Here’s a little fact to wrap your head around: The second-most common reason why CX leaders tell us that their organizations invest in CX initiatives is … to improve their CX score. Yes, you read that correctly. The number two driver of customer experience (CX) investment isn’t to improve your customers’ actual experiences — it’s to improve what your customers say about their experiences. It is true that, in many cases, improving a customer’s actual experience (is it easier, more effective, more emotionally resonant than before?) will result in your CX score improving, but the cause-and-effect is shaky. We’ve all seen examples of score begging — from not-so-subtle Net Promoter Score℠ (NPS) surveys that highlight 9 and 10 in bold green to blatant statements such as “Anything less than a 9 is a failure.” Three other key gaps compound the score obsession: The skills gap: Most CX teams skew toward measurement as a core competency. Defining CX metrics is the most common core competency of CX teams we survey globally. And they are doubling down, because “defining CX metrics” is the second-most common competency that teams tell us they are adding. The technology gap: CX technology budgets focus on metrics, not action. Almost half of CX teams, and three-quarters of measurement-focused teams, use customer feedback management technology, while only one in five use technologies focused on responding and driving action, such as journey orchestration. The process gap: Teams lack processes to drive action. Only around one in five voice-of-the-customer and measurement teams tell us that they have effective or very effective processes to drive outcomes such as the ability to prioritize CX initiatives or act on insights. There’s a clear gap between the overwhelming majority of CX leaders who tell us that their executives believe their firm is customer-obsessed — and the reality of the 3% of global firms that our Customer Obsession Assessment tells us actually are. Learn How To Connect Brand And CX At CX Summit EMEA Many firms still think about acquisition and retention as two discrete, separate activities. In his recent blog post, Introducing Forrester’s Brand Experience Index — Drive Growth With Both Brand And Customer Experience, Forrester VP and Principal Analyst Dipanjan Chatterjee introduced us to the multiplicative value of combining brand and CX measurement. By cross-referencing our Brand Experience Index (BX Index) and Customer Experience Index (CX Index™) scores, he showed how brands that lead on both (i.e., that have strong customer salience, fit and trust, and that deliver experiences that are easy, effective, and emotionally resonant) see an average of 2.3x revenue uplift compared to firms that score low on both indices. We’re leaning into this concept for our CX Summit EMEA this year. On June 2–4 in London, CX, digital, and marketing leaders will come together to explore the future of customer relationships and learn how to build a total experience — one that aligns brand experience and CX to fuel sustainable growth. Through a mix of keynotes, track sessions, interactive experiences, workshops, roundtables, and more, we aim to share how to: Measure and improve total experience using Forrester’s BX Index and CX Index to drive retention and revenue. Harness AI and emerging technology to create intuitive, humanlike customer interactions. Use data-driven insights to refine design strategies and enhance digital experiences. Leverage metrics to shape a customer-centric culture, ensuring that they support transformation rather than hinder it. Be a change leader, effectively managing organizational transformation while maintaining focus on people and processes. Check out the full agenda and register to start taking action that truly benefits customers — and your business. source

By Nadia Dreid ( February 27, 2025, 7:00 PM EST) — Television commercials might be getting too loud again, the Federal Communications Commission recognized Thursday when it voted to take a look at whether its rules about commercial volume are due for an update…. 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 It’s here: OpenAI has announced the release of GPT-4.5, a research preview of its latest and most powerful large language model (LLM) for chat applications. Unfortunately, it’s far-and-away OpenAI’s most expensive model (more on that below). It’s also not a “reasoning model,” or the new class of models offered by OpenAI, DeepSeek, Anthropic and many others that produce “chains-of-thought,” (CoT) or stream-of-consciousness-like text blocks in which they reflect on their own assumptions and conclusions to try and catch errors before serving up responses/outputs to users. It’s still more of a classical LLM. Nonetheless, acording to OpenAI co-founder and CEO Sam Altman’s post on the social network X, GPT-4.5 is: “The first model that feels like talking to a thoughtful person