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AI

Evisort embeds AI into contract management software, raises $100M

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For lawyers and the organizations that employ them, time is quite literally money. The business of contract management software is all about helping to optimize that process, reducing the time and money it takes to understand and manage contracts. 

As it turns out, there is big money in the market for contract management software as well. An increasingly integral part of the business is the use of AI-powered automation. To that end, today contract management vendor Evisort announced that it raised $100 million in a series C round of funding, bringing total funding to date up to $155.6 million. 

Evisort was founded in 2016 and raised a $15 million series A back in 2019. The company was founded by a team of Harvard Law and MIT researchers and discovered early on that there was a market opportunity for using AI to help improve workflow for contracts within organizations.

“If you think about it, every time a company sells something, buys something or hires somebody, there’s a contract,” Evisort cofounder and CEO Jerry Ting told VentureBeat. “Contract data really is everywhere.”

Contract management is a growing market

Evisort fits squarely into a market that analysts often refer to as contract lifecycle management (CLM). Gartner Peer Insights lists at least twenty vendors in the space, which includes both startups and more established vendors.

Among the large vendors in the space is DocuSign, which entered the market in a big way in 2020 with its $188 million acquisition of AI contract discovery startup Seal Software. Startups are also making headway, with SirionLabs announcing this week that it has raised $85 million to help add more AI and automation to its contract management platform.

The overall market for contract lifecycle management is set to grow significantly in the coming years, according to multiple reports. According to Future Market Insights, the global market for CLM in 2021 generated $936 million in revenue and is expected to reach $2.4 billion by 2029. MarketsandMarkets provides a more considerable number, with the CLM market forecast to grow to $2.9 billion by 2024.

Ting commented that while every organization has contracts, in this view many organizations still do not handle contracts with a digital system and rely on spreadsheets and email. That’s one of the key reasons why he expects to see significant growth in the CLM space as organizations realize there is a better way to handle contracts.

Integrating AI to advance the state of contract management

Evisort’s flagship platform uses AI to read contracts that users then upload into the software-as-a-service (SaaS)-based platform.

Ting explained that his company developed its own algorithm to help improve natural language processing and classification of important areas in contracts. Those areas could include terms of a deal, such as deadlines, rates and other conditions of importance for a lawyer who is analyzing a contract. Going a step further, Evisort’s AI will now also analyze the legal clauses in an agreement.

“We can actually pull the pertinent data out of a contract, instead of having a human have to type it into a different system,” Ting said. 

Once all the contract data is understood and classified, the next challenge that faces organizations is what to do with all the data. That’s where the other key part of Evisort’s platform comes into play, with a no-code workflow service. The basic idea with the workflow service is to help organizations collaborate on contract activities, including analysis and approvals.

What $100M of investment into AI will do for Evisort

With the new funding, Ting said that his company will continue to expand its go-to market and sales efforts. Evisort will also be investing in new AI capabilities that Ting hopes will democratize access to AI for contract management.

To date, he explained that Evisort’s AI works somewhat autonomously based on definitions that Evisort creates. With future releases of the platform, Ting wants to enable users to take Evisort’s AI and adjust and train the algorithm for specific and customized needs. The plan is to pair Evisort’s no-code capabilities into the future feature, in an approach that will make it easy for regular users and not just data scientists, to build AI capabilities to better understand and manage contracts.

“I think the 100 million dollar mark tells the market, hey, this company is a serious player and they’re here to stay,” Ting said. “It’s a scale-up, not a startup.”

The new funding round was led by TCV with participation from Breyer Capital as well as existing investors Vertex Ventures, Amity Ventures, General Atlantics and Microsoft’s venture capital fund M12.

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AI

AI will soon oversee its own data management

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AI thrives on data. The more data it can access, and the more accurate and contextual that data is, the better the results will be.

The problem is that the data volumes currently being generated by the global digital footprint are so vast that it would take literally millions, if not billions, of data scientists to crunch it all — and it still would not happen fast enough to make a meaningful impact on AI-driven processes.

AI helping AI

This is why many organizations are turning to AI to help scrub the data that is needed by AI to function properly.

