How to derive actionable insights during data integrations

Amadeus CORE extends your health organisation’s integration engine with secure and scalable Amazon Web Services (AWS) cloud storage to provide a platform for data analytics. Amadeus CORE has out-of-the-box dashboards and reports that provide useful insights to improve an organisation’s operational efficiency. These will help with resolving data integration issues, uncover blockages and forecast future infrastructure or service needs. All the data is queryable via APIs or a SQL interface to enable the use of third-party reporting and analytics tools. Message characteristics and resolving data integration issues Integration development is a relatively straightforward problem when both the source and target systems are within your control. When interfacing with a system that is outside of your control, making changes to the data is a challenging undertaking.

Spotlight

TeamQuest Corporation

TeamQuest Corporation is the global leader in IT Service Optimization (ITSO) software, specializing in Infrastructure Monitoring, Capacity Planning and Capacity Management, and Business Value Dashboards. TeamQuest helps IT organizations consistently meet service levels while minimizing costs and mitigating risks. By combining performance data and business metrics, TeamQuest software enables IT organizations to provide accurate, objective information as input to critical business decisions. Companies around the world trust TeamQuest software to help them proactively improve service delivery and support best practices.

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Big Data Management, Data Science, Big Data

Can you really trust Amazon Product Recommendation?

Article | April 28, 2023

Since the internet became popular, the way we purchase things has evolved from a simple process to a more complicated process. Unlike traditional shopping, it is not possible to experience the products first-hand when purchasing online. Not only this, but there are more options or variants in a single product than ever before, which makes it more challenging to decide. To not make a bad investment, the consumer has to rely heavily on the customer reviews posted by people who are using the product. However, sorting through relevant reviews at multiple eCommerce platforms of different products and then comparing them to choose can work too much. To provide a solution to this problem, Amazon has come up with sentiment analysis using product review data. Amazon performs sentiment analysis on product review data with Artificial Intelligence technology to develop the best suitable products for the customer. This technology enables Amazon to create products that are most likely to be ideal for the customer. A consumer wants to search for only relevant and useful reviews when deciding on a product. A rating system is an excellent way to determine the quality and efficiency of a product. However, it still cannot provide complete information about the product as ratings can be biased. Textual detailed reviews are necessary to improve the consumer experience and in helping them make informed choices. Consumer experience is a vital tool to understand the customer's behavior and increase sales. Amazon has come up with a unique way to make things easier for their customers. They do not promote products that look similar to the other customer's search history. Instead, they recommend products that are similar to the product a user is searching for. This way, they guide the customer using the correlation between the products. To understand this concept better, we must understand how Amazon's recommendation algorithm has upgraded with time. The history of Amazon's recommendation algorithm Before Amazon started a sentiment analysis of customer product reviews using machine learning, they used the same collaborative filtering to make recommendations. Collaborative filtering is the most used way to recommend products online. Earlier, people used user-based collaborative filtering, which was not suitable as there were many uncounted factors. Researchers at Amazon came up with a better way to recommend products that depend on the correlation between products instead of similarities between customers. In user-based collaborative filtering, a customer would be shown recommendations based on people's purchase history with similar search history. In item-to-item collaborative filtering, people are shown recommendations of similar products to their recent purchase history. For example, if a person bought a mobile phone, he will be shown hints of that phone's accessories. Amazon's Personalization team found that using purchase history at a product level can provide better recommendations. This way of filtering also offered a better computational advantage. User-based collaborative filtering requires analyzing several users that have similar shopping history. This process is time-consuming as there are several demographic factors to consider, such as location, gender, age, etc. Also, a customer's shopping history can change in a day. To keep the data relevant, you would have to update the index storing the shopping history daily. However, item-to-item collaborative filtering is easy to maintain as only a tiny subset of the website's customers purchase a specific product. Computing a list of individuals who bought a particular item is much easier than analyzing all the site's customers for similar shopping history. However, there is a proper science between calculating the relatedness of a product. You cannot merely count the number of times a person bought two items together, as that would not make accurate recommendations. Amazon research uses a relatedness metric to come up with recommendations. If a person purchased an item X, then the item Y will only be related to the person if purchasers of item X are more likely to buy item Y. If users who purchased the item X are more likely to purchase the item Y, then only it is considered to be an accurate recommendation. Conclusion In order to provide a good recommendation to a customer, you must show products that have a higher chance of being relevant. There are countless products on Amazon's marketplace, and the customer will not go through several of them to figure out the best one. Eventually, the customer will become frustrated with thousands of options and choose to try a different platform. So Amazon has to develop a unique and efficient way to recommend the products that work better than its competition. User-based collaborative filtering was working fine until the competition increased. As the product listing has increased in the marketplace, you cannot merely rely on previous working algorithms. There are more filters and factors to consider than there were before. Item-to-item collaborative filtering is much more efficient as it automatically filters out products that are likely to be purchased. This limits the factors that require analysis to provide useful recommendations. Amazon has grown into the biggest marketplace in the industry as customers trust and rely on its service. They frequently make changes to fit the recent trends and provide the best customer experience possible.

