Consolidata provides financial planning software to help CFOs and FP\u0026A professionals at investor-backed companies develop and maintain more accurate budgets in less time with fewer company resources

Consolidata

Data Management and Consolidation in the Integration of Corporate Information Systems

Introduction to Data Integration The idea of data integration isn't anything particularly new. It was a lengthy and complicated process in the past. Smaller enterprises carried out the manual process by copying and pasting a lot of information into spreadsheets. During this period, larger marketing departments and agencies mainly used Extract, Transform, and Load (ETL) software to combine data in data warehouses. Data from each data silo would be extracted using ETL software from vendors like Oracle, Microsoft, and IBM, and it would then be converted (transformed) to comply with the marketing data warehouse's data formatting standards. Once the data had been altered, it would be imported into the target database, where marketers could compare it to other data and mine it for insights, frequently with the aid of a specialist business intelligence team. This was acceptable when data came in relatively slowly, but "big data" has opened the floodgates. According to a report, 32% of businesses have abandoned ETL software because they cannot handle the volume of data. The necessity for self-service data across the enterprise is mentioned by another 24%, while 23% claim that better data quality is required for machine learning projects. There are many different alternatives for integrating data today, some of which are more complex than others. Marketers must take into account the finest data integration techniques based on your team's particular data requirements and sources. The Next Generation of Data Integration Cloud-Based Data Warehouses: Most data-driven marketing questions may be answered using data warehouses, such as comparing open email rates to clients who sign up for premium memberships. It's not the most incredible option, though, if you want greater freedom to experiment with how different data streams and sources interact, and you still need a way to standardize formats before your data can be sent to the warehouse. Integration Apps: The simplest data integration technique is application-based data integration. When you execute a query, it employs software to directly connect and integrate data from several sources rather than combine it in a single repository. For on-premise databases, it is most frequently used. The more effective integration apps are easy to set up and suitable for small marketing departments. This is frequently a workable approach in service-based industries when there aren't too many data silos or too much data volume. But application-based integration can't keep up when the volume of data or data sources increases too much. Data Integration Middleware: Middleware connects a web server with your database system via a different layer of software, much like application-based integration does in terms of data integration. You won't necessarily have all of your data stored automatically in a central repository using middleware, but you will be able to view what you need, when you need it, with little to no lag time, as the web server directly accesses each data source to retrieve the data you need for your query. Middleware is more suitable for marketing departments or firms that manage several client accounts since it can function more effectively in the cloud than application-based integrations. Data Lakes: The greatest connections and analysis between various data sources are possible with this option, making it the most advanced one. In contrast to data warehouses, data lakes maintain unstructured data in its raw state in enormous repositories. In this manner, you can access all the data points you require and combine them in any way you like. Large businesses that consume enormous volumes of data every minute, such as those from IoT devices, security sensors, or social media interactions, are best suited to use data lakes. They can handle much more intricate queries, such as keeping track of seasonal patterns related to cyclical economic changes and email subject lines. However, to be able to edit the raw data, you'll need some amount of data science skills. When your marketing data is integrated, you may utilize it to advance the level at which marketing campaign decisions are made. As a result, your team can integrate all available data signals for improved marketing results over time by choosing the best new data integration method for your marketing needs, adhering to best practices to ensure that your integrated data is consistent, compliant, high quality, and uses automated procedures. Integrated Data Produces Better Marketing

Why is Marketing Data Consolidation and Integration Necessary?

It is a challenging time for the tire industry due to global economic conditions, fluctuations in its front-end automotive industry, and fierce business competition. Under these circumstances, cost-effective planning, improved production process, keeping records of manufacturing data, product information, and related assets of tires have more importance than ever before. In particular, a systemic, agile, and centralized data management platform supporting the entire data value chain can significantly contribute to business growth. The increased disposable income of consumers and their willingness to spend on new technologically advanced models have spurred automobile sales across the globe. Tire replacement, however, being a typical maintenance cost for direct consumers, is a different story – taking a back seat with increasing demand for new cars. Tire manufacturers further face hurdles as the soaring rubber prices cause an uncomfortable stir on the cost of manufacturing and purchasing tires. Nevertheless, the industry has significantly matured after going through many cycles and stages of change in the last century. Today, it is highly dependent on data and analytics primarily due to several global economic factors, including consumer spending, budget allocation, and/or budget cut downs, among others. Tire manufacturers are driving intensive research into making their products safer and more efficient than ever before. They are becoming more professional with their operations, making use of the ever-growing volume of data they generate to transform their businesses, which will eventually stabilize the profit. Within the daily measurements of temperature, pressure, and wear, data scientists are looking for patterns that could help improve the safety of the vehicles and prompt more efficient fuel use. The Impact That Resonates for a Long Time Throughout the System At any tire plant, through the entire material flow – from raw materials warehouse to mixing, compounding, component manufacturing, tire-building, curing, finishing, finished tire warehouse, and shipping – there is a humongous amount of data is being generated. Add to that the mountain data coming in from the company's research labs, test tracks, suppliers, customers, weblogs, and social media; and it is impossible to ignore the enormity of the constant pilling up of datasets. From production data to recipe management to testing & reporting, discerning handling of all types of data becomes a massive task for enabling 100% traceability of work in progress and finished tires. With market developments, seismic shifts in consumption patterns, and technology getting integrated into almost every stage of tire production and distribution cycle, a comprehensive data management tool is 'mandatory'. Recording information such as batch numbers, operators, weights, processing times, and test results can ensure accurate product tracking at the individual tire level. In turn, automated analytics can apply these insights to a closed-loop action on the same unit, the entire processing network, or other connected devices. By using readily available data, a tire manufacturer/seller can leverage analytics to generate real-time insights and understand the needs of end consumers and product performance under different operating conditions. Additionally, the data from customers and other sources can help drive contextual analytics and generate further monetization opportunities for cost-containment or revenue-generation initiatives for different stakeholders in the ecosystem. Having one centralized platform to organize and manage this vast amount of data can significantly improve the time-to-value of data and response time by reducing network bottlenecks and service delays. Such an approach focuses on actionable data and creates long-term benefits. The key is to have one solution, a single-stop destination, that keeps records of all manufacturing data. Such a system allows manufacturers to analyze vast volumes of data in a scalable and efficient way, and thus stay ahead of the curve. Case Study: How a Leading Tire Manufacturer Reaped the Benefits of Strategic Data Management A firm believer in investing in R&D to improve product efficiency, one of the top global tire manufacturers, was looking for ways to take its operations to the next level. The company is already present across 61 countries, creates products for 41+ brands, and generates yearly revenue of close to €45 billion. The multinational company is developing intelligent technology for making tires that are not only secure and cost-effective but also ecologically friendly. Yet, its vast reach and multiple production facilities meant they were juggling with an incomprehensible amount of data. Lack of tailored solutions to tackle their problems meant extra manual labor with heightened chances of inaccuracy and errors. One of the issues was product data management and enrichment. There was no consolidated, central PIM database for product assets and marketing text; and modules were maintained on multiple systems. The content was shared manually, and various teams used the product information in multiple ways without complete knowledge of how data was being shared or used by other teams. Continually monitoring and updating the data shared with customers thus became a very time consuming and complicated process. Pimcore platform was chosen to implement the PIM and DAM solution to centralize product data and related digital assets on a single platform. The goal was to consolidate product data, marketing text (in multiple languages), and digital assets in a single platform. and DAM platform has streamlined the workflow and ultimately enriched the marketing text. Pimcore's PIM The implemented solution contains the following features: All product-related information is managed centrally for all sales organizations across the globe in Pimcore PIM. All products are organized in a folder-based structure, segregated based on brands and product divisions. Similar products are grouped under product lines for easy access and re-use of information. Pimcore DAM section is used to manage all digital assets. All assets are stored and organized in a folder structure for easier navigation and management. More than 40 languages are enabled for the content and the assets. Inbound integrations have been configured for various source of information like SAP ERP Data Syndication has been enabled to distribute data to websites, various market places, and other channels. The solution has enabled the tire manufacturer's team to effortlessly manage product information with reduced data retrieval time and improved data structure usability. It has also increased the overall visibility & usability of product information and digital assets for different business teams. With Pimcore's PIM, controlled product enrichment has reduced the risk of incorrect product data, and business users across multiple countries are now able to manage digital assets and product data from a single system. With an elegant , end-customers can now get added benefits. For instance, the system can let drivers know when their tires should be repaired or changed, thus allowing fleets to keep downtime to a minimum. data management solution Step Into the Next Conventional tools are no longer enough for handling the complexities of product data and can seriously damage your brand reputation in the run against the competition. You need solutions that can streamline data flow, automate the process of continually updating digital assets across all channels, improve the quality and consistency of product data, and deliver a compelling product experience to drive exceptional customer experience.

