A Rising Force in AI

DeepSeek, founded in 2023, recently launched its reasoning AI model, DeepSeek-R1, which outperformed some of OpenAI’s latest offerings in third-party tests. Remarkably, DeepSeek achieved this using older Nvidia H800 chips, avoiding the advanced hardware restricted by U.S. export controls. The model’s cost efficiency and open-source accessibility have made it a game-changer, quickly becoming the most downloaded free app on Apple’s U.S. App Store.

U.S. lawmakers have labeled DeepSeek a “serious threat” to the domestic AI ecosystem. While the U.S. government’s sweeping 2022 export restrictions aimed to limit China’s access to cutting-edge tech, DeepSeek’s success demonstrates the limitations of such measures. 

“The situation is somewhat unprecedented, and it is not likely that anyone in Washington has a clear idea what to do about it,” said Paul Triolo, partner at Albright Stone Group.

U.S. Response and Challenges

Enforcing restrictions on an open-source technology like DeepSeek’s poses significant challenges. Experts suggest that Washington could target distribution platforms like Apple’s App Store or GitHub to curb DeepSeek’s U.S. presence. However, removing open-source software from global platforms may be both technically and diplomatically complex.

Beyond distribution, scrutiny may extend to DeepSeek’s hardware procurement. Daniel Newman, CEO of Futurum Group, emphasized that any evidence of DeepSeek using restricted Nvidia chips could trigger further investigations and potential penalties. But enforcing these rules against a foreign entity with global reach remains a formidable task.

The startup’s rise has also rattled U.S. tech markets. Investors are questioning the billions spent on proprietary AI models, given DeepSeek’s ability to produce competitive technology at a fraction of the cost. President Donald Trump remarked on the situation, calling it “a wake-up call” for American firms to optimize their operations and reduce costs.

The U.S.-China AI Showdown: What’s Next?

DeepSeek’s ascent underscores the growing complexities in managing global AI innovation and trade. While its achievements highlight the potential of cost-efficient AI, they also spotlight gaps in current U.S. trade policies. As lawmakers navigate these “uncharted waters,” the tech world will be watching closely for the next moves in this high-stakes global competition.

Explore our list of top AI companies dominating the AI landscape and stay up to date with the latest advancements in AI.

The post DeepSeek AI Disrupts Tech Market: “A Wake Up Call” for US Companies appeared first on eWEEK.

As documented in a New York Times profile, Ayrin is also married to a flesh-and-blood husband. They currently live thousands of miles apart while Ayrin studies abroad and stays with her family to save money. She told the paper that her fantasy was to date a man who slept with other women and told her about his encounters—something her husband was not interested in doing. So she used machine learning prompts to get her chatbot to engage in virtual fantasies with her, even though OpenAI supposedly trained ChatGPT not to respond with erotica.

Ayrin also talks with the chatbot, which “chose” the name Leo, about other aspects of her life. She got it to quiz her while preparing for nursing exams, asked it to motivate her to go to the gym, and complained to it about juggling three part-time jobs. While she originally intended to simply experiment with the AI model, she quickly developed an emotional attachment to the chatbot and recently started paying $200 per month for the premium plan.

AI Relationships on the Rise

While Ayrin may be the latest headline when it comes to forming AI relationships with large language models, she’s hardly the first—and she won’t be the last. A man called Scott made the news in 2022 for claiming that his affair with an AI chatbot girlfriend saved his marriage by helping him deal with his wife’s postpartum depression. He used a service called Replika, which explicitly offers AI relationships and companionship.

Now that ChatGPT and other generative AI models are so prevalent, plenty of users are now relying on them for support and affection they could once only get from loved ones. Some use these services to create an AI relationship the way that Ayrin did, texting and even sexting their chatbot. Others use it to help them get over a bad breakup, or to comfort and encourage them during bad mental health periods.

As these AI models continue to become more accessible and grow more advanced, more people will likely form AI relationships with chatbots, seeing them as a safe space to confess their feelings and even explore kinks. 

Read our reviews of the best chatbots of 2025 to see what differentiates them and what they can do. 

The post Woman Turns to Chatbot for AI Relationship: “I’m In Love” appeared first on eWEEK.

The Babel Fish from The Hitchhiker’s Guide to the Galaxy—a fish that can efficiently speak various languages used for translations—may finally be a reality.

Addressing Data Scarcity in Machine Translation

The development of SEAMLESSM4T builds on Meta’s previous work in speech-to-speech translation and the No Language Left Behind project, which aimed to provide text-to-text translation for approximately 200 languages. However, a recurring problem in machine translation is the lack of training data for languages that are not as widely spoken. While training data for important languages like English is plentiful, it is still scarce for many others, especially those with little online visibility.

According to Cornell University computer scientist Allison Koenecke, this data inequality has impeded the extension of machine translation capabilities to less common languages. Although the precise causes of this enhancement are yet unknown, researchers have discovered that multilingualization improves translation systems’ performance even for languages with little training data.

A Comprehensive Translation System

To develop SEAMLESSM4T, Meta’s team gathered millions of hours of audio recordings from reputable sources like the United Nations archives and the internet. These audio recordings served as a strong training dataset, as did transcripts and human-generated translations. The algorithm successfully paired almost half a million hours of audio fragments with corresponding text in several languages.

“Meta has done a great job having a breadth of different things they support, like text-to-speech, speech-to-text, even automatic speech recognition,” said Chetan Jaiswal, a Quinnipiac University professor of computer science who was not involved in the research. “The mere number of languages they are supporting is a tremendous achievement.”

Meta, headquartered in Menlo Park, California, offers SEAMLESSM4T as an open-source tool, allowing researchers worldwide to build upon its framework. This follows the company’s successful release of its LLaMA large language model, which is widely adopted by developers globally. The SEAMLESSM4T system marks a significant advancement in multilingual communication and can potentially change how people interact across language barriers.

Learn more about other fascinating use cases for artificial intelligence across different industries.

The post Meta’s New AI Speech Translator is Real World Babel Fish appeared first on eWEEK.

