Data-Centric Approach for AI Development

Data-Centric Approach for AI Development
As AI has grown in popularity over the past decade, practitioners have concentrated on gathering as much data as possible, classifying it, preparing it for usage, and then iterating on model architectures and hyper-parameters to attain our desired objectives. While dealing with all of this data has long been known as laborious and time-consuming, it has typically been seen as an upfront, one-time step we take before entering into the essential modeling phase of machine learning. Data quality concerns, label noise, model drift, and other biases are all addressed in the same way: by collecting and labeling more data, followed by additional model iterations.

The foregoing technique has worked successfully for firms with unlimited resources or strategic challenges. It doesn't work well for machine learning's long-tail issues, particularly those with fewer users and little training data.

The discovery that the prevailing method of deep learning doesn't "scale down" to industry challenges has given birth to a new "trend" in the area termed "Data-Centric AI."

Implementing a Data-Centric Approach for AI Development

Leverage MLOps Practices
Data-centric AI prioritizes data over models. Model selection, hyper-parameter tuning, experiment tracking, deployment, and monitoring take time. Data-centric approaches emphasize automating and simplifying ML lifecycle operations.

Standardizing and automating model-building requires MLOps. MLOps automates machine learning lifecycle management pipelines.
An organizational structure improves communication and cooperation.

Involve Domain Expertise
Data-centric AI development requires domain-specific datasets. Data scientists can overlook intricacies in various sectors, business processes, or even the same domain. Domain experts can give ground truth for the AI use case and verify whether the dataset truly portrays the situation.

Complete and Accurate Data
Data gaps cause misleading results. It's crucial to have a training dataset that correctly depicts the underlying real-world phenomenon. Data augmentation or creating synthetic data might be helpful if gathering comprehensive and representative data is costly or challenging for your use case.

Spotlight

M2Gen

M2Gen® is a health informatics solutions company focused on accelerating the discovery and development of personalized medicines. M2Gen partners with the nation’s leading cancer centers through The Oncology Research Information Exchange Network® (ORIEN) to create a large, cancer-focused data warehouse linking clinical and molecular data. Using this information, M2Gen helps biopharmaceutical companies address the greatest challenges in oncology drug development.

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Business Intelligence, Big Data Management, Big Data

Topic modelling. Variation on themes and the Holy Grail

Article | July 10, 2023

Massive amount of data is collected and stored by companies in the search for the “Holy Grail”. One crucial component is the discovery and application of novel approaches to achieve a more complete picture of datasets provided by the local (sometimes global) event-based analytic strategy that currently dominates a specific field. Bringing qualitative data to life is essential since it provides management decisions’ context and nuance. An NLP perspective for uncovering word-based themes across documents will facilitate the exploration and exploitation of qualitative data which are often hard to “identify” in a global setting. NLP can be used to perform different analysis mapping drivers. Broadly speaking, drivers are factors that cause change and affect institutions, policies and management decision making. Being more precise, a “driver” is a force that has a material impact on a specific activity or an entity, which is contextually dependent, and which affects the financial market at a specific time. (Litterio, 2018). Major drivers often lie outside the immediate institutional environment such as elections or regional upheavals, or non-institutional factors such as Covid or climate change. In Total global strategy: Managing for worldwide competitive advantage, Yip (1992) develops a framework based on a set of four industry globalization drivers, which highlights the conditions for a company to become more global but also reflecting differentials in a competitive environment. In The lexicons: NLP in the design of Market Drivers Lexicon in Spanish, I have proposed a categorization into micro, macro drivers and temporality and a distinction among social, political, economic and technological drivers. Considering the “big picture”, “digging” beyond usual sectors and timeframes is key in state-of-the-art findings. Working with qualitative data. There is certainly not a unique “recipe” when applying NLP strategies. Different pipelines could be used to analyse any sort of textual data, from social media and reviews to focus group notes, blog comments and transcripts to name just a few when a MetaQuant team is looking for drivers. Generally, being textual data the source, it is preferable to avoid manual task on the part of the analyst, though sometimes, depending on the domain, content, cultural variables, etc. it might be required. If qualitative data is the core, then the preferred format is .csv. because of its plain nature which typically handle written responses better. Once the data has been collected and exported, the next step is to do some pre-processing. The basics include normalisation, morphosyntactic analysis, sentence structural analysis, tokenization, lexicalization, contextualization. Just simplify the data to make analysis easier. Topic modelling. Topic modelling refers to the task of recognizing words from the main topics that best describe a document or the corpus of data. LAD (Latent Dirichlet Allocation) is one of the most powerful algorithms with excellent implementations in the Python’s Gensim package. The challenge: how to extract good quality of topics that are clear and meaningful. Of course, this depends mostly on the nature of text pre-processing and the strategy of finding the optimal number of topics, the creation of a lexicon(s) and the corpora. We can say that a topic is defined or construed around the most representative keywords. But are keywords enough? Well, there are some other factors to be observed such as: 1. The variety of topics included in the corpora. 2. The choice of topic modelling algorithm. 3. The number of topics fed to the algorithm. 4. The algorithms tuning parameters. As you probably have noticed finding “the needle in the haystack” is not that easy. And only those who can use creatively NLP will have the advantage of positioning for global success.