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Business Intelligence, Big Data Management, Big Data

Top 6 Marketing Analytics Trends in 2021

Article | May 15, 2023

The marketing industry keeps changing every year. Businesses and enterprises have the task of keeping up with the changes in marketing trends as they evolve. As consumer demands and behavior changed, brands had to move from traditional marketing channels like print and electronic to digital channels like social media, Google Ads, YouTube, and more. Businesses have begun to consider marketing analytics a crucial component of marketing as they are the primary reason for success. In uncertain times, marketing analytics tools calculate and evaluate the market status and enhances better planning for enterprises. As Covid-19 hit the world, organizations that used traditional marketing analytics tools and relied on historical data realized that many of these models became irrelevant. The pandemic rendered a lot of data useless. With machine learning (ML) and artificial intelligence (AI) in marketers’ arsenal, marketing analytics is turning virtual with a shift in the marketing landscape in 2021. They are also pivoting from relying on just AI technologies but rather combining big data with it. AI and machine learning help advertisers and marketers to improve their target audience and re-strategize their campaigns through advanced marketing attributes, which in turn increases customer retention and customer loyalty. While technology is making targeting and measuring possible, marketers have had to reassure their commitment to consumer privacy and data regulations and governance in their initiatives. They are also relying on third-party data. These data and analytics trends will help organizations deal with radical changes and uncertainties, with opportunities they bring with them over the next few years. To know why businesses are gravitating towards these trends in marketing analytics, let us look at why it is so important. Importance of Marketing Analytics As businesses extended into new marketing categories, new technologies were implemented to support them. This new technology was usually deployed in isolation, which resulted in assorted and disconnected data sets. Usually, marketers based their decisions on data from individual channels like website metrics, not considering other marketers channels. Website and social media metrics alone are not enough. In contrast, marketing analytics tools look at all marketing done across channels over a period of time that is vital for sound decision-making and effective program execution. Marketing analytics helps understand how well a campaign is working to achieve business goals or key performance indicators. Marketing analytics allows you to answer questions like: • How are your marketing initiatives/ campaigns working? What can be done to improve them? • How do your marketing campaigns compare with others? What are they spending their time and money on? What marketing analytics software are they using that helps them? • What should be your next step? How should you allocate the marketing budget according to your current spending? Now that the advantages of marketing analytics are clear, let us get into the details of the trends in marketing analytics of 2021: Rise of real-time marketing data analytics Reciprocation to any action is the biggest trend right now in digital marketing, especially post Covid. Brands and businesses strive to respond to customer queries and provide them with solutions. Running queries in a low-latency customer data platform have allowed marketers to filter the view by the audience and identify underachieving sectors. Once this data is collected, businesses and brands can then readjust their customer targeting and messaging to optimize their performance. To achieve this on a larger scale, organizations need to invest in marketing analytics software and platforms to balance data loads with processing for business intelligence and analytics. The platform needs to allow different types of jobs to run parallel by adding resources to groups as required. This gives data scientists more flexibility and access to response data at any given time. Real-time analytics will also aid marketers in identifying underlying threats and problems in their strategies. Marketers will have to conduct a SWOT analysis and continuously optimize their campaigns to suit them better. . Data security, regulatory compliance, and protecting consumer privacy Protecting market data from a rise in cybercrimes and breaches are crucial problems to be addressed in 2021. This year has seen a surge in data breaches that have damaged businesses and their infrastructures to different levels. As a result, marketers have increased their investments in encryption, access control, network monitoring, and other security measures. To help comply with the General Data Protection Regulation (GDPR) of the European Union, the California Consumer Privacy Act (CCPA), and other regulatory