Tire Manufacturers Are Rolling Out Consolidated Data Management

The resurgence of web3 social media platforms has led to several businesses and companies using them to engage with customers. Decentralized social media enhances a brand’s digital presence in multiple ways and provides enormous potential for long-term growth. In this article, we will explore in detail why brands are now using web3 social media platforms for their marketing campaigns. Direct Consumer Relationships Unlike traditional social media, web3 platforms don’t have any centralized intermediaries who control content distribution or moderate online interactions. Thus, brands can forge direct customer connections and authentically communicate with them to build stronger relationships. Since web3 social platforms have more transparency, companies find it easier to demonstrate their core brand values to the audience. The absence of third-party intermediaries also reduces marketing costs and fosters trust between the brand and consumers. Moreover, web3 platforms usually have more data privacy and security compared to web2 social media. Most traditional social media harvest user data without their knowledge and sell them to companies for advertising campaigns. However, such an approach doesn’t benefit anyone except the intermediary. The user doesn’t get any share of the profit despite generating valuable datasets. Similarly, companies often run unsuccessful ads that do not cater to the right audience. Web3 users have sovereign data ownership with direct monetization opportunities. Thus, customers can consensually provide or sell their data to brands for targeted advertising. This setup benefits both parties as users gain from sharing data and brands can customize their ads. A personalized marketing strategy through shared data exchange on social media creates deeper connections between brands and customers. Leverage Tokenization Opportunities One of the most important features of web3 social media is digital assets and tokenization. Companies can tokenize anything in the digital space to create valuable tokens for the online marketing space. Businesses can use tokens in social media marketing to reward customers for purchases, create content, and generate interest among other users. Tokenization helps in organic growth, lead generation, and brand advocacy through active token-gated communities. The use of non-fungible tokens (NFTs) in marketing campaigns has provided a sense of exclusivity to customers. These unique virtual assets and collectibles are ideal for rewarding loyal consumers and enhancing brand value. Product users can utilize their limited-edition NFTs as special access keys to attend exclusive live events or have curated digital experiences. These NFTs can also unlock promotional offers, which will further drive customer engagement and create brand affinity. The biggest advantage of NFTs is seamless monetization for companies and consumers. Brands can mint NFTs for customers, and users can sell them in marketplaces to create a sustainable economy without relying on third parties. After the 2021 crypto boom, major brands like Nike, Adidas, Dolce and Gabbana, Louis Vuitton, and others launched their own NFT collections. However, these NFTs are indivisible and suffer from liquidity challenges due to their underlying technologies. The new generation of NFTs leverages the ERC-404 token standard, which enables fractional ownership, more liquidity, and easy trading on NFT marketplaces. Tomo uses the ERC-404 standard to mint Tomojis, which can potentially disrupt digital marketing for big brands. Community-Driven Marketing A company’s success depends on the strengths and foundational integrity of its community. Web3 social media empowers the customer community to lead a brand’s marketing campaign. Companies can collaborate directly with content creators or general users to co-create ads without relying on traditional methods. After all, word of mouth is the strongest marketing technique when paid campaigns fail to make an impact. User-generated content and crowdsourced promotional activities create ripple effects among new and existing customers. Authentic reviews, stories, and testimonials through text, images, or videography consolidate a brand’s credibility and create relatable brand interactions. Web3 social media can incentivize user-generated collaborative marketing efforts through token rewards. The rewards mechanism creates a feedback loop for users to contribute to the company’s growth through brainstorming ideas and content creation. A community’s collective wisdom has immense potential to sway customer behavior either in favor or against a brand. Thus, optimal community involvement on web3 social media can help companies reap enormous dividends through innovative decentered marketing. Collaborative Influencer Marketing Although web2 influencers have massive followers and established personal branding, a major chunk of profits from brand collaborations go to social media companies. Additionally, the collaborations might come off as inauthentic and forced ways of monetizing their digital presence. On the contrary, web3 social media establishes direct connections between influencers and fans, which is more profitable for all stakeholders. Influencers can create new revenue streams through brand marketing on web3 media with potentially higher earnings. Web3 influencers have more control over these partnerships and thereby create meaningful campaigns that resonate with their values or visions. An alignment of beliefs and principles can go a long way in creating lasting impressions on customers. Thus, web3-based ad campaigns reach the audience as genuine endorsements and might have more impact on consumer behavior. Additionally, tokenized economies can incentivize followers to actively participate in these campaigns to earn rewards. These programs can create a mutually beneficial system where the influencers, brands, and customers gain simultaneously from authentic engagements. The Future Potential of Brand Marketing For Web3 Social Media According to a Statista report, brands spent approximately $270 billion in social media advertising campaigns in 2023. The budget expenditure for digital marketing is projected to surpass the $300 billion mark this year. However, as users become more wary of data privacy, they’re unwilling to share their data for irrelevant ads. Thus, brands need to be extra cautious about how they collect user data and harness them for targeted advertising. Web3 social media holds the promise of forging direct and authentic relations between companies, customers, and influencers. Although it’s still very early for decentralized social media, they’re slowly entering the market and amplifying their digital footprint. This is the right opportunity to respect user privacy and leverage the benefits of blockchain-based tokenized social media platforms. Even if 50% of traditional digital marketing spending comes into web3 social media, it means opening up a $135 billion market. Thus, the future holds enormous potential for robust growth in brand marketing on web3 social media platforms. The resurgence of web3 