According to Dell’s 2021 Global Data Protection Index, the average enterprise is now managing ten times more data compared to five years ago, with the global load skyrocketing from “just” 1.45 petabytes in 2016 to 14.6 petabytes today. With data being generated in the datacenter, the cloud, the edge, and on connected devices around the world, we can expect this upward trend to continue well into the future.

In this environment, any organization that isn’t leveraging data to its full potential is literally throwing money out the window. So going forward, the question is not whether to integrate AI into data management solutions, but how.

AI brings unique capabilities to each step of the data management process, not just by virtue of its capability to sift through massive volumes looking for salient bits and bytes, but by the way it can adapt to changing environments and shifting data flows. For instance, according to David Mariani, founder of, and chief technology officer at AtScale, just in the area of data preparation, AI can automate key functions like matching, tagging, joining, and annotating. From there, it is adept at checking data quality and improving integrity before scanning volumes to identify trends and patterns that otherwise would go unnoticed. All of this is particularly useful when the data is unstructured.

One of the most data-intensive industries is health care, with medical research generating a good share of the load. Small wonder, then, that clinical research organizations (CROs) are at the forefront of AI-driven data management, according to Anju Life Sciences Software. For one thing, it’s important that data sets are not overlooked or simply discarded, since doing so can throw off the results of extremely important research.

Machine learning is already proving its worth in optimizing data collection and management, often preserving the validity of data sets that would normally be rejected due to collection errors or faulty documentation. This, in turn, produces greater insight into the results of trial efforts and drives greater ROI for the entire process.

Mastering the data

Still, many organizations are just getting their new master data management (MDM) suites up and running, making it unlikely they will replace them with new intelligent versions any time soon. Fortunately, they don’t have to. According to Open Logic Systems, new classes of intelligent MDM boosters are hitting the channel, giving organizations the ability to integrate AI into existing platforms to support everything from data creation and analysis to process automation, rules enforcement, and workflow integration. Many of these tasks are trivial and repetitive, which frees up data managers’ time for higher-level analysis and interpretation.

This trend toward deploying AI to manage the data it needs to perform other duties in the digital enterprise will change the nature of work for data scientists and other knowledge workers. People will no longer be tasked with doing the work they do now and instead will focus on monitoring the results of AI-driven processes and then making changes should they veer from defined objectives.

More than anything, however, AI-driven data management will speed up the pace of business dramatically. Data is king in the digital universe, and kings don’t like to wait.

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Computing

Apple Launches Business Essentials For Mac IT Management

Apple is now targeting small businesses with a new subscription following the company’s success with its Apple One service for individuals and families. Like Apple One, the new Apple Business Essentials subscription includes iCloud storage, but the business-oriented offering swaps out consumer services — like Apple TV+, Apple Fitness+, and Apple News+ — for device management and onboarding services. as well as access to Apple Support.

Apple’s Business Essentials plan covers all Apple hardware, including the iPhone, iPad, and Mac, and it includes setup, onboarding, backup, security, repairs, and updates. Apple is positioning its Business Essentials service as an IT service for small and midsize businesses (SMBs).

The Cupertino, California, maker of the Mac and iPhone is targeting Apple Business Essentials for small and medium businesses with up to 500 employees. The service launches today in the United States and will be available to SMBs for free while it is still in beta. When it exits beta in spring 2022, pricing will range from $3 per seat per month to $13 per seat per month. The pricing range takes into account the number of devices a user has as well as the amount of iCloud storage for the plan. Up to 2TB of iCloud storage is available on the highest-tier plan.

With its mobile device management service, Apple Business Essentials has a section called Collections within the app that employees can use to download apps that are required for their workflow, including Microsoft Office, Cisco Webex, and more.

“When employees sign in to their corporate or personally owned device with their work credentials, Collections automatically pushes settings such as VPN configurations and Wi-Fi passwords,” Apple detailed of its new service. “In addition, Collections will install the new Apple Business Essentials app on each employee’s home screen, where they can download corporate apps assigned to them, such as Cisco Webex or Microsoft Word.”

If an employee leaves, the Business Essentials service also makes it easy for SMB owners to reassign old devices to new users or issue new devices to new users.

