away from some of the convenience of a particular vendor-specific thing in order to allow themselves fluidity.” Shectman suggested that businesses face similar constraints with their staffing, technology, and ability to create effective products. He added that he’s seen open source software help businesses remove constraints, including proprietary vendor lock-in.

Rodriguez suggested that the advancements open source can provide in regard to speed, volume, and overall business strategy aren’t easily matched. “What everyone now is realizing is you cannot out-innovate, or you cannot out-execute open source,” he said.

Will open source continue to grow in 2021 like it did in 2020? Rodriguez thinks it could. “For any company out there, from the most powerful company in the world to a startup, you’re not going to be able to hire [enough] people to build the software of that quality — open source gives you that immediately.”

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Big Data – VentureBeat

Sisense and Signals Analytics Bring the Power of External Data to the Enterprise

December 30, 2020 Sisense

We’re stronger when we work together. In our Partner Showcase, we highlight the amazing integrations, joint projects, and new functionalities created and sustained by working with our technology partners at companies like AWS, Google, and others.

Business teams constantly want to know how their companies are performing — against their internal goals and those of the market they compete in. They benchmark their performance against their previous results, what their customers are asking for, what their customers are buying, and ideally what their customers will buy. To get their answers, businesses typically rely on data sources that are all internal, showing decision-makers only part of the picture.

That’s now in the past. Today, through a strategic partnership, Signals Analytics and Sisense are making it easy to incorporate external data analytics into a company’s main BI environment. The result is a broader, more holistic view of the market coupled with more actionable and granular insights. Infusing these analytics everywhere, democratizes data usage and access to critical insights across the enterprise.

Organizations who are truly data-driven know how to leverage a wide range of internal and external data sources in their decision-making. The integration of Signals Analytics in the Sisense business intelligence environment gets them there faster and seamlessly, without the need for specialized resources to build complex systems.

Kobi Gershoni, Signals Analytics co-founder and chief research officer

Sisense and Signals Analytics Bring the Power of External Data to the Enterprise

Why external data analytics?

The integration of Signals Analytics with the Sisense platform delivers on the promise of advanced analytics — infusing intelligence at the right place and the right time, upleveling standard decisions to strategic decisions, and speeding the time to deployment. Combining internal and external data unlocks powerful insights that can drive innovation, product development, marketing, partnerships, acquisitions, and more.

Primary use cases for external data analytics

External data is uniquely well-suited to inform decision points across the product life cycle, from identifying unmet needs to predicting sales for specific attributes, positioning against the competition, measuring outcomes, and more. By incorporating a wide range of external data sources that are connected and contextualized, users benefit from a more holistic picture of the market.

For example, when combining product reviews, product listings, social media, blogs, forums, news sites, and more with sales data, the accuracy rate for predictive analytics jumps from 36% to over 70%. Similar results are seen when going from social listening alone to using a fully connected and contextualized external data set to generate predictions.

The Sisense and Signals Analytics partnership: What you need to know

Signals Analytics provides the connected and contextualized datasets for specific fast-moving consumer goods (FMCG) categories
Sisense users can tap into one of the broadest external datasets available and unleash the power of this connected data in their Sisense dashboards
The ROI of the analytics investment dramatically increases when combining historical data, sales, inventory, and customer data with Signals Analytics data

Integrate external data analytics in your Sisense environment in three easy steps

Step 1: Connect

From your Sisense UI, use the Snowflake data connector to connect to the Signals Analytics Data Mart. The data can be queried live in the Sisense ElastiCube.

Step 2: Select

Once the data connection has been established, select the data types needed by filtering the relevant “Catalog.”

Step 3: Visualize

Select the dimensions, measures, and filters to apply, then visualize.

More data sources, better decisions

Your company is sitting on a large supply of data, but unless and until you find the right datasets to complement it, the questions you can answer and the insights you can harness from it will be limited. Whatever your company does and whatever questions you are trying to answer, mashing up data from a variety of sources, inside the right platform, is vital to surfacing game-changing insights.

To get started on the next leg of your analytics journey start a free trial or become a partner.

traditional vs cloud data warehouse blog cta banner 770x250 1 Sisense and Signals Analytics Bring the Power of External Data to the Enterprise

Tags: advanced analytics | analytics implementation

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Blog – Sisense

9 trends in enterprise database technology

December 30, 2020 Big Data

The database has always revolved around rock-solid reliability. Data goes in and then comes out in exactly the same way. Occasionally, the bits will be cleaned up and normalized so all of the dates are in the same format and the text is in the same character set, but other than that, nothing should be different.