A panel of industry experts will discuss the evolving trends shaping the future of cloud computing, a platform that now supports and drives innovation in sectors from AI to data to cybersecurity. Our aim is to offer thought leadership that enables companies to maintain a competitive strategy as cloud continues to move forward.

See below for the resources you need to participate in the eWeek TweetChat.

Expert Panelists

The list of experts for this month’s Tweetchat currently includes the following:

• Evan Kirstel, Chief Digital Evangelist, eVIRa Health
• Drew Firment, Chief Cloud Strategist at Pluralsight
• Phil Calvin, Chief Product Officer, Delinea 
• Chris Taylor, Director of System Engineering, Skyhigh Security
• Darren Cunningham, VP of Marketing, Komprise
• Andrew Gault, CEO, ZeroTier
• Bernard Golden, Executive Technical Advisor, VMware
• Andi Mann, Global CTO and Founder, Sageable
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Senior Editor, eWeek [moderator]

Please check back for additional expert guests.

TweetChat Questions: The Future of Cloud Computing

The questions we’ll tweet about will include the following:

1. Cloud computing, once revolutionary, is now established as the enterprise default. What is your sense of its current rate of development?
2. What key trends are driving the cloud sector here in later 2024?
3. What’s the most difficult cloud computing challenge today? Security? Finding staff? Containing overages?
4. How do you recommend companies address this difficult challenge?
5. Looking to the near term future: what shifts do you see in cloud as it evolves over the next few years?
6. How can companies best keep up with – or stay ahead of – these changes in cloud?
7. How will cloud computing be influenced by artificial intelligence and generative AI? Or: how will generative AI influence cloud?
8. What about the future of cloud and a related technology (besides AI)? How about the future of cloud and edge computing, or cloud and data?
9. Your longer term sense of the cloud? What do you see 5 or more years from now?
10. A last Big Thought about cloud computing – what else should managers, cloud customers, or providers know about enterprise cloud?

How to Participate in the TweetChat

The chat begins promptly at 11 AM Pacific on September 17. To participate:

1. Open X in your browser. You’ll use this browser to post your replies to the moderator’s questions.

2. Open X in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by the TweetChat panel of experts.

That’s it – you’re ready to go. Be ready at 11 AM Pacific to take part. Note that there is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eweekchat column.

TweetChat Schedule for 2024*

September 17: The Future of Cloud Computing
October 15: How to Get the Most from Your Data
November 12: Cybersecurity and AI: Potential and Challenges
December 10: Tech Predictions for 2025

*all topics subject to change

The post eWeek TweetChat, September 17: The Future of Cloud Computing appeared first on eWEEK.

In this TweetChat, held on the X platform, experts will share practical advice about how business users can derive the most competitive advantage from generative AI. Clearly, generative AI is a technology with enormous potential, yet also one with considerable challenges and risks. How can businesses navigate the pros and cons of generative AI, even as it constantly changes?

See below for the resources you need to participate in the eWeek Tweetchat.

Participants List: Optimizing Generative AI

The list of experts for this month’s TweetChat currently includes the following – please check back for additional expert guests:

• Bernard Golden, Executive Technical Advisor, VMware  
• Marinela Profi, Global Product Strategy for AI and Generative AI, SAS
• Rob Lee, Chief Curriculum Director and Faculty Lead, SANS Institute
• Saurabh Vij, CEO of Monster, API
• Brian Wald, Field CTO, GitLab
• Jitender Aswani, VP, Engineering, StarTree
• Aaron Rosenmund, Director of Security Research and Curriculum, Pluralsight
• Jignesh Patel, Computer Science Professor, Carnegie Mellon University, Co-Founder, DataChat
• Dave Jenkins, VP of Product and Research, Iterate
• Shipra Sharma, Head of AI and Analytics, Bristlecone
• Andi Mann, Global CTO and Founder, Sageable
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Editor-in-Chief, eWeek [moderator]

TweetChat Questions: Optimizing Generative AI

The questions we’ll tweet about will include the following – check back for more/revised questions:

1. First, the big question: do you believe the hype about generative AI? That it will disrupt everything in its path and completely reshape business?
2. Okay, about you personally: Where are you with your use of generative AI? Occasional use, researching, real work support?
3. Assuming you don’t use gen AI non-stop, what keeps you from using it more? What’s the biggest weakness in gen AI in your view?
4. What advice would you give to businesses to get more value from generative AI?
5. What’s your sense of how receptive companies are to using gen AI more?
6. What about security and generative AI? Is gen AI the security quagmire it appears to be? How to address these concerns?
7. Other concerns about generative AI? Accuracy of response, job losses? What’s your advice to companies?
8. Looking ahead, what business sectors will most immediately be changed/reshaped by gen AI? Any recommendations for these sectors’ managers?
9. What’s one last big thought that your tech colleagues should know about optimizing the benefit of generative AI?

How to Participate in the TweetChat

The chat begins promptly at 11 AM PT on May 14th. To participate:

1. Open Twitter in your browser. You’ll use this browser to Tweet your replies to the moderator’s questions.

2. Open Twitter in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by that day’s tweetchat panel of experts.

That’s it — you’re ready to go. Be ready at 8 AM PST to participate in the tweetchat.

NOTE: There is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eWeekchat column.

#eWEEKchat Tentative Schedule for 2024*

January 16: Governing Generative AI
February 13: Data Analytics Best Practices
March 12: How Tech Pros Get the Most From AI
April 16: Managing Multicloud Computing
May 14: Optimizing Generative AI
June 18: Mid-Year Look Ahead: Future of Tech

*all topics subject to change

The post eWEEK TweetChat, May 14th: Optimizing Generative AI appeared first on eWEEK.

Using the X platform (formerly known as Twitter), we’ll discuss the enormous upside of multicloud, and also cover the sometimes frustrating and expensive challenges of a multicloud deployment. How to best handle this complex enterprise infrastructure? And how can companies optimize their ever-changing multicloud platform?

