By embracing data science tools and technologies, banks can more effectively inform strategic decision-making, reducing uncertainty and eliminating analysis-paralysis.
Social Media Imperatives for Retail BanksCognizant
Social networking tools can help banks boost their retail operations and rebuild customer trust, but only if the strategy addresses risks, is aligned with business objectives and is backed by top leadership.
Big Data in Retail - Examples in ActionDavid Pittman
This use case looks at how savvy retailers can use "big data" - combining data from web browsing patterns, social media, industry forecasts, existing customer records, etc. - to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions, and monitor real-time analytics and results. For more information, visit http://www.IBMbigdatahub.com
Follow us on Twitter.com/IBMbigdata
Social Media Imperatives for Retail BanksCognizant
Social networking tools can help banks boost their retail operations and rebuild customer trust, but only if the strategy addresses risks, is aligned with business objectives and is backed by top leadership.
Big Data in Retail - Examples in ActionDavid Pittman
This use case looks at how savvy retailers can use "big data" - combining data from web browsing patterns, social media, industry forecasts, existing customer records, etc. - to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions, and monitor real-time analytics and results. For more information, visit http://www.IBMbigdatahub.com
Follow us on Twitter.com/IBMbigdata
Big Data in Industry
Many believe that Big Data is a new asset which will help companies catapult others to become the best in class.
What is it about Big Data that is so appealing across industries? Simply, data is intertwined into every sector and function in the global economy and much of modern economic activity would not be able to take place without data.
Big Data relates to large meres of data which can be brought together and then analyzed to inform decision making and discern patterns. The insights which Big Data brings, will become the basis of competition and growth for companies worldwide through further enhancing productivity as well as generating significant value for the global economy by increasing the quality of goods and services.
Previous trends in IT investment and innovation such as cloud adoption and the impact of this on competitiveness and productivity can be mirrored by Big Data which serves as a crucial way for large companies to outperform their competition. Across industries, time-honored competitors and new entrants to the market will use data-driven strategies to compete, innovate and seize value. The knowledge that big data brings informs the creation of new services and the design of future products. In fact, some companies are using Big Data to conduct controlled experiments to inform better management decisions.
http://www.extentia.com/service/big-data
www.extentia.com/contact-us
Through precise location analytics, retailers now can monitor the entire path to purchase. With this data, marketers better understand what led to the purchase providing the ability to move beyond the traditional blanketed “campaign” to a year-round interaction based on consumer behavior. Customers “opt-in” by mobile app to receive highly-targeted promotions, information about merchandise they may have “visited” but didn’t purchase, and discounts for major events – based on correlations like visits, dwell and intent – to drive sales like never before.
Data analytics environment enables the shortest and most viable route to make use of critical data for making business decisions and much more. For more info visit: https://www.raybiztech.com/blog/data-analytics/how-can-data-analytics-boost-your-business-growth
Employing Analytics to Automate and Optimize Insurance DistributionCognizant
Today's insurers have the opportunity to employ advanced analytics to automate and optimize distribution, analyze and track customer patterns, enhance marketing campaigns, better manage agents and deliver more value to the business and its customers.
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
Analytical CRM - Ecommerce analysis of customer behavior to enhance sales Shrikant Samarth
Task: You are required to choose a dataset (or related datasets) in an area of interest suitable for analyzing customer relationships.
Approach: Topic is chosen – Customer behavior Analysis in Ecommerce Industry for Enhancing Sales. Brazilian E-commerce public dataset was downloaded, cleaned and performed multiple regression in SPSS to check the relationship between the dependent variable and multiple independent variables.
Findings: Customer can be retained if the product delivered in time and if there is a delay in the product delivery, it is a duty of a seller to inform the customer for the same. The payment method has proven to be an important parameter to enhance sales over a period of time. analysis suggests on-time delivery, flexibility in payment method and good customer service would help the seller to gain customer trust which would help them to convert more sales.