That consistency is what makes the database essential for any enterprise — allowing it to conduct things like ecommerce transactions. It’s also why the database remains distinct from the data warehouse, another technology that is expanding its mission for slower-twitch things like analysis. The database acts as the undeniable record of the enterprise, the single source of truth.

Now databases are changing. Their focus is shifting and they’re accepting more responsibilities and offering smarter answers. In short, they’re expanding and taking over more and more of the stack.

Many of us might not notice because we’ve been running the same database for years without a change. Why mess with something that works? But as new options and features come along, it makes sense to rethink the architectures of data flows and take advantage of all the new options. Yes, the data will still be returned exactly as expected, but it will be kept safer and presented in a way that’s easier to use.

Many drivers of the change are startups built around a revolutionary new product, like multi-cloud scaling or blockchain assurance. For each new approach to storing information, there are usually several well-funded startups competing to dominate the space and often several others still in stealth mode.

The major companies are often not far behind. While it can take more time to add features to existing products, the big companies are finding ways to expand, sometimes by revising old offerings or by creating new ones in their own skunkworks. Amazon, for instance, is the master at rolling out new ways to store data. Its cloud has at least 11 different products called databases, and that doesn’t include the flat file options.

The other major cloud providers aren’t far behind. Microsoft has migrated its steadfast SQL Server to Azure and found ways to offer a half-dozen open source competitors, like MySQL. Google delivers both managed versions of relational databases and large distributed and replicated versions of NoSQL key/value pairs.

The old standards are also adding new features that often deliver much of the same promise as the startups while continuing support of older versions. Oracle, for instance, has been offering cloud versions of its database while adding new query formats (JSON) and better performance to handle the endless flood of incoming data.

IBM is also moving dB2 to the cloud while adding new features like integration with artificial intelligence algorithms that analyze the data. It’s also supporting the major open source relational databases while building out a hybrid version that merges Oracle compatibility with the PostgreSQL engine.

Among the myriad changes to old database standards and new emerging players, here (in no particular order) are nine key ways databases are being reborn.

1. Better query language

SQL may continue to do the heavy lifting around the world. But newer options for querying — like GraphQL — are making it easier for front-end developers to find the data they need to present to the user and receive it in a format that can be dropped right into the user interface.

GraphQL follows the standard JavaScript format for serializing objects, making it easier for middle- and front-end code to parse it. It also hides some of the complexity of JOINs, making it simpler for end users to grab just the data they need. Developers are already adding tools like Apollo Studio, an IDE for exploring queries, or Hasura, an open source front-end that wraps GraphQL around legacy databases like PostgreSQL.

2. Streaming databases follow vast flows

The model for a standard database is a big ledger, much like the ones clerks would maintain in fat bound books. Streaming databases like ksqlDB are built to watch an endless stream of data events and answer questions about them. Instead of imagining that the data is a permanent table, the streaming database embraces the endlessly changing possibilities as data flows through them.

3. Time-series database

Most database columns have special formats for tracking date stamps. Time-series databases like InfluxDB or Prometheus do more than just store the time. They track and index the data for fast queries, like how many times a user logged in between January 15 and March 12. These are often special cases of streaming databases where the data in the streams is being tracked and indexed for changes over time.

4. Homomorphic encryption

Cryptographers were once happy to lock up data in a safe. Now some are developing a technique called homomorphic encryption to make decisions and answer queries on encrypted data without actually decrypting it, a feature that vastly simplifies cloud security and data sharing. This allows computers and data analysts to work with data without knowing what’s in it. The methods are far from comprehensive, but companies like IBM are already delivering toolkits that can answer some useful database queries.

5. In-memory database

The original goal of a database was to organize data so it could be available in the future, even when electricity is removed. The trouble is that sometimes even storing the data to persistent disks takes too much time, and it may not be worth the effort. Some applications can survive the occasional loss of data (would the world end if some social media snark disappeared?), and fast performance is more important than disaster recovery. So in-memory databases like Amazon’s ElasticCache are designed for applications that are willing to trade permanence for lightning-fast response times.