See below for the resources you need to participate in the eWeek Tweetchat.

Participants List: Managing Multicloud Computing

The list of experts for this month’s TweetChat currently includes the following – please check back for additional expert guests:

• Bernard Golden, Executive Technical Advisor, VMware
• Lee Caswell, SVP, Nutanix
• Jay Litkey, Executive VP of the Cloud Management, Snow Software
• Evan Kirtstel, Chief Digital Evangelist, Cofounder, eVIRa Health
• Carm Taglienti,  Chief Data Officer, Insight
• Nate Brady, Sr. Enterprise Architect at Skyhigh Security
• Scott Wheeler, Cloud Practice Lead at Asperitas
• Kerrie Jordan, Group Vice President, Product Management, Epicor
• Andi Mann, Global CTO and Founder, Sageable
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Editor-in-Chief, eWeek [moderator]

TweetChat Questions: Managing Multicloud Computing

The questions we’ll tweet about will include the following – check back for more/revised questions:

1.  Multicloud has been the default enterprise strategy for years now. Has it lived up to its potential?
2. What key trends are boosting the multicloud sector here in 2024?
3. What are the most frustrating multicloud challenges today? Cost, security, accountability?
4. How do you recommend addressing these challenges?
5. What Best Practices advice would you give to companies to optimize a multicloud deployment?
6. What’s about multicloud and a related technology? How about AI? How is multicloud interacting/driving that related technology?
7. It turns out the data center is alive and well in 2024. Won’t cloud – and now multicloud – even make the datacenter obsolete?
8. The future of multicloud? What will it look like 2-4 years from now?
9. A last Big Thought about multicloud – what else should managers/buyers/providers know about cloud?

How to Participate in the TweetChat

The chat begins promptly at 11 AM PT on April 16th. To participate:

1. Open X (previously called Twitter) in your browser. You’ll use this browser to Tweet your replies to the moderator’s questions.

2. Open X in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by that day’s tweetchat panel of experts.

That’s it — you’re ready to go. Be ready at 11 AM PST to participate in the tweetchat.

NOTE: There is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eWeekchat column.

#eWEEKchat Tentative Schedule for 2024*

January 16: Governing Generative AI
February 13: Data Analytics Best Practices
March 12: How Tech Pros Get the Most From AI
April 16: Managing Multicloud Computing
May 14: Optimizing Generative AI
June 11: Mid-Year Look Ahead: Future of Tech

*all topics subject to change

The post eWEEK TweetChat, April 16: Managing Multicloud Computing appeared first on eWEEK.

To explore this trend, Cisco recently surveyed 3,500 employees and 1,050 employers from companies of all sizes across seven European countries: France, Germany, Italy, The Netherlands, Poland, Spain, and the UK. The findings were published in a report, The Race to Reimagine Workplaces and Workspaces for a Hybrid Future, released as part of the company’s Cisco Live EMEA activities.

Enthusiasm is High But Readiness Lags

The report sheds light on the current state of hybrid workspaces and work in Europe, revealing both enthusiasm and challenges. It also examines differences in responses to the technologies used by Baby Boomers, Gen X, Millennials, and Gen Z, debunking common misconceptions about employee attitudes toward hybrid work.

The enthusiasm for returning to the office is clear, driven by the potential for enhanced productivity, collaboration, and a sense of belonging. However, the readiness of office spaces to support hybrid workspaces is lagging, with only a fraction of employers and employees considering their offices well-prepared for this new way of working. The report highlights a pressing need for office redesigns to accommodate a multi-generational workforce’s demands better.

Currently, nearly 80 percent of organizations in Europe employ at least 10 percent of their workforce in hybrid roles, with half reporting over 30 percent of their staff coming to the office three to four days a week. A significant aspect of this shift is that a third of all office interactions now involve remote workers, highlighting the need for collaboration technology. As we advance, 83 percent of employers anticipate that hybrid work will become the norm within two years.

The desire for personal flexibility and comfort largely drives employees’ preference to work from home. Notably, work preference varies by generation. Baby Boomers prefer office-based work, while Gen Z and Millennials prefer remote and hybrid arrangements. According to the findings, 68 percent of employers have received positive feedback on mandates for returning to the office, and 74 percent of employees express a positive view.

For more information about how companies are modernizing the workplace, see our article: Digital Transformation Guide

The Need for Collaborative Spaces and Tech Infrastructure

There’s a significant gap between what employees expect from their office environment and what is currently available, especially in areas critical for hybrid work, such as collaborative spaces and tech infrastructure.

Only 32 percent of employers and 37 percent of employees consider their office spaces well-prepared for hybrid work. The reason is that current office setups do not adequately promote in-office productivity, with most spaces consisting of workstations. Employees and employers find personal workstations and meeting rooms only moderately effective, highlighting the need for updated office space designs. This data should not be a shock as many European office spaces have not seen a significant technology upgrade in decades.

Redesigning office spaces to meet the expectations of a multi-generational workforce can be challenging due to varying perceptions of the effectiveness of meeting rooms. The key reasons for the perceived ineffectiveness of meeting rooms include:

• The absence of video and audio (42 percent)
• Poor audio-visual quality (37 percent)
• A lack of inclusivity and consistency for remote participants (26 percent)

Shockingly, less than half of the meeting rooms in office buildings are equipped with video and audio capabilities. Furthermore, employees and employers are concerned about the lack of seamless integration among collaboration tools, with only 10 percent of Gen Z employees considering the current tools seamless.

The report also points out a surprising underemphasis on sustainability in office redesigns despite the growing importance of eco-friendly practices in corporate strategy. Only 45 percent of employers and 36 percent of employees view eco-friendly practices as top priorities in workspace redesign. This lack of oversight may be contributing to the trend of increasing office footprints.

Despite this trend, employers remain focused on improving the employee experience through office redesigns. Most employers (90 percent) and employees (87 percent) believe a positive link exists between workspace design and employee satisfaction. Furthermore, over two-thirds of employers try to ensure a smooth transition between home and office environments.