Tools: IBM SPSS , Excel (pivot tables and charts), Tableau
How Big Data helps banks know their customers betterHEXANIKA
Enterprises today mine customer data to ensure maximum success by targeting their products and solutions to the right audience. Let us have a look at how Big Data and Customer Analytics are helping businesses use their customer data for maximum benefits.
With flickery markets, edgy economy, organizational change and the evolving regulatory landscape, the finance divisions are caught up in a fast increase in the amount of public support and changes. All this while, the need for cost cutting and delivering transparent reports stays stable. Rolta’s Financial Analytics solution CFO Impact helps you bring cost effective and sustainable transformations to financial processes and systems with the help of big data analytic technologies.
Big Data in Industry
Many believe that Big Data is a new asset which will help companies catapult others to become the best in class.
What is it about Big Data that is so appealing across industries? Simply, data is intertwined into every sector and function in the global economy and much of modern economic activity would not be able to take place without data.
Big Data relates to large meres of data which can be brought together and then analyzed to inform decision making and discern patterns. The insights which Big Data brings, will become the basis of competition and growth for companies worldwide through further enhancing productivity as well as generating significant value for the global economy by increasing the quality of goods and services.
Previous trends in IT investment and innovation such as cloud adoption and the impact of this on competitiveness and productivity can be mirrored by Big Data which serves as a crucial way for large companies to outperform their competition. Across industries, time-honored competitors and new entrants to the market will use data-driven strategies to compete, innovate and seize value. The knowledge that big data brings informs the creation of new services and the design of future products. In fact, some companies are using Big Data to conduct controlled experiments to inform better management decisions.
http://www.extentia.com/service/big-data
www.extentia.com/contact-us
Through precise location analytics, retailers now can monitor the entire path to purchase. With this data, marketers better understand what led to the purchase providing the ability to move beyond the traditional blanketed “campaign” to a year-round interaction based on consumer behavior. Customers “opt-in” by mobile app to receive highly-targeted promotions, information about merchandise they may have “visited” but didn’t purchase, and discounts for major events – based on correlations like visits, dwell and intent – to drive sales like never before.
Data analytics environment enables the shortest and most viable route to make use of critical data for making business decisions and much more. For more info visit: https://www.raybiztech.com/blog/data-analytics/how-can-data-analytics-boost-your-business-growth
Employing Analytics to Automate and Optimize Insurance DistributionCognizant
Today's insurers have the opportunity to employ advanced analytics to automate and optimize distribution, analyze and track customer patterns, enhance marketing campaigns, better manage agents and deliver more value to the business and its customers.
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
Analytical CRM - Ecommerce analysis of customer behavior to enhance sales Shrikant Samarth
Task: You are required to choose a dataset (or related datasets) in an area of interest suitable for analyzing customer relationships.
Approach: Topic is chosen – Customer behavior Analysis in Ecommerce Industry for Enhancing Sales. Brazilian E-commerce public dataset was downloaded, cleaned and performed multiple regression in SPSS to check the relationship between the dependent variable and multiple independent variables.
Findings: Customer can be retained if the product delivered in time and if there is a delay in the product delivery, it is a duty of a seller to inform the customer for the same. The payment method has proven to be an important parameter to enhance sales over a period of time. analysis suggests on-time delivery, flexibility in payment method and good customer service would help the seller to gain customer trust which would help them to convert more sales.
Tools: IBM SPSS , Excel (pivot tables and charts), Tableau
How Big Data helps banks know their customers betterHEXANIKA
Enterprises today mine customer data to ensure maximum success by targeting their products and solutions to the right audience. Let us have a look at how Big Data and Customer Analytics are helping businesses use their customer data for maximum benefits.
With flickery markets, edgy economy, organizational change and the evolving regulatory landscape, the finance divisions are caught up in a fast increase in the amount of public support and changes. All this while, the need for cost cutting and delivering transparent reports stays stable. Rolta’s Financial Analytics solution CFO Impact helps you bring cost effective and sustainable transformations to financial processes and systems with the help of big data analytic technologies.