6. Microservice engines

Developers have traditionally built their code as a separate layer that lives outside the database itself, and this code treats the database as a black box. But some are noticing that the databases are so feature-rich they can act as microservice engines on their own. PostgreSQL, for instance, now allows embedded procedures to commit full transactions and initiate new ones before spitting out answers in JSON. Developers are recognizing that the embedded code that has been part of databases like Oracle for years may be just enough to build many of the microservices imagined by today’s architects.

Jupyter notebooks started out as a way for data scientists to bundle their answers with the Python code that produced it. Then data scientists started integrating the data access with the notebooks, which meant going where the information was stored: the database. Today, SQL is easy to integrate, and users are becoming comfortable using the notebooks to access the database and generate smart reports that integrate with data science (Julia or R) and machine learning tools. The newer Jupyter Lab interface is turning the classic notebook into a full-service IDE, complete with extensions that pull data directly from SQL databases.

7. Graph databases

The network of connections between people or things is one of the dominant data types on the internet, so it’s no surprise that databases are evolving to make it easier to store and analyze these relationships.

Neo4j now offers a visualization tool (Bloom) and a collection of data science functions for developing complex reports about the network. GraphDB is focusing on developing “semantic graphs” that use natural language to capture linguistic structures for big analytic projects. TerminusDB is aimed at creating knowledge graphs with a versioning system much like Git. All of them bring efficiency to storing a complex set of relationships that don’t fit neatly into standard tables.

8. Merging data storage with transport

Databases were once hidden repositories to keep data safe in the back office. Delivering this information to the user was the job of other code. Now, databases like Firebase treat the user’s phone or laptop as just another location for replicating data.

Databases like FaunaDB are baking replication into the stack, thus saving the DBA from moving the bits. Now, developers don’t need to think about getting information to the user. They can just read and write from the local data store and assume the database will handle the grubby details of marshaling the bytes across the network while keeping them consistent.

9. Data everywhere

A few years ago, all the major browsers began supporting the Local Storage and Indexed Storage APIs, making it easier for web applications to store significant amounts of data on the client’s machine. The early implementations limited the data to 5MB, but some have bumped the limits to 10MB. The response time is much faster, and it will also work even when the internet connection is down. The database is not just running on one box in your datacenter, but in every client machine running your code.

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Big Data – VentureBeat

Accelerate Your Enterprise Connectivity with TIBCO Cloud

December 24, 2020 TIBCO Spotfire

Reading Time: 2 minutes

Businesses today face unprecedented challenges responding to market volatility, increasing the importance of business agility as the means to successfully adapt to changing market conditions. One key enabler of business agility is the ability to quickly connect digital assets no matter where they are hosted to create new capabilities or streamline processes.

Accelerated Connectivity = Greater Agility

This growing pressure means many teams are looking for ways to reduce time to market for new connectivity applications as much as possible. As the number of apps created across the company increases, there is an opportunity to streamline development through the reuse of digital assets like APIs. However, the process of tracking down API specs and manually documenting them for consumption in applications can be a time-consuming process that could be spent on more valuable tasks.

New connections could be created even faster if there was a way for developers to easily discover APIs created by different teams across the business to reuse in new integration apps. To help our customers navigate this challenge and reduce time to market, the TIBCO Cloud makes it easy for application developers across your business to share and discover digital assets.

Service Discovery with TIBCO Cloud

The TIBCO Cloud provides a centralized resource for developers to discover and share APIs or other REST services created across the TIBCO Cloud. Using a simple drop-down box, developers can easily find and consume APIs or register a new API that can be reused by other developers.

Accelerate Your Enterprise Connectivity with TIBCO Cloud

In today’s volatile market environment, even the smallest time saving can make a difference as businesses quickly pivot to meet new demands. Utilizing the registry allows developers to focus on more valuable work such as defining and building new product capabilities by promoting the reuse of assets across teams.

The TIBCO Cloud makes it easy for application developers across your business to share and discover digital assets. Click To Tweet

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For an example using API discovery to streamline integration app development, check out this demo where you will learn how to easily use TIBCO Cloud Integration to automate your marketing lead generation process.

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The TIBCO Blog

Zoomin has raised $21 million to unify enterprise product content

December 16, 2020 Big Data

Zoomin, a “knowledge orchestration” platform that helps users extract answers from enterprise documentation, today revealed it has raised $ 21 million. Investors include Salesforce Ventures, Bessemer Venture Partners, and Viola Growth, and the investment, which had been undisclosed before today, came in the form of several tranches starting in 2018.