to me. I have had several moments where I’ve sat back in my chair and been astonished at getting actually good advice from an AI.” However, he cautioned that the company is bumping up against the upper end of its supply of graphics processing units (GPUs) and has had to limit access as a result: “Bad news: It is a giant, expensive model. We really wanted to launch it to plus and pro at the same time, but we’ve been growing a lot and are out of GPUs. We will add tens of thousands of GPUs next week and roll it out to the plus tier then. (Hundreds of thousands coming soon, and I’m pretty sure y’all will use every one we can rack up.) This isn’t how we want to operate, but it’s hard to perfectly predict growth surges that lead to GPU shortages.“ Starting today, GPT-4.5 is available to subscribers of OpenAI’s most expensive subscription tier, ChatGPT Pro ($200 USD/month), and developers across all paid API tiers, with plans to expand access to the far less costly Plus and Team tiers ($20/$30 monthly) next week. GPT‑4.5 is able to access search and OpenAI’s ChatGPT Canvas mode, and users can upload files and images to it, but it doesn’t have other multimodal features like voice mode, video and screensharing — yet. Advancing AI with unsupervised learning GPT-4.5 represents a step forward in AI training, particularly in unsupervised learning, which enhances the model’s ability to recognize patterns, draw connections and generate creative insights. During a livestream demonstration, OpenAI researchers noted that the model was trained on data generated by smaller models and that this improved its “world model.” They also said it was pre-trained across multiple data centers concurrently, suggesting a decentralized approach similar to that of rival lab Nous Research. This training regimen apparently helped GPT-4.5 learn to produce more natural and intuitive interactions, follow user intent more accurately and demonstrate greater emotional intelligence. The model builds on OpenAI’s previous work in AI scaling, reinforcing the idea that increasing data and compute power leads to better AI performance. Compared to its predecessors and contemporaries, GPT-4.5 is expected to produce far fewer hallucinations (37.1% instead of 61.8% for GPT-4o), making it more reliable across a broad range of topics. What makes GPT-4.5 stand out? According to OpenAI, GPT-4.5 is designed to create warm, intuitive and naturally flowing conversations. It has a stronger grasp of nuance and context, enabling more human-like interactions and a greater ability to collaborate effectively with users. The model’s expanded knowledge base and improved ability to interpret subtle cues allow it to excel in various applications, including: Writing assistance: Refining content, improving clarity and generating creative ideas. Programming support: Debugging, suggesting code improvements and automating workflows. Problem-solving: Providing detailed explanations and assisting in practical decision-making. GPT-4.5 also incorporates new alignment techniques that enhance its ability to understand human preferences and intent, further improving user experience. How to access GPT-4.5 ChatGPT Pro users can select GPT-4.5 in the model picker on web, mobile and desktop. Next week, OpenAI will begin rolling it out to Plus and Team users. For developers, GPT-4.5 is available through OpenAI’s API, including the chat completions API, assistants API, and batch API. It supports key features like function calling, structured outputs, streaming, system messages and image inputs, making it a versatile tool for various AI-driven applications. However, it currently does not support multimodal capabilities such as voice mode, video or screen sharing. Pricing and implications for enterprise decision-makers Enterprises and team leaders stand to benefit significantly from the capabilities introduced with GPT-4.5. With its lower hallucination rate, enhanced reliability and natural conversational abilities, GPT-4.5 can support a wide range of business functions: Improved customer engagement: Businesses can integrate GPT-4.5 into support systems for faster, more natural interactions with fewer errors. Enhanced content generation: Marketing and communications teams can produce high-quality, on-brand content efficiently. Streamlined operations: AI-powered automation can assist in debugging, workflow optimization and strategic decision-making. Scalability and customization: The API allows for tailored implementations, enabling enterprises to build AI-driven solutions suited to their needs. At the same time, the pricing for GPT-4.5 through OpenAI’s API for third-party developers looking to build applications on the model appears shockingly high, at $75/$180 per million input/output tokens compared to $2.50/$10 for GPT-4o. And with other rival models released recently — from Anthropic’s Claude 3.7, to Google’s Gemini 2 Pro, to