Business Essentials is notable in that it will bring a more managed IT experience that employees at larger corporations have relied upon to the world of small businesses. In addition to device management and hardware support, Apple will also allow small businesses to bundle its AppleCare+ optional extended warranty services to Business Essentials starting in the spring. Employees will have access to two device repairs per year on their plan, and the repairs can be initiated directly within the Apple Business Essentials app. Apple will also offer on-site repair services in addition to mail-in repairs with its small business offering, and technicians can arrive on site in as little as four hours.

Apple has been making an aggressive push into its services business, which includes Apple Music. In its most recent earnings report, the company announced that its net sales from the services business grew by more than 27% year-over-year for the fiscal year ended September 25, 2021. Apple reported that its services businesses generated $68.4 billion, compared to $297.4 billion for products.

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Game

Pokimane is starting a talent management company for streamers

It can be difficult to ‘make it’ as a full-time streamer, but Pokimane (aka Imane Anys) thinks there’s a better way to nurture budding internet broadcasters. The well-known Twitch personality is co-founding RTS, a talent management and brand consulting company that plans to fix “what is broken” for both game streamers and esports. The firm will rethink management to help creators run a “stable business” that survives in the long run, and to support game developers and other brands wanting to make a significant impact.

Pokimane saw RTS tackling the problems she and others faced getting started. There are plenty of talented streamers who are “spinning their wheels on basic stuff” and forging partnerships that don’t work for either side, she said. Ideally, her new company will reflect her experience and give rookies the support they need to avoid many of those early headaches.

The startup will include a slew of game industry veterans, not to mention some major customers. Twitch and Endeavor veteran Stuart Saw will serve as CEO, while the remaining executives include alumni from Twitch, Blizzard and PAX. The board includes Twitch co-founder Kevin Lin, PUBG Corp’s Americas head Brian Corrigan and Endeavor Executive VP Karen Brodkin. RTS will work with Pokimane as well as Epic Games and Facebook. It will own and co-manage the Evo fighting game tournament.

It could take a while before it’s clear how well RTS fares compared to existing online talent management outfits. However, it’s notable that Pokimane and her team are focused on growing small-time streamers rather than courting big names can already fetch major deals. While this certainly won’t guarantee fame, it might lead to more people pursuing streaming as a job (if not a full-fledged career) instead of a hobby.

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AI

AI-powered spend management platform Yokoy secures $26M

Spend management platform Yokoy today announced that it raised $26 million in series A financing from Left Lane, with participation from prominent European investor Balderton Capital. The Zurich, Switzerland-based company says that it’ll use the new funding — which brings its total raised to $27.1 million — to expand across the U.S., Europe, and other regions around the world while enhancing the technologies underpinning its platform.

The spend management software industry is projected to be worth $3.97 billion by 2027, up from $1.08 billion in 2019, according to Verified Market Research. The Global Business Travel Association pegs the cost to companies of processing a single expense report at $58 each time, while processing a single invoice is estimated to cost between $12 and $32. Indeed, the biggest cost drivers when it comes to spend management are often errors, manual work, and transaction fees. Yokoy asserts that these costs can be cut by half if certain resource-intensive processes are automated.

Founded in Switzerland in 2019, Yokoy provides an AI-powered spend management suite for midsize and enterprise companies. Through a combination of machine learning, automation, and API integrations, the startup offers expense management, supplier invoice management, and corporate credit cards.

CEO Philippe Sahli, and chief technology officer Devis Lussi (who previously worked at CERN) met while working at Ernst & Young’s management consultancy, while chief customer officer Lars Mangelsdorf had been building software-as-a-service (SaaS) products at other startups. Meanwhile, CFO Thomas Inhelder — who’s held accounting roles at KPMG — came into contact with Sahli at a previous startup.

“With Yokoy, we’re building a highly intelligent, highly secure, and highly customizable global spend management platform that empowers our customers to take control of their vast corporate spending processes and fine-tune their workflows,” Lussi said in a statement. “We’re helping them to cut costs, save time and bring clarity to their global operations in a way that fits their ambitions. It’s this that we believe will see us becoming the leading spend management platform in the world.”

AI-powered spend management

With Yokoy’s platform, midsize to enterprise companies can configure and build their own process flows and integrate Yokoy with third-party tools. Lussi claims that the platform is “self-learning,” enabling Yokoy to monitor individual workflows and processes to make them more efficient and scalable over time.