Zoomin was founded in 2015 and has hubs in New York and Tel Aviv. The company aims to help businesses make their vast pools of technical content easier to find and more usable. Companies may have many thousands of manuals, guides, training paraphernalia, online community discussions, and more, but all this disparate content is typically created and managed by different teams, people, and systems and often exists in silos. Zoomin “unifies” this content and delivers it in a more “intuitive and personalized way,” according to CEO and cofounder Gal Oron.

White label

Zoomin’s product can perhaps be crudely described as a white label search engine for enterprise product content, though Oron argues traditional federated search solutions focus on indexing content and taking users from their point of search to whatever external channel contains the results. Zoomin, on the other hand, can bring answers to the user wherever they conduct the search from.

“This means they don’t need to navigate across different sites and experience the fragmentation and drop-off that naturally accompanies this kind of ‘context switching,’” Oron told VentureBeat.

How Zoomin is used largely depends on what the customer needs from it. It could be a standalone technical resource center, perhaps something akin to a companywide intranet or even a public portal, transforming disparate static content into a dynamic search interface replete with filters, auto-suggestions, recommendations, and more. Or it could be a widget that offers content relevant to the context of a given situation, baked into the customer’s own applications, such as a customer relationship management (CRM) tool.

“In some cases, customers replace their existing portals with Zoomin, in other cases they keep their portal but use Zoomin to create an enhanced, intuitive, personalized experience,” Oron added.

Zoomin has raised $21 million to unify enterprise product content

Above: Zoomin “in-product”

Zoomin ships with various integration options, including REST APIs, JavaScript APIs, and command line interfaces (CLIs). It also offers prebuilt apps that can be downloaded, customized, and integrated with Salesforce or ServiceNow.

Above: Zoomin integrated into Salesforce

Under the hood, Zoomin says it uses both supervised and unsupervised machine learning (ML) models, developed and trained in-house, alongside off-the-shelf ML services.

“Zoomin’s knowledge graph ties together enterprise content, users, and interactions, powering the platform’s text analysis and classification, dynamic ranking, content recommendations, and predictive insights,” Oron explained.

Analytics also play a sizable role in Zoomin’s offering, including “traffic insights” that detail where traffic is coming from (including the referring domain and location); “content insights” that surface which topics and publications receive the most engagement; and “search insights” that give companies search pattern data that can be used to tweak the UX.

“These insights are designed to help our customers understand what users are searching for, learn which search terms are yielding no results, analyze the usage of search filters, and more,” Oron added.

Above: Zoomin: Content freshness analytics

Although Zoomin has operated fairly under the radar, it has amassed a number of notable clients, including now Adobe-owned Workfront, Chinese hospitality giant Shiji, and cybersecurity veteran Imperva.

Zoomin was entirely bootstrapped up until Bessemer’s inaugural investment in 2018, which was followed by Salesforce Ventures’ investment in 2019. Both VC firms reinvested in the startup this year, alongside Israel’s Viola Growth.

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Big Data – VentureBeat

What enterprise CISOs need to know about AI and cybersecurity

November 19, 2020 Big Data

Hari Sivaraman is the Head of AI Content Strategy at Venturebeat.

Modern day enterprise security is like guarding a fortress that is being attacked on all fronts, from digital infrastructure to applications to network endpoints.

That complexity is why AI technologies such as deep learning and machine learning have emerged as a game-changing defensive weapon in the enterprise’s arsenal over the past three years. There is no other technology that can keep up. It has the ability to rapidly analyze billions of data points, and glean patterns to help a company act intelligently and instantaneously to neutralize many potential threats.

Beginning about five years ago, investors started pumping hundreds of millions of dollars into a wave of new security startups that leverage AI, including CrowdStrike, Darktrace, Vectra AI, and Vade Secure, among others. (More on these companies lower down).

But it’s important to note that cyber criminals can themselves leverage increasingly easy-to-use AI solutions as potent weapons against the enterprise. They can unleash counter attacks against AI-led defenses, in a never-ending battle of one-upmanship. Or they can hack into the AI itself. After all, most AI algorithms rely on training data, and if hackers can mess with the training data, they can distort the algorithms that power effective defense. Cyber criminals can also develop their own AI programs to find vulnerabilities much faster than they used to, and often faster than the defending companies can plug them.