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Business Intelligence, Big Data Management, Data Science

Transforming the Gaming Industry with AI Analytics

Article | April 13, 2023

In 2020, the gaming market generated over 177 billion dollars, marking an astounding 23% growth from 2019. While it may be incredible how much revenue the industry develops, what’s more impressive is the massive amount of data generated by today’s games. There are more than 2 billion gamers globally, generating over 50 terabytes of data each day. The largest game companies in the world can host 2.5 billion unique gaming sessions in a single month and host 50 billion minutes of gameplay in the same period. The gaming industry and big data are intrinsically linked. Companies that develop capabilities in using that data to understand their customers will have a sizable advantage in the future. But doing this comes with its own unique challenges. Games have many permutations, with different game types, devices, user segments, and monetization models. Traditional analytics approaches, which rely on manual processes and interventions by operators viewing dashboards, are insufficient in the face of the sheer volume of complex data generated by games. Unchecked issues lead to costly incidents or missed opportunities that can significantly impact the user experience or the company’s bottom line. That’s why many leading gaming companies are turning to AI and Machine Learning to address these challenges. Gaming Analytics AI Gaming companies have all the data they need to understand who their users are, how they engage with the product, and whether they are likely to churn. The challenge is gaining valuable business insights into the data and taking action before opportunities pass and users leave the game. AI/ML helps bridge this gap by providing real-time, actionable insights on near limitless data streams so companies can design around these analytics and act more quickly to resolve issues. There are two fundamental categories that companies should hone in on to make the best use of their gaming data: The revenue generating opportunities in the gaming industry is one reason it’s a highly competitive market. Keeping gamers engaged requires emphasizing the user experience and continuous delivery of high-quality content personalized to a company’s most valued customers. Customer Engagement and User Experience Graphics and creative storylines are still vital, and performance issues, in particular, can be a killer for user enjoyment and drive churn. But with a market this competitive, it might not be enough to focus strictly on these issues. Games can get an edge on the competition by investing in gaming AI analytics to understand user behaviors, likes, dislikes, seasonality impacts and even hone in on what makes them churn or come back to the game after a break. AI-powered business monitoring solutions deliver value to the customer experience and create actionable insights to drive future business decisions and game designs to acquire new customers and prevent churn. AI-Enhanced Monetization and Targeted Advertising All games need a way to monetize. It’s especially true in today’s market, where users expect games to always be on and regularly deliver new content and features. A complex combination of factors influences how monetization practices and models enhance or detract from a user’s experience with a game. When monetization frustrates users, it’s typically because of aggressive, irrelevant advertising campaigns or models that aren’t well suited to the game itself or its core players. Observe the most successful products in the market, and one thing you will consistently see is highly targeted interactions. Developers can use metrics gleaned from AI analytics combined with performance marketing to appeal to their existing users and acquire new customers. With AI/ML, games can use personalized ads that cater to users’ or user segments’ behavior in real-time, optimizing the gaming experience and improving monetization outcomes. Using AI based solutions, gaming studios can also quickly identify growth opportunities and trends with real-time insight into high performing monetization models and promotions. Mobile Gaming Company Reduces Revenue Losses from Technical Incident One mobile gaming company suffered a massive loss when a bug in a software update disrupted a marketing promotion in progress. The promotion involved automatically pushing special offers and opportunities for in-app purchases across various gaming and marketing channels. When a bug in an update disrupted the promotions process, the analytics team couldn’t take immediate action because they were unaware of the issue. Their monitoring process was ad hoc, relying on the manual review of multiple dashboards, and unfortunately, by the time they discovered the problem, it was too late. The result was a massive loss for the company – a loss of users, a loss of installations, and in the end, more than 15% revenue loss from in-app purchases. The company needed a more efficient and timely way to track its cross-promotional metrics, installations, and revenue. A machine learning-based approach, like Anodot’s AI-powered gaming analytics, provides notifications in real-time to quickly find and react to any breakdowns in the system and would have prevented the worst of the impacts. Anodot’s AI-Powered Analytics for Gaming The difference between success and failure is how companies respond to the ocean of data generated by their games and their users. Anodot’s AI-powered Gaming Analytics solutions can learn expected behavior in the complex gaming universe across all permutations of gaming, including devices, levels, user segments, pricing, and ads. Anodot’s Gaming AI platform is specifically designed to monitor millions of gaming metrics and help ensure a seamless gaming experience. Anodot monitors every critical metric and establishes a baseline of standard behavior patterns to quickly alert teams to anomalies that might represent issues or opportunities. Analytics teams see how new features impact user behavior, with clear, contextual alerts for spikes, drops, purchases, and app store reviews without the need to comb over dashboards trying to find helpful information. The online gaming space represents one of the more recent areas where rapid data collection and analysis can provide a competitive differentiation. Studios using AI powered analytics will keep themselves and their players ahead of the game.