OpenAI’s own reasoning “o” series (o1, o3-mini high, o3) — the question will become if GPT-4.5’s value is worth the relatively high cost, especially through the API. Early reactions from fellow AI researchers and power users vary widely The release of GPT-4.5 has sparked mixed reactions from AI researchers and tech enthusiasts on the social network X, particularly after a version of the model’s “system card” (a technical document outlining its training and evaluations) was leaked, revealing a variety of benchmark results ahead of the official announcement. The actual final system card published by OpenAI following the leak contains notable differences, including the removal of a line that “GPT-4.5 is not a frontier model, but it is OpenAI’s largest LLM, improving on GPT-4’s computational efficiency by

Americans who go to religious services tend to worship where most other congregants and senior leaders share their race or ethnicity. Two-thirds of U.S. adults who attend religious services in person at least a few times a year (called “attenders” in this chapter) say they go to a church, synagogue, mosque, temple or other house of worship in which all or most other congregants share their race or ethnicity. Two-thirds also go to congregations where the senior religious leaders share their race or ethnicity. The new Religious Landscape Study (RLS) also asked about the religious composition of the congregation that respondents attended as children. Most people who grew up attending religious services at least a few times a year (78%) say they went to religious services in which most other congregants had the same race or ethnicity as they did. One-in-ten say that as children they went to a house of worship where most attendees were of a different race than their own. A similar share (10%) say they attended a congregation where no single racial group made up a majority. Today, half of U.S. adults who attend religious services at least a few times a year report that all or most other people in their house of worship are White, 11% say most of their fellow congregants are Hispanic, 10% say most are Black, and 4% say most are Asian. And when it comes to leadership in their houses of worship, 53% of U.S. adults who attend services at least a few times a year say that all or most of the senior leaders there are White, 11% say most are Black, 10% say most are Hispanic, and 4% say most are Asian. Additionally, 21% of attenders go to congregations where no one racial or ethnic group accounts for a majority of congregants, and 18% go to a congregation where no one racial or ethnic group makes up a majority of senior leaders. Read more about how religious service attenders answer the survey’s questions about the racial and ethnic compositions of congregations and congregational leaders. The racial and ethnic composition of congregations Two-thirds of Americans who attend religious services at least a few times a year say that all or most of their fellow worshippers are the same race or ethnicity as themselves. An additional 11% say they go to a congregation where all or most other congregants have a different race or ethnicity than they do, and 21% attend a congregation in which no single racial group makes up a majority. Most Jews (81%) worship where all or most attendees are the same race or ethnicity as themselves, as do 73% of mainline Protestants, 72% of members of the historically Black Protestant tradition, and 71% of Hindus. Muslim Americans are an exception. Just 35% of Muslim Americans say they worship in congregations where they share the race or ethnicity of all or most of their fellow worshippers. A majority of Muslims who attend religious services do so either where all or most congregants are a different race than they are (26%), or where no one racial group makes up a majority (39%). We do not discuss results for religious “nones” in this section because the vast majority of them – 88% – seldom or never attend religious services. Most of the rest say they attend religious services a few times a year (9%). Just 3% of religious “nones” say they attend religious services at least once or twice a month. The survey did not include enough interviews with Orthodox Christians or members of other religiously affiliated groups who attend religious services in person at least a few times a year to be able to show their results. The survey finds that 85% of Jewish attenders say they go to a congregation in which all or most other congregants are White, as do 70% of members of the Church of Jesus Christ of Latter-day Saints (widely known as Mormons), 70% of mainline Protestants and 61% of evangelical Protestants. Two-thirds of attenders who identify with the historically Black Protestant tradition say they go to a congregation in which all or most other people are Black or African American. And about three-quarters of Hindu attenders (77%) report that all or