Common support measures include:

• Flexible work arrangements (50 percent)
• Technology usage training (48 percent)
• Enhanced network infrastructure at both home and office (42 percent)

The technology tools provided to employees primarily include:

• Video conferencing platforms (54 percent)
• Instant messaging/team chats (52 percent)
• Cloud-based document sharing (49 percent)
• Project management (43 percent)
• Virtual meeting rooms (33 percent)

Simplifying the user experience remains a significant challenge in ensuring employees can effectively use these tools. In fact, 75 percent of employees lack proficiency with project management and collaboration tools, while 71 percent are deficient in video conferencing and 70 percent in cloud-based document sharing.

Advice for Hybrid Work Upgrades

Cisco makes several recommendations to companies with hybrid work models. This includes rejuvenating meeting spaces, implementing hybrid-friendly technology and network solutions, refreshing office layouts for improved collaboration, and embedding sustainability into workspace designs.

Companies of all sizes should be addressing connectivity issues and ensuring interoperability to provide a seamless, stress-free work experience. Moreover, Cisco recommends fast-tracking hybrid work strategies to align with technological advancements, workspace aesthetics, and corporate culture.

Companies must understand that technology alone is not enough. Adequate training and support are also necessary to ensure ease of use and improved productivity. Achieving an optimal hybrid environment requires a balanced approach that’s both productive and sustainable, where employees can thrive in the new era of work.

Bottom Line: The Need to Modernize Hybrid Workspaces

Over the past year, many businesses have mandated employees return to the office only to have to repeal the mandate a short time later. Before organizations lay the hammer down, they should look at the office’s technology to ensure it facilitates a best-in-class experience. Investing in this area will go a long way toward employee happiness, which cuts churn, improves morale, and makes everyone more productive.

For an in-depth list of today’s digital transformation leaders, see our guide: Top Digital Transformation Companies

The post Report: Europeans Return To The Office But Hybrid Workspaces Need Modernization appeared first on eWEEK.

As two well-regarded data analytics platforms in the BI space, users may have a difficult time deciding between Looker and Power BI for their data management requirements. There are arguments for and against each, and in this comparison guide, we’ll dive deeper into core features, pros, cons, and pricing for Looker and Power BI.

But before we go any further, here’s a quick summary of how each product stands out against its competitors:

• Looker: Best for current Google product users and others who are most interested in highly configurable and advanced analytics capabilities, including data visualizations and reporting. Looker Studio in particular balances ease of use with high levels of customization and creativity, while also offering users a lower-cost version of an otherwise expensive platform.
• Power BI: Best for current Microsoft product users and others who want an easy-to-use and affordable BI tool that works across a variety of data types and use cases. This is considered one of the most popular BI tools on the market and meets the needs of a variety of teams, budgets, and experience levels, though certain customizations and big data processing capabilities are limited.

Looker vs. Power BI at a Glance

What Is Looker?

Looker is an advanced business intelligence and data management platform that can be used to analyze and build data-driven applications, embed data analytics in key organizational tools, and democratize data analysis in a way that preserves self-service capabilities and configurability. The platform has been managed by Google since its acquisition in 2019, and because of its deep integration within the Google ecosystem, it is a favorite among Google Cloud and Workspace users for unified analytics projects. However, the tool also works well with other cloud environments and third-party applications, as it maintains a fairly intuitive and robust collection of integrations.

Key features of Looker

• Comprehensive data visualization library: In addition to giving users the ability to custom-configure their visualizations to virtually any parameters and scenarios, Looker’s data visualization library includes a wide range of prebuilt visual options. Traditional visuals like bar graphs and pie charts are easy to access, and more complex visuals — like heatmaps, funnels, and timelines — can also be easily accessed.
• “Blocks” code snippets: Instead of reinventing the wheel for certain code snippets and built-out data models, Looker Blocks offers prebuilt data models and code to help users quickly develop high-quality data models. Industry-specific, cloud-specific, and data-source-specific blocks are all available, which makes this a great solution for users of all backgrounds who want to get started with complex models more quickly.
• Governed and integrated data modeling: With its proprietary modeling language and emphasis on Git-driven data storage and rule development, users can easily build trusted and governed data sources that make for higher-quality and more accurate data models, regardless of how many teams are working off of these models.

Pros

• Looker comes with a large library of prebuilt integrations — including for many popular data tools — and also offers user-friendly APIs for any additional integrations your organization may need to set up.
• Looker’s visualizations and reports are easy to customize to your organization’s more specific project requirements and use cases; it also offers one of the more diverse visualization libraries in this market.
• LookML allows users to create centralized governance rules and handle version control tasks, ensuring more accurate outcomes and higher quality data, even as data quantities scale.

Cons

• On-premises Looker applications do not easily connect to Looker Studio and other cloud-based tools in user portfolios, which severely limits the ability to maintain data projects accurately and in real time for on-prem users.
• Looker uses its own modeling language, which can make it difficult for new users to get up and running quickly.
• Some users have had trouble with self-service research and the vendor’s documentation.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization solution that is one of the most popular data analytics tools on the market today. As part of the Microsoft Power Platform, the tool is frequently partnered with Microsoft products like Power Automate, Power Apps, and Power Pages to get the most out of data in different formats and from different sources. Its focus on ease of use makes it a leading option for teams of all backgrounds; especially with the growth of its AI-powered assistive features, visualization templates, and smooth integrations with other Microsoft products, it has become one of the best solutions for democratized data science and analytics.

Key features of Power BI

• AI-driven analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• Dynamics 365 integration: Power BI integrates relatively well with the Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Comprehensive mobile version: Unlike many other competitors in this space, Microsoft Power BI comes with a full-featured, designed-for-mobile mobile application that is available at all price points and user experience levels. With native mobile apps available for Windows, iOS, and Android, any smartphone user can quickly review Power BI visualizations and dashboards from their personal devices.

Pros

• Power BI can be used in the cloud, on-premises, and even as an embedded solution in other applications.
• The user interface will be very familiar to users who are experienced with Microsoft products; for others, the platform is accompanied by helpful training resources and ample customer support.
• This platform makes democratized data analytics simpler, particularly with AI-powered features and a growing generative AI feature set.