bodies, organizations have made the shift to platforms where all consumer data is in one place. Advanced encryptions and stateless computing have made it possible to securely store and share governed data that can be kept in a single location. Interacting with a single copy of the same data will help compliance officers tasked with identifying and deleting every piece of information related to a particular customer much easier and the possibility of overseeing something gets canceled. Protecting consumer privacy is imperative for marketers. They offer consumers the control to opt out, eradicate their data once they have left the platform, and remove information like location, access control to personally identifiable information like email addresses and billing details separated from other marketing data. Predictive analytics Predictive analytics’ analyzes collected data and predicts future outcomes through ML and AI. It maps out a lookalike audience and identifies which strata are most likely to become a high-value customer and which customer strata has the highest likelihood of churn. It also gauges people’s interests based on their browsing history. With better ML models, predictions have become better overtime, leading to increased customer retention and a drop in churn. According to the research by Zion Market Research, by 2022, the global market for predictive analytics is set to hit $11 billion. Investment in first-party data Cookies-enabled website tracking led marketers to know who was visiting their website and re-calibrate their ads to these people throughout the web. However, in 2020, Google announced cookies would be phased out of Chrome within two years while they had already removed them from Safari and Firefox. Now that adding low-friction tracking to web pages will be tough, marketers will have to gather more limited data. This will then be then integrated with first-party data sets to get a rounded view of the customer. Although a big win for consumer privacy activists, it is difficult for advertisers and agencies to find it more difficult to retarget ads and build audiences in their data management platforms. In a digital world without cookies, marketers now understand how customer data is collected, introspect on their marketing models, and evaluate their marketing strategy. Emergence of contextual customer experience These trends in marketing analytics have become more contextually conscious since the denunciation of cookies. Since marketers are losing their data sets and behavioral data, they have an added motivation to invest in insights. This means that marketers have to target messaging based on known and inferred customer characteristics like their age, location, income, brand affinity, and where these customers are in their buying journey. For example, marketers should tailor messaging in ads to make up consumers based on the frequency of their visits to the store. Effective contextual targeting hinges upon marketers using a single platform for their data and creates a holistic customer profile. Reliance on third-party data Even though there has been a drop in third-party data collection, marketers will continue to invest in third-party data which have a complete understanding of their customers that augments the first-party data they have. Historically, third-party data has been difficult to source and maintain for marketers. There are new platforms that counter improvement of data like long time to value, cost of maintaining third-party data pipelines, and data governance problems. U.S. marketers have spent upwards of $11.9 billion on third-party audience data in 2019, up 6.1% from 2018, and this reported growth curve is going to be even steeper in 2021, according to a study by Interactive Advertising Bureau and Winterberry Group. Conclusion Marketing analytics enables more successful marketing as it shows off direct results of the marketing efforts and investments. These new marketing data analytics trends have made their definite mark and are set to make this year interesting with data and AI-based applications mixed with the changing landscape of marketing channels. Digital marketing will be in demand more than ever as people are purchasing more online. Frequently Asked Questions Why is marketing analytics so important? Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity. What is the use of marketing analytics? Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods. Which industries use marketing analytics? Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically. What are the types of marketing analytics tools? Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity." } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods." } },{ "@type": "Question", "name": "Which industries use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically." } },{ "@type": "Question", "name": "What are the types of marketing analytics tools?", "acceptedAnswer": { "@type": "Answer", "text": "Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc." } }] }

