Data Integration Platform: Leveraging the Power of Data

Data Integration Platform: Leveraging the Power of Data
Data is not stored in a single database, file system, data lake, or repository. Data generated in a system of record must meet various business requirements, connect with other data sources, and then be utilized for analytics, customer-facing apps, or internal procedures. A well-established data integration solution provides a unified picture of data received from various places and formats. This can also happen when two organizations merge or when internal applications are consolidated. Data integration can also facilitate the development of a more complete data warehouse, resulting in more accurate and effective analysis. Data integration establishes the foundation for effective Business Intelligence (BI) and decision-making.

Data Integration as a Tool for Business Strategy

The gathering, analysis, and integration of data is essential to the success of businesses. Let’s have a look at the way in which data integration technology enables business strategy.


Set Data-Integration Goals

These objectives should be part of the larger company objective. A thorough awareness of your consumers, for example, is corporate goal. To do this, your integration strategy should aim to embed customer data into your service, sales, and marketing platforms.


Improved Financial Data Management

A robust data integration strategy allows you to monitor and manage vital financial and operational data through simple dashboards that combine all business and financial data into a single platform.

Any effective financial management system will include basic accounting capabilities that will enable you to track revenue and spending, assets and liabilities, and amortizations in order to provide accurate financial reports.


Enhanced Marketing Analytics

Data about competitors, industry trends, consumer behavior, and campaign performance should drive your marketing strategy. Update often as fresh data becomes available.

By assessing your marketing tools and channels, you can determine the optimum time, place, and technique to advertise your company. Gather data from social media, email marketing tools, CRMs, CMSs, and other platforms for marketing analytics. This also allows you to evaluate where you should spend additional resources to improve the consistency of your marketing effort.


Save Time and Resources

Business intelligence experts have a huge workload of sifting through business data.

Analysts worry less when teams have direct access to essential data. This frees them to concentrate on difficult, valuable data sets.

Without data integration platforms, even the simplest business report requires manual processing of all sources, creating code or automatically uploading data to the database, and exhausting systematization.

Not to mention the challenge to monitor and correct any human factor errors. This will be completely eliminated by integration automation.


How AI Is Enhancing the Data Integration Process?

  • Data Mapping: Businesses can map data faster using AI for insights generation and decision-making.
  • Autonomous learning: An ML-based data integration process enables autonomous learning to discover patterns and trends in the stored data.
  • Big data processing: Machine learning (ML) makes it possible to quickly and accurately transform unstructured and inconsistent data into desirable formats.


Closing Lines

Regardless of the size of your company or resources, processing and managing data correctly can expand vision of your business and customers.

As organizations rely on data analytics and business intelligence, data integration will become more user-friendly in the coming years. Data integration is an unavoidable aspect of every organization's digital transformation path. Implement the most current data integration techniques to stay ahead of your competition.

Spotlight

Soosto

We weave big-data analytics and predictive modeling into advanced software platforms to help our clients boost their key financial metrics on the e-commerce and marketing channels. We take advantage of the latest methods in the fields of Artificial Intelligence and Machine Learning to drive increases in customer satisfaction, conversions, revenue and other critical domain-specific objectives. By focusing on the research, design and development of cutting edge software technologies, our team produces next generation software products that unlock major growth potential in our clients. Our expertise in Functional Programming helps us craft highly concurrent, mission critical and sophisticated-yet-scalable solutions at a rapid pace.

OTHER ARTICLES
Business Intelligence, Big Data Management, Big Data

How Should Data Science Teams Deal with Operational Tasks?