Humans are the strongest link

So how does an enterprise CISO ensure the optimal use of this technology to secure the enterprise? The answer lies in leveraging something called Moravec’s paradox, which suggests that tasks that are easy for computers/AI are difficult for humans and vice-versa. In other words, combine the best technology with the CISO’s human intelligence resources.

If clear guidelines can be distilled in the form of training data for AI, technology can do a far better job than humans at detecting security threats. For instance, if there are guidelines on certain kinds of IP addresses or websites that are known for being the source of malicious malware activity, the AI can be trained to look for them, take action, learn from this, and become smarter at detecting such activity in the future. When such attacks happen at scale, AI will do a far more efficient job of spotting and neutralizing such threats compared to humans.

On the other hand, humans are better at judgement-based daily decisions, which might be difficult for computers. For instance, let’s say a particular well-disguised spear phishing email talks about a piece of information, which only an insider ‘could’ have known. A vigilant human security expert with that knowledge and intelligence, will be able to connect the dots and detect that this is ‘probably’ an insider attack and flag the email as suspicious. It’s important to know in this instance, that AI will find it difficult to perform this kind of abductive reasoning and arrive at such a decision. Even if you cover some such use cases with appropriate training data, it is nigh on impossible to cover all the scenarios. As every AI expert will tell you, AI is not quite ready to replace human general intelligence or what we call ‘wisdom’ in the foreseeable future.

But…humans could also be the weakest link

At the same time, humans can be your weakest link. For instance most phishing attacks rely on the naivety and ignorance of an untrained user, and get them to unwittingly reveal information or perform an action which opens up the enterprise for attack. If all your people are not trained to recognize such threats, the risks increase dramatically.

The key is to know that AI and human intelligence can join forces and form a formidable defense against cybersecurity threats. AI, while being a game-changing potent weapon in the fight against cybercrime, cannot be left unsupervised, at least in the foreseeable future, and will always need human assistance by trained, experienced security professionals and a vigilant workforce. This two-factor AI plus human intelligence (HI) security, if implemented fastidiously as a policy guideline across the enterprise, will go a long way in winning the war against cybercrime .

7 AI-based cybersecurity companies

Below is more about the leading emerging AI-first cybersecurity companies. Each of them bite off a section of enterprise security needs. A robust cybersecurity strategy, which has to defend at all points, is almost impossible for a single company to manage. Attack fronts include hardware infrastructure (data centers and clouds), desktops, mobile devices (cellphones, laptops, tablets, external storage devices, etc.), IoT devices, software applications, data, data pipelines, operational processes, physical sites including home offices, communication channels (email, chat, social networks), insider attacks, and perhaps most importantly, employee and contractor security awareness training. With bad actors leveraging an ever widening range of attack techniques against enterprises (phishing, malware, DoS, DDoS, MitM, XSS, etc.), security technical leaders need all the help they can get.

CrowdStrike

CrowdStrike’s Falcon suite of products are could-native, AI-powered cyber security solutions for companies of all sizes. These products cover next-gen antivirus, endpoint detection and response, threat intelligence, threat hunting, IT hygiene, incident response, and proactive services. CrowdStrike says it uses something called ‘signatureless’ artificial intelligence/machine learning, which means it does not rely on a signature ( i.e. a unique set of characteristics within the virus that differentiates it from other viruses). The AI can detect hitherto unknown threats using something it calls Indicator of Attack (IOA) — a way to determine the intent of a potential attack — to stop known and unknown threats in real-time. Based in Sunnyvale, California, this company has raised $ 481 million in funding and says it has almost 5,000 customers. The company has grown rapidly by focusing mainly on its endpoint threat detection and response product called Falcon Prevent, which leverages behavioral pattern matching techniques from crowd-sourced data. It gained recognition for handling the high-profile DNC cyber attacks in 2016.

Darktrace

Darktrace offers cloud-native, self learning, AI-based enterprise cyber security. The system works by understanding your organization’s ‘DNA’ and its normal healthy state. It then uses machine learning to identify any deviations from this healthy state, i.e. any intrusions that can affect the health of the enterprise and then triggers instantaneous and autonomous defense mechanisms. In this way, it describes itself as similar to antibodies in a human immune system. It protects the enterprise on various fronts including workforce devices and IoT, SaaS, and email. It leverages unsupervised machine learning techniques in a system called Antigena to scan for potential threats and stop attacks before they can happen. The Cambridge, U.K.- and San Francisco, U.S.-based company has raised more than $ 230M in funding and says it has more than 4,000 customers.

