Understanding Big Data and Artificial Intelligence

Data is an important asset. Data leads to innovation and organizations tend to compete for leading these innovations on a global scale. Today, every business requires data and insights to stay relevant in the market. Big Data has a huge impact on the way organizations conduct their businesses. Big Data is used in different enterprises like travel, healthcare, manufacturing, governments, and more.  If they need to determine their audience, understand what clients want, forecast the needs of the customers and the clients, AI and big data analysis is vital to every decision-making scenario. When companies process the collected data accurately, they get the desired results, which leads them to their desired goals.

The term Big Data has been around since the 1990s. By the time we could fully comprehend it, Big Data had already amassed a huge amount of stored data. If this data is analyzed properly, it would reveal valuable industry insights into the industry to which the data belonged.

IT professionals and computer scientists realized that going through all of the data and analyzing it for the purpose was too big of a task for humans to undertake. When artificial intelligence (AI) algorithm came into the picture, it accomplished analyzing the accumulated data and deriving insights. The use of AI in Big Data is fundamental to get desired results for organizations.

According to Northeastern University, the amount of data in the world was 4.4 zettabytes in 2013. By of 2020, the data rose to 44 zettabytes.

When there is this amount of data produced globally, this information is invaluable to the enterprises and now can leverage AI algorithms to process it. Because of this, the companies can understand and influence customer behavior. By 2018, over 50% of countries had adopted Big Data.

Let us understand what Big Data, convergence of big data and AI, and impact of AI on big data analytics. 

Understanding Big Data

In simple words, Big Data is a term that comprises every tool and process that helps people use and manage vast sets of data. According to Gartner, Big Data is a “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision-making, and process automation.”

The concept of Big Data was created to capture trends, preferences, and user behavior in one place called the data lake. Big Data in enterprises can help them analyze and configure their customers’ motivations and come up with new ideas for the creation of new offerings. Big Data studies different methods of extracting, analyzing, or dealing with data sets that are too complicated for traditional data processing systems. To analyze a large amount of data requires a system designed to stretch its extraction and analysis capability.

Data is everywhere. This stockpile of data can give us insights and business analytics to the industry belonging to the data set. Therefore, the AI algorithms are written to benefit from large and complex data.

Importance of Big Data

Data is an integral part of understanding customer demographics and their motivations.
When customers interact with technology in active or passive manner, these actions create a new set of data. What contributes to this data creation is what they carry with them every day - their smartphones. Their cameras, credit cards, purchased products all contribute to their growing data profile. A correctly done analysis can tell a lot about their behavior patterns, personality, and events in the customer’s life. Companies can use this information to rethink their strategies, improve on their product, and create targeted marketing campaigns, which would ultimately lead them to their target customer.

Industry experts, for years and years, have discussed Big Data and its impact on businesses. Only in recent years, however, has it become possible to calculate that impact. Algorithms and software can now analyze large datasets quickly and efficiently.The forty-four zettabyte of data will only quadruple in the coming years. This collection and analysis of the data will help companies get the AI insights that will aid them in generating profits and be future-ready.

Organizations have been using Big Data for a long time. Here’s how those organizations are using Big Data to drive success:

Answering customer questions

Using big data and analytics, companies can learn the following things:

• What do customers want?
• Where are they missing out on?
• Who are their best and loyal customers?
• Why people choose different products?

Every day, as organizations gather more information, they can get more insights into sales and marketing. Once they get this data, they can optimize their campaigns to suit the customer’s needs. Learning from their online habits and with correct analysis, companies can send personalized promotional emails. These emails may prompt this target audience to convert into full-time customers.

Making confident decisions

As companies grow, they all need to make complex decisions. With in-depth analysis of marketplace knowledge, industry, and customers, Big Data can help you make confident choices. Big Data gives you a complete overview of everything you need to know. With the help of this, you can launch your marketing campaign or launch a new product in the market, or make a focused decision to generate the highest ROI. Once you add machine learning and AI to the mix, your Big Data collections can form a neural network to help your AI suggest useful company changes.