For example, customers can import expense receipts and invoices by snapping pictures of them through Yokoy’s mobile app. The platform’s algorithms enhance the picture quality before extracting the words and numbers via optical character recognition, validating more than 300 data points in a single receipt or invoice. In the case of invoices, Yokoy can also recognize suppliers, match them with data from a company’s enterprise resource planning software, and fill any missing data into the scanned document. With the information it extracts from documents, Yokoy checks relevant policies, gauges the potential for fraud, and validates the data for outliers and rules violations.

Yokoy

Above: Yokoy’s spend management platform.

Image Credit: Yokoy

Any scans that don’t pass Yokoy’s quality assurance benchmarks are set aside for manual review, while the rest are automatically exported to an accounting system.

“Yokoy is able to reconstruct [the] context [of documents] based on various features. Such features can be the relative position of a sequence of characters on the paper — top, bottom, left, right — or the presence of certain keywords. With the help of keywords (Yokoy’s list comprises more than 100,000 entries in many different languages), something can be found out about the type of an expense,” Sahli told VentureBeat via email. “Yokoy has described the relationships between these features within [AI] models. In the fraction of a second [that] it takes the software to digitize and analyze an expense receipt, several such models are processed. The models have been trained with millions of examples, and they are constantly being refined.”

Yokoy competes with Ramp and others in the spend management solutions segment. But in two years, the company has managed to attract 80,000 users across 400 customers including DPD Group, Stadler Rail, Russia’s Sberbank, the Swiss bank Swissquote. Part of the latest investment will be put toward growing the company’s headcount by the end of 2021, Sahli says.

“Spend management includes the processing of supplier invoices, travel expenses, corporate credit card expenses, and all such accounts payable categories. While the travel expenses have decreased during the pandemic, other categories — like online purchases over corporate credit cards — have increased during that time,” Sahli added. “The total spend volume processed over Yokoy per customer during the pandemic has actually increased. Yokoy is a true winner of the pandemic, but for a completely different reason: COVID-19 has truly accelerated the digitization and automation in companies and that’s what has pushed us, especially the new customers number.”

Yokoy employs about 100 people throughout its five offices. It expects to more than double that number to 250 by the end of 2022.

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AI

Virtru launches zero-trust key management for entire Google ecosystem

Virtru, a well-known name in data encryption and privacy, has launched an external zero-trust key-management solution expressly for admins of the Google Cloud Platform (GCPs).

Virtru’s cloud-based software protects data throughout its lifecycle as it travels through email and file-sharing platforms, including SaaS solutions, cloud environments, and a diverse range of file ecosystems. It is designed to smooth out a normally thorny and painstaking routine for security admins.

Virtru enables admins to manage encryption keys separately from data. This allows them to mitigate breaches, prevent unauthorized access, and ensure no third party can access the data — Google and Virtru included — without disturbing data stores. Users also have choices on how they want to host their encryption keys: on-premises, in a private cloud, fully hosted, or as optional HSM integrations.

Advantages of managing encryption keys separately from data

With the help of the external key-management solution, enterprises can securely manage their encryption keys independently of their data across Google Workspaces, GCPs, and other cloud applications. The solution safeguards the information in data lakes, databases, and other various containers that pass through Google’s cloud computing services and AI capabilities.

With Virtru, all data moving across the Google ecosystem has a single global framework and policy language, irrespective of where the data source is generated. It could be created by users, devices, or systems.

Virtru’s cofounder and CEO John Ackerly said that this solution directly addresses a widespread lack of security in “leveraging big-data cloud computing.”

Zero-trust security for Google portfolio

The zero-trust data standard of the solution means that Google users can now optimize the GCP by extending data sovereignty from collaboration suites to cloud applications, as use cases dictate.

“Virtru is now the only Google partner bringing data security to the entire Google portfolio, including key management, supporting regulations like ITAR in the U.S. and Schrems II in the EU,” Ackerly said in a media advisory.

Although Virtru has been a longstanding partner of Google for data protection and Google-recommended encryption key management, the new solution extends its jurisdiction to Gmail, Google Drive, Google Docs, Sheets, and Slides. Virtru leverages the open Trusted Data Format (TDF), a well-known encryption technology that is also used by the U.S. National Security Agency, to build these solutions.