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Performance Management

Exploiting IoT Data Analytics for Business Success

Article | May 30, 2023

The Internet of Things has been the hype in the past few years. It is set to play an important role in industries. Not only businesses but also consumers attempt to follow developments that come with the connected devices. Smart meters, sensors, and manufacturing equipment all can remodel the working system of companies. Based on the Statista reports, the IoT market value of 248 billion US dollars in 2020 is expected to reach a worth of 1.6 Trillion USD by 2025. The global market is in the support of IoT development and its power to bring economic growth. But, the success of IoT without the integration of data analytics is impossible. This major growth component of IoT is the blend of IoT and Big Data - together known as IoT Data Analytics. Understanding IoT Data Analytics IoT Data Analytics is the analysis of large volumes of data that has been gathered from connected devices. As IoT devices generate a lot of data even in the shortest period, it becomes complex to analyze the enormous data volumes. Besides, the IoT data is quite similar to big data but has a major difference in their size and number of sources. To overcome the difficulty in IoT data integration, IoT data analytics is the best solution. With this combination, the process of data analysis becomes cost-effective, easier, and rapid. Why Data Analytics and IoT Will Be Indispensable? Data analytics is an important part of the success of IoT investments or applications. IoT along with Data analytics will allow businesses to make efficient use of datasets. How? Let’s get into it! Impelling Revenue Using data analytics in IoT investments businesses will become able to gain insight into customer behavior. It will lead to the crafting offers and services accordingly. As a result, companies will see a hike in their profits and revenue. Volume The vast amount of data sets that are being used by IoT applications needs to be organized and analyzed to obtain patterns. It can easily be achieved by using IoT analytics software. Competitive Advantage In an era full of IoT devices and applications, the competition has also increased. You can gain a competitive advantage by hire developers that can help with the IoT analytics implementations. It will assist businesses in providing better services and stand out from the competition. Now the next question arises: Where is it being implemented? Companies like Amazon, Microsoft, Siemens, VMware, and Huawei are using IoT data analytics for product usage analysis, sensor data analysis, camera data analysis, improved equipment maintenance, and optimizing operations. The Rise of IoT Data Analytics With the help of IoT Data Analytics, companies are ready to achieve more information that can be used to improve their overall performance and revenue. Although it has not reached every corner of the market yet, it is still being used for making the workplace more efficient and safe. The ability to analyze and predict data in real-time is definitely a game-changer for companies that need all of their equipment to work efficiently all the time. It is continuously growing to provide insights that were never possible before.

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Data Science

Thinking Like a Data Scientist

Article | December 23, 2020

Introduction Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist. The challenges of the Data Science professional Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them. Why Data Science automation often fails Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually. The Data Science mindset overall The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward. Specific aspects of the Data Science mindset Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand. The Data Science process The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture. Learning more about the topic Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!

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Spotlight

TeamQuest Corporation

TeamQuest Corporation is the global leader in IT Service Optimization (ITSO) software, specializing in Infrastructure Monitoring, Capacity Planning and Capacity Management, and Business Value Dashboards. TeamQuest helps IT organizations consistently meet service levels while minimizing costs and mitigating risks. By combining performance data and business metrics, TeamQuest software enables IT organizations to provide accurate, objective information as input to critical business decisions. Companies around the world trust TeamQuest software to help them proactively improve service delivery and support best practices.

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Data Science

J.D. Power Acquires Autovista Group to Expand Automotive Data Portfolio

J.D. Power | September 18, 2023

J.D. Power, a prominent global leader in data analytics, has recently announced a definitive agreement to acquire Autovista Group, a renowned pan-European and Australian automotive data, analytics, and industry insights provider. This strategic acquisition complements J.D. Power's existing strengths in vehicle valuation and intricate vehicle specification data and analytics while significantly expanding its presence within the European and Australian automotive markets. This acquisition represents a crucial moment, as it delivers substantial value to the customers of both companies. It brings together Autovista Group's extensive European and Australian market intelligence with J.D. Power's market-leading predictive analytics, valuation data, and customer experience datasets. These complementary offerings will empower original equipment manufacturers (OEMs), insurers, dealers, and financing companies with a truly global perspective on critical industry trends. They will also provide the tools to accurately predict risk, capitalize on emerging trends, and align sales strategies with real-time market dynamics. Pete Cimmet, Chief Strategy Officer at J.D. Power, stated: The addition of Autovista Group broadens our global presence allowing us to serve our customers across key global markets including North America, Europe and Asia/Australia. We look forward to partnering with the Autovista team to launch innovative new products and pursue strategic add-on acquisitions in Europe and Australia. [Source: Business Wire] Autovista Group, through its five prominent brands—Autovista, Glass's, Eurotax, Schwacke, and Rødboka—standardizes and categorizes a multitude of technical attributes for nearly every vehicle manufactured in European and Australian markets. This comprehensive approach offers clients a 360-degree view of detailed vehicle data, which is invaluable for valuations, forecasts, and repair estimates. Furthermore, Autovista Group's robust analytical solutions and its team of seasoned experts are trusted by stakeholders across the automobile industry for their in-depth insights and benchmarks related to vehicle values, ownership, replacements, and repair costs. Under this agreement, Autovista Group's senior leadership, along with its 700 employees, will remain part of the organization, serving as J.D. Power's automotive data and analytics platform for Australia and Europe. Lindsey Roberts will continue to lead the team in her role as President of J.D. Power Europe, reporting to CEO Dave Habiger. Currently, Autovista Group is owned by Hayfin Capital Management, a prominent European alternative asset management firm. The anticipated closure of the Autovista Group acquisition is set for conclusion by the end of 2023, pending customary closing conditions and regulatory review and approval. For this transaction, RBC Capital Markets acted as the exclusive financial advisor, and Kirkland & Ellis provided legal counsel to J.D. Power. TD Cowen served as the exclusive financial advisor, with Macfarlanes, Cravath, Swaine & Moore, and Mishcon de Reya acting as legal advisors to Autovista Group and Hayfin. About J.D. Power J.D. Power, a renowned consumer insights, advisory services, and data and analytics firm, has consistently spearheaded the use of big data, artificial intelligence (AI), and algorithmic modeling to illuminate the intricacies of consumer behavior for more than half a century. With a storied legacy of providing in-depth industry intelligence on customer interactions with brands and products, J.D. Power serves as the trusted leader for the world's preeminent enterprises, spanning diverse major sectors, profoundly influencing and refining their customer-centric strategies.