Vectra

Vectra’s Cognito NDR platform uses behavioral detection algorithms to analyze metadata from captured packets revealing hidden and unknown attackers in real time, whether traffic is encrypted or not. By providing real-time attack visibility and non-stop automated threat hunting that’s powered by always-learning behavioral models, it cuts cybercriminal dwell times and speeds up response times. The Cognito product uses a combination of supervised and unsupervised machine learning and deep learning techniques to glean patterns and act upon them automatically. The San Jose, California-headquartered Vectra has raised $ 223M in funding and claims “thousands” of enterprise clients.

SparkCognition

SparkCognition’s DeepArmor is an AI-built end-point cybersecurity solution for enterprises that provides protection against known software vulnerabilities exploitable by cyber criminals. It protects against attack vectors such as ransomware, viruses, malware, and offers threat visibility and management. DeepArmor’s technology leverages big data, NLP, and SparkCognition’s patented machine learning algorithms to protect enterprises from what it says are more than 400 million new malware variants discovered each year. Lenovo partnered with SparkCognition in October 2019 to launch DeepArmor Small Business. SparkCognition has raised roughly $ 175M in funding and boasts “thousands” of enterprise clients.

Vade Secure

Vade Secure is one of the leading products in predictive email defense. It claims it protects a billion mailboxes across 76 countries. Its product helps protect users from advanced email security threats, including phishing, spear phishing, and malware. Vade Secure’s AI products leverage a multi-layered approach, including using supervised machine learning models trained on a massive dataset of more than 600 million mailboxes administered by the world’s largest ISPs. The France- and U.S.-based company has raised almost $ 100 million in funding and says it has more than 5,000 clients.

SAP NS2

SAP NS2’s approach is to apply the latest advancements in AI and machine learning to problems like cybersecurity and counterterrorism, working with a variety of U.S. security agencies and enterprises. Its technology adopts the philosophy that security in this new era requires a balance of human and machine intelligence. In 2019, NS2 won the Defense Security Service James S. Cogswell Outstanding Industrial Security Achievement Award.

Blue Hexagon

Blue Hexagon offers deep learning-based real-time security for network threat detection and response in both enterprise network and cloud environments. It claims to deliver industry-leading sub-second threat detection with full AI-verdict explanation, threat categorization, and killchain (i.e. the structure of an attack starting with identifying the target, counter attack used to nullify the target, and proof of the destruction of the target). The Sunnyvale, California-based company has raised $ 37M in funding.

VentureBeat is the host of Transform, the world’s leading AI event focused on business and technology decision makers in applied AI, and in our July 2021 event (12-16 July), AI in cybersecurity will be one of the key areas we will be focusing on. Register early and join us to learn more.

The author will be speaking at the DTX Cyber Security event next week. Register early to learn more.

Best practices for a successful AI Center of Excellence:

A guide for both CoEs and business units Access here

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Big Data – VentureBeat

The secrets of small data: How machine learning finally reached the enterprise

October 9, 2020 Big Data

Over the past decade, “big data” has become Silicon Valley’s biggest buzzword. When they’re trained on mind-numbingly large data sets, machine learning (ML) models can develop a deep understanding of a given domain, leading to breakthroughs for top tech companies. Google, for instance, fine-tunes its ranking algorithms by tracking and analyzing more than one trillion search queries each year. It turns out that the Solomonic power to answer all questions from all comers can be brute-forced with sufficient data.

But there’s a catch: Most companies are limited to “small” data; in many cases, they possess only a few dozen examples of the processes they want to automate using ML. If you’re trying to build a robust ML system for enterprise customers, you have to develop new techniques to overcome that dearth of data.

Two techniques in particular — transfer learning and collective learning — have proven critical in transforming small data into big data, allowing average-sized companies to benefit from ML use cases that were once reserved only for Big Tech. And because just 15% of companies have deployed AI or ML already, there is a massive opportunity for these techniques to transform the business world.

The secrets of small data: How machine learning finally reached the enterprise

Above: Using the data from just one company, even modern machine learning models are only about 30% accurate. But thanks to collective learning and transfer learning, Moveworks can determine the intent of employees’ IT support requests with over 90% precision.

Image Credit: Moveworks

From DIY to open source

Of course, data isn’t the only prerequisite for a world-class machine learning model — there’s also the small matter of building that model in the first place. Given the short supply of machine learning engineers, hiring a team of experts to architect an ML system from scratch is simply not an option for most organizations. This disparity helps explain why a well-resourced tech company like Google benefits disproportionately from ML.