Learn how financial institutions are betting on the Big Data and Artificial Intelligence through APIs that help banks to define products, segmenting customers and detect possible fraud. Throughout this ebook we offer a review of the APIs bank data aggregation. More information in http://bbva.info/2t1NEv7
Banking in the Digital Era: Regaining Consumer TrustCognizant
Amid wavering consumer confidence, changing banking behaviors, widespread hacks and new competition, here’s what traditional banks can do to rebuild trust in the digital era.
Retail Banking: Delivering a Meaningful Digital Customer ExperienceCognizant
To compete effectively, banks must fully adopt digital technologies to enhance customer experience, by providing mobile banking, omni-channel banking options, digital personal financial management, and more.
U.S. Consumer Banks and the Potential of Location-Based OffersCognizant
The increasing use of mobile devices, plus advances in location-aware technologies, are driving the adoption of location-based services across customer-facing industries, including retail. U.S. consumer banks can take advantage of this trend by using the vast amount of customer data they collect to help retailers develop contextually relevant, location-based offers that strengthen and grow customer relationships and position retail banking services as more than mere commodities.
A Survey on Bigdata Analytics using in Banking Sectorsijtsrd
Current days, banking industry is generating large amount of data. Already, most banks have failed to utilize this data. However, nowadays, banks have starts using this data to reach their main objectives of marketing. By using this data, many secrets can be discovering like money movements, thefts, failure. This paper aims to find out how big data analytics can be used in banking sector to find out spending patterns of customer, sentiment and feedback analysis etc. Big data analytics can aid banks in understanding customer behavior based on the inputs receive from their investment patterns, shopping trends, motivation to invest and personal or financial backgrounds. This data plays a necessary role in leading customer loyalty by designing personalized banking solutions for them. Gagana H. S | Roja H. N | Gouthami H. S "A Survey on Bigdata Analytics using in Banking Sectors" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31016.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31016/a-survey-on-bigdata-analytics-using-in-banking-sectors/gagana-h-s
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
La base para optimizar y potenciar la toma de decisiones en cualqueir empresa es la información. Pero no la información en bruto, sino aquella de la que podemos obtener valor tras su análisis.
Going Digital: What Banking Leaders Need to KnowCognizant
To compete in the digital era, banks need to embrace data, put customers first and manage organizational change -- three concepts, one payoff. Here's how your bank can put it all together.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is evolving into a strategy that reaches across technology companies. We offer guidance on the rise of experience and its role in business modernization, with details on how orgnizations can build the ecosystem to support it.
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Enhancing Desirability: Five Considerations for Winning Digital InitiativesCognizant
To be a modern digital business in the post-COVID era, organizations must be fanatical about the experiences they deliver to an increasingly savvy and expectant user community. Getting there requires a mastery of human-design thinking, compelling user interface and interaction design, and a focus on functional and nonfunctional capabilities that drive business differentiation and results.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
According to our research, manufacturers are well ahead of other industries in their IoT deployments but need to marshal the investment required to meet today’s intensified demands for business resilience.
The Work Ahead in Higher Education: Repaving the Road for the Employees of To...Cognizant
Higher-ed institutions expect pandemic-driven disruption to continue, especially as hyperconnectivity, analytics and AI drive personalized education models over the lifetime of the learner, according to our recent research.
Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
In recent years, insurers have invested in technology platforms and process improvements to improve
claims outcomes. Leaders will build on this foundation across the claims landscape, spanning experience,
operations, customer service and the overall supply chain with market-differentiating capabilities to
achieve sustainable results.
Profitability in the Direct-to-Consumer Marketplace: A Playbook for Media and...Cognizant
Amid constant change, industry leaders need an upgraded IT infrastructure capable of adapting to audience expectations while proactively anticipating ever-evolving business requirements.
Green Rush: The Economic Imperative for SustainabilityCognizant
Green business is good business, according to our recent research, whether for companies monetizing tech tools used for sustainability or for those that see the impact of these initiatives on business goals.
