By Lauren Berg ( December 20, 2024, 11:06 PM EST) — The second Texas judge to oversee litigation filed by Elon Musk’s X Corp. accusing the World Federation of Advertisers and others of conspiring to withhold advertising revenue from the company has recused himself from the case…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

According to a new report, cloud computing was worth between $9 billion and $10 billion to the Australian economy from 2022 to 2023. However, continued enterprise cloud migrations and a boom in AI computing could push its annual contribution to GDP up to $81 billion by 2029. The report, endorsed by the Australian Information Industry Association and compiled by DT Economics for Amazon Web Services, indicated that Australia has seen a 300% growth in the cloud sector’s GDP contribution in Australia since 2007. Cloud computing also supports between 47,000 and 56,000 jobs across the Australian economy, boosting productivity by 0.2%-1.0% of GDP annually. AIIA CEO Simon Bush called the cloud “a driver of national productivity and prosperity.” Cloud computing’s economic contributions are rising ADAPT Research, an Australian technology research firm, suggested that 55% of Australian organisations’ workloads will be hosted on public clouds by 2025. Meanwhile, Gartner has predicted the proportion of Australian and global workloads in the cloud will rise to 70% by 2028. The AIIA said this uptake in cloud services propelled a compound annual growth in contribution to GDP of 10% between 2007 and 2023, now representing 0.4% of GDP. The report projected the cloud industry will contribute between $68 billion and $81 billion to the nation’s GDP by 2029, accounting for over 0.5% of GDP. Many Australian jobs are connected to cloud computing The cloud computing sector supported up to 56,000 jobs in 2022-23, according to the report. This marks a significant growth from an estimated 20,000 to 23,000 jobs in 2007-08. Projections indicated that cloud-related jobs will grow to between 71,000 and 84,000 by 2028-29. SEE: Australia Could Have 200,000 AI Tech Workers by 2030 The report noted that these jobs include those employed directly by cloud service providers and many others across supply chains. The cloud will also facilitate regional job creation more broadly as more cloud storage facilities are built in urban and regional areas across Australia. More Australia coverage Additional benefits flowing to the economy from the cloud According to the report, the GDP figures or job numbers do not capture several other productivity benefits flowing from the cloud. The report said that benefits relate to more efficient use of labour and capital resources in producing goods and services because the cloud is intertwined with business. The report noted how the cloud’s ability to conserve energy leads to enhanced market access opportunities locally and globally, enhanced capabilities, cost savings, improved operational resilience, reduced cyber security risks, and reduced energy and carbon emissions. SEE: Resilience in Focus: How Australian Boards Are Preparing for CPS 230 AI supports cloud growth Artificial intelligence computing is another key and emerging benefit of the cloud for Australia. The AIIA said that cloud computing service providers support integrating emerging technologies, such as AI and machine learning, across Australia’s public and private sectors, as evidenced by the recent growth in AI. AIIA urges caution in future cloud computing regulation The Securing Australia’s Cloud Potential report was produced, in part, to caution regulators about imposing strict rules on cloud services providers. This follows regulatory recommendations made by the Australian Competition and Consumer Commission as part of its ongoing Digital Platform Services Inquiry. The ACCC recommended implementing legally binding, service-specific codes of conduct for certain designated digital platforms. These codes aim to address issues such as anti-competitive self-preferencing, tying the sale of one product to another, and exclusive pre-installation agreements. Bush explained that “Australia has at times focused on regulating technology rather than supporting innovation,” and that “we must strike a balance to safeguard citizens and customers while fostering creativity, investment, and growth.” He added that poorly designed regulations risk dampening the sector’s potential and undermining Australia’s position as a leader in the digital economy. “We need forward-thinking regulation that empowers, not encumbers, the sector to remain at the forefront of global innovation.” source

Munich-based startup Vaeridion has secured €14mn to develop an electric aircraft that it hopes will whisk passengers on short-haul routes around Europe by 2030.  “The microliner looks like a regular plane and it takes off from a runway — the only difference is that it will be powered by batteries,” Vaeridion’s co-founder and CEO, Ivor van Dartel, told TNW in an interview last month. “For operators and passengers, the experience will be essentially the same.” Berlin-based climate tech VC World Fund led the Series A investment, with participation from Project A Ventures, Vsquared Ventures, Schwarz Holding, InnovationQuarter, and angel investor Andreas Kupke.  “Our new funding will significantly accelerate development efforts, paving the way for certification-conforming prototype flights to take off in 2027, followed by a first commercial flight by 2030,” said Van Dartel. The 💜 of EU tech The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now! The news comes just a month after Vaeridion became the first general aviation manufacturer to secure a pre-application contract (PAC) with the European Union Aviation Safety Agency (EASA), in a big step towards commercial flight.  Vaeridion’s head of engineering, Markus Kochs Kämper, called it “a huge milestone” in the development of its microliner. “This initiative allows us to de-risk our core technology and the path to certifying our electric aircraft prior to submitting a type certificate application,” he told TNW at the time.   Van Dartel and Sebastian Seemann — both former Airbus and ZF engineers — co-founded Vaeridion in 2021. Their vision was to build an electric plane to replace jet-fueled aircraft on regional flights.   Preliminary tests put the range of the microliner at about 500km, said the company. In 2022, almost a third of flights in the EU covered this distance or less, according to Eurocontrol.  Vaeridion’s design is similar to existing regional aircraft, which could reduce development and manufacturing costs compared to more experimental electric vertical takeoff and landing (eVTOL) models that often require intricate propulsion systems and vertical lift capabilities.   The company has already signed up its first customers: Dutch private jet operator ASL Group, German business airline Aero-Dienst, and Danish companies Copenhagen AirTaxi and Copenhagen Helicopter.   Aero-Dienst and Vaeridion are also working together on the potential roll-out of an electric plane ambulance service for Germany’s ADAC, Europe’s largest automobile association.   “Our partnerships and market-focused strategy reflect our commitment to not only decarbonising short-haul flights across Europe but also to setting a new standard for sustainable and energy-efficient aviation at a competitive price point,” said Van Dartel. Vaeridion estimates that a trip in the microliner will cost between €150–300. The aircraft will initially serve business passengers before expanding into consumer travel, the company said.   source

As businesses grow and tech stacks become more complex, scalability remains a top issue.  “Companies face significant challenges scaling across both physical and virtual spaces. While a holistic approach to operations across regions provides advantages, it also introduces complexity,” says Dustin Johnson, CTO of advanced analytics software provider Seeq. “The cloud can assist, but it’s not always a one-size-fits-all solution, especially regarding compute needs. Specialized resources like GPUs for AI workloads versus CPUs for standard processes are essential, and technologies like Kubernetes allow for effective clustering and scaling. However, applications must be designed to fully leverage these features, or they won’t realize the benefits.”  The variety of technologies involved creates significant complexity.   “Today, a vertically integrated tech stack isn’t practical, as companies rely on diverse applications, infrastructure, AI/ML tools and third-party systems,” says Johnson. “Integrating all these components — ensuring compatibility, security, and scalability — requires careful coordination across the entire tech landscape.  A common mistake is treating scalability as a narrow technology issue rather than a foundational aspect of system design. Approaching it with a short-term, patchwork mentality limits long-term flexibility and can make it difficult to respond to growing demands.  Related:Tech Company Layoffs: The COVID Tech Bubble Bursts Following are some more things that need to scale better in 2025.  1. Processes   A lot of organizations still have manual processes that prevent velocity and scale. For example, if a user needs to submit a ticket for a new server to implement a new project, someone must write the ticket, someone receives the ticket, someone must activate it, and then something must be done with it. It’s an entire sequence of steps.  “That’s not a scalable way to run your environment so I think scaling processes by leveraging automation is a really important topic,” says Hillery Hunter, CTO and GM of innovation at IBM and an IBM Fellow. “There are a bunch of different answers to that [ranging] from automation to what people talk about, such as is IT ops or orchestration technologies. If you have a CIO who is trying to scale something and need to get permission separately from the chief information security officers, the chief risk officer or the chief data officer team, that serialization of approvals blocks speed and scalability.”  Organizations that want to achieve higher velocities should make it a joint responsibility among members of the C-suite.  Related:Things CIOs and CTOs Need To Do Differently in 2025 “You don’t just want to automate inefficient things in your organization. You really want to transform the business process,” says Hunter. “When you bring together the owners of IT, information, and security at the same table, you remove that serialization of the decision process, and you remove the impulse to say no and create a collective impetus to say yes because everyone understands the transformation is mutual and a team goal.”  2. IT operations  IT is always under pressure to deliver faster without sacrificing quality, but the pressure to do more with less leaves IT leaders and their staff overwhelmed.  “Scalability needs to be done though greater efficiency and automation and use things like AIOps to oversee the environment and make sure that as you scale, you maintain your security and resiliency standards,” says Hunter. “I think re-envisioning the extent of automation within IT and application management is not done until those processes break. It’s maybe not investing soon enough so they can scale soon enough.”  3. Architectures  In the interest of getting to market quickly, startups might be tempted to build a new service from existing pre-made components that can be coupled together in ways that “mostly fit” but will demonstrate the business idea. This can lead to unintentionally complicated systems that are impossible to scale because of their sheer complexity. While this approach may work well in the beginning, getting business approval later to completely re-architect a working service that is showing signs of success may be very difficult.  Related:How CIOs Can Contribute to Corporate Strategy “First of all, be very careful in the architectural phase of a solution [because] complexity kills. This is not just a reliability or security argument, it is very much a scalability argument,” says Jakob Østergaard, CTO at cloud backup and recovery platform Keepit. “A complex structure easily leads to situations where one cannot simply ‘throw hardware at the problem’ this can lead to frustrations on both the business side and the engineering side.”  He advises: “Start with a critical mindset, knowing that upfront investment in good architecture will pay for itself many times over.”  4. Data visibility  Organizations are on a constant mission to monetize data. To do that they need to actively manage that data throughout the entire lifecycle at scale.   “While cloud computing has gained popularity over the past few decades, there is still a lot of confusion, resulting in challenges including understanding where your cloud data lives, what it contains, and how to ensure it is properly protected,” says Arvind Nithrakashyap, co-founder and CTO at data security company Rubrik. “When it comes to scalability one blind spot is unstructured and semi-structured data.”  Unstructured data poses a security risk, as it can contain sensitive business data or personally identifiable information. And since all unstructured data is shared with end-user applications using standard protocols over TCP/IP networks, it’s a prime target for threat actors. Since most companies have hybrid and multi-cloud implementations IT needs to understand where sensitive data is, where it is going and how it is being secured.   “One of the toughest hurdles for organizations whose unstructured data portfolio includes billions of files, and/or petabytes of data, is maintaining an accurate, up-to-date count of those datasets and their usage patterns,” says Nithrakashyap. “[You need to understand] things [such as] how many files [exist], where they are, how old they are, and whether they’re still in active use. Without reliable, up-to-date visibility into the full spectrum of critical business files, your organization can easily be overwhelmed by the magnitude of your data footprint, not knowing where critical datasets

By Michael Ellenberger ( December 20, 2024, 5:45 PM EST) — As 2024 draws to a close, several crucial developments — some aimed at modernizing long-standing legal practices, others addressing emerging challenges — have reached patent law…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Two years on from the public release of ChatGPT, conversations about AI are inescapable as companies across every industry look to harness large language models (LLMs) to transform their business processes. Yet, as powerful and promising as LLMs are, many business and IT leaders have come to over-rely on them and to overlook their limitations. This is why I anticipate a future where specialized language models, or SLMs, will play a bigger, complementary role in enterprise IT. SLMs are more typically referred to as “small language models” because they require less data and training time and are “more streamlined versions of LLMs.” But I prefer the word “specialized” because it better conveys the ability of these purpose-built solutions to perform highly specialized work with greater accuracy, consistency and transparency than LLMs. By supplementing LLMs with SLMs, organizations can create solutions that take advantage of each model’s strengths. Trust and the LLM ‘black box’ problem LLMs are incredibly powerful, yet they are also known for sometimes “losing the plot,” or offering outputs that veer off course due to their generalist training and massive data sets. That tendency is made more problematic by the fact that OpenAI’s ChatGPT and other LLMs are essentially “black boxes” that don’t reveal how they arrive at an answer.  This black box problem is going to become a bigger issue going forward, particularly for companies and business-critical applications where accuracy, consistency and compliance are paramount. Think healthcare, financial services and legal as prime examples of professions where inaccurate answers can have huge financial consequences and even life-or-death repercussions. Regulatory bodies are already taking notice and will likely begin to demand explainable AI solutions, especially in industries that rely on data privacy and accuracy. While businesses often deploy a “human-in-the-loop” approach to mitigate these issues, an over-reliance on LLMs can lead to a false sense of security. Over time, complacency can set in and mistakes can slip through undetected. SLMs = greater explainability Fortunately, SLMs are better suited to address many of the limitations of LLMs. Rather than being designed for general-purpose tasks, SLMs are developed with a narrower focus and trained on domain-specific data. This specificity allows them to handle nuanced language requirements in areas where precision is paramount. Rather than relying on vast, heterogeneous datasets, SLMs are trained on targeted information, giving them the contextual intelligence to deliver more consistent, predictable and relevant responses. This offers several advantages. First, they are more explainable, making it easier to understand the source and rationale behind their outputs. This is critical in regulated industries where decisions need to be traced back to a source.  