Optimizing and Understanding Business Processes

Cloud computing and machine learning help you to stay ahead by identifying opportunities in your company’s practices. Big Data analytics can tell you if your email strategy is working even when your social media marketing isn’t gaining you any following. You can also check which parts of your company culture have the right impact and result in the desired turnover. The existing evidence can help you make quick decisions and ensure you spend more of your budget on things that help your business grow.

Convergence of Big Data and AI

Big Data and Artificial Intelligence have a synergistic relationship. Data powers AI. The constantly evolving data sets or Big Data makes it possible for machine learning applications to learn and acquire new skills. This is what they were built to do. Big Data’s role in AI is supplying algorithms with all the essential information for developing and improving features, pattern recognition capabilities.

AI and machine learning use data that has been cleansed of duplicate and unnecessary data. This clean and high-quality big data is then utilized to create and train intelligent AI algorithms, neural networks, and predictive models.

AI applications rarely stop working and learning. Once the “initial training” is done (initial training is preparing already collected data), they adjust their work as and when the data changes. This makes it necessary for data to be constantly collected.

When it comes to businesses using this technology, AI helps them use Big Data for analytics by making advanced tools accessible and obtainable to help users gain insights that would otherwise have been hidden in the huge amount of data. Once firms and businesses gain a hold on using AI and Big Data, they can provide decision-makers with a clear understanding of factors that affect their businesses. 

Impact of AI on Big Data Analytics

AI supports users in the Big Data cycle, including aggregation, storage, and retrieval of diverse data types from different data sources. This includes data management, context management, decision management, action management, and risk management.

Big Data can help alert problems and help find new solutions and get ideas about any new prospects. With the amount of information stream that comes in, it can be difficult to determine what is important and what isn’t. This is where AI and machine learning come in. It can help identify unusual patterns in the processes, help in the analysis, and suggest further steps to be taken.

It can also learn how users interact with analytics and learn subtle differences in meanings or context-specific nuances to understand numeric data sources. AI can also caution users about anomalies, unforeseen data patterns, monitoring events, and threats from system logs or social networking data.

Application of Big Data and Artificial Intelligence

After establishing how AI and Big Data work together, let us look at how some applications are benefitting from their synergy:

Banking and financial sectors

The banking and financial sectors apply these to monitor financial marketing activities. These institutions also use AI to keep an eye on any illegal trading activities. Trading data analytics are obtained for high-frequency trading, and decision making based on trading, risk analysis, and predictive analysis. It is also used for fraud warning and detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc.

Healthcare

AI has simplified health data prescriptions and health analysis, thus benefitting healthcare providers from the large data pool. Hospitals are using millions of collected data that allow doctors to use evidence-based medicine. Chronic diseases can be tracked faster by AI.

Manufacturing and supply chain

AI and Big Data in manufacturing, production management, supply chain management and analysis, and customer satisfaction techniques are flawless. The quality of products is thus much better with higher energy efficiency, reliable increase in levels, and profit increase.

Governments

Governments worldwide use AI applications like facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy explorations, environmental conservation, criminal investigations, and more.

Other sectors that use AI are mainly retail, entertainment, education, and more.

Conclusion

According to Gartner’s predictions, artificial intelligence will replace one in five workers by 2022. Firms and businesses can no longer afford to avoid using artificial intelligence and Big Data in their day-to-day. Investments in AI and Big Data analysis will be beneficial for everyone. Data sets will increase in the future, and with it, its application and investment will grow over time. Human relevance will continue to decrease as time goes by.

AI enables machine learning to be the future of the development of business technologies. It will automate data analysis and find new insights that were previously impossible to imagine by processing data manually. With machine learning, AI, and Big Data, we can redraw the way we approach everything else.

Frequently Asked Questions

Why does big data affect artificial intelligence?

Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents.

Is AI or big data better?

AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale.

Is AI used in big data?

When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI.

What is the future of AI in big data?

AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics.

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Audiencepoint

AudiencePoint is an email data company that tracks and indexes subscriber activity and engagement levels. Their software products – ListFit and Send Time Optimization – help marketers increase subscriber engagement by leveraging data in smart, innovative ways.

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Will Quantum Computers Make Supercomputers Obsolete in the Field of High Performance Computing?