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Business Intelligence, Big Data Management, Big Data

SQream Expands its End-To-End Low-Code Analytics Platform with Flex Connector AI Assistant

PR Newswire | August 17, 2023

SQream, the scalable data analytics company built for massive data stores and AI/ML workloads, announced today that its low-code ELT and analytics platform Panoply, is launching an AI Flex Connector helper which leverages generative AI to streamline the path to business intelligence. This tool will make it even easier for users to collect all of their business data - from CRMs, user applications, and other tools - into one single source, and further minimize the technical requirements to generate quick data insights. While there are multiple ingestion tools already on the market, these tools are often limited in terms of which data sources can connect with them. Released in April 2023, Panoply's Flex Connector has enabled greater platform flexibility by supporting connections to any RestAPI or GraphQL data source. The Flex Connector currently requires users or the Panoply Customer Success team to sift through multiple API documents to find the configuration that meets their needs, but the new Flex Connector AI helper takes these capabilities to the next level by removing this manual process and instead relying on generative AI to complete the required research. This will enable users to skip the majority of the steps previously required and provide a working configuration that analysts will then customize with minimal information (authentication details, domain names, dates etc.). "We're excited about the future of AI in data and how it can make data in general even simpler to use and more accessible for non-technical users," said Ittai Bareket, GM of SQream Americas and Panoply. "With our upcoming AI focused product enhancements, we're looking to automate and outsource the more technical and time consuming aspects of gaining insights from your data." The new feature is prompted by Open AI LLM models, which are deployed on Microsoft Azure and enable applications built on top of the LangChain framework, allowing users to switch between models in the future. The user provides two parameters to prompt the tool to scan the web for the most up-to-date API documentation of the selected service, and within it all the requirements needed to extract the selected resource. About Panoply by SQream Panoply's managed data warehouse plus ELT and dashboards make it easy for users to sync, store, access, and visualize their data without complex code. Panoply is a product line of SQream, a data analytics company that helps organizations break through barriers to ask the biggest, most important questions from their data. SQream's GPU-based technology empowers businesses to overcome dataset limits and query complexity to analyze exponentially more data, and get substantially faster insights at dramatic cost-savings. By leveraging SQream's advanced analytics capabilities for AI/ML, enterprises can stay ahead of their competitors while reducing hardware usage. If you want to take your data initiatives to the next level, Ask Bigger and unlock new opportunities with SQream. About SQream SQream is a data analytics company that helps organizations Ask Bigger by providing them with accurate insights at a lower cost. Our unique technology empowers businesses to analyze exponentially more data, and get substantially faster insights at dramatic cost-savings. By leveraging SQream's advanced analytics capabilities, organizations are able to stay ahead of their competitors while reducing hardware usage. If you want to take your data exploration to the next level, Ask Bigger and unlock new opportunities with SQream.

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Business Intelligence, Big Data Management, Data Science

Unravel Data Launches Cloud Data Cost Observability and Optimization for Google Cloud BigQuery