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Business Intelligence, Big Data Management, Big Data

Predictive Analytics: Implementation in Business Processes

Article | July 18, 2023

Knowledge is power in business, and knowing what will happen in the future is a superpower. When data analytics, statistical algorithms, AI, and machine learning are combined, this superpower, also known as predictive analytics, becomes a skill that can significantly influence on a company's choices and outcomes. Predictive analytics is the use of modern analytical tools. For example, machine learning concludes about the future based on historical data. Businesses can consider application of predictive analytics tools and models to forecast trends and generate accurate future predictions by leveraging historical and current data. Let’s look at the top three reasons why predictive analytics is important for your business. Why is Predictive Analytics Important for Businesses? Businesses are looking at predictive analytics to help them solve challenges and discover new opportunities. Here are some of the most common benefits of predictive business analytics and an understanding of how is predictive analytics used in business. Fraud Detection In general, various analyzing techniques are merged to analyze data to enhance the accuracy of pattern recognition and discover criminal behavior, thereby reducing the incidence of frequent fraud. With behavioral analytics, you can look at any suspicious behavior and activities that happen on a network in real-time to look for fraud, zero-day breaches, and underlying threats. Enhancing Business Campaigns The predictive analytics process can help you optimize marketing campaigns and promotional events. Predictive designs helps businesses attract, retain, and increase valuable customers by determining their purchase responses and promoting cross-sell opportunities. Minimizing Potential Risk The predictive analytics process helps businesses decide on appropriate steps to avoid or reduce losses. Predictive analytics is revolutionizing risk management by alerting businesses about future developments. For example, credit scores, which financial institutions use to predict defaulters depending on a user's purchasing behavior. How Does Predictive Analytics Help the C-Suite? The C-suite is the final decision maker, so they are the ones who must use predictive analytics the most for insightful decision-making. Let’s look at ways in which predictive analytics can help C-level executives. Predict Customer Behavior Predictive analytics utilizes data to forecast future customer behavior. Customer intent becomes the primary aspect rather than historical transactional data, allowing for hyper-personalized marketing and communications. For example, researchers at China's Renmin University used predictive analytics and machine learning to figure out that data on consumer interests and jobs can predict customer preferences and purchase intent for cars. Predicting customer requirements accurately is a huge opportunity for businesses. Companies can use AI and predictive analytics models to figure out what customers will do based on data instead of guesswork. Pricing Optimization Predictive business analytics can help companies improve pricing optimization quickly and affordably. A business can use predictive analytics to figure out how to make a product more affordable in the future by looking at past data, industry trends, competitive prices, and other data sources. Each customer provides a unique value to the products. To add to the complexity, a consumer's value of a product may vary depending on the purchase circumstances and environment. Simplicity in pricing misses opportunities and can result in a significant drop in