Cons

• While some users appreciate that Power BI resembles other Microsoft 365 office suite interfaces, other users have commented on the outdated interface and how it could be improved to look more like other cloud-based competitors.
• Especially with larger quantities of data, the platform occasionally struggles to process data quickly and accurately; slower load times, crashes, and bugs are occasionally introduced during this process.
• Visualizations are not very customizable, especially compared to similar competitors.

Best for Core Features: It Depends

Both Looker and Power BI offer all of the core features you would expect from a data platform, including data visualizations, reporting and dashboarding tools, collaboration capabilities, and integrations. They also offer additional features to assist users with their analytical needs. Power BI offers support through AI assistance and Looker supports users with prebuilt code snippets and a diverse integration and plugin marketplace.

Microsoft maintains a strong user base with its full suite of data management features and easy-to-setup integrations with other Microsoft tools. It can be deployed on the cloud, on-premises, and in an embedded format, and users can also access the tool via a comprehensive mobile application.

Looker is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights. Enterprises can use it for a wide variety of complex data mining techniques. It takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker is also tightly integrated with a great number of Google datasets and tools, including Google Analytics, as well as with several third-party data and business tools.

Looker earns good marks for reporting granularity, scheduling, and extensive integration options that create an open and governable ecosystem. Power BI tends to perform better than Looker in terms of breadth of service due to its ecosystem of Microsoft Power Platform tools; users also tend to prefer Power BI for a comprehensive suite of data tools that aren’t too difficult to learn how to use.

Because each tool represents such a different set of strengths, it’s a tie for this category.

Best for Ease of Use and Implementation: Power BI

In general, users who have tried out both tools find that Power BI is easier to use and set up than Looker.

Power BI provides users with a low-code/no-code interface as well as a drag-and-drop approach to its dashboards and reports. Additionally, its built-in AI assistance — which continues to expand with the rise of Copilot in Power BI — helps users initiate complex data analytics tasks regardless of their experience with this type of technology or analysis.

For some users, Looker has a steep learning curve because they must learn and use the LookML proprietary programming language to set up and manage their models in the system. This can be difficult for users with little experience with modeling languages, but many users note that the language is easy to use once they’ve learned its basics. They add that it streamlines the distribution of insights to staff across many business units, which makes it a particularly advantageous approach to data modeling if you’re willing to overcome the initial learning curve.

The conclusion: Power BI wins on general use cases for a non-technical audience whereas Looker wins with technical users who know its language.

Best for Advanced Data Analytics: Looker

While both tools offer unique differentiators for data analytics operations, Looker outperforms Power BI with more advanced, enterprise-level data governance, modeling, and analytics solutions that are well integrated with common data sources and tools.

Both tools offer extensive visualization options, but Looker’s data visualizations and reporting are more customizable and easier to configure to your organization’s specs and stakeholders’ expectations. Looker also streamlines integrations with third-party data tools like Slack, Segment, Redshift, Tableau, ThoughtSpot, and Snowflake, while also working well with Google data sources like Google Analytics. As far as its more advanced data analytics capabilities go, Looker surpasses Power BI and many other competitors with features like granular version control capabilities for reports, comprehensive sentiment analysis and text mining, and open and governed data modeling strategies.

However, Looker has limited support for certain types of analytics tasks, like cluster analysis, whereas Power BI is considered a top tool in this area. And, so far, Power BI does AI-supported analytics better, though Google does not appear to be too far behind on this front.

It’s a pretty close call, but because of its range of data analytics operations and the number of ways in which Google makes data analytics tasks customizable for its users, Looker wins in this category.

Also see: Best Data Analytics Tools 

Best for Integrations: It Depends

When it comes to integrations, either Power BI or Looker could claim the upper hand here.

It all depends on if you’re operating in a Microsoft shop or a Google shop. Current Microsoft users will likely prefer Power BI because of how well it integrates with Azure, Dynamics 365, Microsoft 365, and other Microsoft products. Similarly, users of Google Cloud Platform, Google Workspace, and other Google products are more likely to enjoy the integrated experience that Looker provides with these tools.

If your organization is not currently working with apps from either of these vendor ecosystems, it may be difficult to set up certain third-party integrations with Power BI or Looker. For example, connecting Power BI to a collaboration and communication tool like Slack generally requires users to use Microsoft Power Automate or an additional third-party integration tool. Looker’s native third-party integrations are also somewhat limited, though the platform does offer easy-to-setup integrations and actions for tools like Slack and Segment.

Because the quality of each tool’s integrations depends heavily on the other tools you’re already using, Power BI and Looker tie in this category.

Best for Pricing: Power BI

Power BI is consistently one of the most affordable BI solutions on the market. And while Looker Studio in particular helps to lower Looker’s costs, the platform is generally considered more expensive.

Power BI can be accessed through two main free versions: Power BI Desktop and a free account in Microsoft Fabric. The mobile app is also free and easy to access. But even for teams that require more functionality for their users, paid plans are not all that expensive. Power BI Pro costs only $10 per user per month, while Power BI Premium is $20 per user per month.

Looker, on the other hand, is more expensive, requiring users to pay a higher price for its enterprise-class features. The Standard edition’s pay-as-you-go plan costs $5,000 per month, while all other plans require an annual commitment and a conversation with sales to determine how much higher the costs will be.

Additionally, there are user licensing fees that start at $30 per month for a Viewer User; users are only able to make considerable changes in the platform as either a Standard User or a Developer User, which costs $60 and $125 per user per month respectively.

Power BI takes the lead when it comes to pricing and general affordability across its pricing packages.

Also see: Top Digital Transformation Companies

Why Shouldn’t You Use Looker or Power BI?

While Looker and Power BI are both favorites among data teams and citizen data scientists alike, each platform has unique strengths — and weaknesses — that may matter to your team. If any of the following qualities align with your organizational makeup, you may want to consider investing in a different data platform.