Business Wire | August 11, 2023

Unravel Data, the first AI-enabled data observability and FinOps platform built to address the speed and scale of modern data platforms, today announced the release of Unravel 4.8.1, enabling Google Cloud BigQuery customers to see and better manage their cloud data costs by understanding specific cost drivers, allocation insights, and performance and cost optimization of SQL queries. This launch comes on the heels of the recent BigQuery pricing model change that replaced flat-rate and flex slot pricing with three new pricing tiers, and will help BigQuery customers to implement FinOps in real-time to select the right new pricing plan based on their usage, and maximize workloads for greater return on cloud data investments. As today’s enterprises implement artificial intelligence (AI) and machine learning (ML) models, to continually garner more business value from their data, they are experiencing exploding cloud data costs, with a lack of visibility into cost-drivers and a lack of control for managing and optimizing their spend. As cloud costs continue to climb, managing cloud spend remains a top challenge for global business leaders. Data management services are the fastest-growing category of cloud service spending, representing 39% of the total cloud bill. Unravel 4.8.1 enables visibility into BigQuery compute and storage spend and provides cost optimization intelligence using its built-in AI to improve workload cost efficiency. Unravel’s purpose-built AI for BigQuery delivers insights based on Unravel’s deep observability of the job, user, and code level to supply AI-driven cost optimization recommendations for slots and SQL queries, including slot provisioning, query duration, autoscaling efficiencies, and more. With Unravel, BigQuery users can speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads. BigQuery customers can also use Unravel to customize dashboards and alerts with easy-to-use widgets that offer insights on spend, performance, and unit economics. “As AI continues to drive exponential data usage, companies are facing more problems with broken pipelines and inefficient data processing which slows time to business value and adds to the exploding cloud data bills. Today, most organizations do not have the visibility into cloud data spend or ways to optimize data pipelines and workloads to lower spend and mitigate problems,” said Kunal Agarwal, CEO and Co-founder, Unravel Data. “With Unravel’s built-in AI, BigQuery users have data observability and FinOps in one solution to increase data pipeline reliability and cost efficiency so that businesses can bring even more workloads to the cloud for the same spend.” “Enterprises are increasingly concerned about lack of visibility into and control of their cloud-related costs, especially for cloud-based analytics projects,” says Kevin Petrie, VP of Research at The Eckerson Group. "By implementing FinOps programs, they can predict, measure, monitor, optimize and account for cloud-related costs related to data and analytics projects." At the core of Unravel Data’s platform is its AI-powered Insights Engine, purpose-built for data platforms, which understands all the intricacies and complexities of each modern data platform and the supporting infrastructure to optimize efficiency and performance. The Insights Engine ingests and interprets the continuous millions of ongoing metadata streams to provide real-time insights into application and system performance, and recommendations to optimize costs and performance for operational and financial efficiencies. Unravel 4.8.1 includes additional features, such as: Recommendations for baseline and max setting for reservations Scheduling insights for recurring jobs SQL insights and anti-patterns Recommendations for custom quotas for projects and users Top-K projects, users, and jobs Showback by compute and storage types, services, pricing plans, etc. Chargeback by projects and users Out-of-the-box and custom alerts and dashboards Project/Job views of insights and details Side-by-side job comparisons Data KPIs, metrics, and insights such as size and number of tables and partitions, access by jobs, hot/warm/cold tables About Unravel Data Unravel Data radically transforms the way businesses understand and optimize the performance and cost of their modern data applications – and the complex data pipelines that power those applications. Providing a unified view across the entire data stack, Unravel’s market-leading data observability platform leverages AI, machine learning, and advanced analytics to provide modern data teams with the actionable recommendations they need to turn data into insights. A recent winner of the Best Data Tool & Platform of 2023 as part of the annual SIIA CODiE Awards, some of the world’s most recognized brands like Adobe, Maersk, Mastercard, Equifax, and Deutsche Bank rely on Unravel Data to unlock data-driven insights and deliver new innovations to market. To learn more, visit https://www.unraveldata.com.