revenue. Product information, consumer segmentation, and purchase circumstances are all enhanced by predictive analytics. Businesses can use this data to uncover trends and patterns to help them price more profitably. Predicting Growth and Market Trends Businesses can use predictive market analysis to decipher existing and future market trends. With this data, businesses can develop a plan to maximize opportunities, expand market share, and sustain disruption and new competition. Companies can use it to detect unmet customer demand and fill any gaps. Consumption sentiment is revealed through social media data. A product that does not match customer demand creates a market opportunity for a new product or service. Predictive market analysis can uncover customer perceptions of a product or service and unmet consumer demands. Predictive business analytics helps businesses better understand their customers, meet their needs, and find new ways to earn revenue and grow. Example: Reu La La Uses Predictive Analytics to Increase its Revenue by 10% You often hear about giant enterprises like Amazon, Airbnb, Microsoft, Google, and others utilizing predictive analytics to extend their reach, boost sales, and more. Today let’s look at Reu La La and how they used predictive analytics to enhance their revenue. Rue La La, a boutique retailer, often needs to predict sales and fix pricing for products being sold for the first time in its online store with no existing sales data. They observed that many products were either sold out within the first few hours of release or did not sell, which lead to revenue loss. Rue La La took action by creating a set of quantitative qualities for its items and predicting future demand by utilizing historical sales data. They used statistical and computing technologies, such as regression analysis and machine learning, to create a demand forecast and pricing optimization model. In partnership with the Massachusetts Institute of Technology, they created an automated price decision assistance tool. Revenue increased from 10% to 13% across all departments because they used the pricing tool's proposed optimal rates. Conclusion “As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.” Eric Siegel You can consider the predictions that predictive analytics makes as gold, but, using predictive analytics is like a crystal ball that shows the future. You can look into the future, prevent issues in your company from escalating, and recognize profitable possibilities. If you haven't started leveraging predictive analytics, start by experimenting with it on a modest scale and gradually build up as you acquire expertise and observe positive outcomes. FAQ How can Predictive Analytics Improve Performance Measurement? Predictive analytics improves performance measurements by expanding an organization's understanding of the important performance drivers. It also helps with the weighting of different performance metrics based on how important they are. What Are the Four Steps in Predictive Analytics? In simple terms, predictive analytics involves four steps: creating a baseline prediction, assessing it, adding assumptions, and building a consensus demand plan. To do so, we must first choose a modeling technique, create a test design, then construct the model, evaluate the mode, and achieve alignment. What Are the Three Different Types of Predictive Analytics? Businesses utilize three forms of analytics to drive their decision-making: Descriptive analytics — tells something that has already happened; Predictive analytics — shows what can happen; Prescriptive analytics — tells what should happen in the future

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Big Data Management

How can machine learning detect money laundering?