Second, their smaller size means they can often perform faster than LLMs, which can be a crucial factor for real-time applications. Third, SLMs offer businesses more control over data privacy and security, especially if they’re deployed internally or built specifically for the enterprise. Moreover, while SLMs may initially require specialized training, they reduce the risks associated with using third-party LLMs controlled by external providers. This control is invaluable in applications that demand stringent data handling and compliance. Focus on developing expertise (and be wary of vendors who overpromise) I want to be clear that LLMs and SLMs are not mutually exclusive. In practice, SLMs can augment LLMs, creating hybrid solutions where LLMs provide broader context and SLMs ensure precise execution. It’s also still early days even where LLMs are concerned, so I always advise technology leaders to continue exploring the many possibilities and benefits of LLMs.  In addition, while LLMs can scale well for a variety of problems, SLMs may not transfer well to certain use cases. It is therefore important to have a clear understanding upfront as to what use cases to tackle.  It’s also important that business and IT leaders devote more time and attention to building the distinct skills required for training, fine-tuning and testing SLMs. Fortunately, there is a great deal of free information and training available via common sources such Coursera, YouTube and Huggingface.co. Leaders should make sure their developers have adequate time for learning and experimenting with SLMs as the battle for AI expertise intensifies.  I also advise leaders to vet partners carefully. I recently spoke with a company that asked for my opinion on a certain technology provider’s claims. My take was that they were either overstating their claims or were simply out of their depth in terms of understanding the technology’s capabilities.  The company wisely took a step back and implemented a controlled proof-of-concept to test the vendor’s claims. As I suspected, the solution simply wasn’t ready for prime time, and the company was able to walk away with relatively little time and money invested.  Whether a company starts with a proof-of-concept or a live deployment, I advise them to start small, test often and build on early successes. I’ve personally experienced working with a small set of instructions and information, only to find the results veering off course when I then feed the model more information. That’s why slow-and-steady is a prudent approach. In summary, while LLMs will continue to provide ever-more-valuable capabilities, their limitations are becoming increasingly apparent as businesses scale their reliance on AI. Supplementing with SLMs offers a path forward, especially in high-stakes fields that demand accuracy and explainability. By investing in SLMs, companies can future-proof their AI strategies, ensuring that their tools not only drive innovation but also meet the demands of trust, reliability and control.  AJ Sunder is co-founder, CIO and CPO at Responsive. DataDecisionMakers Welcome to the VentureBeat community! DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation. If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers. You might even consider contributing an article of your own! Read More From DataDecisionMakers source

The European Commission has approved NVIDIA’s proposed acquisition of Run:ai, an Israel-based provider of a compute management platform. It is pushing for Apple to enhance interoperability between iOS and third-party devices. NVIDIA supplies GPUs for data centres, while Run:ai supplies GPU orchestration software. These products must be compatible, and it was thought that the acquisition could result in the companies intentionally hampering their respective products’ compatibility with competitors. However, the Commission’s investigation found that NVIDIA could not prevent its GPUs from being compatible with the orchestration software of Run:ai’s competitors due to the widespread nature of tools that ensure such compatibility. Furthermore, Run:ai lacks a dominant position in the GPU orchestration software market, as many equivalent alternatives are available or can be built in-house. This led to the deal being approved unconditionally. NVIDIA’s purchase of Run:ai intended to improve customer satisfaction, efficiency Big Tech firms are rapidly investing in young AI startups to gain early control and capitalise on the AI boom. Notably, this can be seen through partnerships such as Microsoft and OpenAI, NVIDIA and Inflection AI, and Google and Anthropic. However, such collaborations can lead to market dominance, making it more difficult for other independent companies to get funding, attract talent, or compete with the advanced technology and