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The top 10 web3 impacts on traditional industries due to decentralization are enlisted in this article

The Top 10 Web3 Impacts on Traditional Industries Due to Decentralization, it can disrupt and alter sectors ranging from banking and art to healthcare and education. Web3 has had a tremendous influence on the financial industry. Blockchain-enabled Decentralized Finance (DeFi) protocols enable peer-to-peer lending, borrowing, and trading without the need for middlemen.

1. Decentralized Finance (DeFi): Web3 is revolutionizing the financial sector by introducing DeFi protocols that enable peer-to-peer lending, borrowing, and trading without the use of middlemen. This upends established banking and investing paradigms by giving customers greater control over their cash and allowing them to earn higher profits.

2. Digital Art: The introduction of non-fungible tokens (NFTs) and blockchain-based digital art markets has transformed the art business. This decentralized technique allows artists to monetize their work without the need for intermediaries while also ensuring the arts validity and provenance.

3. Supply Chain Management: Web3 is changing supply chain management by offering a transparent and decentralized ledger of all transactions, allowing for increased supply chain transparency and efficiency.

4. Cybersecurity: lockchain-based decentralized security protocols are more secure and less resistant to attack than centralized ones. This has the potential to disrupt existing cybersecurity approaches, providing people and companies with enhanced safety.

5. Education: Decentralized education systems increase access to education while also introducing new financial options for instructors. Peer-to-peer learning and credential verification can be facilitated via Web3-enabled applications, upsetting established education structures.

6. Healthcare: Decentralized healthcare solutions based on blockchain technology provide improved security and transparency in the exchange and management of medical data. This innovation has the potential to completely revolutionize the healthcare business by improving patient outcomes and lowering costs.

7. Digital Identity: Web3 enables self-sovereign identification, giving people authority over their digital identities and data. This has the potential to disrupt established identity verification approaches while also providing consumers with improved privacy and security.

8. Social Media: Decentralized social media platforms challenge established business models by giving users more control and ownership over their data. This can change the advertising business while also providing consumers with improved privacy and security.

9. Real Estate: Traditional real estate models are being disrupted by Web3-enabled platforms that enable peer-to-peer property transactions and eliminate the need for intermediaries. This disruption can completely revolutionize the real estate sector by making it more accessible and inexpensive to individuals.

10. Governance: Through the usage of DAOs, Web3 enables decentralized decision-making. (Decentralized autonomous organizations). This has the potential to upend existing governance paradigms by allowing for more participative and democratic decision-making processes.

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Top 10 Web3 Impacts on Traditional Industries Due to Decentralization - Analytics Insight

Decentralized trials (DCTs) are not a novel concept, but the disruption of nearly68 percent of clinical trials during the height of the pandemic led to more widespread interest in and adoption of hybrid and virtual trial models.Studies have shown that DCTs can lead to shorter development cycle times, lower clinical trial screen failure rates, and fewer protocol amendments. With wider DCT adoption, however, comes an increase in data sources (particularly external data sources), leading to a surge in data volume. Good data management is crucial to control this. From acquisition and analysis to data cleaning and statistical processes, data managers are the stewards responsible for guiding a modern data strategy amid the perfect storm of growing data complexity, digitization initiatives, and ever-increasing pressure to accelerate timelines.

In the past, data management was often siloed, focused on cleaning and querying listings of electronic data capture (EDC) data. However, non-EDC and external data sources now contribute significantly to overall data volume. The percentage of data coming from outside EDC continues to rise, while a rise in outsourced models has prompted the data management role to become more oversight focused.

As DCTs become more widely adopted and as the volume of disparate data continues to grow, data management processes will become even more complex. An Industry Standard Researchsurvey in 2019 revealed that 38 percent of pharma and contract research organizations anticipated DCTs to make up a large portion of their portfolios, and 48 percent expected trials to operate with the majority of activities taking place from the participants homes. When revisiting the same questions only one year later, all of the respondents anticipated decentralized trials would make up a significant portion of their research profiles.

Although remote participation is pleasing for patients, it results in even greater data source volume and variety, which is difficult for clinical trial teams to manage.Research from the Tufts Center for the Study of Drug Development in 2019 found 75 percent of life sciences organizations still using SAS and Excel to integrate and analyze data. Over 80 percent of respondents reported data management activities as time consuming and labor intensive.