Policy Administration Modernization: Four Paths for InsurersCognizant
The pivot to digital is fraught with numerous obstacles but with proper planning and execution, legacy carriers can update their core systems and keep pace with the competition, while proactively addressing customer needs.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Operations Workforce Management: A Data-Informed, Digital-First ApproachCognizant
As #WorkFromAnywhere becomes the rule rather than the exception, organizations face an important question: How can they increase their digital quotient to engage and enable a remote operations workforce to work collaboratively to deliver onclient requirements and contractual commitments?
Five Priorities for Quality Engineering When Taking Banking to the CloudCognizant
As banks move to cloud-based banking platforms for lower costs and greater agility, they must seamlessly integrate technologies and workflows while ensuring security, performance and an enhanced user experience. Here are five ways cloud-focused quality assurance helps banks maximize the benefits.
Getting Ahead With AI: How APAC Companies Replicate Success by Remaining FocusedCognizant
Changing market dynamics are propelling Asia-Pacific businesses to take a highly disciplined and focused approach to ensuring that their AI initiatives rapidly scale and quickly generate heightened business impact.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Bank(ing) on Data Science
1. Bank(ing) on Data Science
By embracing data science tools and technologies, banks can more
effectively inform strategic decision-making, reducing uncertainty
and eliminating analysis-paralysis.
Executive Summary
Amid the ever-present big data buzz, some large
global banks have mastered the art of using
data science to improve customer engagement,
revamp products and optimize marketing
outreach, risk management, pricing and ongoing
cost reductions. Meanwhile, others are still trying
to make sense of where these emerging technolo-
gies and techniques fit in. At some point, banks of
all sizes, shapes and forms need to incorporate
data science into their operating models.
The future of banking will be determined by how
well banks use technology to maximize their
accumulated wealth of transactional and interac-
tional data to better understand hidden patterns
of customer behavior. Using these insights, they
can make necessary service improvements and
customize existing offerings to properly align the
right products with the right customers.
To successfully implement data science, banks
need to start small and adopt a structured
approach, based on a strategic roadmap. Banks
that can analyze the data they collect and utilize
it for strategic decision-making will maximize
their competitive advantage; those that cannot
will place their profitability, if not their survival,
at risk.
By understanding data and applying insights
gleaned from customers, partners and employees,
banks can more effectively compete on code and
gain incredible competitive advantage. Companies
such as Google, Pandora, Netflix, Amazon — and
many others — are winning decisively in their
markets because of their refined ability to mine
insight from the digital information surrounding
people, organizations and devices, or what we call
a Code Halo™. When properly harnessed, Code
Halos contain a treasure trove of business value.1
This white paper details the growing importance
of Code Halos in data science and analytics ini-
tiatives. Importantly, it highlights potential areas
of fit, ways to overcome challenges and a recom-
mended implementation strategy for key data
science initiatives.
Banking’s Evolving World
In the aftermath of last decade’s global financial
meltdown, the banking industry is undergoing a
radical transformation due to rapidly changing
consumer behaviors and expectations; more
stringent regulatory guidelines; and a highly
competitive environment with a proliferation of
new channels (mobile banking and social media)
and competitors (nonbanks, such as Paypal and
Google Wallet).
This ongoing transformation, while difficult, is
also opening doors to new opportunities. Banks
must ensure that they can cost-effectively acquire
new customers while retaining existing ones. And
• Cognizant 20-20 Insights
cognizant 20-20 insights | june 2014
2. 2
to expand their reach and profitability, they must
also tighten their focus on the expanding digital
world. Analytics, big data and data science can
unlock a world of new possibilities. With proper
use of data science, banks can better understand
prospect/customer relationships by exploring
ever-changing transactional and interactional
behaviors. New digital marketing technologies,
such as Web sites, e-mail, mobile apps and social
networks, are helping banks better target their
customers and improve engagement. Moreover,
advanced segmentation strategies are helping
them boost their marketing effectiveness by iden-
tifying niches based on consumer behavior.
Growing Importance of Data Science
The goal of data science is to extract hidden
insights and knowledge from data. In our view,
the key word is “science,” since, done properly,
data science requires a systematic study of obser-
vation, backed by proven scientific techniques.