reach of the big players. Innovation within AI specifically depends only on a few elements, with GPUs being one of them. NVIDIA announced its plans to buy Run:ai in April “to help customers make more efficient use of their AI computing resources.” Run:ai’s platform dynamically allocates GPU resources, whether on-premises, in public clouds, or at the edge, allowing companies to get the most out of their hardware and reduce operational costs. “Together with Run:ai, NVIDIA will enable customers to have a single fabric that accesses GPU solutions,” NVIDIA said in the acquisition announcement. The two companies have been working together since about 2020. The deal is worth $700 million, according to TechCrunch, and NVIDIA does not currently have plans to change Run:ai’s business model. Initially, Italy flagged the deal to the E.U. Merger Regulation, which allows for mergers that don’t have an E.U. dimension but could impact trade and competition within the region. While it did not meet the E.U.’s or Italy’s turnover thresholds, at the time, the Italian competition authority determined that the acquisition either posed concrete risks to competition or met other conditions outlined in the Italian Competition Act. SEE: UK Probes Alphabet’s Partnership With Anthropic Over Competition Concerns What’s hot at TechRepublic EU continues to hold Apple accountable, proposing interoperability measures for Digital Markets Act compliance On Dec. 19, the Commission proposed measures to enhance interoperability between Apple’s iOS and iPadOS and third-party devices, which is required under the DMA. Apple has expressed concerns that granting access to its operating system could compromise user privacy. The Commission’s recommended measures include improving compatibility between iOS and features of devices such as smartwatches and earbuds. These features include notifications, automatic Wi-Fi connections, AirPlay, AirDrop, and automatic Bluetooth audio switching. The authority also suggests that Apple make its process for developers to request interoperability within iOS and iPadOS features more transparent and predictable. This involves providing clear information about its internal features and timely status updates for requests. Apple says measures will impact privacy, security In response to the measures, Apple published a document outlining how granting access to its technology stack and, thus, user data could compromise privacy and security. It also highlights how Meta Platforms has made 15 requests for access to Apple’s software tools, including messages, iPhone mirroring, and connectivity to all a user’s Apple devices, under the DMA. “If Apple were to have to grant all of these requests, Facebook, Instagram, and WhatsApp could enable Meta to read on a users device all of their messages and emails, see every phone call they make or receive, track every app that they use, scan all of their photos, look at their files and calendar events, log all of their passwords, and more,” Apple wrote. “This is data that Apple itself has chosen not to access in order to provide the strongest possible protection to users.” Apple also highlighted that Meta “has been fined by regulators time and again for privacy violations.” In 2019, Meta agreed to pay a $5 billion penalty to the U.S. Federal Trade Commission to settle an investigation into privacy, including the unauthorised sharing of user data with third parties. It was also fined €1.2 billion in 2023 by the Irish Data Protection Commission for violating GDPR. SEE: Meta Offers Less Personalised Ads for EU Users to Appease Regulators However, Meta has not taken this lying down. Meta Communications Director Andy Stone posted on X: “Here’s what Apple is actually saying: they don’t believe in interoperability. In fact, every time Apple is called out for anticompetitive behavior, they defend themselves on privacy grounds that have no basis in reality.” The Commission is now collecting feedback on its proposed measures, which may impact the final set that is put to Apple. Apple’s ongoing tussle with the Digital Markets Act The DMA has been a point of contention for Apple since it was enacted in September 2022. On Nov. 4, the Commission announced its investigation into whether Apple’s iPadOS operating system complies with the legislation. The DMA’s requirements apply only to the 24 core platform services offered by the seven “gatekeeper” companies, including Alphabet, Amazon, Apple, Booking, ByteDance, Meta, and Microsoft. The gatekeepers have a major economic impact in the E.U. and more than 45 million monthly users in the region or more than 10,000 yearly business users for at least three years. iPadOS, along with the App Store, Safari, and iOS, is on the list of core platform services as it provides “an important gateway for business users to reach end users.” However, the platforms must comply with the DMA’s requirements. iPadOS users should be able to choose their default web browser, use third-party app stores, and explore features with non-Apple accessories such as

By Ivan Moreno ( December 20, 2024, 9:11 PM EST) — Ahead of a trial in February in Oregon federal court, Nike Inc. and Converse Inc. on Thursday blasted trade secret theft allegations involving an anti-counterfeiting system from Valmarc Corp., saying that Valmarc failed to protect its claimed secrets, that the technology at issue has been around for years and that the company’s complaint is time-barred…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