Developers will likely be pleased to know that the solution also has integration compatibility with Google Cloud Platform’s Kubernetes Engine, Secret Engine, Compute Engine, BigQuery, Dataflow, Cloud SQL, and Pub/Sub. Virtru’s Data Protection Gateway, working in conjunction with Google Cloud EKM, is also compatible with enterprise applications like Salesforce, Zendesk, and Looker.

The company said its service is used to secure more than 7,000 customers and enhances collaboration capabilities for a network of more than 260,000 domains. The average number of emails and files that draw protection from Virtru’s platform exceeds an average of 2 million per day.

Virtru’s client base includes companies and institutions such as Next Insurance, DNA Worldwide, the state of Maryland, and the Ivy League institution, Brown University.

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AI

Compliance and risk management startup Hyperproof lands $16.5M

Hyperproof, a compliance software provider, today announced that it raised $16.5 million in series A funding led by Toba Capital at a $72.5 million post-money valuation. The company says that it’ll use the new capital to support product R&D and grow its headcount as it looks to expand to new markets.

The need for risk and compliance management solutions is increasing as companies move their operations to digital channels. More than 900 regulatory agencies issued a combined more than 200 regulatory updates every day, on average, in 2017. Unsurprisingly, many enterprises don’t feel equipped to address the challenges from a talent or technology standpoint. According to a survey from The Financial Times Group, 57% of senior-level executives rank “risk and compliance” as one of the top two risk categories they’re least prepared to address.

Hyperproof was founded in 2018 by Craig Unger, whose previous startup — cloud-based automation company Azuqua — was acquired by Okta in 2019. Hyperproof offers a software-as-a-service (SaaS) compliance operations platform that lets customers track and manage organizational risks, such as vendor risks, on a continuous basis. Using collaboration tools and workflows as well as integrations with cloud services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, team members can leverage Hyperproof to complete tasks like evidence collection, dashboard creation, and reporting for more than 50 frameworks including FedRAMP, SOX, and CSA.

Hyperproof

Above: Hyperproof’s compliance dashboard.

Image Credit: Hyperproof

“The pandemic has really underscored the importance of maintaining strong security programs and compliance programs to organizations’ leaders, because it has made a number of risks more prominent and introduced new risks,” Unger told VentureBeat via email. “For instance, as [society] shifted to mass remote work, security teams had to maintain cybersecurity under such a radically different environment and implement new security controls. Moving to remote work meant that compliance work also had to be done remotely. Because Hyperproof supports … collaboration between compliance professionals and business units, compliance professionals found it to be a valuable platform that helped them get their work done efficiently.”

Applying AI to compliance

Hyperproof supports compliance projects and comes with a risk register and a place for tracking an organization’s vendors and conducting vendor assessments. Using Hyperproof, companies can document risk mitigation plans, map risks to controls and compliance requirements, and review software and technology vendors to assess their compliance and security posture.

“[Hyperproof can map] between different compliance standards and frameworks, so work such as control design, implementing and testing, and evidence gathering can be streamlined as much as possible,” Unger said. “[The platform keeps] people accountable for completing security and compliance tasks, [streamlines] audit preparation and efficiently comply with multiple standards and frameworks, [and] automate[s] evidence collection and testing.”

In the coming months, Hyperproof plans to launch and environmental, social, and governance software product designed to help organizations manage their businesses more sustainably while meeting reporting requirements. Beyond this, the 51-employee startup is investing heavily in automation solutions powered by AI and machine learning technologies.

“Hyperproof is planning to leverage machine learning to help our users automatically identify and flag the overlapping requirements across various compliance frameworks, so they can see areas where they’re already meeting requirements and reuse their compliance artefacts to satisfy new requirements,” Unger said. “[In addition, we] will use [AI] to address an area of compliance that’s really tedious: Gathering [compliance] evidence from different systems to show auditors that internal controls meant to enhance security and mitigate risks were operating correctly and effectively … In 2022, we plan to [tap] machine learning to automatically identify opportunities for our users to set up integrations that will pull in compliance data from third-party systems into Hyperproof.”