Article | March 15, 2024

If you want an explicit answer without having to know the extra details, then here it is: Yes, there is a possibility that quantum computers can replace supercomputers in the field of high performance computing, under certain conditions. Now, if you want to know how and why this scenario is a possibility and what those conditions are, I’d encourage you to peruse the rest of this article. To start, we will run through some very simple definitions. Definitions If you work in the IT sector, you probably would have heard of the terms ‘high performance computing’, ‘supercomputers’ and ‘quantum computers’ many times. These words are thrown around quite often nowadays, especially in the area of data science and artificial intelligence. Perhaps you would have deduced their meanings from their context of use, but you may not have gotten the opportunity to explicitly sit down and do the required research on what they are and why they are used. Therefore, it is a good idea to go through their definitions, so that you have a better understanding of each concept. High Performance Computing: It is the process of carrying out complex calculations and computations on data at a very high speed. It is much faster than regular computing. Supercomputer: It is a type of computer that is used to efficiently perform powerful and quick computations. Quantum Computing: It is a type of computer that makes use of quantum mechanics’ concepts like entanglement and superposition, in order to carry out powerful computations. Now that you’ve gotten the gist of these concepts, let’s dive in a little more to get a wider scope of how they are implemented throughout the world. Background High performance computing is a thriving area in the sector of information technology, and rightly so, due to the rapid surge in the amount of data that is produced, stored, and processed every second. Over the last few decades, data has become increasingly significant to large corporations, small businesses, and individuals, as a result of its tremendous potential in their growth and profit. By properly analysing data, it is possible to make beneficial predictions and determine optimal strategies. The challenge is that there are huge amounts of data being generated every day. If traditional computers are used to manage and compute all of this data, the outcome would take an irrationally long time to be produced. Massive amounts of resources like time, computational power, and expenses would also be required in order to effectuate such computations. Supercomputers were therefore introduced into the field of technology to tackle this issue. These computers facilitate the computation of huge quantities of data at much higher speeds than a regular computer. They are a great investment for businesses that require data to be processed often and in large amounts at a time. The main advantage of supercomputers is that they can do what regular computers need to do, but much more quickly and efficiently. They have an overall high level of performance. Till date, they have been applied in the following domains: • Nuclear Weapon Design • Cryptography • Medical Diagnosis • Weather Forecasting • Online Gaming • Study of Subatomic Particles • Tackling the COVID-19 Pandemic Quantum computers, on the other hand, use a completely different principle when functioning. Unlike regular computers that use bits as the smallest units of data, quantum computers generate and manipulate ‘qubits’ or ‘quantum bits’, which are subatomic particles like electrons or photons. These qubits have two interesting quantum properties which allow them to powerfully compute data – • Superposition: Qubits, like regular computer bits, can be in a state of 1 or 0. However, they also have the ability to be in both states of 1 and 0 simultaneously. This combined state allows quantum computers to calculate a large number of possible outcomes, all at once. When the final outcome is determined, the qubits fall back into a state of either 1 or 0. This property iscalled superposition. • Entanglement: Pairs of qubits can exist in such a way that two members of a pair of qubits exist in a single quantum state. In such a situation, changing the state of one of the qubits can instantly change the state of the other qubit. This property is called entanglement. Their most promising applications so far include: • Cybersecurity • Cryptography • Drug Designing • Financial Modelling • Weather Forecasting • Artificial Intelligence • Workforce Management Despite their distinct features, both supercomputers and quantum computers are immensely capable of providing users with strong computing facilities. The question is, how do we know which type of system would be the best for high performance computing? A Comparison High performance computing requires robust machines that can deal with large amounts of data - This involves the collection, storage, manipulation, computation, and exchange of data in order to derive insights that are beneficial to the user. Supercomputers have successfully been used so far for such operations. When the concept of a quantum computer first came about, it caused quite a revolution within the scientific community. People recognised its innumerable and widespread abilities, and began working on ways to convert this theoretical innovation into a realistic breakthrough. What makes a quantum computer so different from a supercomputer? Let’s have a look at Table 1.1 below. From the table, we can draw the following conclusions about supercomputers and quantum computers - 1. Supercomputers have been around for a longer duration of time, and are therefore more advanced. Quantum computers are relatively new and still require a great depth of research to sufficiently comprehend their working and develop a sustainable system. 2. Supercomputers are easier to provide inputs to, while quantum computers need a different input mechanism. 3. Supercomputers are fast, but quantum computers are much faster. 4. Supercomputers and quantum computers have some similar applications. 5. Quantum computers can be perceived as extremely powerful and highly advanced supercomputers. Thus, we find that while supercomputers surpass quantum computers in terms of development and span of existence, quantum computers are comparatively much better in terms of capability and performance. The Verdict We have seen what supercomputers and quantum computers are, and how they can be applied in real-world scenarios, particularly in the field of high performance computing. We have also gone through their differences and made significant observations in this regard. We find that although supercomputers have been working great so far, and they continue to provide substantial provisions to researchers, organisations, and individuals who require intense computational power for the quick processing of enormous amounts of data, quantum computers have the potential to perform much better and provide faster and much more adequate results. Thus, quantum computers can potentially make supercomputers obsolete, especially in the field of high performance computing, if and only if researchers are able to come up with a way to make the development, deployment, and maintenance of these computers scalable, feasible, and optimal for consumers.