Data science builds on elements, techniques
and theories from many fields, including signal
processing, mathematics, probability models,
machine learning, computer programming,
statistics, data engineering, pattern recognition
and learning, visualization, uncertainty modeling,
data warehousing and high-performance
computing. The exponential growth of data, par-
ticularly unstructured data, makes big data an
important aspect of data science. Every day, 2.5
exabytes of data are created; just one exabyte
is equal to 50,000 years’ worth of DVD-quality
video.2
For years, financial institutions have leveraged
customer insights gleaned from systems of record
to manage risk and fraud, as well as to improve
product development, marketing and customer
communications. Today, new and enhanced
technologies, coupled with the availability of a
vast pool of structured and unstructured data,
allows for real-time, multichannel decision-mak-
ing processes that can save money and increase
revenues.
Many banks are just beginning to consolidate and
utilize the internal data elements at their disposal,
such as debit and credit transactions, purchase
histories, channel usage, communication prefer-
ences, loyalty behavior, etc. And when it comes
to big data, banks have collected large amounts
of information from a variety of sources, such as
transaction details and spending behaviors. The
addition of newer sources, including Web server
logs, Internet clickstreams, social media activity
and mobile-phone call details, has opened the
floodgates on the data sets that can be mined for
meaning.
However, this is easier said than done, as these
data sets come in a variety of structured, semi-
structured and unstructured formats, and arrive
at an ever-increasing velocity and complexity.
Analyzing this data is now mission-critical, since
it can provide more timely and precise insights
to guide business planning and decision-making.
With so much transparent content generated
daily through social media, data science can
help banks deliver a consistent and integrated
customer experience.
To use this data for business advantage, banks
must set up data analysis teams to collect, sift and
apply meaning from this data to advance business
goals. According to Gartner, big data in the
banking industry has the highest level of oppor-
tunity because of the high volume and velocity of
data in play. Moreover, 78% of CFOs have labeled
BI and analytics as the top technology initiative
for their departments — beating out even financial
management applications.3
Key Inputs for Data Science
As noted earlier, data can be broadly categorized
as structured and unstructured. At a broad level,
structured data comprises transactional data,
which includes customer buying/spending habits,
and unstructured data can be obtained from
various social media sites, such as Facebook and
Twitter. Precise analysis of social data is of great
importance because it provides valuable insight
into individual customers’ likes, dislikes, prefer-
ences, etc.
Analysis of both structured and unstructured data
can help banks better target the right product to
the right customer at the right time. For example,
by correlating the social activities (unstructured
data) of a customer with a spending pattern
(structured data), banks can customize and
optimize the timing of their product offerings.
For even more precise targeting, organizations
can add new third-party data sources, compiled
from a variety of sources, such as public reposi-
tories, mobile devices and cars. As such, data
science involves three aspects of data: velocity,
volume and diversity (see Figure 1).
cognizant 20-20 insights
3. 3cognizant 20-20 insights
Data Science: Usage Areas
Many business areas can benefit from data sci-
ence (see Figure 2). To properly ascertain how
customers prefer to be served, banks can apply
such data science techniques as hypothesis test-
ing, crowdsourcing, data fusion and integration,
machine learning, natural language processing,
signal processing, simulation, time series analysis
and visualization. Using the insights gleaned from
these approaches, marketers can derive the right
marketing strategy through a mix of marketing
messages and offers that resonate with individual
customers and segments.
For example, using a mobile app, banks can
analyze individual consumer behaviors and
spending activities and combine that data with
credit bureau information. When analyzed, the
resulting insights can lead to better targeted
messaging around a potential offer, such as a
pre-approved home loan to a customer who is
qualified based on analysis of the data contained
in his transactional files and interactions on social
media.