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Data Science

J.D. Power Acquires Autovista Group to Expand Automotive Data Portfolio

J.D. Power | September 18, 2023

J.D. Power, a prominent global leader in data analytics, has recently announced a definitive agreement to acquire Autovista Group, a renowned pan-European and Australian automotive data, analytics, and industry insights provider. This strategic acquisition complements J.D. Power's existing strengths in vehicle valuation and intricate vehicle specification data and analytics while significantly expanding its presence within the European and Australian automotive markets. This acquisition represents a crucial moment, as it delivers substantial value to the customers of both companies. It brings together Autovista Group's extensive European and Australian market intelligence with J.D. Power's market-leading predictive analytics, valuation data, and customer experience datasets. These complementary offerings will empower original equipment manufacturers (OEMs), insurers, dealers, and financing companies with a truly global perspective on critical industry trends. They will also provide the tools to accurately predict risk, capitalize on emerging trends, and align sales strategies with real-time market dynamics. Pete Cimmet, Chief Strategy Officer at J.D. Power, stated: The addition of Autovista Group broadens our global presence allowing us to serve our customers across key global markets including North America, Europe and Asia/Australia. We look forward to partnering with the Autovista team to launch innovative new products and pursue strategic add-on acquisitions in Europe and Australia. [Source: Business Wire] Autovista Group, through its five prominent brands—Autovista, Glass's, Eurotax, Schwacke, and Rødboka—standardizes and categorizes a multitude of technical attributes for nearly every vehicle manufactured in European and Australian markets. This comprehensive approach offers clients a 360-degree view of detailed vehicle data, which is invaluable for valuations, forecasts, and repair estimates. Furthermore, Autovista Group's robust analytical solutions and its team of seasoned experts are trusted by stakeholders across the automobile industry for their in-depth insights and benchmarks related to vehicle values, ownership, replacements, and repair costs. Under this agreement, Autovista Group's senior leadership, along with its 700 employees, will remain part of the organization, serving as J.D. Power's automotive data and analytics platform for Australia and Europe. Lindsey Roberts will continue to lead the team in her role as President of J.D. Power Europe, reporting to CEO Dave Habiger. Currently, Autovista Group is owned by Hayfin Capital Management, a prominent European alternative asset management firm. The anticipated closure of the Autovista Group acquisition is set for conclusion by the end of 2023, pending customary closing conditions and regulatory review and approval. For this transaction, RBC Capital Markets acted as the exclusive financial advisor, and Kirkland & Ellis provided legal counsel to J.D. Power. TD Cowen served as the exclusive financial advisor, with Macfarlanes, Cravath, Swaine & Moore, and Mishcon de Reya acting as legal advisors to Autovista Group and Hayfin. About J.D. Power J.D. Power, a renowned consumer insights, advisory services, and data and analytics firm, has consistently spearheaded the use of big data, artificial intelligence (AI), and algorithmic modeling to illuminate the intricacies of consumer behavior for more than half a century. With a storied legacy of providing in-depth industry intelligence on customer interactions with brands and products, J.D. Power serves as the trusted leader for the world's preeminent enterprises, spanning diverse major sectors, profoundly influencing and refining their customer-centric strategies.

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Business Intelligence, Big Data Management, Big Data

SQream Expands its End-To-End Low-Code Analytics Platform with Flex Connector AI Assistant

PR Newswire | August 17, 2023

SQream, the scalable data analytics company built for massive data stores and AI/ML workloads, announced today that its low-code ELT and analytics platform Panoply, is launching an AI Flex Connector helper which leverages generative AI to streamline the path to business intelligence. This tool will make it even easier for users to collect all of their business data - from CRMs, user applications, and other tools - into one single source, and further minimize the technical requirements to generate quick data insights. While there are multiple ingestion tools already on the market, these tools are often limited in terms of which data sources can connect with them. Released in April 2023, Panoply's Flex Connector has enabled greater platform flexibility by supporting connections to any RestAPI or GraphQL data source. The Flex Connector currently requires users or the Panoply Customer Success team to sift through multiple API documents to find the configuration that meets their needs, but the new Flex Connector AI helper takes these capabilities to the next level by removing this manual process and instead relying on generative AI to complete the required research. This will enable users to skip the majority of the steps previously required and provide a working configuration that analysts will then customize with minimal information (authentication details, domain names, dates etc.). "We're excited about the future of AI in data and how it can make data in general even simpler to use and more accessible for non-technical users," said Ittai Bareket, GM of SQream Americas and Panoply. "With our upcoming AI focused product enhancements, we're looking to automate and outsource the more technical and time consuming aspects of gaining insights from your data." The new feature is prompted by Open AI LLM models, which are deployed on Microsoft Azure and enable applications built on top of the LangChain framework, allowing users to switch between models in the future. The user provides two parameters to prompt the tool to scan the web for the most up-to-date API documentation of the selected service, and within it all the requirements needed to extract the selected resource. About Panoply by SQream Panoply's managed data warehouse plus ELT and dashboards make it easy for users to sync, store, access, and visualize their data without complex code. Panoply is a product line of SQream, a data analytics company that helps organizations break through barriers to ask the biggest, most important questions from their data. SQream's GPU-based technology empowers businesses to overcome dataset limits and query complexity to analyze exponentially more data, and get substantially faster insights at dramatic cost-savings. By leveraging SQream's advanced analytics capabilities for AI/ML, enterprises can stay ahead of their competitors while reducing hardware usage. If you want to take your data initiatives to the next level, Ask Bigger and unlock new opportunities with SQream. About SQream SQream is a data analytics company that helps organizations Ask Bigger by providing them with accurate insights at a lower cost. Our unique technology empowers businesses to analyze exponentially more data, and get substantially faster insights at dramatic cost-savings. By leveraging SQream's advanced analytics capabilities, organizations are able to stay ahead of their competitors while reducing hardware usage. If you want to take your data exploration to the next level, Ask Bigger and unlock new opportunities with SQream.