social media platforms has led to several businesses and companies using them to engage with customers. Decentralized social media enhances a brand’s digital presence in multiple ways and provides enormous potential for long-term growth. In this article, we will explore in detail why brands are now using web3 social media platforms for their marketing campaigns. web3 Direct Consumer Relationships Unlike traditional social media, web3 platforms don’t have any centralized intermediaries who control content distribution or moderate online interactions. Thus, brands can forge direct customer connections and authentically communicate with them to build stronger relationships. Since web3 social platforms have more transparency, companies find it easier to demonstrate their core brand values to the audience. The absence of third-party intermediaries also reduces marketing costs and fosters trust between the brand and consumers. Moreover, web3 platforms usually have more data privacy and security compared to web2 social media. Most traditional social media harvest user data without their knowledge and sell them to companies for advertising campaigns. However, such an approach doesn’t benefit anyone except the intermediary. The user doesn’t get any share of the profit despite generating valuable datasets. Similarly, companies often run unsuccessful ads that do not cater to the right audience. Web3 users have sovereign data ownership with direct monetization opportunities. Thus, customers can consensually provide or sell their data to brands for targeted advertising. This setup benefits both parties as users gain from sharing data and brands can customize their ads. A personalized marketing strategy through shared data exchange on social media creates deeper connections between brands and customers. Leverage Tokenization Opportunities One of the most important features of web3 social media is digital assets and tokenization. Companies can tokenize anything in the digital space to create valuable tokens for the online marketing space. Businesses can use tokens in social media marketing to reward customers for purchases, create content, and generate interest among other users. Tokenization helps in organic growth, lead generation, and brand advocacy through active token-gated communities. The use of non-fungible tokens (NFTs) in marketing campaigns has provided a sense of exclusivity to customers. These unique virtual assets and collectibles are ideal for rewarding loyal consumers and enhancing brand value. Product users can utilize their limited-edition NFTs as special access keys to attend exclusive live events or have curated digital experiences. These NFTs can also unlock promotional offers, which will further drive customer engagement and create brand affinity. The biggest advantage of NFTs is seamless monetization for companies and consumers. Brands can mint NFTs for customers, and users can sell them in marketplaces to create a sustainable economy without relying on third parties. After the 2021 crypto boom, major brands like Nike , Adidas , Dolce and Gabbana , Louis Vuitton , and others launched their own NFT collections. However, these NFTs are indivisible and suffer from liquidity challenges due to their underlying technologies. Nike Nike Adidas Adidas Dolce and Gabbana Dolce and Gabbana Louis Vuitton Louis Vuitton The new generation of NFTs leverages the ERC-404 token standard, which enables fractional ownership, more liquidity, and easy trading on NFT marketplaces. Tomo uses the ERC-404 standard to mint Tomojis, which can potentially disrupt digital marketing for big brands. Community-Driven Marketing A company’s success depends on the strengths and foundational integrity of its community. Web3 social media empowers the customer community to lead a brand’s marketing campaign. Companies can collaborate directly with content creators or general users to co-create ads without relying on traditional methods. After all, word of mouth is the strongest marketing technique when paid campaigns fail to make an impact. User-generated content and crowdsourced promotional activities create ripple effects among new and existing customers. Authentic reviews, stories, and testimonials through text, images, or videography consolidate a brand’s credibility and create relatable brand interactions. Web3 social media can incentivize user-generated collaborative marketing efforts through token rewards. The rewards mechanism creates a feedback loop for users to contribute to the company’s growth through brainstorming ideas and content creation. A community’s collective wisdom has immense potential to sway customer behavior either in favor or against a brand. Thus, optimal community involvement on web3 social media can help companies reap enormous dividends through innovative decentered marketing. Collaborative Influencer Marketing Although web2 influencers have massive followers and established personal branding, a major chunk of profits from brand collaborations go to social media companies. Additionally, the collaborations might come off as inauthentic and forced ways of monetizing their digital presence. On the contrary, web3 social media establishes direct connections between influencers and fans, which is more profitable for all stakeholders. Influencers can create new revenue streams through brand marketing on web3 media with potentially higher earnings. Web3 influencers have more control over these partnerships and thereby create meaningful campaigns that resonate with their values or visions. An alignment of beliefs and principles can go a long way in creating lasting impressions on customers. Thus, web3-based ad campaigns reach the audience as genuine endorsements and might have more impact on consumer behavior. Additionally, tokenized economies can incentivize followers to actively participate in these campaigns to earn rewards. These programs can create a mutually beneficial system where the influencers, brands, and customers gain simultaneously from authentic engagements. The Future Potential of Brand Marketing For Web3 Social Media According to a Statista report, brands spent approximately $270 billion in social media advertising campaigns in 2023. The budget expenditure for digital marketing is projected to surpass the $300 billion mark this year. spent spent However, as users become more wary of data privacy, they’re unwilling to share their data for irrelevant ads. Thus, brands need to be extra cautious about how they collect user data and harness them for targeted advertising. Web3 social media holds the promise of forging direct and authentic relations between companies, customers, and influencers. Although it’s still very early for decentralized social media, they’re slowly entering the market and amplifying their digital footprint. This is the right opportunity to respect user privacy and leverage the benefits of blockchain-based tokenized social media platforms. Even if 50% of traditional digital marketing spending comes into web3 social media, it means opening up a $135 billion market. Thus, the future holds enormous potential for robust growth in brand marketing on web3 social media platforms.