most of their fellow worshippers are Asian or Asian American. Nearly half of Catholic attenders (47%) go to Mass at a church in which all or most other congregants are White, while 27% of Catholic attenders go to a church that is mostly Hispanic or Latino, and 19% say they attend a church where no racial group makes up a majority. The differences across religious groups in the way respondents describe the racial and ethnic makeup of their congregations partly reflects the racial and ethnic composition of the groups themselves. For details on the racial and ethnic composition of people within these religious categories, refer to Chapter 24. The racial and ethnic composition of congregational leaders As with the racial and ethnic composition of congregants, two-thirds of Americans who attend religious services say they go to congregations in which all or most of the senior religious leaders are the same race or ethnicity as they are. Here again, Muslims are a notable exception. Fewer than half of Muslim respondents say they share the same race as most of the religious leaders at their mosque. Roughly nine-in-ten Jewish Americans who attend religious services at least a few times a year say all or most of the leaders at their congregation are White. Three-quarters of Latter-day Saints and mainline Protestants say the same, as do two-thirds of evangelical Protestants. About half of Catholics say all or most of the senior religious leaders at their parishes are White. source

Image: Envato/Weedezign_photo The U.S. Federal Bureau of Investigation has confirmed that North Korea was behind the theft of $1.5 billion worth of digital tokens from cryptocurrency exchange firm Bybit last week. This is thought to be the biggest crypto heist of all time. The FBI’s PSA about this “TraderTraitor” attack In a Public Service Announcement, the FBI referred to the attack as “TraderTraitor,” a malicious campaign linked to North Korean state-sponsored hackers targeting cryptocurrency firms. “TraderTraitor actors are proceeding rapidly and have converted some of the stolen assets to Bitcoin and other virtual assets dispersed across thousands of addresses on multiple blockchains,” it said. The FBI expected the stolen assets to be laundered and eventually converted to “fiat currency” —  money issued by a government that is not backed by a physical commodity like gold or silver. It also provided a list of Ethereum addresses the threat actors have used or are using to launder the stolen assets, which it recommends crypto organisations block. Must-read security coverage How the crypto was stolen from Bybit and its response to customers The crypto was taken during a routine internal transfer from its Ethereum coin “cold wallet,” a digital wallet typically stored offline and considered more secure, according to Bybit CEO Ben Zhou. The attacker exploited vulnerabilities in the transaction to gain access to the cold wallet, and then transferred about 401,000 ETH to an unidentified address. After the theft, the value of Ethereum fell by around 4% on Friday, leaving it worth $2,641.41 per coin. SEE: Deepfakes Can Fool Facial Recognition on Crypto Exchanges The scale of the Bybit theft surpasses the previous record crypto heist, involving the theft of $615 million of Ethereum and U.S. coins from the Ronin Network in 2022. It also exceeds the largest known non-crypto heist; Saddam Hussein’s 2003 theft of $1 billion in assets from the Iraqi Central Bank. Zhou wrote on X Sunday that Bybit has replenished its reserves since the incident through a mix of emergency loans and large deposits. The company also told clients that their funds were “safe,” and it would refund anyone affected. Bybit was founded in 2018 and reportedly counts President Donald Trump and former PayPal chief Peter Thiel among its early investors. The company says it has more than 60 million users worldwide and offers access to various cryptocurrencies. Lazarus Group suspected as responsible for the theft The Lazarus Group, a hacking organization under North Korea’s Reconnaissance General Bureau, has been identified by blockchain security experts. Blockchain analyst ZachXBT provided evidence to the blockchain analytics platform Arkham linking the attack to Lazarus, citing patterns consistent with recent North Korean cyberattacks in a post for TRM Labs. Lazarus has been active since about 2009 and has been responsible for a number of high-profile cyber attacks, including the 2017 Wannacry ransomware outbreak, which infected over 300,000 computers worldwide and caused significant disruption to the U.K.’s NHS. Estimates indicate that it cost the NHS £92 million due to disruptions in patient care. The group continues to develop new forms of malware to help it avoid detection. SEE: Ransomware Cheat Sheet: Everything You Need To Know ZachXBT also linked Lazarus’ Bybit hack to a January attack on another cryptocurrency exchange called Phemex, which lost at least $69 million, according to The Record. North Korea has been accused of multiple hacks of cryptocurrency exchanges to steal digital assets, launder the funds, and use them to finance its nuclear weapons program. In 2024, North Korean hackers stole a record $1.3 billion in digital assets, nearly doubling the $660 million they took in 2023. Cryptocurrency has become a preferred method for money laundering by criminals to cleanse their illicit funds. The authors of this news story are TechnologyAdvice staff writer Fiona Jackson and contributing writer Esther Shein. source