The vast amounts of online data have much
to offer banks seeking consumer insights. For
instance, by combining information from travel
Web sites and spending patterns gleaned from
internal databases, banks can optimize their
product mix and offers. Analysis of transactional
behavior like recency, frequency and monetary
value can be sliced and diced to derive customer
profiles that can improve the effectiveness and
efficiency of targeted marketing efforts. An
example is an Australian bank that is working
with a retailer to better understand where the
retailers’ customers live, when and where they
shop, and how much they spend. This informa-
tion is then used to refine the retailer’s branch
location/relocation strategy.4
Another example is a bank that uses point of sale
data to determine whether a customer frequents
a certain area for shopping or lunch and then use
this information to deliver online offers that are
highly personalized even to the type of food the
customer prefers, increasing the probability that
the offer would be accepted. Adding device-spe-
cific capabilities, the offer could be delivered by
SMS at the most logical time for decision-making.
Data Science Trio
•
Velocity
• Batch process
• Near real-time
• Real-time
Volume
• Records in terabytes,
petabytes
Diversity
• Structure transactional data
• Unstructured/semi-structured data
from social source
Figure 1
Applying Data Science
Intelligent
Forecasting
Consumer
Sentiment
Fraud
Detection
Customer
Service
Target
Marketing
Data
Science Areas
of Usage
Consumer
Profiling
Figure 2
4. Quick Take
As an early warning system, data science solutions
can help banks quickly identify potentially
fraudulent behavior before the fraud becomes
material. For example, individual cardholders are
creatures of habit. Cardholders have “favorites“
or recurrences over a wide variety of objects in
their transaction streams. These objects might
include favorite ATMs that are close to work or
home or gas stations along a daily commute, as
well as preferred grocery stores and online sites
for shopping.
An analytics technique that could be used to
improve fraud management is to identify card-
holder favorites, in order to distinguish between
“in-pattern,” or normal, customer spending and
“out-of-pattern” suspicious transaction activity.
This enables faster fraud detection at much lower
false positive rates (declines on legitimate trans-
actions).
Text analytics of unstructured data can help banks
identify patterns of information that indicate the
likelihood of fraud. Text mining of insurance claim
descriptions (written and recorded) provided by
bogus claimants uncovered some very interest-
ing facts. It turns out that certain phraseologies
(the use of “ed” rather than “ing” on the end of
verbs, for instance), are extremely indicative of
fraudulent claims. This is due to the different ways
in which people relay stories they actually expe-
rienced vs. those they concocted; for instance “I
was walking” is indicative of someone recounting
an actual experience whereas “I walked” often
turns out to be indicative of someone describing
a fictitious event.
Applying Data Science to Detect Fraud Before The Fact
4cognizant 20-20 insights
This is the same approach perfected by Amazon
and other retailers.
Unstructured data, such as social media com-
ments, can help banks gain insight into what cus-
tomers like and don’t like about various brands,
products and service and also gather feedback
about their own products and services. By closely
tracking customer comments, banks can quickly
identify issues and take action to improve the cus-
tomer experience. The instant feedback of social
media also enables banks to capitalize on oppor-
tunities to proactively counteract negative per-
ceptions, exceeding customer expectations and
driving loyalty. Banks can also use social media
data to target customers with offers or services
aligned with recent life events (e.g., graduation,
marriage, new job).
Data science can help banks recognize behavior
patterns, providing a complete view of individual
customers and segments. For example, when a
customer enters a bank, customer representa-
tives can be better equipped to offer the right
products and provide a quicker resolution to
customer queries by analyzing their Code Halos.
Data science can also be used by banks to analyze
the average cost for each channel (e.g., call center,
branch banking, etc.) and design strategies to
migrate customers to low-cost channels.
Analytics techniques can also play a signifi-
cant role in the early warning, detection and
monitoring of fraud. These techniques allow
organizations to extract, analyze, interpret and
transform business data to help detect potential
instances of fraud and implement effective fraud
monitoring programs (see sidebar).
Advanced data science techniques could enable
institutions to improve underwriting decisions
and increase revenues while reducing risk costs.
These techniques can be fruitful across all asset
classes, all types of credit risk models and the
entire credit life cycle, including profit maximiza-
tion and portfolio management.