Hyperproof

In addition, Hyperproof aims to develop algorithms that intelligently scan the content of evidence attached to internal policies set up in a company’s account. The algorithm will review the evidence against a specific user-defined parameter or value range to see if the evidence falls within or outside of that acceptable range. According to Unger, Hyperproof is also exploring machine learning to help users gauge how prepared they are for an upcoming compliance audit. The goal is to analyze user activities like how many policies have been implemented and how many requirements have functioning controls linked to them to generate a preparedness score that’ll be updated in real time.

“We plan to leverage machine learning in our products in several different ways, both to eliminate much of the repetitive work compliance professionals have to deal with today and to surface meaningful risk insights to users so that they can make better, more strategic decisions,” Unger continued. “This will not only save compliance professionals hours every month — it will also provides organizations’ leaders peace of mind knowing that the system automatically detects issues as soon as they occur and alerts the right people.”

Bellevue, Washington-based Hyperproof competes with Vanta, LogicGate, MetricStream, and others in a global enterprise governance, risk, and compliance solutions market that was estimated to be worth $35.1 billion in 2020, according to Grand View Research. But Hyperproof claims it’s on track for 400% year-over-year growth thanks to contracts with clients including Sophos, ForgeRock, AppGate, Fortinet, 3M, and Motorola Solutions.

Existing investors participated in Hyperproof’s latest round, which brings the company’s total raised to $26.5 million. Hyperproof previously closed three seed rounds totaling $10 million.

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AI

Meow Wolf, Anthos team for multi-cloud app management in art shows

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Meow Wolf’s work with SADA, a Google Cloud Premier Partner with multiple specializations, and its use of Anthos multi-cloud app management were featured in a spotlight session on immersive art experiences at the Google Cloud Next ’21 conference held online through October 14.

Meow Wolf is an American arts and entertainment company that creates large-scale immersive art installations and produces streaming content, music videos, and arts and music festivals. SADA is a cloud-computing consultant based in North Hollywood, California. Google Anthos is a next-gen, hybrid- and multi-cloud application management platform that aims to provide a consistent development and operations experience for cloud and on-premises environments.

Scalable, flexible multi-cloud app management

Known more for its work with enterprise clients, SADA is helping Meow Wolf design and apply solutions for its permanent multimedia installations, such as Omega Mart, now open in Las Vegas. Anthos fit Meow Wolf’s requirements for a modern cloud application that could be deployed on-premises to ensure low latency and fault tolerance.

The complex, always-on nature of Omega Mart required the scalable IT infrastructure Anthos offers. Anthos allows apps to run unmodified on existing on-premises hardware and many public clouds in simple, flexible, and secure ways.

“Anthos has helped us create a groundbreaking experience that immerses guests in a way that’s never been done before,” said Jordan Snyder, vice president of platform at Meow Wolf. “It gives us a ‘single pane of glass’ to monitor, maintain, and quickly push out app updates.”

Omega Mart, an interactive “supermarket,” is Meow Wolf’s second permanent art exhibition leveraging the hybrid cloud platform to run sensory installations.

Omega Mart, which opened in February, is an art installation billed as the world’s most surreal supermarket and sensory playground, featuring otherworldly displays, hidden portals, immersive art experiences, and shelves stocked with peculiar products. With live, interactive displays that can be accessed via RFID-powered Boop Card readers, shoppers become part of the experience.

“It’s exciting to know that technology like Anthos can be applied to bring artistic visions to life in new and creative ways,” said Miles Ward, CTO at SADA. “Omega Mart is one of many amazing ways to apply Anthos technology.”

“SADA has been instrumental to this process, from helping us conceive the technical solutions to tackling various hurdles along the way,” Snyder said.  SADA’s consultants worked with Anthos and helped Meow Wolf design and apply solutions to meet Omega Mart’s needs. He added, “Their guidance, expertise, and support helped make the launch of Omega Mart a huge success.”

Anthos is now used to host Meow Wolf’s applications and various installations that capture customer interactions with the various Boop and computer stations, which facilitate the interactive gameplay element of the experience and drive the exhibit’s story. Since the opening of the exhibit, SADA has continued to provide technical account management and support.

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AI

Why unstructured data is the future of data management

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Enterprises are increasingly relying on unstructured data for regulatory, analytic, and decision-making purposes. Unstructured data will power analytics, machine learning, and business intelligence.