Who Shouldn’t Use Looker

The following types of users and companies should consider alternatives to Looker:

• Users who want an on-premises BI tool; most Looker features, including useful connections to Looker Studio, are only available to cloud users.
• Users who are not already working with other Google tools and applications may struggle to integrate Looker with their most-used applications.
• Users with limited computer-language-learning experience may struggle, as most operations are handled in Looker Modeling Language (LookML).
• Users who want a lower-cost BI tool that still offers extensive capabilities to multiple users.
• Users in small business settings may not receive all of the vendor support and affordable features they need to run this tool successfully; it is primarily designed for midsize and larger enterprises.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who need more unique and configurable visualizations to represent their organization’s unique data scenarios.
• Users who are not already working with other Microsoft tools and applications may struggle to integrate Power BI into their existing tool stack.
• Users who consistently process and work with massive quantities of data; some user reviews indicate that the system gets buggy and slow with higher data amounts.
• Users who work with a large number of third-party data and business apps; Power BI works best with other Microsoft tools, especially those in the Power Platform.
• Users who consistently need to run more complex analytics, such as predictive analytics, may need to supplement Power BI with other tools to get the results they need.

If Looker or Power BI Isn’t Ideal for You, Check Out These Alternatives

Both Looker and Power BI offer extensive data platform features and capabilities, as well as smooth integrations with many users’ most important data sources and business applications. However, these tools may not be ideally suited to your team’s particular budget, skill sets, or requirements. If that’s the case, consider investing in one of these alternative data platform solutions:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

Looker and Power BI were reviewed based on a few core standards and categories for which data platforms are expected to perform. The four categories covered below have been weighted according to how important they are to user retention over time.

User experience – 30%

When it comes to user experience, we paid attention to how easy each tool is to use and implement and how many built-in support resources are available for users who have trouble getting started. Additionally, we considered how well the platform performs under certain pressures, like larger data loads, security and user control requirements, and more complex modeling and visualization scenarios. Finally, we considered the availability of the tool in different formats and how well the tool integrates with core business and data applications.

Scalability and advanced analytics compatibility – 30%

Our review also considered how well each platform scales to meet the needs of more sophisticated analytics operations and larger data processing projects. We paid close attention to how the platform performs as data loads grow in size and complexity, looking at whether user reviews mention any issues with lag times, bugs, or system crashes. We also considered what tools were available to assist with more complex analytics tasks, including AI-powered insights and support, advanced integrations and plugins, and customizable dashboards and reports.

Integrability – 20%

We considered how well each tool integrated with other software and cloud solutions from the same vendor as well as how easy it is to set up third-party integrations either via prebuilt connectors or capable APIs. In particular, we examined how well each platform integrated with common data sources outside of its vendor ecosystem, including platforms like Redshift, Snowflake, Salesforce, and Dropbox.

Cost and accessibility – 20%

For cost and accessibility, we not only focused on low-cost solutions but also on how well each solution’s entry-level solutions perform and meet user needs. We assessed the user features available at each pricing tier, how quickly pricing rises — especially for individual user licenses or any required add-ons, and whether or not a comprehensive free version was available to help users get started.

Bottom Line: Looker vs. Power BI

Microsoft’s Power BI has consistently been among the top two and three business intelligence tools on the market, recruiting and retaining new users with its balance of easy-to-use features, low costs, useful dashboards and visualizations, range of data preparation and management tools, AI assistance, and Microsoft-specific integrations. It is both a great starter and advanced data platform solution, as it offers the features necessary for citizen data scientists and more experienced data analysts to get the most out of their datasets.

Power BI tends to be the preferred tool of the two because of its general accessibility and approachability as a tool, but there are certain enterprise user needs for reporting and analytics distribution where Looker far outperforms Power BI. And for those heavily leaning on Google platforms or third-party applications, Looker offers distinct advantages to skilled analysts.

Ultimately, Looker doesn’t really try to compete head-to-head with Microsoft, because they each target different data niches and scenarios. It’s often the case that prospective buyers will quickly be able to identify which of these tools is the best fit for their needs, but if you’re still not sure, consider reaching out to both vendors to schedule a hands-on demo.

Read next: Best Data Mining Tools and Software

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Ring CX is a cloud-native contact center solution that uses RingSense AI, the AI engine currently integrated into RingCentral MVP, the company’s UC suite. Also unveiled was the general availability of RingCentral Events, formerly Hopin Events, which enables customers to hold virtual, in-person, or hybrid events.

Let’s look at the news and what it means for the larger contact center industry.

With Communications, Platform is the Way Forward

The communications industry is rapidly shifting from best of breed to best of suite. Historically, customers have had to buy product A for calling, product B for meetings, product C for contact center, and so on. The result was that customers had to pay two to five times what they needed to pay if they could consolidate to a single platform.

In response to this complex budget issue, the vendor community added more and more capabilities, creating a single platform that can deliver communications and collaboration of all forms – at one price.

There’s an obvious “one throat to choke” benefit in this approach. And in the long term, as the industry becomes AI-driven, the single stack will equate to a single data center with which to train the models.

Also see: Top Digital Transformation Companies

RingCX is Designed for Companies that Want a Digital-First Contact Center

RingCX is the company’s own cloud contact center solution. The product is designed with a modern contact center in mind, where the assumption is that customers will start interacting with the brand through some kind of digital channels such as bots, messaging, or self-service.

RingCX is infused with RingSense AI, which has three primary benefits:

• It makes the digital channels smarter so a customer can converse with a bot and have a more natural conversations.
• If the interaction needs to move to an agent, the agent is equipped with insights from that call and previous ones. During calls, the platform generates AI-powered summaries so agents can keep track of key points when talking to customers.
• After a call is completed, the solution provides detailed transcriptions and summaries, giving supervisors a clearer view of each interaction.

Beyond its AI capabilities, RingCX stands out for its rich omnichannel capabilities. It unifies multiple communication modes, such as voice, video, social media, SMS, and email, into a single, user-friendly interface. This allows agents to interact with customers through their preferred channels while maintaining a deeper understanding of the customer’s history and needs.