Why Should Brands Turn To Web3 Social Media?

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Kiriaki Frangias;
(2) Andrew Lin;
(3) Ellen Vitercik;
(4) Manolis Zampetakis. Table of Links Abstract and Introduction Warm-up: Agents with known equal disutility Agents with unknown disutilites Experiments Conclusions and future directions, References A Summary of notation B Omitted proofs from Section 2 C Omitted proofs from Section 3 D Additional Information about Experiments Abstract Ranking is fundamental to many areas, such as search engine and recommender system optimization, as well as peer grading. Crowdsourcing, which is often used for these tasks, requires proper incentivization to ensure accurate inputs. In this work, we draw on the field of contract theory from Economics to propose a novel mechanism that enables a principal to accurately rank a set of items by incentivizing agents to provide pairwise comparisons of the items. Our mechanism implements these incentives by verifying a subset of each agent’s comparisons with a ground-truth ordering, a task we assume to be costly. The agent is compensated (for example, monetarily or with class credit) based on the accuracy of these comparisons. Our mechanism achieves the following guarantees: (1) it only requires the principal to verify O(log s) comparisons, where s is the total number of agents, and (2) it provably achieves higher total utility for principal compared to ranking the items herself with no crowdsourcing. 1 Introduction Ranking is a central task across a wide range of applications including search engine optimization, recommender systems, and peer grading: search engines rank pages by relevance, recommender systems rank content by entertainment value, and peer graders rank submissions by quality. Platforms often use crowdsourcing to determine rankings [7, 9, 15], especially when ranking many alternatives. In many such cases, it is often more feasible and accurate for agents to provide pairwise comparisons than cardinal evaluations [26, 27]. However, rational agents will only invest the effort necessary to provide accurate comparisons when properly incentivized. In many real world scenarios, such as peer grading, these incentives involve a principal (the ranking’s end-user) verifying a selection of an agent’s comparisons against a ground-truth sorting. The agent’s payment depends on these verified comparisons’ accuracy. We propose a novel mechanism that enables a principal to rank a set of items from pairwise comparisons while minimizing the requisite payments and number of verifications, which are costly for the principal. To model the principal and agents, we draw on contract theory, a classical field in economics [16] that has become increasingly relevant in computer science [e.g., 6, 10, 14, 32]. The principal assigns comparisons to each agent and verifies a subset of these comparisons against a ground-truth ordering. The agent does not know the subset’s identity. The principal will compensate each agent for correctly evaluating all verified pairs. Given the principal’s offered payment, the agent decides whether they will exert effort. As in the standard model of contract theory, if the agent exerts effort, they will be “good” at the task with some probability and “bad” at the task with some smaller probability. If they decide not to exert effort, they will be “bad” with certainty. A “good” agent will return the correct comparisons, but a “bad” agent will not. When modeling the agents, we introduce heterogeneity through differing values of the per-comparison disutility that they experience. We therefore assume that every agent can potentially be “good”, but might require more or less compensation to be incentivized to exert effort. As we show, this also allows the principal to incentivize and pay only agents with small disutilities, even though these values are not a priori known to the principal. Key challenges. The primary obstacle we face in designing a mechanism for this problem lies in determining how to distribute the comparisons among the agents while satisfying several critical requirements: (a) The number of items assigned to each agent (i.e., the subset of items that make up their pairwise comparisons) should be small, minimizing the agent’s workload. (b) Every comparison must be evaluated by at least one “good” agent with high probability. In other words, we want each agent’s comparisons to cover as few items as possible to satisfy (a). However, we need the union of all agents’ comparisons to have sufficiently high redundancy to satisfy (b). These conditions are fundamentally at odds, so satisfying both requires care. 1.1 Our contributions We summarize our four primary contributions below. 2. Aggregating the noisy comparisons. Next, we present an algorithm for identifying “bad” agents and aggregating comparisons from “good” agents. Our algorithm returns the correct ranking with high probability. 3. Determining payments. We identify the payments necessary to ensure enough agents are “good” to recover the ground-truth sorting. This depends on the disutility it costs agents to make comparisons. We study a range of settings, from a warm-up where the principal knows the disutilities (as is typical in contract theory), to a setting where the disutilities are drawn from a distribution about which the principal has limited information. 4. Principal’s utility. We show that it suffices for the principal to verify O(log s) comparisons to ensure that the ground-truth sorting is recovered. We also identify natural conditions under which, by implementing our proposed contract, the principal achieves higher total utility than she would if she performed all pairwise comparisons herself. 5. Experiments. In experiments, we stress-test our results by relaxing our model along several axes. We demonstrate that even when the assumed model is inaccurate, with a sufficient increase in the payment the principal can recover the ground-truth ranking. 1.2 Related work Rank Aggregation. Ranking via pairwise comparisons can be considered as a subfield of rank aggregation [2, 19, 22, 28]. Recent work on rank aggregation with pairwise comparisons has built upon and extended these techniques [4, 5, 8, 18, 30]. Lin [17] provides an extensive introduction. Rank aggregation differs from our work, which models the data sources as agents with differing reliability. Crowdsourcing. Crowdsourcing offers a cost-effective and efficient way to consolidate feedback from a large number of non-experts. With the rise of crowdsourcing platforms, such as Amazon Mechanical Turk, crowdsourcing has been commonly used in a variety of applications, including sorting [1, 4, 25]. A central task in crowdsourcing is modeling the reliability of annotators [4, 25]. Chen et al. [4], for example, use the Bradley-Terry model for ranking aggregation, assuming that annotators are either perfect or spammers (comparisons are correct with probability 0.5). In contrast, we study a dynamic setting in which annotators can be incentivized to exert effort and therefore increase the accuracy of their work. Ordinal peer grading. This area is most related to our work. With the rise of massive online open courses (MOOCs), it has become increasingly important to decentralize the grading process in an efficient manner that comes at a low cost to teaching staff [21]. Peer grading can be a solution to this technical hurdle. Ordinal peer grading (in which students are asked to rank other students’ work) is an inexpensive, accurate, and robust way to assess performance [27]. However, many works on ordinal peer grading draw on probabilistic methods to estimate grader reliability and lack perspective on strategic interactions [20, 23, 24]. In contrast, works relating to aggregating feedback from strategic data sources focus mostly on regression or cardinal evaluations of items [3, 11–13, 31]. Our work aims to bridge this gap using a contract between the principal and the agents. The contract structures their interaction and leverages their strategic behavior by incentivizing agents to perform accurate comparisons. It also comes at a low cost to the principal while provably retrieving the correct ranking of items. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. This paper is available on Arxiv under CC 4.0 license. Arxiv Authors: Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. Table of Links Abstract and Introduction Abstract and Introduction Warm-up: Agents with known equal disutility Warm-up: Agents with known equal disutility Agents with unknown disutilites Agents with unknown disutilites Experiments Experiments Conclusions and future directions, References Conclusions and future directions, References A Summary of notation A Summary of notation B Omitted proofs from Section 2 B Omitted proofs from Section 2 C Omitted proofs from Section 3 C Omitted proofs from Section 3 D Additional Information about Experiments D Additional Information about Experiments Abstract Ranking is fundamental to many areas, such as search engine and recommender system optimization, as well as peer grading. Crowdsourcing, which is often used for these tasks, requires proper incentivization to ensure accurate inputs. In this work, we draw on the field of contract theory from Economics to propose a novel mechanism that enables a principal to accurately rank a set of items by incentivizing agents to provide pairwise comparisons of the items. Our mechanism implements these incentives by verifying a subset of each agent’s comparisons with a ground-truth ordering, a task we assume to be costly. The agent is compensated (for example, monetarily or with class credit) based on the accuracy of these comparisons. Our mechanism achieves the following guarantees: (1) it only requires the principal to verify O(log s) comparisons, where s is the total number of agents, and (2) it provably achieves higher total utility for principal compared to ranking the items herself with no crowdsourcing. 1 Introduction Ranking is a central task across a wide range of applications including search engine optimization, recommender systems, and peer grading: search engines rank pages by relevance, recommender systems rank content by entertainment value, and peer graders rank submissions by quality. Platforms often use crowdsourcing to determine rankings [7, 9, 15], especially when ranking many alternatives. In many such cases, it is often more feasible and accurate for agents to provide pairwise comparisons than cardinal evaluations [26, 27]. However, rational agents will only invest the effort necessary to provide accurate comparisons when properly incentivized. In many real world scenarios, such as peer grading, these incentives involve a principal (the ranking’s end-user) verifying a selection of an agent’s comparisons against a ground-truth sorting. The agent’s payment depends on these verified comparisons’ accuracy. We propose a novel mechanism that enables a principal to rank a set of items from pairwise comparisons while minimizing the requisite payments and number of verifications, which are costly for the principal. To model the principal and agents, we draw on contract theory, a classical field in economics [16] that has become increasingly relevant in computer science [e.g., 6, 10, 14, 32]. The principal assigns comparisons to each agent and verifies a subset of these comparisons against a ground-truth ordering. The agent does not know the subset’s identity. The principal will compensate each agent for correctly evaluating all verified pairs. Given the principal’s offered payment, the agent decides whether they will exert effort. As in the standard model of contract theory, if the agent exerts effort, they will be “good” at the task with some probability and “bad” at the task with some smaller probability. If they decide not to exert effort, they will be “bad” with certainty. A “good” agent will return the correct comparisons, but a “bad” agent will not. When modeling the agents, we introduce heterogeneity through differing values of the per-comparison disutility that they experience. We therefore assume that every agent can potentially be “good”, but might require more or less compensation to be incentivized to exert effort. As we show, this also allows the principal to incentivize and pay only agents with small disutilities, even though these values are not a priori known to the principal. Key challenges. The primary obstacle we face in designing a mechanism for this problem lies in determining how to distribute the comparisons among the agents while satisfying several critical requirements: Key challenges. (a) The number of items assigned to each agent (i.e., the subset of items that make up their pairwise comparisons) should be small, minimizing the agent’s workload. (b) Every comparison must be evaluated by at least one “good” agent with high probability. In other words, we want each agent’s comparisons to cover as few items as possible to satisfy (a). However, we need the union of all agents’ comparisons to have sufficiently high redundancy to satisfy (b). These conditions are fundamentally at odds, so satisfying both requires care. 1.1 Our contributions We summarize our four primary contributions below. 2. Aggregating the noisy comparisons. Next, we present an algorithm for identifying “bad” agents and aggregating comparisons from “good” agents. Our algorithm returns the correct ranking with high probability. 2. Aggregating the noisy comparisons. 3. Determining payments. We identify the payments necessary to ensure enough agents are “good” to recover the ground-truth sorting. This depends on the disutility it costs agents to make comparisons. We study a range of settings, from a warm-up where the principal knows the disutilities (as is typical in contract theory), to a setting where the disutilities are drawn from a distribution about which the principal has limited information. 3. Determining payments. 4. Principal’s utility. We show that it suffices for the principal to verify O(log s) comparisons to ensure that the ground-truth sorting is recovered. We also identify natural conditions under which, by implementing our proposed contract, the principal achieves higher total utility than she would if she performed all pairwise comparisons herself. 4. Principal’s utility. 5. Experiments. In experiments, we stress-test our results by relaxing our model along several axes. We demonstrate that even when the assumed model is inaccurate, with a sufficient increase in the payment the principal can recover the ground-truth ranking. 5. Experiments. 1.2 Related work Rank Aggregation. Ranking via pairwise comparisons can be considered as a subfield of rank aggregation [2, 19, 22, 28]. Recent work on rank aggregation with pairwise comparisons has built upon and extended these techniques [4, 5, 8, 18, 30]. Lin [17] provides an extensive introduction. Rank aggregation differs from our work, which models the data sources as agents with differing reliability. Rank Aggregation. Crowdsourcing . Crowdsourcing offers a cost-effective and efficient way to consolidate feedback from a large number of non-experts. With the rise of crowdsourcing platforms, such as Amazon Mechanical Turk, crowdsourcing has been commonly used in a variety of applications, including sorting [1, 4, 25]. A central task in crowdsourcing is modeling the reliability of annotators [4, 25]. Chen et al. [4], for example, use the Bradley-Terry model for ranking aggregation, assuming that annotators are either perfect or spammers (comparisons are correct with probability 0.5). In contrast, we study a dynamic setting in which annotators can be incentivized to exert effort and therefore increase the accuracy of their work. Crowdsourcing Ordinal peer grading. This area is most related to our work. With the rise of massive online open courses (MOOCs), it has become increasingly important to decentralize the grading process in an efficient manner that comes at a low cost to teaching staff [21]. Peer grading can be a solution to this technical hurdle. Ordinal peer grading (in which students are asked to rank other students’ work) is an inexpensive, accurate, and robust way to assess performance [27]. Ordinal peer grading. However, many works on ordinal peer grading draw on probabilistic methods to estimate grader reliability and lack perspective on strategic interactions [20, 23, 24]. In contrast, works relating to aggregating feedback from strategic data sources focus mostly on regression or cardinal evaluations of items [3, 11–13, 31]. Our work aims to bridge this gap using a contract between the principal and the agents. The contract structures their interaction and leverages their strategic behavior by incentivizing agents to perform accurate comparisons. It also comes at a low cost to the principal while provably retrieving the correct ranking of items. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Algorithmic Contract Design for Crowdsourced Ranking: An Introduction to Get You Started