Slack reported an outage on February 26 and 27, with most features back up by Thursday but some issues with messaging still persisting; in particular, some custom apps and bots did not work as expected. In addition, a problem with Slack Connect meant coworkers might not get notified if someone pinged them with the @ command. According to Downdetector, about 3,000 people had reported issues at the peak of the problem. As of Thursday morning, Slack said most features had been restored. Users still experiencing trouble were advised to reload Slack (Command + Shift + R  on Mac or Ctrl + Shift + R on Windows or Linux). Slack team’s response to the outage, including Slack Connect tips The loss of functionality on Wednesday was “​​related to Events API and is a symptom of an earlier incident about trouble connecting to or loading Slack,” plus an unrelated problem with Slack Connect causing the lack of notifications, Slack said. Our workplace sysadmin team announced problems with Slack at around 1:00 p.m. EST on February 26, with some employees being unable to post messages. (My work was not disrupted.) SEE: Slack added tools for automatically building workflows, including for developers.  By Wednesday evening, Slack’s status site said, “We’re continuing to diligently work on database shard repair and have made progress on restoring affected replicas, though users may still be experiencing impact.” Later that day, Slack personnel were working on “re-processing of the backend job to attempt to serve queued Events API requests.” Slack suggested the following mitigation for users having trouble mentioning others using Slack Connect: Open the Slack desktop app, then click Help in your computer’s top menu bar. (On Windows 10, click the three horizontal lines icon in the top left of the Slack app.) Select Troubleshooting. Click Clear Cache and restart. The Slack team said the cause of the outage was “a symptom of an earlier incident,” and did not specify further. Messages, workflows, threads, and other API-related features were last to come back online. What’s hot at TechRepublic Past outages and Slack’s place in the market Slack hasn’t had a major outage like this since July 2023. Prior to that, the service went down several times in 2022 and in January 2021. Slack is a major partner in Salesforce’s Agentforce generative AI effort. Competitors to Slack include Microsoft Teams, Google Chat, and Pumble. source

As the US and China pursue dominance in the global technology race, concerns are mounting among European founders that the region’s entrenched bureaucracy is impeding its capacity for innovation and growth. The EU is going “overboard on tech regulation,” said Job van der Voort, CEO and founder of Remote, an HR tech company valued at over $3bn. “It’s stifling innovation and it’s a massive risk for Europe.” Van der Voort told TNW that many business leaders share his view. “Most entrepreneurs agree this is a huge problem,” he said.   Indeed, such concerns are being raised with growing frequency. 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! At a conference in Paris earlier this month, the likes of Mistral CEO Arthur Mensch and DeepMind founder Demis Hassabis repeatedly called for regulation in Europe that is “flexible enough” to support innovation and competitiveness. A little over a week later, Dutch software unicorn Bird took drastic steps to escape “overregulation,” announcing plans to move most of its operations out of Europe. “I think more companies will be forced to do the same [as Bird],” said van der Voort. “But the biggest impact will be at the early stage.” The founder highlighted the ongoing trend of European startups crossing the Atlantic to scale. Research by London-based VC Hoxton Ventures found that nearly all European startups with over $500mn in revenue — including Spotify, Wise, and Adyen — succeeded by winning the US market.  Van der Voort believes burdensome tech regulation is encouraging moves beyond the continent. “It’s becoming unattractive to start and maintain a business here,” he said. “That’s why I tell startups to leave Europe if they want to succeed.” Van der Voort has followed his own advice. After he and Marcelo Lebre founded Remote in 2019, the partners decided to base the company in San Francisco. “It was simply easier to start it there,” he said. Remote, which helps businesses hire and manage remote teams, has grown rapidly since then. In 2022, the company raised $300mn in a Series C round that pushed its valuation above $3bn.  “The EU needs to consider its own fate over the next decades,” warned van der Voort. “Regulation standing in the way of innovation — that makes it harder for startups and is incredibly hurtful for the economy.”   Job van der Voort is a former speaker at TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets for the event are now on sale. Use the code TNWXMEDIA2025 at the check-out to get 30% off the price tag. source