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Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | August 17, 2023

Explore the benefits of using industrial big data for predictive maintenance strategies. Learn how businesses can shift from reactive to proactive maintenance approaches and optimize operations with the power of predictive analytics. Contents 1 Importance of Predictive Maintenance 2 Challenges of Traditional Reactive Maintenance for Enterprises 3 Emergence of Proactive Strategies for Predictive Maintenance 4 Reactive vs. Proactive Strategies 5 Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications 6 Navigating Implementation Challenges 6.1 Overcoming Data Collection and Pre-processing Challenges 6.2 Addressing Data Integration Challenges 6.3 Model Selection and Implementation Solutions 6.4 Staffing and Training Solutions 7 Leverage Predictive Maintenance for Optimal Operations 8 Final Thoughts 1. Importance of Predictive Maintenance Predictive maintenance (PdM) is a proactive maintenance approach that employs advanced downtime tracking software to evaluate data and predict when maintenance on equipment should be conducted. With PdM constantly monitoring equipment performance and health using sensors, maintenance teams can be alerted when equipment is nearing a breakdown, allowing them to take mitigation measures before any unscheduled downtime occurs. The global predictive maintenance market is expected to expand at a 25.5% CAGR to reach USD 23 billion in 2025 during the forecast period. (Market Research Future) Organizations often prefer PdM as a maintenance management method as it reduces costs with an upfront investment compared to preventive and reactive maintenance. Furthermore, maintenance has become crucial to ensuring smooth system functioning in today's complex industrial environment. Therefore, predictive maintenance is an essential strategy for industrial organizations, as it improves safety and productivity and reduces costs. As industrial equipment becomes more automated and diagnostic tools become more advanced and affordable, more and more plants are taking a proactive approach to maintenance. The immediate goal is to identify and fix problems before they result in a breakdown, while the long-term goal is to reduce unexpected outages and extend asset life. Plants that implement predictive maintenance processes see a 30% increase in equipment mean time between failures (MTBF), on average. This means your equipment is 30% more reliable and 30% more likely to meet performance standards with a predictive maintenance strategy. (Source: FMX) 2. Challenges of Traditional Reactive Maintenance for Enterprises The waning popularity of reactive maintenance is attributed to several inherent limitations, such as exorbitant costs and a heightened likelihood of equipment failure and safety hazards. At the same time, the pursuit of maintaining industrial plants at maximum efficiency with minimal unplanned downtime is an indispensable objective for all maintenance teams. However, the traditional reactive approach, which involves repairing equipment only when it malfunctions, can result in substantial expenses associated with equipment downtime, product waste, and increased equipment replacement and labor costs. To overcome these challenges, organizations can move towards proactive maintenance strategies, which leverage advanced downtime tracking software to anticipate maintenance needs and forestall potential breakdowns. 3. Emergence of Proactive Strategies for Predictive Maintenance The constraints of reactive maintenance have instigated the emergence of proactive approaches, including predictive analytics. It employs real-time data gathered from equipment to predict maintenance needs and employs algorithms to recognize potential issues before they result in debilitating breakdowns. The data collected through sensors and analytics facilitates the establishment of a more thorough and precise assessment of the general well-being of the operation. With such proactive strategies, organizations can: Arrange maintenance undertakings in advance, Curtail downtime, Cut expenses, and Augment equipment reliability and safety 4. Reactive vs. Proactive Strategies As of 2020, 76% of the respondents in the manufacturing sector reported following a proactive maintenance strategy, while 56% used reactive maintenance (run-to-failure). (Source: Statista) Proactive maintenance strategies, such as predictive maintenance, offer many benefits over reactive maintenance, which can be costly and time-consuming. By collecting baseline data and analyzing trends, proactive maintenance strategies can help organizations perform maintenance only when necessary, based on real-world information. However, establishing a proactive maintenance program can be challenging, as limited maintenance resources must be directed to address the most critical equipment failures. Analyzing data from both healthy and faulty equipment can help organizations determine which failures pose the biggest risk to their operation. A proactive maintenance approach may assist in avoiding the fundamental causes of machine failure, addressing issues before they trigger failure, and extending machine life, making it a crucial strategy for any industrial operation. 5. Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications Big data analytics is a key enabler of predictive maintenance strategies. Its capability to process vast amounts of data provides valuable insights into equipment health and performance, making predictive maintenance possible. With their wide-ranging applications, industrial big data analytics tools can predict maintenance needs, optimize schedules, and detect potential problems before they escalate into significant problems. It can also monitor equipment performance, identify areas for improvement, and refine processes to increase equipment reliability and safety. Industrial big data is indispensable in realizing the shift from reactive to proactive predictive maintenance, which is accomplished through the optimal utilization of available datasets. Industrial big data can glean insights into equipment condition, including patterns of maintenance that may not be readily apparent. Moreover, it has the capacity to attain actionable intelligence capable of effecting a closed loop back to the plant floor. Integration of big data technologies with industrial automation is key to this accomplishment. Nevertheless, this transition will necessitate investment in supplementary assets, such as new maintenance processes and employee training. 