The same study also found that over two-thirds of clinical trial sponsors were using or piloting at least four types of data. The number of sources has nearly doubled since then and will continue to rise as DCT models are widely operationalized. A 40 percent increase in last patient, last visit (LPLV) to database lock cycle times for companies with five or more data sources was reported; the study concluded that contending with disparate data sources was contributing to longer database lock cycle times. In our services organization, trials frequently average eight or more data sources but many include over 15!

The trends that contributed to the Tufts study findings have only accelerated since the onset of the pandemic, which means one thing: data chaos. If the industry doesnt adopt new approaches, data management will only get more challenging.

Identifying and creating a data strategy roadmap in the midst of these growing pains can present a challenge, but it is essential, if you want your organization to be ready to face the future. The increased adoption of virtual and DCT approaches to clinical trials necessitates a balance between the use of advanced solutions that connect trials with a greater number of patients, and maintaining efficient, high-quality data review and analysis. Improving the overall patient experience is a motivating factor for DCTs, as is easing the burden of traveling to and from sponsor sites. The problem is that many organizations lack the infrastructure to accommodate the shift.

Operational leaders plagued by oversight and monitoring challenges in DCTs need methods to streamline and standardize data from increasingly non-traditional sources. However, there are now a number of data solutions available in the industry that can help. Below are just two examples of how companies are using data management platforms to help manage more external data streams.

With an increasing volume of external data streams, Bristol Myers Squibb (BMS) sought out a data management platform that would integrate with and support its current EDC platform, while also supporting data curation and aggregation. Implementing the platform streamlined clinical data flow, providing quicker access to clean data, streamlined data acquisition, and mapping and standardization all of which resulted in faster access to data by downstream teams. The platform alleviated pain points experienced with BMS previous infrastructure by compiling all data into a unified source, giving the company the ability to create cross-study analytics reports for deeper insights.

A second example:Karyopharm Therapeutics worked on randomized clinical trials with hospital patients suffering from severe COVID-19 it was the first study of an XP01 inhibitor in patients with viral infections. To support rapid data collection, cleaning, and review for this program, Karyopharm partnered with a data management platform, working closely to build a fully validated database to collect data from physicians and patients in just 15 days. This accelerated timeline enabled Karyopharm to meet the first patient milestone in its critical research initiative.

Cloud-based centralized data management platforms allow clinical trial teams to manage their data more efficiently, mitigate costs, minimize timeline delays, and improve cycle timelines. Put simply, cloud-based platforms modernize data infrastructure by compiling data sources into a unified source of truth. By implementing a cloud-based platform with expert data configuration, management, and statistical analysis, some companies have seen up to a50 percent decrease in cycle time was experienced from LPLV to database lock in 2021.

In short, the right data management tools facilitate data transformation, delivering consistent real-time updates and allowing researchers to analyze data faster and uncover insights needed for critical decision making. Moreover, identifying areas and opportunities to pivot earlier in the trial process can help prevent avoidable delays. To keep up with the evolving clinical trial landscape, companies must employ a modern strategy, which requires three core elements: an interoperable approach to DCTs and other non-traditional trial models, investment in resources to ease the data management burden, and the ability to generate meaningful insights from a good data platform.

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Pharma Clinical Trials: Decentralization and Digital Trends - The Medicine Maker

As of February 2023, over 44.15 million unique addresses have a non-zero balance of Bitcoin (BTC). While this may seem impressive, lets face it blockchain technology has come a long way since Bitcoins inception in 2009.

Bitcoin addresses with a non-zero balance. Source: Glassnode

However, as the technology continues to evolve and gain mainstream adoption, scalability remains one of the biggest challenges facing the industry. Bitcoin and Ethereum, two of the largest blockchain networks, are highly decentralized, with thousands of nodes operating on each network (17,553 nodes for Bitcoin and 7,099 nodes for Ethereum as of April 14, 2023).

Ethereum mainnet statistics. Source: Ethernodes

While this decentralization provides greater security, it also results in slower transaction speeds and scalability issues due to the significant computational resources required to maintain the continuously growing sum of nodes.

Hence, the blockchain trilemma, coined by Vitalik Buterin, suggests that blockchains can only have two out of three properties: scalability, security and decentralization. As a result, this fundamental trade-off represents a significant barrier to the widespread adoption of blockchain technology.

There are two primary strategies that have been introduced to tackle the scalability challenge: layer-1 (L1) and layer-2 (L2) solutions. While L1 solutions seek to optimize the base layer of a blockchain, L2 solutions provide an additional layer on top of the base layer to facilitate faster and more affordable transactions. Needless to say, this has sparked an ongoing battle between the two approaches as each demonstrates unique strengths and weaknesses.