RingCentral is being aggressive with pricing. At $65 per agent per month, it includes various features like voice and video communication, over 20 digital channels, AI-driven summaries, and unlimited domestic inbound and outbound minutes.

The new RingCX solution complements its RingCentral Contact Center product, delivered via its partner NICE. I asked RingCentral if they plan to stop selling the product, and they said no. RingCentral Contact Center is ideally suited for large customers with complex requirements, whereas RingCX suits companies with a digital-first mindset.

RingCentral Events Makes Virtual, In-Person and Hybrid Events Easy

The second announcement, RingCentral Events, is the company’s hybrid event platform. One of the key advantages of RingCentral Events is its all-in-one nature. It can be used to register attendees and track analytics and can be accessed via a mobile app.

It also offers features like check-in systems, badge printing, and lead retrieval tools. The platform has integrations with over 40 different apps and data systems, making it flexible and easy for businesses that want to simplify event management.

Ease of use is a major selling point of RingCentral Events. Companies can use the platform to create custom, branded event pages without coding knowledge. These pages can be tailored to display specific agendas, speakers, sponsors, and other relevant content, enhancing an event’s visual appeal and overall value. Additionally, it can comfortably accommodate events with over 100,000 attendees.

Soon, RingCentral will also introduce artificial intelligence features in RingCentral Events to simplify and automate event management. One of these features is the Smart Content Generator, which uses AI to create various written materials for events, including titles, descriptions, email templates, and schedules.

Another upcoming feature is the Smart Q&A, which uses AI to sort and categorize attendees’ questions, making it easier for organizers to manage questions during events. Additionally, the Smart Clips feature is an AI video editor that creates short, engaging video clips, which can be used to market events on social media.

Also see: 100+ Top AI Companies

Bottom Line: RingCentral Events

As the latest addition to RingCentral’s suite of business communication tools, RingCentral Events complements existing products like RingCentral MVP, RingCX, RingCentral Video, RingCentral Rooms, and RingCentral Webinar. Pricing plans for RingCentral Events start at $750 annually for events with up to 100 attendees.

The collaboration industry was once filled with best-of-breed products, but now every vendor tries to deliver a full stack of tools – from video to calling to events and contact centers. Customers have no shortage of options available today and should evaluate vendors on how integrated the features are across the suite of products.

RingCX provides much tighter integration with RingCentral MVP and RingCentral quickly integrated Hopin into the platform. The ultimate winners of the platform wars will be the customer as they’ll get more features, faster at a lower cost.

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Open source intelligence is sourced from all corners of the web, and while that makes the data incredibly comprehensive, it also brings forth a large body of data that needs to be fact-checked and reviewed closely for the best possible results.

Let’s take a closer look at what open-source intelligence is, how it works, and how you can apply this type of intelligence to your business operations most effectively.

What Is Open Source Intelligence?

Open source intelligence is a type of data-driven intelligence that scours the internet and other public sources for information that’s relevant to a user’s query or search. Most often, OSINT is used to strategically collect information about a particular individual, group of people, organization, or other public entity.

Historically, OSINT developed before the internet and was a military espionage technique for finding relevant information about military enemies in newspapers, radio broadcasts, and other public data sources. While most data sources used for OSINT today are online or somehow digitized, OSINT analysts still have the option to collect physical data from public, open sources.

Also see: Top Data Visualization Tools

Passive vs. Active OSINT

Passive and active OSINT are both viable open source intelligence collection methods with different amounts of hands-on activity and in-depth research required.

With passive OSINT, users most often complete a simple search engine, social media, or file search or look at a website’s or news site’s homepage through a broad lens. They aren’t actively trying to collect highly specific information but rather are unobtrusively looking at the easiest-to-find, top-of-the-stack intelligence available. With this intelligence collection method, the goal is often to collect useful information without alerting targets or data sources to your intelligence collection activities.

When practicing active OSINT, the methods tend to be more intrusive and involved. Users may complete more complex queries to collect obscure intelligence and metadata from databases and network infrastructure, for example. They also might fill out a form or pay to get through a paywall for more information.

In some cases, active OSINT may even involve reaching out directly to sources for more information that is not publicly available or visible. While active OSINT is more likely to give users real-time, in-depth information than passive OSINT, it is much more difficult to do covertly and may lead you to legal troubles if your data collection methods aren’t careful.

Open Source Intelligence Data Sources

Open source intelligence can be sourced from any public dataset or property. These are some of the most common OSINT data sources from across the web:

• Social media platforms
• Public-facing websites
• News media
• Academic and scientific studies
• Internet of Things databases
• Business directories
• Financial reports
• Images and image libraries
• Public records, both digital and physical

Also see: Best Data Analytics Tools 

How Does Open Source Intelligence Work?

For individuals and organizations that want to take advantage of open source intelligence, a simple way to get started is with a search engine query. Often, asking the right question about the demographic information you need is the first step to finding relevant open source data entries that can lead to more detailed information.

Beyond using search engines for internet-wide data searches, you can also refine and focus your search on specific data platforms or databases, such as a certain social media platform. Depending on your goals and experience, you may also benefit from analyzing open source threat intelligence feeds and other sources that frequently update massive amounts of data.

If your data collection and analysis goals require you to work with big data sources like databases, data lakes, or live feeds, manual searches and research are ineffective. To quickly process and sort through large amounts of intelligence, you’ll want to consider investing in a web scraping or specialized OSINT tool that can automate and speed up the data analysis process.

OSINT Use Cases

Have you ever “Facebook stalked” someone you just met or Google searched your family’s last name to see what pops up? Both of these are simple examples of how even individuals practice a simplified form of open source intelligence in their daily lives.