Article | December 16, 2020

In this article, we will explore different techniques to detect money laundering activities. Notwithstanding, regardless of various expected applications inside the financial services sector, explicitly inside the Anti-Money Laundering (AML) appropriation of Artificial Intelligence and Machine Learning (ML) has been generally moderate. What is Money Laundering, Anti Money Laundering? Money Laundering is where someone unlawfully obtains money and moves it to cover up their crimes. Anti-Money Laundering can be characterized as an activity that forestalls or aims to forestall money laundering from occurring. It is assessed by UNO that, money-laundering exchanges account in one year is 2��5% of worldwide GDP or $800 billion — $3 trillion in USD. In 2019, regulators and governmental offices exacted fines of more than $8.14 billion. Indeed, even with these stunning numbers, gauges are that just about 1 % of unlawful worldwide money related streams are ever seized by the specialists. AML activities in banks expend an over the top measure of manpower, assets, and cash flow to deal with the process and comply with the guidelines. What are the punishments for money laundering? In 2019, Celent evaluated that spending came to $8.3 billion and $23.4 billion for technology and operations, individually. This speculation is designated toward guaranteeing anti-money laundering. As we have seen much of the time, reputational costs can likewise convey a hefty price. In 2012, HSBC laundering of an expected £5.57 billion over at least seven years.   What is the current situation of the banks applying ML to stop money laundering? Given the plenty of new instruments the banks have accessible, the potential feature risk, the measure of capital involved, and the gigantic expenses as a form of fines and punishments, this should not be the situation. A solid impact by nations to curb illicit cash movement has brought about a huge yet amazingly little part of money laundering being recognized — a triumph rate of about 2% average. Dutch banks — ABN Amro, Rabobank, ING, Triodos Bank, and Volksbank announced in September 2019 to work toward a joint transaction monitoring to stand-up fight against Money Laundering. A typical challenge in transaction monitoring, for instance, is the generation of a countless number of alerts, which thusly requires operation teams to triage and process the alarms. ML models can identify and perceive dubious conduct and besides they can classify alerts into different classes such as critical, high, medium, or low risk. Critical or High alerts may be directed to senior experts on a high need to quickly explore the issue. Today is the immense number of false positives, gauges show that the normal, of false positives being produced, is the range of 95 and 99%, and this puts extraordinary weight on banks. The examination of false positives is tedious and costs money. An ongoing report found that banks were spending near 3.01€ billion every year exploring false positives. Establishments are looking for increasing productive ways to deal with crime and, in this specific situation, Machine Learning can end up being a significant tool. Financial activities become productive, the gigantic sum and speed of money related exchanges require a viable monitoring framework that can process exchanges rapidly, ideally in real-time.   What are the types of machine learning algorithms which can identify money laundering transactions? Supervised Machine Learning, it is essential to have historical information with events precisely assigned and input variables appropriately captured. If biases or errors are left in the data without being dealt with, they will get passed on to the model, bringing about erroneous models. It is smarter to utilize Unsupervised Machine Learning to have historical data with events accurately assigned. It sees an obscure pattern and results. It recognizes suspicious activity without earlier information of exactly what a money-laundering scheme resembles. What are the different techniques to detect money laundering? K-means Sequence Miner algorithm: Entering banking transactions, at that point running frequent pattern mining algorithms and mining transactions to distinguish money laundering. Clustering transactions and dubious activities to money laundering lastly show them on a chart. Time Series Euclidean distance: Presenting a sequence matching algorithm to distinguish money laundering detection, utilizing sequential detection of suspicious transactions. This method exploits the two references to recognize dubious transactions: a history of every individual’s account and exchange data with different accounts. Bayesian networks: It makes a model of the user’s previous activities, and this model will be a measure of future customer activities. In the event that the exchange or user financial transactions have. Cluster-based local outlier factor algorithm: The money laundering detection utilizing clustering techniques combination and Outliers.   Conclusion For banks, now is the ideal opportunity to deploy ML models into their ecosystem. Despite this opportunity, increased knowledge and the number of ML implementations prompted a discussion about the feasibility of these solutions and the degree to which ML should be trusted and potentially replace human analysis and decision-making. In order to further exploit and achieve ML promise, banks need to continue to expand on its awareness of ML strengths, risks, and limitations and, most critically, to create an ethical system by which the production and use of ML can be controlled and the feasibility and effect of these emerging models proven and eventually trusted.

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Spotlight

M2Gen

M2Gen® is a health informatics solutions company focused on accelerating the discovery and development of personalized medicines. M2Gen partners with the nation’s leading cancer centers through The Oncology Research Information Exchange Network® (ORIEN) to create a large, cancer-focused data warehouse linking clinical and molecular data. Using this information, M2Gen helps biopharmaceutical companies address the greatest challenges in oncology drug development.