In theory, telemarketing lists are great. Pay for a spreadsheet full of leads instead of doing the insanely labor-intensive work of getting that information yourself. Any decent call center dialer enables a rep to have a hundred conversations a day, sometimes much more. Keeping a hungry rep busy full-time is going to require thousands of leads. For a team, you need an even longer telemarketing list to draw from. List-building is hard. And yes, I know about all the Google Maps scrapers and AI lead-gen tools out there. Some are amazing, but building a list of contacts that is actually going to help a sales team make money still takes a high-degree of human involvement. I have paid people $5k per month (sometimes much more) for list-builder roles. So the prospect of a telemarketing list that has all that research and verification done ahead of time is really attractive. I understand how buying business leads works, and the labor that goes into creating these lists. $25k for a telemarketing list? If the volume and quality of leads is good, that price can easily pencil out for me. But the list has to be good. It has to be full of fresh, relevant contacts, and free of potential legal exposure for my agents and organization. The truth is that not every telemarketing list hits these requirements. Even a good list that has aged or been recycled is going to cause problems. 1 RingCentral RingEx Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Hosted PBX, Managed PBX, Remote User Ability, and more 2 Talkroute Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Call Management/Monitoring, Call Routing, Mobile Capabilities, and more The risks of buying low-quality telemarketing lists Buying low-quality telemarketing lists just isn’t worth it. There are too many serious risks if you accidentally use a bad one: Wasted resources: The information on these lists often isn’t worth what you pay for them, and you can spend a lot of money on outdated, useless information. Unproductive outreach: If your agents are spending all their time calling numbers that are out of service or irrelevant for your needs, that’s time they’re not spending calling actual leads or customers. A negative impact on your business’s reputation: Everyone hates spam calls, and that’s exactly what your company’s calls will feel like if you work from a bad list. The risk to your reputation just isn’t worth it. Even the best cold call script in the world won’t help a rep who is calling a list of dead leads. A low-quality list puts reps in a no-win situation and can easily lead to call center burnout. Using a bad list also comes with a number of legal and financial risks. These lists often have numbers that are on the National Do Not Call (DNC) Registry, and if you call one of these numbers, your company can be held liable for up to $50,120 per call. That’s right. Over $50,000 for one phone call. This is true even if you make the call using an outbound dialer, so you need to be super cautious about who you’re calling. If you continually call numbers on the DNC registry, the Federal Trade Commission (FTC) can also sue your business. Signs of a garbage telemarketing list So how do you know if you’ve got your hands on a bad list? Here are some signs. Incomplete or inconsistent contact information: If the list is missing data or has inconsistently formatted entries, it’s likely to be a bad list. This isn’t always true, but it’s not a great sign, and can create issues or extra work when you import/export the list. Outdated info: Sometimes you can find a timestamp that indicates the data is old. Often you will find addresses of stores that have moved locations, people who have switched employers, and other details that can clue you into when the list was built. High bounce rates: All lists are going to have some level of bounce rate, but if the list you’re looking at has a bounce rate over 10%, you shouldn’t buy it. Low response rates: Similarly, if the list you’re looking at has lower than average response rates, leave it alone. Anything lower than 10% is a red flag. Duplicate entries: This is a sign that the list hasn’t been reviewed or cleaned in a long time, if at all, and is likely of lower quality. Be on the lookout for names that might not immediately appear to be duplicates. For instance, it may include the same contact with their middle name in one entry and without it in another. Irrelevant or unqualified contacts: Some people go into the process of buying telemarketing lists thinking that any batch of contacts is better than no contacts, but this isn’t true. If the list you’re looking at has a bunch of people who have never indicated any interest in your industry or company, leave it alone. Poor data sources: The quality of data sourcing varies widely from company to company, so do some digging and see if you can find out where your prospective list’s data comes from. The better ones have data from verifiable sources, like networking platforms, business websites, and utility lists. The lists you don’t want either won’t list their data sources at all, or just list vague, unverifiable sources. Lack of segmentation: Low quality telemarketing lists are a free for all — anybody’s contact info can be on them, regardless of who they are and what they’re interested in. Avoid any lists that don’t allow for customer segmentation. Any good vendor in this space is going to give you tools to make sure that your list is populated with contacts that fit your industry. For instance, if you’re fundraising for a nonprofit, a good