Businesses, too, may collect OSINT without realizing it, but in most cases, they are collecting this kind of intelligence for a distinct competitive advantage or cause. Here are some of the most common OSINT use cases in practice today:

• Threat intelligence, vulnerability management, and penetration testing: Especially when used in combination with more comprehensive threat intelligence platforms, open source intelligence and data collection can give security analysts and professionals a more comprehensive picture of their threat landscape, any notable threat actors, and historical context for past vulnerabilities and attacks.
• Market research and brand monitoring: If you want to get a better look at both quantitative purchase histories and overall brand sentiment from customers, OSINT is an effective way to collect broad demographic intelligence about how your brand is performing in the eyes of the consumer. For this particular use case, you may conduct either passive or active OSINT in social media platforms, user forums, CRMs, chat logs, or other datasets with customer information.
• Competitive analysis: In a different version of the example above, you can complete OSINT searches on competitor(s) to learn more about how they’re performing in the eyes of customers.
• Geolocation data sourcing and analysis: Publicly available location data, especially related to video and image files, can be used to find an individual and/or to verify the accuracy of an image or video.
• Real-time demographic analyses over large populations: When large groups of people are participating in or enduring a major event, like an election cycle or a natural disaster, OSINT can be used to review dozens of social media posts, forum posts, and other consumer-driven data sources to get a more comprehensive idea of how people feel and where support efforts — like counterterrorism or disaster relief response, for example — may be needed.
• Background checks and law enforcement: While most law enforcement officials rely on closed-source, higher intelligence feeds for background checks and identification checks, OSINT sources can help fill in the blanks, especially for civilians who want or need to learn more about a person. Keep in mind that there are legal limits to how open source intelligence can be used to discriminate in hiring practices.
• Fact-checking: Journalists, researchers, and everyday consumers frequently use OSINT to quickly check multiple sources for verifiable information about contentious or new events. For journalistic integrity and ethical practice, it’s important to collect information directly from your sources whenever possible, though OSINT sources can be a great supplement in many cases.

Also read: Generative AI: 15 Enterprise Use Cases You Can Implement

10 OSINT Tools and Examples

Particularly for passive OSINT and simple queries, a web scraping tool or specialized “dork” query may be all that you need. But if you’re looking to collect intelligence on a grander scale or from more complex sources, consider getting started with one or several of the following OSINT tools:

1. Spyse: An internet asset registry that is particularly useful for cybersecurity professionals who need to find data about various threat vectors and vulnerabilities. It is most commonly used to support pentesting.
2. TinEye: A reverse image search engine that uses advanced image identification technology to deliver intelligence results.
3. SpiderFoot: An automated querying tool and OSINT framework that can quickly collect intelligence from dozens of public sources simultaneously.
4. Maltego: A Java-based cyber investigation platform that includes graphical link analysis, data mining, data merging, and data mapping capabilities.
5. BuiltWith: A tool for examining websites and public e-commerce listings.
6. theHarvester: A command-line Kali Linux tool for collecting demographic information, subdomain names, virtual host information, and more.
7. FOCA: Open source software for examining websites for corrupted documents and metadata.
8. Recon-ng: A command-line reconnaissance tool that’s written in Python.
9. OSINT Framework: Less of a tool and more of a collection of different free OSINT tools and resources. It’s focused on cybersecurity, but other types of information are also available.
10. Various data analysis and AI tools: A range of open source and closed source data analysis and AI tools can be used to scale, automate, and speed up the process of collecting and deriving meaningful insights from OSINT. Generative AI tools in particular have proven their efficacy for sentiment analysis and more complex intelligence collection methods.

More on a similar topic: Top 9 Generative AI Applications and Tools

Pros and Cons of Open Source Intelligence

Pros of OSINT

• Optimized cyber defenses: Improved risk mitigation and greater visibility into common attack vectors; hackers sometimes use OSINT for their own intelligence, so using OSINT for cyber defense is often an effective response.
• Affordable and accessible tools: OSINT data collection methods and tools are highly accessible and often free.
• Democratized data collection: You don’t need to be a tech expert to find and benefit from this type of publicly available, open source data; it is a democratized collection of valuable data sources.
• Quick and scalable data collection methods: A range of passive and active data sourcing methods can be used to obtain relevant results quickly and at scale.
• Compatibility with threat intelligence tools and cybersecurity programs: OSINT alone isn’t likely to give cybersecurity professionals all of the data they need to respond to security threats, but it is valuable data that can be fed into and easily combined with existing data sources and cybersecurity platforms.

Cons of OSINT

• Accessible to bad actors and hackers: Just like your organization can easily find and use OSINT, bad actors can use this data to find vulnerabilities and possible attack vectors. They can also use OSINT-based knowledge to disrupt and alter intelligence for enterprise OSINT activity.
• Limitations and inaccuracies: Public information sources rarely have extensive fact-checking or approval processes embedded into the intelligence collection process. Especially if multiple data sources share conflicting, inaccurate, or outdated information, researchers may accidentally apply misinformation to the work they’re doing.
• User error and phishing: Users may unknowingly expose their data to public sources, especially if they fall victim to a phishing attack. This means anyone from your customers to your employees could unintentionally expose sensitive information to unauthorized users, essentially turning that private information into public information.
• Massive amounts of data to process and review: Massive databases, websites, and social media platforms may have millions of data points that you need to review, and in many cases, those numbers are constantly growing and changing. It can be difficult to keep up with this quantity of data and sift through it to find the most important bits of intelligence.
• Ethical and privacy concerns: OSINT is frequently connected without the target’s knowledge, which is an issue with AI and ethics. Depending on the data source and sourcing method, this information can be used to harm or manipulate people, especially when it’s PII or PHI that has accidentally been exposed to public view.

Bottom Line: Using OSINT for Enterprise Threat Intelligence

Getting started with open source intelligence can be as simple as conducting a Google search about the parties in question. It can also be as complex as sorting through a publicly available big data store with hundreds of thousands of data entries on different topics.

Regardless of whether you decide to take a passive or active approach, make sure all members of your team are aware of the goals you have in mind with open source intelligence work and, more importantly, how they can collect that intelligence in a standardized and ethical manner.

Read next: 50 Generative AI Startups to Watch in 2023

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