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Big Data Management

NICE Actimize X-Sight DataIQ ClarityKYC Wins Best Data Solution for Regulatory Compliance in A-Team Group’s 2023 Data Management Insight Awards

Business Wire | November 01, 2023

NICE Actimize, (Nasdaq: NICE) was named a winner in A-Team Group's Data Management Insight Awards USA 2023 in the category for Best Data Solution for Regulatory Compliance. NICE Actimize’s X-Sight DataIQ ClarityKYC was the recipient of the most online votes in its category derived from reader/online nominations from within the data management community and verified by A-Team Group editors and its advisory board. NICE Actimize’s X-Sight DataIQ ClarityKYC is a SaaS workflow solution that automates data aggregation and simplifies KYC for financial services organization users. The solution facilitates compliance with KYC/Anti-Money Laundering (AML) requirements by integrating disparate datasets and streamlining the customer identification, due diligence, and credit investigation process. Customer onboarding is a critical first step in any financial services organization’s risk management strategy. Onboarding new customers and conducting ongoing reviews presents numerous competitive challenges, which include manual and error-prone processes, long onboarding times which result in longer time to revenue for the banks, and no practical way to make sure the bank’s global regulatory policies are met in an auditable process, said Craig Costigan, CEO, NICE Actimize. NICE Actimize’s DataIQ ClarityKYC addresses these issues effectively. We thank the A-Team group and the data management community for recognizing the innovation we offer with X-Sight DataIQ. “These awards recognize both established solution vendors and innovative newcomers providing leading data management solutions, services, and consultancy to capital markets participants across North America. Congratulations go to NICE Actimize for winning Best Data Solution for Regulatory Compliance,” said Angela Wilbraham, CEO of A-Team Group and host of the Data Management Insight Awards USA 2023. X-Sight DataIQ ClarityKYC leverages AI-powered technologies to access traditional content while intelligently orchestrating data from various global data sources. X-Sight DataIQ Clarity reduces the amount of effort needed to conduct research. Long IT integration projects and tasks formerly done manually or requiring steps can be completed quickly, automatically saving time and effort while enabling teams to comply with confidence while reducing customer friction.

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Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

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Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

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Big Data Management

NICE Actimize X-Sight DataIQ ClarityKYC Wins Best Data Solution for Regulatory Compliance in A-Team Group’s 2023 Data Management Insight Awards

Business Wire | November 01, 2023

NICE Actimize, (Nasdaq: NICE) was named a winner in A-Team Group's Data Management Insight Awards USA 2023 in the category for Best Data Solution for Regulatory Compliance. NICE Actimize’s X-Sight DataIQ ClarityKYC was the recipient of the most online votes in its category derived from reader/online nominations from within the data management community and verified by A-Team Group editors and its advisory board. NICE Actimize’s X-Sight DataIQ ClarityKYC is a SaaS workflow solution that automates data aggregation and simplifies KYC for financial services organization users. The solution facilitates compliance with KYC/Anti-Money Laundering (AML) requirements by integrating disparate datasets and streamlining the customer identification, due diligence, and credit investigation process. Customer onboarding is a critical first step in any financial services organization’s risk management strategy. Onboarding new customers and conducting ongoing reviews presents numerous competitive challenges, which include manual and error-prone processes, long onboarding times which result in longer time to revenue for the banks, and no practical way to make sure the bank’s global regulatory policies are met in an auditable process, said Craig Costigan, CEO, NICE Actimize. NICE Actimize’s DataIQ ClarityKYC addresses these issues effectively. We thank the A-Team group and the data management community for recognizing the innovation we offer with X-Sight DataIQ. “These awards recognize both established solution vendors and innovative newcomers providing leading data management solutions, services, and consultancy to capital markets participants across North America. Congratulations go to NICE Actimize for winning Best Data Solution for Regulatory Compliance,” said Angela Wilbraham, CEO of A-Team Group and host of the Data Management Insight Awards USA 2023. X-Sight DataIQ ClarityKYC leverages AI-powered technologies to access traditional content while intelligently orchestrating data from various global data sources. X-Sight DataIQ Clarity reduces the amount of effort needed to conduct research. Long IT integration projects and tasks formerly done manually or requiring steps can be completed quickly, automatically saving time and effort while enabling teams to comply with confidence while reducing customer friction.

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Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

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Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

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