Language models (LMs) represent a highly versatile category of technology, processing text inputs to generate textual outputs. Contemporary research has illuminated that this adaptable text-to-text framework can be harnessed to address a myriad of tasks without necessitating task-specific adjustments (i.e., absent fine-tuning or architectural changes) through the use of prompting methods to enable precise zero and few-shot inference. In essence, LMs are initially trained on extensive, unlabelled text corpora (guided by a language modeling objective), after which the LMs are directed via textual prompts to resolve specific problems, thereby allowing the pre-trained model to be efficiently repurposed for varied applications. Despite the immense promise of LMs as task-neutral foundation models, initial endeavours to apply pre-trained LMs to downstream tasks (e.g., GPT and GPT-2 [4, 5]) encountered significant challenges. This overview will explore how recent advancements have improved upon these preliminary efforts, leading to LMs that deliver significantly enhanced task-neutral performance. A critical discovery in this research trajectory is the enhanced capability of LMs as they are scaled up. (from [1] and [2]) More detailed examination reveals that large LMs (LLMs) are (i) more sample-efficient compared to their smaller counterparts and (ii) exhibit greater proficiency in task-neutral transfer to downstream applications. Notably, the performance of these LLMs demonstrates predictable patterns relative to various parameters (e.g., model scale and training data volume). This empirical evidence has culminated in the development of GPT-3, a colossal LLM with 175 billion parameters, which not only surpasses its predecessors in task-neutral performance but also exceeds contemporary, supervised deep learning approaches in certain domains. Key concepts necessary for understanding LMs have been discussed in my previous posts, including the language modeling objective, the architecture of decoder-only transformer models, and their integration to forge robust foundation models. For a more thorough understanding, refer to the provided link. Background I will offer a succinct overview of these concepts here, while also delineating a few additional notions pertinent to comprehending LLMs like GPT-3. Modern LMs utilize standard pre-training methods to tackle a broad spectrum of tasks without subsequent modifications (i.e., no architectural adjustments, fine-tuning, etc.). Employing a voluminous corpus of unlabelled text, we pre-train our LM with a language modeling objective that (i) extracts a text sample from our corpus and (ii) attempts to predict the succeeding word. This represents a form of self-supervised learning, where the correct next word can always be verified by examining the data in our corpus. Language modeling at a glance Modern LMs employ decoder-only transformer architectures, which apply a series of layers that include masked self-attention and forward transformations to the input. Masked self-attention, as opposed to bidirectional self-attention, precludes the model from 'peeking ahead' in a sequence to predict the subsequent word. Architecture Beyond these decoder-only layers, the LM architecture encompasses embedding layers that store vectors for all potential tokens within a predefined vocabulary size. Through these embedding layers, raw text is transformed into a matrix that the model can process, achieved by: Tokenizing raw text into discrete tokens (i.e., words or sub-words), Retrieving the corresponding embedding vector for each token, Concatenating these token embeddings to form a matrix or sequence of token vectors, Integrating position (and other) embeddings to each token vector. By pre-training LMs over expansive text corpora, we obtain models capable of accurately predicting the next token within a given token sequence. However, to utilize these models for language understanding tasks such as sentence classification and language translation, we do not modify the LM. Instead, we leverage the model’s inherent generic text-to-text input-output structure by supplying textual "prompts," such as: Adaptation “Translate this sentence to English: <sentence> =>”
“Summarize the following document: <document> =>”. With these problem-solving prompts, a proficient LM should yield a textual sequence that addresses the query effectively. For scenarios requiring a selection from a predetermined set of responses (e.g., multiple choice or classification), the LM can be used to assess the likelihood of generating each potential response and select the most probable outcome. The essence of modern LLMs lies in utilizing language model pre-training as a mechanism to develop generic foundation models capable of resolving diverse challenges without the need for adaptation or fine-tuning. While previous LMs like GPT and GPT-2 [4, 5] demonstrated inferior performance relative to tailored or supervised language understanding methods, this learning paradigm holds considerable promise and—as evidenced by GPT-3—can yield impressive results when the underlying LM is substantially enlarged. Main takeaway Power Laws In this overview, we will frequently refer to the concept of power laws. To illustrate, consider a statement from a research paper: "The LM’s test loss demonstrates a power law dependency on the number of model parameters." This assertion establishes a correlation between two variables—the loss and the number of model parameters—wherein adjustments to one variable induce proportionate, scale-independent alterations in the other. For clarity, a power law is mathematically expressed as follows. In this equation, x and y represent the variables under examination, while a and p influence the power law's shape and dynamics. When this power law (with a = 1, p = 0.5, and 0 < x, y < 1) is graphed, converting both axes to a logarithmic scale reveals a distinct linear trend, characteristic of power laws. Power laws describe how one variable scales as a power of another. Our discussions will include an inverse version of a power law, represented as: This equation employs a negative exponent for p, producing a graph where one variable decreases as the other increases. In our analysis of LMs, we observe that the LM loss decreases in accordance with a power law across various dimensions, such as model or dataset size. Further elaboration on this will follow in subsequent sections. Additional Useful Concepts In addition to the foundational principles of language modeling, there are several ancillary concepts that will be beneficial to understand moving forward. The primary focus of the papers discussed in this overview is on scaling models like GPT and GPT-2 [4, 5]. As models grow in complexity, training them becomes increasingly challenging due to heightened computational and memory demands. Distributed training methods, which utilize additional hardware resources (e.g., more servers/GPUs), facilitate the scalability of large training operations. Distributed Training There are various strategies for distributing the training workload of neural networks. One such strategy is data parallel training, which involves: Taking a large mini-batch, Dividing it into smaller sub-batches, Performing computations for each sub-batch in parallel across different GPUs, Consolidating the results from each GPU to update the central model. This method enhances training efficiency by parallelizing computations across multiple GPUs. Alternatively, model-parallel training divides the model itself across several GPUs. For instance, each layer—or even sub-components of each layer—might be allocated to different GPUs. This distribution allows the forward pass of the training process to occur across multiple devices, enabling the training of larger models and potentially improving efficiency through sophisticated pipelining and parallelization. For the purposes of this overview, it is important to recognize that leveraging multiple GPUs can make training of large language models (LLMs) more manageable and efficient. Data and model parallel training represent popular techniques in distributed training. There are numerous other strategies and considerations in this field that significantly enhance practical outcomes in deep learning. Utilizing large batches in data parallel training is generally advantageous for computational efficiency. However, excessively large batches may impair model performance and escalate computational costs, requiring more hardware. This phenomenon of diminishing returns becomes evident beyond a certain batch size; see below. Critical Batch Size (from [3]) Research has empirically determined the 'critical batch size', a metric that estimates the optimal batch size across various applications. Beyond this critical size, performance gains and computational efficiency start to diminish. As we will explore, some research adopts this critical batch size as a standard for efficient resource utilization during training. Language models generate text sequences in response to prompts through an autoregressive process, predicting each subsequent word based on the previous context. The simplest method continuously selects the most likely next word, but this approach may not yield the optimal overall sequence. Beam Search Beam search offers an alternative: instead of choosing the single most probable next word, it selects the top-k most probable options at each step, maintaining a list of potential sequences. At the conclusion of the generation process, the most likely overall sequence from this list is selected. This method provides a more effective approximation of the best sequence by considering multiple potential continuations at each step, thereby enhancing the quality of the generated text. Key Insights and Strategic Implications The foundational goal of GPT models was to develop universal language models capable of addressing a broad spectrum of tasks without necessitating task-specific tuning. This ambition is grounded in the premise that a profound comprehension of language modeling—specifically, the prediction of subsequent words in sequences—can be extrapolated to a variety of applications. Initially, models such as GPT and GPT-2 did not meet these expectations, delivering subpar performance in task-agnostic settings compared to supervised models. However, this overview has demonstrated that amplifying the scale of these language models (LMs) is an effective strategy for achieving high-performance, task-agnostic language understanding capabilities. This approach culminated in the conceptualization and deployment of GPT-3, an enormously scaled language model (approximately 100 times larger than GPT-2) that significantly outstrips the task-agnostic performance of its predecessors. The principle of scaling—enhancing model performance through the augmentation of model size, data volume, and computational resources—has been pivotal. Empirical data suggest that to optimize the efficacy of language model training, it is critical to substantially expand the model's dimensions while moderately increasing the dataset and batch size used during pre-training. This results in models that not only exhibit greater sample efficiency but also improve performance in accordance with power laws related to model size, data volume, and computational investment. Scaling Laws The advent of GPT-3, a model boasting 175 billion parameters, exemplifies the practical validation of these scaling theories. When such a colossal model is pre-trained across extensive text corpora, it demonstrates marked enhancements in task-agnostic, few-shot learning capabilities. Although GPT-3 may still lag behind specialized, supervised methods in certain benchmarks, subsequent studies have convincingly shown that as LLMs increase in size, their capacity for in-context learning also intensifies. Despite GPT-3’s architectural similarities to GPT-2, engineering a model of this magnitude underscores the transformative potential and scalability of foundational language models. Prospects of Scaling In conclusion, the journey from the initial GPT models to GPT-3 highlights a significant shift in the understanding and application of language models. The scalability of these systems not only advances their performance but also broadens their applicability across a diverse range of language understanding tasks, setting a new benchmark for future developments in the field of artificial intelligence. Bibliography [1] Kaplan, Jared, et al. “Scaling laws for neural language models.” arXiv preprint arXiv:2001.08361 (2020). [2] Brown, Tom, et al. “Language models are few-shot learners.” Advances in neural information processing systems 33 (2020): 1877–1901. [3] McCandlish, Sam, et al. “An empirical model of large-batch training.” arXiv preprint arXiv:1812.06162 (2018). [4] Radford, Alec, et al. “Improving language understanding by generative pre-training.” (2018). [5] Radford, Alec, et al. “Language Models are Unsupervised Multitask Learners.” [6] Child, Rewon, et al. “Generating long sequences with sparse transformers.” arXiv preprint arXiv:1904.10509 (2019). [7] Raffel, Colin, et al. “Exploring the limits of transfer learning with a unified text-to-text transformer.” J. Mach. Learn. Res. 21.140 (2020): 1–67. [8] Liu, Yinhan, et al. “Roberta: A robustly optimized bert pretraining approach.” arXiv preprint arXiv:1907.11692 (2019).