Researchers at the University of Oxford have demonstrated distributed quantum computing for the first time by connecting two separate quantum processors via a photonic network interface. By using optical fibers to entangle quantum bits in separate modules, quantum logic operations can be performed across the modules via quantum teleportation. The method makes it possible to link together small quantum units, which could potentially lead to functioning quantum computer systems on a large scale that could perform calculations in a few hours that would take today’s supercomputers several years. Quantum computing has long had a scalability problem, in that packing together the large number of qubits necessary to achieve theorized quantum processing leaps would require computers of immense size. By linking together smaller quantum devices, the researchers suggest that this vast processing scale could be achieved through a distributed network rather than a single machine. source

Many questions in the 2023-24 Religious Landscape Study (RLS) previously were asked in the 2007 and 2014 landscape studies. Measuring change over time on these questions is one key goal of the new RLS. But the 2023-24 survey was conducted using different methods from the 2007 and 2014 surveys, and this “mode switch” complicates comparisons between the results of the new survey and the findings of the previous surveys. In the new survey, we mailed invitations to a random sample of U.S. households using address-based sampling (ABS). Participants were given the option of completing the survey online, on paper, or on the phone (by calling a toll-free number we gave them). For more details, read this report’s Methodology. Overall, 60% of the respondents in the 2023-24 RLS participated online, 37% completed the survey on paper, and 3% took the survey on the phone. By contrast, both the 2007 and 2014 surveys were conducted entirely by phone. In both of those surveys, we recruited respondents using random-digit dialing, and the survey was administered by live interviewers who asked the questions and recorded the answers. Respondents answer some survey questions differently when they are talking with an interviewer (as in the 2007 and 2014 surveys) than when they participate in surveys online or on paper (as most respondents in the new survey did). This means that for some survey questions, the results we obtained in previous surveys (using an interviewer-administered mode) cannot be directly compared with the results from the new survey that we conducted using mainly self-administered modes, even if the wording of the questions is identical. But not all questions are equally subject to these “mode effects.” While testing indicates that some questions are not comparable across different modes, others can be safely compared, and still others fall somewhere in between – the data can provide basic information about the direction of trends, even if varying modes don’t produce identical results. Using a bridge study and other data to decide how to compare results To help us determine which results from the new survey are comparable with those of past surveys, we conducted a telephone bridge study using a methodological approach similar to the one we used for the 2007 and 2014 landscape studies. In the bridge study, we used a random-digit-dialing method and conducted telephone interviews with 1,519 respondents separately from the main survey. We asked these respondents the same questions that were administered to respondents in the main survey. But the 1,519 respondents in the telephone bridge study are not included in the main RLS survey; the bridge study was conducted only as a test, exclusively for the purpose of helping us determine which questions in the new survey can safely be compared with the 2007 and 2014 results, which questions clearly cannot be compared, and which fall somewhere in between. (Read about the bridge study’s Methodology.) We compared the results of the telephone bridge study with those of the main address-based sample. For each question, we examined whether the balance of opinion was similar across the differing modes (phone for the bridge study, mostly online/paper for the main survey). We also considered whether the different survey