Layer-1 blockchains, such as Bitcoin and Ethereum, are designed to optimize the foundational layer of a blockchain protocol to increase transaction throughput and reduce fees. Their maximum capacity is often limited by network congestion and other factors, so L1 scaling solutions directly extend the blockchain protocol to improve scalability.

A prominent example of this is the introduction of Ethereum 2.0 and the subsequent development of (dank) sharding. Sharding aims to increase Ethereums transaction processing speed and reduce fees by splitting the network into smaller, more manageable shards. Each shard can then process transactions in parallel, significantly increasing the networks overall speed.

Layer-2 blockchains, on the other hand, refer to a network or technology that operates on top of an underlying blockchain protocol with the aim to improve scalability too. The idea behind L2s is to shift transactions from the base layer blockchain to an adjacent system architecture that is able to process the majority of the data and then report back to the base blockchain to finalize the result.

For instance, Ethereum is a layer-1 network, and a number of layer-2 solutions have been built to improve transaction speeds on the Ethereum network, including Polygon (MATIC), Optimism (OP) and Arbitrum (ARB).

Undoubtedly, the scalability battle has come to the forefront with recent developments in L1 and L2 blockchains. While this may be the case, understanding the differences between L1 and L2 blockchain networks is crucial to gain insight and distinguish the primary differences between both layers.

Layer-1 blockchains and layer-2 scaling solutions differ not only in their purpose but also in their fundamental design and architecture. L1 blockchains are designed to be self-sufficient, meaning that all the necessary layers for data availability, consensus, and execution are integrated into a single system. This design is intended to provide the security, decentralization, and immutability, that are the hallmarks of blockchain technology.

In contrast, layer-2 scaling solutions are designed to enhance the performance of L1 blockchains rather than operate as independent blockchains. Layer-2 scaling solutions use off-chain techniques such as state channels, nested blockchains, rollups and sidechains to process transactions faster and more efficiently. In this way, layer-2 scaling solutions can increase the transaction throughput of L1 blockchains without compromising their security and decentralization.

Another significant difference between L1 and L2 scaling solutions lies in their scalability methods. L1 blockchains depend on various techniques such as consensus mechanism changes, chain forking and sharding to boost their transaction throughput. While these methods can improve transaction speeds, they can also lead to network congestion, security risks and fragmentation. L2 scaling solutions, on the other hand, process transactions off-chain, allowing for increased speed and efficiency while still relying on the primary network for security and decentralization. This approach reduces the risk of network congestion, minimizes fragmentation and enhances the overall performance of the blockchain ecosystem.

The Nakamoto coefficient is an important metric to consider when evaluating the level of decentralization in a blockchain network. It is crucial to consider the trade-off between scalability and decentralization when measuring up the difference between L1 and L2 solutions.

Often, L1 solutions such as Near protocol (NEAR) or Solana (SOL) have a higher coefficient because they offer a high degree of decentralization due to their reliance on a large number of validators. On the other hand, L2 solutions such as Opside or zkSync could offer improved scalability through the use of off-chain processing, but in turn, would be less decentralized due to their reliance on a smaller set of validators.

The ongoing battle between L1 and L2 solutions has its fair share of pros and cons. While L1 blockchains offer superior security and decentralization, they suffer from scalability issues. In contrast, L2 solutions offer scalability and lower fees, but may come at the cost of compromising the security and decentralization of the underlying blockchain.

Evidently, L2 solutions are not a one-size-fits-all solution to the scalability challenge. They rely on the base layers security and decentralization, and if the base is compromised, it could affect the very foundation of the layer-2 solutions in question.

Needless to say, as blockchain technology continues to mature, the outcome of this showdown will likely determine the path forward for scaling the technology to meet the demands of real-world applications. In the meantime, it is vital for both L1 and L2 solutions to work together to effectively address the scalability challenge.

Digi516 has been a crypto researcher and NFT enthusiast for almost a decade, with experience in educating and managing several crypto communities. Now, as head of community lead at XGo, Digi516 is on a mission to onboard the next 100 million users to Web3 and empower sovereign financial freedom.

Disclaimer. Cointelegraph does not endorse any content or product on this page. While we aim at providing you with all important information that we could obtain, readers should do their own research before taking any actions related to the company and carry full responsibility for their decisions, nor can this article be considered as investment advice.