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Business Intelligence, Big Data Management, Data Science

Unravel Data Launches Cloud Data Cost Observability and Optimization for Google Cloud BigQuery

Business Wire | August 11, 2023

Unravel Data, the first AI-enabled data observability and FinOps platform built to address the speed and scale of modern data platforms, today announced the release of Unravel 4.8.1, enabling Google Cloud BigQuery customers to see and better manage their cloud data costs by understanding specific cost drivers, allocation insights, and performance and cost optimization of SQL queries. This launch comes on the heels of the recent BigQuery pricing model change that replaced flat-rate and flex slot pricing with three new pricing tiers, and will help BigQuery customers to implement FinOps in real-time to select the right new pricing plan based on their usage, and maximize workloads for greater return on cloud data investments. As today’s enterprises implement artificial intelligence (AI) and machine learning (ML) models, to continually garner more business value from their data, they are experiencing exploding cloud data costs, with a lack of visibility into cost-drivers and a lack of control for managing and optimizing their spend. As cloud costs continue to climb, managing cloud spend remains a top challenge for global business leaders. Data management services are the fastest-growing category of cloud service spending, representing 39% of the total cloud bill. Unravel 4.8.1 enables visibility into BigQuery compute and storage spend and provides cost optimization intelligence using its built-in AI to improve workload cost efficiency. Unravel’s purpose-built AI for BigQuery delivers insights based on Unravel’s deep observability of the job, user, and code level to supply AI-driven cost optimization recommendations for slots and SQL queries, including slot provisioning, query duration, autoscaling efficiencies, and more. With Unravel, BigQuery users can speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads. BigQuery customers can also use Unravel to customize dashboards and alerts with easy-to-use widgets that offer insights on spend, performance, and unit economics. “As AI continues to drive exponential data usage, companies are facing more problems with broken pipelines and inefficient data processing which slows time to business value and adds to the exploding cloud data bills. Today, most organizations do not have the visibility into cloud data spend or ways to optimize data pipelines and workloads to lower spend and mitigate problems,” said Kunal Agarwal, CEO and Co-founder, Unravel Data. “With Unravel’s built-in AI, BigQuery users have data observability and FinOps in one solution to increase data pipeline reliability and cost efficiency so that businesses can bring even more workloads to the cloud for the same spend.” “Enterprises are increasingly concerned about lack of visibility into and control of their cloud-related costs, especially for cloud-based analytics projects,” says Kevin Petrie, VP of Research at The Eckerson Group. "By implementing FinOps programs, they can predict, measure, monitor, optimize and account for cloud-related costs related to data and analytics projects." At the core of Unravel Data’s platform is its AI-powered Insights Engine, purpose-built for data platforms, which understands all the intricacies and complexities of each modern data platform and the supporting infrastructure to optimize efficiency and performance. The Insights Engine ingests and interprets the continuous millions of ongoing metadata streams to provide real-time insights into application and system performance, and recommendations to optimize costs and performance for operational and financial efficiencies. Unravel 4.8.1 includes additional features, such as: Recommendations for baseline and max setting for reservations Scheduling insights for recurring jobs SQL insights and anti-patterns Recommendations for custom quotas for projects and users Top-K projects, users, and jobs Showback by compute and storage types, services, pricing plans, etc. Chargeback by projects and users Out-of-the-box and custom alerts and dashboards Project/Job views of insights and details Side-by-side job comparisons Data KPIs, metrics, and insights such as size and number of tables and partitions, access by jobs, hot/warm/cold tables About Unravel Data Unravel Data radically transforms the way businesses understand and optimize the performance and cost of their modern data applications – and the complex data pipelines that power those applications. Providing a unified view across the entire data stack, Unravel’s market-leading data observability platform leverages AI, machine learning, and advanced analytics to provide modern data teams with the actionable recommendations they need to turn data into insights. A recent winner of the Best Data Tool & Platform of 2023 as part of the annual SIIA CODiE Awards, some of the world’s most recognized brands like Adobe, Maersk, Mastercard, Equifax, and Deutsche Bank rely on Unravel Data to unlock data-driven insights and deliver new innovations to market. To learn more, visit https://www.unraveldata.com.

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