6. Navigating Implementation Challenges 6.1 Overcoming Data Collection and Pre-processing Challenges One of the primary challenges in implementing industrial big data analytics for predictive maintenance is the collection and pre-processing of data. The voluminous industrial data, which comes in various formats and from multiple sources, makes it necessary for organizations to develop robust data collection and pre-processing strategies to ensure data accuracy and integrity. To achieve this, organizations need to establish sensor and data collection systems and ensure that the data undergoes appropriate cleaning, formatting, and pre-processing to obtain accurate and meaningful results. 6.2 Addressing Data Integration Challenges Integrating data from heterogeneous sources is a daunting challenge that organizations must overcome when implementing industrial big data analytics for predictive maintenance. It involves processing multiple datasets from different sensors and maintenance detection modalities, such as vibration analysis, oil analysis, thermal imaging, and acoustics. While utilizing data from various sources leads to more stable and accurate predictions, it requires additional investments in sensors and data collection, which is generally very hard to achieve in most maintenance systems. A well-crafted data architecture is critical to managing the copious amounts of data that come from different sources, including various equipment, sensors, and systems. Organizations must devise a comprehensive data integration strategy that incorporates relevant data sources to ensure data integrity and completeness. 6.3 Model Selection and Implementation Solutions Selecting appropriate predictive models and implementing them effectively is another significant challenge. To overcome this, organizations need to have an in-depth understanding of the various models available, their strengths and limitations, and their applicability to specific maintenance tasks. They must also possess the necessary expertise to implement the models and seamlessly integrate them into their existing maintenance workflows to achieve timely and accurate results. Furthermore, it is crucial to align the selected models with the organization's business objectives and ensure their ability to deliver the desired outcomes. 6.4 Staffing and Training Solutions In order to ensure successful implementation, organizations must allocate resources toward staffing and training solutions. This entails hiring proficient data scientists and analysts and then providing them with continual training and professional development opportunities. Moreover, it is imperative to have personnel with the requisite technical expertise to manage and maintain the system. Equally crucial is providing training to employees on the system's usage and equipping them with the necessary skills to interpret and analyze data. 7. Leverage Predictive Maintenance for Optimal Operations Predictive maintenance is widely acknowledged among plant operators as the quintessential maintenance vision due to its manifold advantages, such as higher overall equipment effectiveness (OEE) owing to a reduced frequency of repairs. Furthermore, predictive maintenance data analytics facilitate cost savings by enabling optimal scheduling of repairs and minimizing planned downtimes. It also enhances employees' productivity by providing valuable insights on the appropriate time for component replacement. Additionally, timely monitoring and addressing potential problems can augment workplace safety, which is paramount for ensuring employee well-being. In a survey of 500 plants that implemented a predictive maintenance program, there was an average increase in equipment availability of 30%. Simply implementing predictive maintenance will ensure your equipment is running when you need it to run. (Source: FMX) By synchronizing real-time equipment data with the maintenance management system, organizations can proactively prevent equipment breakdowns. Successful implementation of predictive maintenance data analytic strategies can substantially reduce the time and effort spent on maintaining equipment, as well as the consumption of spare parts and supplies for unplanned maintenance. Consequently, there will be fewer instances of breakdowns and equipment failures, ultimately leading to significant cost savings. On average, predictive maintenance reduced normal operating costs by 50%. (Source: FMX) 8. Final Thoughts Traditional reactive maintenance approaches need to be revised in today's industrial landscape. Proactive strategies, such as predictive maintenance, are necessary to maintain equipment health and performance. Real-time predictive maintenance using big data collected from equipment can help prevent costly downtime, waste, equipment replacement, and labor expenses, thus enhancing safety and productivity. The shift from reactive to proactive maintenance is crucial for organizations, and industrial big data analytics is vital for realizing this transition. Although big data analytics applications for predictive maintenance pose challenges, they can be overcome with the right measures. Ultimately, the effective implementation of big data analytics solutions is a vital enabler of big data predictive maintenance strategies and an essential tool for any industrial plant seeking to optimize its maintenance approach. By embracing predictive maintenance strategies and leveraging the power of industrial big data and analytics, organizations can ensure the longevity and reliability of their equipment, enhancing productivity and profitability.