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Moving beyond the blockchain trilemma: L1 vs. L2 - Cointelegraph

The rise of digital subscription businesses has revolutionized the way we consume products and services. From entertainment to health and wellness, people want tailored experiences that can be delivered right to their phones or inboxes. But a looming global recession, unstable markets, rising inflation and cost of living has caused many people to cut back on subscriptions, prompting companies to focus on retention over acquisition.

Our conversations with more than 20 leaders in consumer subscriptions organizations over the past few months underscored the need for this strategy change, but also shone a spotlight on the internal impact these shifts have on priorities, organizational structures and leadership. In this article, we explore key actions leaders need to take now to ensure their teams are able to capitalize on this strategy shift instead of being hampered by it.

While centralized structures are often the most cost-effective and have a clear leadership structure, most digital subscription companies need an element of decentralization to be competitive today. Innovation and entrepreneurship tend to thrive in decentralized environments, where leaders feel and act like empowered owners and have the freedom to make decisions quickly and take necessary risks.

However, there are a few caveats to decentralization. When theres a wholesale shift in strategy (e.g., the change from acquisition to retention), functional teams can suffer if they don't have a voice in large-scale organizational decisions. Additionally, decentralization also often means there is duplicate work and positions. The willingness of a company to accept this duplication of effort depends on whether the primary focus is on growth or profitability. For companies focusing on growth, its more likely this duplication is part of the trade off in rapid scaling. For those focusing on profit, the cost of duplicative work is likely less palatable.

Before making a shift to a different organizational model because of a strategy change, take stock of what is working and where you can improve. It may turn out that you dont need to make a structural change, but instead you could work on developing employees muscles around communications and creating clarity across decentralized teams. Leaders can also focus on building cross-functional rituals to ensure communication is intentional and deepens relationships across teams.

If youre asking this question, you may have already reached the limit your current leaders and workforce can handle. Reorganizations in digital companies happen rather frequently, triggered by personnel changes, mergers and acquisitions, new business priorities and market expansion. However, even for the most agile employees, frequent reorganizations can take a toll. Employees who are part of companies that reorganize every six months or every year may experience burnout and stress among their teams, as they are expected to deliver results on their current projects while also implementing new operating models and ways of working at the same time. This high level of stress and expectation can erode employee morale over time, leading to burnout and disengagement from the purpose and outputs of the organization.

A finding from one of our recent studies across generations and roles in the workplace found that employees value their physical and mental wellbeing above all else, including compensation. While you may not be able to limit your reorganizations, consider offering your employees relevant additional perks. This could include paid time off for additional professional development or mental health days for recharging or simple ideas like eliminating internal meetings on Friday afternoons. Its also important that people feel heard when it comes to voicing their frustrations. Leaders need to actively listen and take stock of company morale. An environment that constantly feels unstable and employees who feel underappreciated will undermine productivity and engagement, potentially leading to an increase in talent turnover.

As subscription businesses shift from focusing on acquisition to retention, they need strong functional leaders across the critical areas of product, data, marketing, engineering and others, who are aligned across the companys goals, strategies, and decision-making processes. For example, your Chief Marketing Officer, Chief Product Officer and Chief Data Officer should operate in lockstep, with data at the core of driving both product development and product positioning. Bringing the strengths of each of these teams together creates a powerful cross-functional trio able to understand the business strategy from multiple angles and add context from each of their functions. For this deep alignment to be successful, these leaders must also possess high emotional intelligence, comfort with ambiguity and approach every challenge with the mindset of the customer.

In addition to being strongly connected across functions, these functional leaders should have a voice at the very top of the organization, with access to the CEO and the board when applicable. This is extremely important when new strategies and products are being considered. They will have the business insights that are critical to making informed decisions.

This doesnt mean endless WhatsApp and Slack messages at all hours. What it does mean is intentional time for connecting on shared leadership priorities and establishing agreed upon rules of engagement and a decision-making framework. Then these leaders must translate this shared passion for connection to their teams, which will result in higher employee morale and will benefit the business with greater alignment across functions. This collaboration may take some time to build, especially if your company is accustomed to frequent reorganizations. The teams you work with may shift and you need to devote time to building trust in these new relationships. It may also be a time to consider team coaching to help build stronger alignment across functions.

Digital subscription businesses that change customer strategy without considering their organizational structure and people may find themselves in danger of being canceledfirst by their employees and then by customers if they lack the talent to fulfill customer needs. Leaders must remember that customer centricity shouldnt come at the cost of their employees; businesses that thrive know that to build customer loyalty they need to build employee loyalty first.

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Decoding Digital Subscription Success - Egon Zehnder