But over the past several years, a number of open source ML models — including the famous BERT model for understanding language, which Google released in 2018 — have started to change the game. The complexity of creating a model the caliber of BERT, whose aptly named “large” version has about 340 million parameters, means that few organizations can even consider quarterbacking such an initiative. However, because it’s open source, companies can now tweak that publicly available playbook to tackle their specific use cases.

To understand what these use cases might look like, consider a company like Medallia, a Moveworks customer. On its own, Medallia doesn’t possess enough data to build and train an effective ML system for an internal use case, like IT support. Yet its small data does contain a treasure trove of insights waiting for ML to unlock them. And by leveraging new techniques to glean these insights, Medallia has become more efficient, from recognizing which internal workflows need attention to understanding the company-specific language its employees use when asking for tech support.

Massive progress with small data

So here’s the trillion-dollar question: How do you take an open source ML model designed to solve a particular problem and apply that model to a disparate problem in the enterprise? The answer starts with transfer learning, which, unsurprisingly, entails transferring knowledge gained from one domain to a different domain that has less data.

For example, by taking an open source ML model like BERT — designed to understand generic language — and refining it at the margins, it is now possible for ML to understand the unique language employees use to describe IT issues. And language is just the beginning, since we’ve only begun to realize the enormous potential of small data.

Above: Transfer learning leverages knowledge from a related domain — typically one with a greater supply of training data — to augment the small data of a given ML use case.

Image Credit: Moveworks

More generally, this practice of feeding an ML model a very small and very specific selection of training data is called “few-shot learning,” a term that’s quickly become one of the new big buzzwords in the ML community. Some of the most powerful ML models ever created — such as the landmark GPT-3 model and its 175 billion parameters, which is orders of magnitude more than BERT — have demonstrated an unprecedented knack for learning novel tasks with just a handful of examples as training.

Taking essentially the entire internet as its “tangential domain,” GPT-3 quickly becomes proficient at these novel tasks by building on a powerful foundation of knowledge, in the same way Albert Einstein wouldn’t need much practice to become a master at checkers. And although GPT-3 is not open source, applying similar few-shot learning techniques will enable new ML use cases in the enterprise — ones for which training data is almost nonexistent.

The power of the collective

With transfer learning and few-shot learning on top of powerful open source models, ordinary businesses can finally buy tickets to the arena of machine learning. But while training ML with transfer learning takes several orders of magnitude less data, achieving robust performance requires going a step further.

That step is collective learning, which comes into play when many individual companies want to automate the same use case. Whereas each company is limited to small data, third-party AI solutions can use collective learning to consolidate those small data sets, creating a large enough corpus for sophisticated ML. In the case of language understanding, this means abstracting sentences that are specific to one company to uncover underlying structures:

Above: Collective learning involves abstracting data — in this case, sentences — with ML to uncover universal patterns and structures.

Image Credit: Moveworks

The combination of transfer learning and collective learning, among other techniques, is quickly redrawing the limits of enterprise ML. For example, pooling together multiple customers’ data can significantly improve the accuracy of models designed to understand the way their employees communicate. Well beyond understanding language, of course, we’re witnessing the emergence of a new kind of workplace — one powered by machine learning on small data.

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Big Data – VentureBeat

Tag Archives: Enterprise

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More on the Magic Quadrant report

Why TIBCO was named a Leader

Speed: Accelerating technology with a remote, distributed workforce

Volume: Innovating with code reuse and existing tools

Novelty: Energizing IT teams and long-term growth

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Why external data analytics?

Primary use cases for external data analytics

The Sisense and Signals Analytics partnership: What you need to know

Integrate external data analytics in your Sisense environment in three easy steps

Step 1: Connect

Step 2: Select

Step 3: Visualize

More data sources, better decisions

1. Better query language

2. Streaming databases follow vast flows

3. Time-series database

4. Homomorphic encryption

5. In-memory database

6. Microservice engines

7. Graph databases

8. Merging data storage with transport

9. Data everywhere

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Accelerated Connectivity = Greater Agility

Service Discovery with TIBCO Cloud

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Humans are the strongest link

But…humans could also be the weakest link

7 AI-based cybersecurity companies

From DIY to open source

Massive progress with small data

The power of the collective

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