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Predictive Analytics

Exploiting IoT Data Analytics for Business Success

Article | June 28, 2024

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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Here’s How Analytics are Transforming the Marketing Industry

Article | July 13, 2021

When it comes to marketing today, big data analytics has become a powerful being. The raw material marketers need to make sense of the information they are presented with so they can do their jobs with accuracy and excellence. Big data is what empowers marketers to understand their customers based on any online action they take. Thanks to the boom of big data, marketers have learned more about new marketing trends and preferences, and behaviors of the consumer. For example, marketers know what their customers are streaming to what groceries they are ordering, thanks to big data. Data is readily available in abundance due to digital technology. Data is created through mobile phones, social media, digital ads, weblogs, electronic devices, and sensors attached through the internet of things (IoT). Data analytics helps organizations discover newer markets, learn how new customers interact with online ads, and draw conclusions and effects of new strategies. Newer sophisticated marketing analytics software and analytics tools are now being used to determine consumers’ buying patterns and key influencers in decision-making and validate data marketing approaches that yield the best results. With the integration of product management with data science, real-time data capture, and analytics, big data analytics is helping companies increase sales and improve the customer experience. In this article, we will examine how big data analytics are transforming the marketing industry. Personalized Marketing Personalized Marketing has taken an essential place in direct marketing to the consumers. Greeting consumers with their first name whenever they visit the website, sending them promotional emails of their favorite products, or notifying them with personalized recipes based on their grocery shopping are some of the examples of data-driven marketing. When marketers collect critical data marketing pieces about customers at different marketing touchpoints such as their interests, their name, what they like to listen to, what they order most, what they’d like to hear about, and who they want to hear from, this enables marketers to plan their campaigns strategically. Marketers aim for churn prevention and onboarding new customers. With customer’s marketing touchpoints, these insights can be used to improve acquisition rates, drive brand loyalty, increase revenue per customer, and improve the effectiveness of products and services. With these data marketing touchpoints, marketers can build an ideal customer profile. Furthermore, these customer profiles can help them strategize and execute personalized campaigns accordingly. Predictive Analytics Customer behavior can be traced by historical data, which is the best way to predict how customers would behave in the future. It allows companies to correctly predict which customers are interested in their products at the right time and place. Predictive analytics applies data mining, statistical techniques, machine learning, and artificial intelligence for data analysis and predict the customer’s future behavior and activities. Take an example of an online grocery store. If a customer tends to buy healthy and sugar-free snacks from the store now, they will keep buying it in the future too. This predictable behavior from the customer makes it easy for brands to capitalize on that and has been made easy by analytics tools. They can automate their sales and target the said customer. What they would be doing gives the customer chances to make “repeat purchases” based on their predictive behavior. Marketers can also suggest customers purchase products related to those repeat purchases to get them on board with new products. Customer Segmentation Customer segmentation means dividing your customers into strata to identify a specific pattern. For example, customers from a particular city may buy your products more than others, or customers from a certain age demographic prefer some products more than other age demographics. Specific marketing analytics software can help you segment your audience. For example, you can gather data like specific interests, how many times they have visited a place, unique preferences, and demographics such as age, gender, work, and home location. These insights are a golden opportunity