Article | May 15, 2023

Introduction There are many articles explaining advanced methods on AI, Machine Learning or Reinforcement Learning. Yet, when it comes to real life, data scientists often have to deal with smaller, operational tasks, that are not necessarily at the edge of science, such as building simple SQL queries to generate lists of email addresses to target for CRM campaigns. In theory, these tasks should be assigned to someone more suited, such as Business Analysts or Data Analysts, but it is not always the case that the company has people dedicated specifically to those tasks, especially if it’s a smaller structure. In some cases, these activities might consume so much of our time that we don’t have much left for the stuff that matters, and might end up doing a less than optimal work in both. That said, how should we deal with those tasks? In one hand, not only we usually don’t like doing operational tasks, but they are also a bad use of an expensive professional. On the other hand, someone has to do them, and not everyone has the necessary SQL knowledge for it. Let’s see some ways in which you can deal with them in order to optimize your team’s time. Reduce The first and most obvious way of doing less operational tasks is by simply refusing to do them. I know it sounds harsh, and it might be impractical depending on your company and its hierarchy, but it’s worth trying it in some cases. By “refusing”, I mean questioning if that task is really necessary, and trying to find best ways of doing it. Let’s say that every month you have to prepare 3 different reports, for different areas, that contain similar information. You have managed to automate the SQL queries, but you still have to double check the results and eventually add/remove some information upon the user’s request or change something in the charts layout. In this example, you could see if all of the 3 different reports are necessary, or if you could adapt them so they become one report that you send to the 3 different users. Anyways, think of ways through which you can reduce the necessary time for those tasks or, ideally, stop performing them at all. Empower Sometimes it can pay to take the time to empower your users to perform some of those tasks themselves. If there is a specific team that demands most of the operational tasks, try encouraging them to use no-code tools, putting it in a way that they fell they will be more autonomous. You can either use already existing solutions or develop them in-house (this could be a great learning opportunity to develop your data scientists’ app-building skills). Automate If you notice it’s a task that you can’t get rid of and can’t delegate, then try to automate it as much as possible. For reports, try to migrate them to a data visualization tool such as Tableau or Google Data Studio and synchronize them with your database. If it’s related to ad hoc requests, try to make your SQL queries as flexible as possible, with variable dates and names, so that you don’t have to re-write them every time. Organize Especially when you are a manager, you have to prioritize, so you and your team don’t get drowned in the endless operational tasks. In order to do this, set aside one or two days in your week which you will assign to that kind of work, and don’t look at it in the remaining 3–4 days. To achieve this, you will have to adapt your workload by following the previous steps and also manage expectations by taking this smaller amount of work hours when setting deadlines. This also means explaining the paradigm shift to your internal clients, so they can adapt to these new deadlines. This step might require some internal politics, negotiating with your superiors and with other departments. Conclusion Once you have mapped all your operational activities, you start by eliminating as much as possible from your pipeline, first by getting rid of unnecessary activities for good, then by delegating them to the teams that request them. Then, whatever is left for you to do, you automate and organize, to make sure you are making time for the relevant work your team has to do. This way you make sure expensive employees’ time is being well spent, maximizing company’s profit.

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Business Intelligence, Big Data Management, Big Data

How I Made a Bot to “Fake” Responses for a Survey

Article | August 17, 2023

We had to make a project presentation in which we (as in me and my friend) had to conduct an additional survey to back up our point. Now, like any other student, we waited till the deadline. Well, to be fair, we didn’t have a choice. We had a lot of work from college. But that’s the story of any student, I guess. A snap of the form which was used to collect the information So, we sent the link and got back a few responses. But, since the deadline was the next day and the survey had to be on a larger scale, I decided to create a bot for it. (Because that’s the obvious answer for everything. [Pun intended]). Here it is: • Link to the Code. A few things which I learnt are that while using the click() method, if we’re using xpath, it’s not easy for the web driver to locate a clickable element if it’s embedded under div tags and span tags. It throws a “no such element exception”. The best thing to do would be: • Use the label of the element you want to locate on the form. • To use the pre-filled links feature of Google forms. (Basically a pre-filled link, which uses parameters to fill the form). Best suited for small forms.

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Big Data Management, Data Science, Big Data

3 steps to build a data fabric to integrate all your data tools

Article | May 16, 2023

One approach for better data utilization is the data fabric, a data management approach that arranges data in a single "fabric" that spans multiple systems and endpoints. The goal of the fabric is to link all data so it can easily be accessed. "DataOps and data fabric are two different but related things," said Ed Thompson, CTO at Matillion, which provides a cloud data integration platform. "DataOps is about taking practices which are common in modern software development and applying them to data projects. Data fabric is about the type of data landscape that you create and how the tools that you use work together."

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Topic modelling. Variation on themes and the Holy Grail

Article | September 2, 2021

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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Spotlight

Soosto

We weave big-data analytics and predictive modeling into advanced software platforms to help our clients boost their key financial metrics on the e-commerce and marketing channels. We take advantage of the latest methods in the fields of Artificial Intelligence and Machine Learning to drive increases in customer satisfaction, conversions, revenue and other critical domain-specific objectives. By focusing on the research, design and development of cutting edge software technologies, our team produces next generation software products that unlock major growth potential in our clients. Our expertise in Functional Programming helps us craft highly concurrent, mission critical and sophisticated-yet-scalable solutions at a rapid pace.