modes led to different distributions on particular response options. For example, people who completed the survey by phone in the bridge study sometimes volunteered “I don’t know” as a response, an option that is not available to respondents on the web and mail surveys. This sometimes resulted in fewer phone respondents choosing one of the substantive response options, compared with those who completed the survey via other modes. The bridge study is one important tool we used to help determine how to compare the results from the 2023-24 RLS with the results from the previous landscape studies, but it is not the only tool we used for this purpose. In addition to the bridge study, we also used other Pew Research Center analyses and experiences to help inform our decisions about whether to make comparisons between the new survey and previous RLS results. For example, we conducted an analysis in 2021 assessing whether it is possible to compare phone results with web results on key questions about religious identity, frequency of prayer, religious service attendance, and religion’s importance in people’s lives. Several RLS questions also have been asked on other recent Center surveys, which can provide additional information about comparability. We drew on all this information – the telephone bridge study, previous efforts to understand mode effects, and ongoing Pew Research Center surveys – to categorize RLS questions into three buckets, color coded as green (“go”), yellow (“caution”) and red (“stop”). Green questions produce results in the new survey that can safely be compared with the 2007 and 2014 RLS findings. There is no evidence that these questions are subject to significant mode effects. In polling lingo, they are “trendable” even though the new RLS was conducted using different modes than the previous studies. Red questions cannot be compared with the previous studies. The mode effects on these questions are relatively large. Comparing the results of these questions from the new study with previous studies could be misleading. Doing so might suggest that big changes have occurred in public opinion over the last 10 or 15 years when, in reality, the different results could be produced by the mode switch alone. Yellow questions fall between the safely trendable green questions, on the one hand, and the clearly non-trendable red questions, on the other. Yellow questions appear to be subject to modest mode effects, but they nevertheless can be cautiously compared with previous results while bearing in mind the size and direction of the mode effects on the particular question at hand.   Green: Questions we can compare with previous Religious Landscape Studies Many key questions in the new RLS can be safely compared with the 2007 and 2014 landscape studies, including the study’s core questions about religious identity (also sometimes called religious affiliation, preference or adherence). The bridge study finds little

Making decisions based on data To ensure that the best people end up in management positions and diverse teams are created, HR managers should rely on well-founded criteria, and big data and analytics provide these. By collecting and evaluating large amounts of data, HR managers can make better personnel decisions faster that are not (only) based on intuition and experience. In addition, they can use statistical methods, algorithms and machine learning to more easily establish correlations and patterns, and thus make predictions about future developments and scenarios. Viole Kastrati: “Without systematic and continuous reporting, it is almost impossible to get a complete picture of the personnel situation and make informed decisions based on it.” Kastrati – Nagarro The problem is that many companies still make little use of their data. In particular, human resources is still one of the least data-driven areas of a company, and potential is often not fully exploited. While data tends to be used in tactical-operational areas such as HR reporting and controlling, there is still room for improvement in the strategic area of people analytics. Most use master data to make daily processes more efficient and to optimize the use of existing resources. Aspects such as employee satisfaction and talent development are often neglected. This is due, on the one hand, to the uncertainty associated with handling confidential, sensitive data and, on the other hand, to a number of structural problems. As a rule, a lot of data is available in the company, but it is stored in different systems and comes from various sources, which makes it difficult to link it. “In addition, there is silo thinking in many companies. Each department evaluates its own key figures, if at all, and looks at them in isolation from others. This makes it impossible to identify any correlations,” explains Viole Kastrati, Senior Consultant SAP – BI & Analytics at Nagarro. source