According to the latest figures from research firm ITC, the volume of unstructured data is set to grow from 33 zettabytes in 2018 to 175 zettabytes, or 175 billion terabytes, by 2025. There has to be some kind of data management so organizations have the right kind of data available at the right time. Krishna Subramanian, president and COO of Komprise, a data management software provider, sat down with VentureBeat to discuss the business benefits and challenges associated with unstructured data.

Venturebeat: Does the average enterprise IT organization know how much unstructured data they have and how fast it is growing?

Krishna Subramanian: Intuitively they know a lot is unstructured and it is growing in double digits, but they don’t know exactly how much they have and how fast it’s growing. We know that 80-90% of the world’s data is unstructured.

Venturebeat: What’s the problem with this data growth — there is now endless cloud storage after all, right?

Subramanian: The big issue is the cost – over two-thirds of the cost of data is not in the storage, but in its active management. For every piece of data, companies typically keep a few backup copies and a replication copy for disaster recovery. If you think your data is growing at 30%, it’s more like 90-100% when you factor in all the copies of the data. It’s also wise to consider that cloud storage is not necessarily cheaper. For instance, AWS itself today offers over 16 tiers of unstructured file and object storage. If you don’t put your data in the right place and control egress costs, you may end up paying more than if you were storing it on premises because every time you even read the data you’ll be charged. The key here is that over 80% of data is not actually actively accessed and is cold. This cold data can be stored on cheaper storage and does not require the same level of backup and replication. Therefore, you need to manage hot data that is actively used and cold data that is rarely used differently. As just one example, Pfizer researchers generate between 8TB and 10TB a day, and they were running out of datacenter space. They were able to use a data management product to identify the cold data and eliminate it from their expensive storage, backups, and replication by moving it to lower cost-resilient storage in the cloud and taking it out of active management. The company wound up cutting 75% of their data storage and backup costs, all without users having to notice any change. What’s hard about data growth is that a lot of organizations don’t like to delete data. You never know when you might need it. And when you do, you want to be able to find it easily. And users and applications should not have to change their behavior when you move data around. In the past, with archiving to tape, that wasn’t possible, but now it is with cloud storage and with data management software.

Venturebeat: Why is it important to be strategic about how you manage it, store it — isn’t it just about making sure you can find it for the BI team?

Subramanian: Today, data is a valuable corporate asset. You’ve got to be strategic with it because it’s not just for your BI teams, but for the R&D and customer success teams. They need historical data to build new products or to improve the ones they already have. This is super relevant in manufacturing, such as in the semiconductor chip industry, but also in other industries that are so important to our economy, such as pharmaceuticals. COVID researchers depended upon access to SARS data when developing vaccines and treatments. Data often becomes valuable again later, and what if you don’t know what you have or you can’t find it? We’ve had customers in the media and entertainment business, and in the past when they wanted to find an old show, they’d need access to a tape archive. Then, they needed an asset tag to locate the tape. That can be very difficult, and it’s why archiving is not popular. Live archive solutions that are available today make archived data instantly accessible and transparently tier data so users can easily locate files and access them anytime.

Venturebeat: How will tools and practices evolve to help IT departments better leverage this unstructured data for the organization/business users? What’s needed, where are the gaps?

Subramanian: You need a storage-independent way to look at data across all of your storage technologies, whether in your datacenter or in the cloud, to not only move data to the right place, but also to help businesses extract value from the data. Gartner calls this category “data management software,” and it includes companies like Cirrus Data for block data and Komprise for file and object data. The ultimate goal is to help business users leverage historical data, and this requires data search, data analytics, and data intelligence. These are hot areas where a lot of innovation is happening. The cloud providers offer several data warehousing and data analytics solutions that can be leveraged in conjunction with data management software, such as AWS Redshift and QuickSight. For instance, we use distributed Elastic Search in our software to rapidly search billions of files and find just the data relevant to a user, such as all the data for a particular project, and export this data to RedShift for further analysis. Why have all this data if you can’t detect significant trends, such as anomalies or ransomware? I believe we need more predictive analytics around data.

Venturebeat: Will the data management challenge spur a whole new sector of startups in the coming year or two?