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Snowflake Accelerates How Users Build Next Generation Apps and Machine Learning Models in the Data Cloud

Business Wire | November 03, 2023

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake's secure and fully managed infrastructure. “The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.” Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include: Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform. Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures. Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference. Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement. Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale, said Saad Zaheer, VP of Data Science and Engineering, Endeavor. With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines. Snowflake Advances Developer Capabilities Across the App Lifecycle The Snowflake Native App Framework (general availability soon on AWS, public preview soon on Azure) now provides every organization with the necessary building blocks for app development, including distribution, operation, and monetization within Snowflake’s platform. Leading organizations are monetizing their Snowflake Native Apps through Snowflake Marketplace, with app listings more than doubling since Snowflake Summit 2023. This number is only growing as Snowflake continues to advance its developer capabilities across the app lifecycle so more organizations can unlock business impact. For example, Cybersyn, a data-service provider, is developing Snowflake Native Apps exclusively for Snowflake Marketplace, with more than 40 customers running over 5,000 queries with its Financial & Economic Essentials Native App since June 2022. In addition, LiveRamp, a data collaboration platform, has seen the number of customers deploying its Identity Resolution and Transcoding Snowflake Native App through Snowflake Marketplace increase by more than 80% since June 2022. Lastly, SNP has been able to provide its customers with a 10x cost reduction in Snowflake data processing associated with SAP data ingestion, empowering them to drastically reduce data latency while improving SAP data availability in Snowflake through SNP’s Data Streaming for SAP - Snowflake Native App. With Snowpark Container Services (public preview soon in select AWS regions), developers can run any component of their app — from ML training, to LLMs, to an API, and more — without needing to move data or manage complex container-based infrastructure. Snowflake Automates DevOps for Apps, Data Pipelines, and Other Development Snowflake is giving developers new ways to automate key DevOps and observability capabilities across testing, deploying, monitoring, and operating their apps and data pipelines — so they can take them from idea to production faster. With Snowflake’s new Database Change Management (private preview soon) features, developers can code declaratively and easily templatize their work to manage Snowflake objects across multiple environments. The Database Change Management features serve as a single source of truth for object creation across various environments, using the common “configuration as code” pattern in DevOps to automatically provision and update Snowflake objects. Snowflake also unveiled a new Powered by Snowflake Funding Program, innovations that enable all users to securely tap into the power of generative AI with their enterprise data, enhancements to further eliminate data silos and strengthen Snowflake’s leading compliance and governance capabilities through Snowflake Horizon, and more at Snowday 2023.

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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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Data Science

Snowflake Accelerates How Users Build Next Generation Apps and Machine Learning Models in the Data Cloud

Business Wire | November 03, 2023

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake's secure and fully managed infrastructure. “The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.” Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include: Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform. Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures. Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference. Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement. Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale, said Saad Zaheer, VP of Data Science and Engineering, Endeavor. With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines. Snowflake Advances Developer Capabilities Across the App Lifecycle The Snowflake Native App Framework (general availability soon on AWS, public preview soon on Azure) now provides every organization with the necessary building blocks for app development, including distribution, operation, and monetization within Snowflake’s platform. Leading organizations are monetizing their Snowflake Native Apps through Snowflake Marketplace, with app listings more than doubling since Snowflake Summit 2023. This number is only growing as Snowflake continues to advance its developer capabilities across the app lifecycle so more organizations can unlock business impact. For example, Cybersyn, a data-service provider, is developing Snowflake Native Apps exclusively for Snowflake Marketplace, with more than 40 customers running over 5,000 queries with its Financial & Economic Essentials Native App since June 2022. In addition, LiveRamp, a data collaboration platform, has seen the number of customers deploying its Identity Resolution and Transcoding Snowflake Native App through Snowflake Marketplace increase by more than 80% since June 2022. Lastly, SNP has been able to provide its customers with a 10x cost reduction in Snowflake data processing associated with SAP data ingestion, empowering them to drastically reduce data latency while improving SAP data availability in Snowflake through SNP’s Data Streaming for SAP - Snowflake Native App. With Snowpark Container Services (public preview soon in select AWS regions), developers can run any component of their app — from ML training, to LLMs, to an API, and more — without needing to move data or manage complex container-based infrastructure. Snowflake Automates DevOps for Apps, Data Pipelines, and Other Development Snowflake is giving developers new ways to automate key DevOps and observability capabilities across testing, deploying, monitoring, and operating their apps and data pipelines — so they can take them from idea to production faster. With Snowflake’s new Database Change Management (private preview soon) features, developers can code declaratively and easily templatize their work to manage Snowflake objects across multiple environments. The Database Change Management features serve as a single source of truth for object creation across various environments, using the common “configuration as code” pattern in DevOps to automatically provision and update Snowflake objects. Snowflake also unveiled a new Powered by Snowflake Funding Program, innovations that enable all users to securely tap into the power of generative AI with their enterprise data, enhancements to further eliminate data silos and strengthen Snowflake’s leading compliance and governance capabilities through Snowflake Horizon, and more at Snowday 2023.

Read More

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.

Read More

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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