for marketers to create bold campaigns optimizing their return on investment. They can cluster customers into specific groups and target these segments with highly relevant data marketing campaigns. The main goal of customer segmentation is to identify any interesting information that can help them increase revenue and meet their goals. Effective customer segmentation can help marketers with: • Identifying most profitable and least profitable customers • Building loyal relationships • Predicting customer patterns • Pricing products accordingly • Developing products based on their interests Businesses continue to invest in collecting high-quality data for perfect customer segmentation, which results in successful efforts. Optimized Ad Campaigns Customers’ social media data like Facebook, LinkedIn, and Twitter makes it easier for marketers to create customized ad campaigns on a larger scale. This means that they can create specific ad campaigns for particular groups and successfully execute an ad campaign. Big data also makes it easier for marketers to run ‘remarketing’ campaigns. Remarketing campaigns ads follow your customers online, wherever they browse, once they have visited your website. Execution of an online ad campaign makes all the difference in its success. Chasing customers with paid ads can work as an effective strategy if executed well. According to the rule 7, prospective customers need to be exposed to an ad minimum of seven times before they make any move on it. When creating online ad campaigns, do keep one thing in mind. Your customers should not feel as if they are being stalked when you make any remarketing campaigns. Space out your ads and their exposure, so they appear naturally rather than coming on as pushy. Consumer Impact Advancements in data science have vastly impacted consumers. Every move they make online is saved and measured. In addition, websites now use cookies to store consumer data, so whenever these consumers visit these websites, product lists based on their shopping habits pop up on the site. Search engines and social media data enhance this. This data can be used to analyze their behavior patterns and market to them accordingly. The information gained from search engines and social media can be used to influence consumers into staying loyal and help their businesses benefit from the same. These implications can be frightening, like seeing personalized ads crop up on their Facebook page or search engine. However, when consumer data is so openly available to marketers, they need to use it wisely and safeguard it from falling into the wrong hands. Fortunately, businesses are taking note and making sure that this information remains secure. Conclusion The future of marketing because of big data and analytics seems bright and optimistic. Businesses are collecting high-quality data in real-time and analyzing it with the help of machine learning and AI; the marketing world seems to be up for massive changes. Analytics are transforming marketing industry to a different level. And with sophisticated marketers behind the wheel, the sky is the only limit. Frequently Asked Questions Why is marketing analytics so important these days? Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team, and the resources you invest in channels and efforts are critical steps to achieving marketing team success. What is the use of marketing analytics? Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels. Which companies use marketing analytics? Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important these days?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team and the resources you invest in channels and efforts are critical steps to achieving marketing team success" } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels." } },{ "@type": "Question", "name": "Which companies use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well." } }] }

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Audiencepoint

AudiencePoint is an email data company that tracks and indexes subscriber activity and engagement levels. Their software products – ListFit and Send Time Optimization – help marketers increase subscriber engagement by leveraging data in smart, innovative ways.

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Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.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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Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.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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