Understanding Scaling Law through Data Science Lenses

Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about. That doesn’t mean these tools are strictly unsafe, but it does warrant attention. You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about . That doesn’t mean these tools are strictly unsafe, but it does warrant attention. you should know about You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 14 states have data privacy laws 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. recognized quantum-proof encryption methods 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. it increases visibility and reduces downtime Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. more than a hundred apps You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. outlines software supply chain security steps 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. 95% of cybersecurity issues Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf

How Can You Integrate Cybersecurity Into Your Content Automation Process?

5 SEO Mistakes That Will Get Your Website Penalized by Google and How to Fix Them Introduction In the ever-evolving landscape of search engine optimization (SEO), staying on top of best practices is crucial to maintaining your website's visibility and ranking on Google. However, certain mistakes can lead to penalties from the search giant, ultimately harming your site's performance. In this guide, we'll delve into five common SEO mistakes that can land your website in hot water with Google and provide step-by-step solutions to rectify them. Mistake 1: Keyword Stuffing What is Keyword Stuffing?
Keyword stuffing is the practice of overloading web pages with keywords or numbers to manipulate a site's ranking in search results. Why is it a Problem?
Google penalizes websites that engage in keyword stuffing because it detracts from the user experience and undermines the quality of search results. How to Fix It: Audit Your Content: Identify pages where keyword stuffing occurs by reviewing the density of keywords compared to the overall content.


Rewrite Content Naturally: Replace excessive keywords with synonyms or related terms to maintain readability while still optimizing for search engines.


Focus on Quality: Prioritize creating high-quality, valuable content that addresses user intent rather than obsessing over keyword density. Mistake 2: Duplicate Content What is Duplicate Content?
Duplicate content refers to identical or substantially similar content that appears on multiple URLs. Why is it a Problem?
Google may struggle to determine which version of the content to index, leading to lower rankings for affected pages. How to Fix It: Use 301 Redirects: If multiple URLs lead to the same content, set up 301 redirects to consolidate link equity and direct users to the preferred URL.


Canonical Tags: Implement canonical tags to indicate the preferred version of a page to search engines.


Regularly Check for Scraped Content: Monitor your site for instances of scraped content, and take action to have it removed or properly attributed. Mistake 3: Broken Links and Redirects What are Broken Links and Redirects?
Broken links are hyperlinks that lead to pages that no longer exist or return error messages, while redirects occur when one URL forwards to another. Why are They a Problem?
Broken links and redirects disrupt the user experience and can prevent search engine crawlers from properly indexing your site. How to Fix Them: Regularly Check for Broken Links: Utilize tools like Google Search Console or third-party plugins to identify and fix broken links on your site.


Update Redirects: Ensure that redirects are implemented correctly and lead to relevant, functioning pages.


Monitor Site Changes: Whenever you make changes to your site's structure or URLs, double-check that all internal and external links are updated accordingly. Mistake 4: Thin or Low-Quality Content What is Thin Content?
Thin content refers to pages that offer little to no value to users, often consisting of minimal text or duplicate content. Why is it a Problem?
Google prioritizes high-quality, informative content in its search results, so pages with thin content are unlikely to rank well. How to Fix It: Content Audit: Evaluate your website's pages to identify thin content and either improve it with additional information or remove it altogether.


Focus on E-A-T: Emphasize expertise, authoritativeness, and trustworthiness (E-A-T) in your content creation to establish credibility with both users and search engines.


Diversify Content Formats: Incorporate diverse content formats such as videos, infographics, and interactive elements to enhance user engagement and provide value. Mistake 5: Ignoring Mobile Optimization Why is Mobile Optimization Important?
With the majority of internet traffic coming from mobile devices, optimizing your website for mobile users is essential for providing a seamless browsing experience. Why is it a Problem?
Failure to optimize for mobile can result in higher bounce rates, lower engagement, and decreased visibility in mobile search results. How to Fix It: Responsive Design: Implement a responsive design that adapts to various screen sizes and devices, ensuring consistent functionality and usability.


Page Speed Optimization: Prioritize loading speed by optimizing images, minifying code, and leveraging browser caching to enhance mobile performance.