Subramanian: Definitely. Analysts are beginning to recognize data management software as a new category. Beyond the use cases above, consider all the new types of data analytics companies getting funded, such as SnowFlake, DataBricks, and Apache Spark. So many companies are coming to light right now to solve data management and data analytics issues at scale.

Venturebeat: How are the big cloud providers responding to problems and opportunities with unstructured data growth?

Subramanian: They are all offering more services to store data at different performance and price points. Amazon Elastic File System (Amazon EFS) and Azure Files were born to address the need for file storage in the cloud. The major CSPs are investing in partners across many areas of unstructured data management, including migration and analytics.

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Categories
AI

AI brings promise and peril to customer relations management

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Artificial intelligence is proving to be most valuable when it is applied to rote, predictable functions. At first blush, this may not sound like an ideal fit for customer relations management (CRM), but keeping customers happy requires a lot of tedious work.

In today’s increasingly digital world, CRM lives and dies by data — not just the amount of data gathered but the quality of that data, which can only be achieved by cutting-edge analysis and interpretation. But today’s volumes are simply too much for human analysts to cope with (at least, in a timely fashion), so CRM platforms of all stripes are starting to incorporate AI to handle the load.

Bad data, bad decisions

Faulty data management is a key factor in poor customer relationship outcomes, with more than 85% of sales agents citing it as the cause of embarrassing mistakes, according to marketing analysis firm MarTech Series. Upwards of 2.5 quintillion bytes of data are being created every day, 90% of it unstructured, so the mere task of putting all of this data into context is functionally impossible without AI. And AI can be integrated directly into CRM workflows to handle the tedious tasks most people don’t want to do anyway — and usually can’t do without introducing numerous errors. At the same time, AI can be trained to communicate directly with customers, either by text or voice, to address simple questions or easily solvable complaints.

As counterintuitive as it may seem, AI is likely to produce a more personalized approach to CRM than is currently possible. AI can assemble and assess a customer’s digital history — including purchases, emails, and other events — to determine their needs and temperament much faster and more thoroughly than a human representative could, Anzhelika Danielkievich recently wrote on the Keen Ethics blog. This helps resolve problems in a timely manner, and with a higher degree of satisfaction, but it also enables a more accurate representation of brand sentiment to further hone marketing and communication efforts.

In addition, AI can do wonders for much of the behind-the-scenes work of CRM, such as lead scoring, cross-selling, price optimization, and sales forecasting. This information can then be used to improve business strategies, right down to targeted advice to sales reps to guide them through each stage of the sales pipeline.

A friendly voice

The ultimate goal is to provide better customer service, according to software developer Nahla Davies. At the moment, one of the chief complaints aimed at companies large and small is the long wait times at call centers and in email replies. A properly trained AI-driven CRM platform will be able to handle most common queries with little to no delay, sending the more complicated requests to service reps, who should have greater availability. AI will also be able to more effectively communicate with customers over the web, social media, and mobile platforms.

AI can also help people interact with enterprise services in a more streamlined and secure fashion. Davies notes that mobile banking is already pushing software that allows customers to take complete control of their finances, with AI programs constantly monitoring for threats and then pushing out the appropriate updates to security tools like encryption and two-factor authentication.

As with any software, it’s important to note that all AI-based CRM platforms are not created equal, nor are they immune to vendors’ tendency to overpromise and underdeliver. For instance, all the talk about almost humanlike interaction between customers and service bots tends to skip the fact that this level of technology is still a few generations away. Right now, AI bots are being purpose-built for specific use cases, such as data entry and task scheduling.

On the other hand, AI is giving a major and immediate boost to functions like predictive analytics for everything from trend and market forecasting to driving inefficiencies out of supply chains. In the end, enterprise executives should take a hard look at what AI can do best and target it at specific areas where its efficacy can be measured against established metrics.

Perhaps the most important thing to keep in mind when augmenting a CRM with AI is that the technology should be a conduit to successful outcomes, not a barrier. Many people will gladly engage with AI if it provides a quick, simple resolution to their problem. But frustration will mount if the problem is not solved and they can’t get past AI to a human representative.

Likewise, customers will likely be unhappy if they are talking or texting with what they think is human but turns out to be a bot. The use of AI should be made clear up front, and it must be used at the customer’s discretion.

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Repost: Original Source and Author Link