Mobile-Friendly Content: Format content for easy readability on smaller screens, with concise paragraphs, larger fonts, and clickable elements spaced for touch navigation. FAQ Q: How long does it take to recover from a Google penalty?
A: The recovery time can vary depending on the severity of the penalty and the actions taken to address the issues. In some cases, recovery can take weeks or even months as Google reevaluates your site's compliance with its guidelines. Q: Are there any tools to help identify SEO issues on my website?
A: Yes, several tools, such as Google Search Console, SEMrush, and Moz, offer features to audit your site for SEO issues, including keyword stuffing, duplicate content, broken links, and more. Q: Can I avoid penalties altogether by following SEO best practices from the start?
A: While adhering to SEO best practices significantly reduces the risk of penalties, it's still possible to encounter issues over time, especially as search engine algorithms evolve. Regular monitoring and maintenance are essential for long-term success. Conclusion Avoiding these common SEO mistakes is essential for maintaining a healthy website presence and avoiding penalties from Google. By following the provided solutions and adhering to best practices, you can improve your site's visibility, user experience, and ultimately, its success in search engine rankings. 5 SEO Mistakes That Will Get Your Website Penalized by Google and How to Fix Them Introduction In the ever-evolving landscape of search engine optimization (SEO), staying on top of best practices is crucial to maintaining your website's visibility and ranking on Google. However, certain mistakes can lead to penalties from the search giant, ultimately harming your site's performance. In this guide, we'll delve into five common SEO mistakes that can land your website in hot water with Google and provide step-by-step solutions to rectify them. Mistake 1: Keyword Stuffing What is Keyword Stuffing? Keyword stuffing is the practice of overloading web pages with keywords or numbers to manipulate a site's ranking in search results. What is Keyword Stuffing? Why is it a Problem? Google penalizes websites that engage in keyword stuffing because it detracts from the user experience and undermines the quality of search results. Why is it a Problem? How to Fix It: How to Fix It: Audit Your Content: Identify pages where keyword stuffing occurs by reviewing the density of keywords compared to the overall content. Rewrite Content Naturally: Replace excessive keywords with synonyms or related terms to maintain readability while still optimizing for search engines. Focus on Quality: Prioritize creating high-quality, valuable content that addresses user intent rather than obsessing over keyword density. Audit Your Content: Identify pages where keyword stuffing occurs by reviewing the density of keywords compared to the overall content. Audit Your Content : Identify pages where keyword stuffing occurs by reviewing the density of keywords compared to the overall content. Audit Your Content Rewrite Content Naturally: Replace excessive keywords with synonyms or related terms to maintain readability while still optimizing for search engines. Rewrite Content Naturally : Replace excessive keywords with synonyms or related terms to maintain readability while still optimizing for search engines. Rewrite Content Naturally Focus on Quality: Prioritize creating high-quality, valuable content that addresses user intent rather than obsessing over keyword density. Focus on Quality : Prioritize creating high-quality, valuable content that addresses user intent rather than obsessing over keyword density. Focus on Quality Mistake 2: Duplicate Content What is Duplicate Content? Duplicate content refers to identical or substantially similar content that appears on multiple URLs. What is Duplicate Content? Why is it a Problem? Google may struggle to determine which version of the content to index, leading to lower rankings for affected pages. Why is it a Problem? How to Fix It: How to Fix It: Use 301 Redirects: If multiple URLs lead to the same content, set up 301 redirects to consolidate link equity and direct users to the preferred URL. Canonical Tags: Implement canonical tags to indicate the preferred version of a page to search engines. Regularly Check for Scraped Content: Monitor your site for instances of scraped content, and take action to have it removed or properly attributed. Use 301 Redirects: If multiple URLs lead to the same content, set up 301 redirects to consolidate link equity and direct users to the preferred URL. Use 301 Redirects : If multiple URLs lead to the same content, set up 301 redirects to consolidate link equity and direct users to the preferred URL. Use 301 Redirects Canonical Tags: Implement canonical tags to indicate the preferred version of a page to search engines. Canonical Tags : Implement canonical tags to indicate the preferred version of a page to search engines. Canonical Tags Regularly Check for Scraped Content: Monitor your site for instances of scraped content, and take action to have it removed or properly attributed. Regularly Check for Scraped Content : Monitor your site for instances of scraped content, and take action to have it removed or properly attributed. Regularly Check for Scraped Content Mistake 3: Broken Links and Redirects What are Broken Links and Redirects? Broken links are hyperlinks that lead to pages that no longer exist or return error messages, while redirects occur when one URL forwards to another. What are Broken Links and Redirects? Why are They a Problem? Broken links and redirects disrupt the user experience and can prevent search engine crawlers from properly indexing your site. Why are They a Problem? How to Fix Them: How to Fix Them: Regularly Check for Broken Links: Utilize tools like Google Search Console or third-party plugins to identify and fix broken links on your site. Update Redirects: Ensure that redirects are implemented correctly and lead to relevant, functioning pages. Monitor Site Changes: Whenever you make changes to your site's structure or URLs, double-check that all internal and external links are updated accordingly. Regularly Check for Broken Links: Utilize tools like Google Search Console or third-party plugins to identify and fix broken links on your site. Regularly Check for Broken Links : Utilize tools like Google Search Console or third-party plugins to identify and fix broken links on your site. Regularly Check for Broken Links Update Redirects: Ensure that redirects are implemented correctly and lead to relevant, functioning pages. Update Redirects : Ensure that redirects are implemented correctly and lead to relevant, functioning pages. Update Redirects Monitor Site Changes: Whenever you make changes to your site's structure or URLs, double-check that all internal and external links are updated accordingly. Monitor Site Changes : Whenever you make changes to your site's structure or URLs, double-check that all internal and external links are updated accordingly. Monitor Site Changes Mistake 4: Thin or Low-Quality Content What is Thin Content? Thin content refers to pages that offer little to no value to users, often consisting of minimal text or duplicate content. What is Thin Content? Why is it a Problem? Google prioritizes high-quality, informative content in its search results, so pages with thin content are unlikely to rank well. Why is it a Problem? How to Fix It: How to Fix It: Content Audit: Evaluate your website's pages to identify thin content and either improve it with additional information or remove it altogether. Focus on E-A-T: Emphasize expertise, authoritativeness, and trustworthiness (E-A-T) in your content creation to establish credibility with both users and search engines. Diversify Content Formats: Incorporate diverse content formats such as videos, infographics, and interactive elements to enhance user engagement and provide value. Content Audit: Evaluate your website's pages to identify thin content and either improve it with additional information or remove it altogether. Content Audit : Evaluate your website's pages to identify thin content and either improve it with additional information or remove it altogether. Content Audit Focus on E-A-T: Emphasize expertise, authoritativeness, and trustworthiness (E-A-T) in your content creation to establish credibility with both users and search engines. Focus on E-A-T : Emphasize expertise, authoritativeness, and trustworthiness (E-A-T) in your content creation to establish credibility with both users and search engines. Focus on E-A-T Diversify Content Formats: Incorporate diverse content formats such as videos, infographics, and interactive elements to enhance user engagement and provide value. Diversify Content Formats : Incorporate diverse content formats such as videos, infographics, and interactive elements to enhance user engagement and provide value. Diversify Content Formats Mistake 5: Ignoring Mobile Optimization Why is Mobile Optimization Important? With the majority of internet traffic coming from mobile devices, optimizing your website for mobile users is essential for providing a seamless browsing experience. Why is Mobile Optimization Important? Why is it a Problem? Failure to optimize for mobile can result in higher bounce rates, lower engagement, and decreased visibility in mobile search results. Why is it a Problem? How to Fix It: How to Fix It: Responsive Design: Implement a responsive design that adapts to various screen sizes and devices, ensuring consistent functionality and usability. Page Speed Optimization: Prioritize loading speed by optimizing images, minifying code, and leveraging browser caching to enhance mobile performance. Mobile-Friendly Content: Format content for easy readability on smaller screens, with concise paragraphs, larger fonts, and clickable elements spaced for touch navigation. Responsive Design: Implement a responsive design that adapts to various screen sizes and devices, ensuring consistent functionality and usability. Responsive Design : Implement a responsive design that adapts to various screen sizes and devices, ensuring consistent functionality and usability. Responsive Design Page Speed Optimization: Prioritize loading speed by optimizing images, minifying code, and leveraging browser caching to enhance mobile performance. Page Speed Optimization : Prioritize loading speed by optimizing images, minifying code, and leveraging browser caching to enhance mobile performance. Page Speed Optimization Mobile-Friendly Content: Format content for easy readability on smaller screens, with concise paragraphs, larger fonts, and clickable elements spaced for touch navigation. Mobile-Friendly Content : Format content for easy readability on smaller screens, with concise paragraphs, larger fonts, and clickable elements spaced for touch navigation. Mobile-Friendly Content FAQ Q: How long does it take to recover from a Google penalty? A: The recovery time can vary depending on the severity of the penalty and the actions taken to address the issues. In some cases, recovery can take weeks or even months as Google reevaluates your site's compliance with its guidelines. Q: How long does it take to recover from a Google penalty? Q: Are there any tools to help identify SEO issues on my website? A: Yes, several tools, such as Google Search Console, SEMrush, and Moz, offer features to audit your site for SEO issues, including keyword stuffing, duplicate content, broken links, and more. Q: Are there any tools to help identify SEO issues on my website? Q: Can I avoid penalties altogether by following SEO best practices from the start? A: While adhering to SEO best practices significantly reduces the risk of penalties, it's still possible to encounter issues over time, especially as search engine algorithms evolve. Regular monitoring and maintenance are essential for long-term success. Q: Can I avoid penalties altogether by following SEO best practices from the start? Conclusion Avoiding these common SEO mistakes is essential for maintaining a healthy website presence and avoiding penalties from Google. By following the provided solutions and adhering to best practices, you can improve your site's visibility, user experience, and ultimately, its success in search engine rankings.

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