For debt collections and recoveries, analytics
is a critical part of the process, as it can enable
organizations to create an accurate picture of
the customer’s propensity and ability to pay and,
therefore, the amount likely to be recovered. This
behavioral scoring is used to segment customers
and prioritize collections activities to maximize
recoveries and reduce collections costs.
Overcoming Challenges
What follows are the common obstacles banks
encounter when attempting to implement an
effective data science strategy.
5. cognizant 20-20 insights 5
Data Volume
Over the last decade, banks have accumulated
huge volumes of data, especially following the
introduction of smartphones, tablets and now
wearables that enable multi-channel access;
however, many still suffer from a scarcity of
insight. Managing enormous data sets, as well as
analyzing and correlating structured, semi-struc-
tured and unstructured formats, makes the data
science job increasingly complex.
Distinguishing “signal” (meaningful insight)
from “noise” (massive amounts of unmanaged
data) remains a fundamental challenge and a
significant opportunity. There are various data
cleansing techniques, such as clustering, outlier
detection, etc., that can help organizations find
correlations within date sets.
Budget Constraints
Banks must be willing to invest significantly in
people, infrastructure and platforms to effective-
ly analyze and make strategic decisions from big
data. Beyond these investments, such initiatives
also need to strategically align with the bank’s
overall vision and business mission. Such initia-
tives require qualitative and quantitative scrutiny
in order to prioritize the projects with the highest
payback. Priorities can be determined by strategic
and tactical benefits, cost, duration, people and
technology availability.
Privacy Concerns
Gaining permission to use and process data from
mobile and social media is a huge challenge.
Numerous concerns have been raised over
identity theft, privacy and social media stalking,
among other issues. Within the bank, it is also
important to ensure that the right people across
the organization (i.e., bank decision-makers) can
access the right data, at the right time.
Organizations must also decide who owns the
data before a data science project is implement-
ed, so that accountability and workflow can be
properly set and followed.
Skilled Talent
There is a huge demand for data scientists, and
the pool of available talent is insufficient to meet
the needs of every organization. Finding highly
skilled data scientists is not easy; they do not
simply report on data but look at it from many
angles, running complex queries to find correla-
tions and patterns. They also need to communi-
cate their findings and recommendations to top
leadership. Some of the top skills required for
data scientists include analytics know-how, statis-
tical acumen, domain expertise data mining and
the ability to clearly and effectively communicate.
Looking Forward
Today’s knowledge economy provides businesses
of all kinds with access to big data that’s growing
exponentially in volume, variety, velocity and
complexity. With more data coming from more
sources faster than ever, the questions will only
continue to unfold. Some examples:
• What is your organization’s data science
strategy?
• How is your enterprise combining new and
existing data sources to make better decisions?
• How could new data sources, including social,
sensors, location and video, help improve your
organization’s business performance?
• Will your organization take advantage of big
data or remain paralyzed through endless
analysis?
A savvy, experienced team of data science con-
sultants can help organizations create a roadmap
that results in a meaningful, business-aligned
approach to data science. Experts can help
implement data science technologies, manage big
data, accurately predict customer demand and
make better decisions faster than ever before.
The best approach is to start small rather than
setting off a big bang. The mantra for successful
data science projects depends on the organiza-
tion’s business objectives, but one constant is
focus and agility. For example, if the business
need is to define customer segments to drive
pricing-elasticity models, the IT organization
should first discover which customer data needs
to be gathered before building an enterprise data
warehouse and an enterprise analytics platform.
Experts can develop an initial proof of concept by
analyzing the internal, external, structured and
unstructured data and conclude with meaningful,
business-aligned recommendations.
A blueprint can help guide the organization to
develop and implement data science solutions in
ways that deliver business value. From there, an
implementation strategy followed by a detailed
plan can be built (see Figure 3, next page).
6. cognizant 20-20 insights 6
Data Science Implementation Plan
Figure 3
3
BussiinneessB
oobjective
Internal, external,I
social data analysis by
data science
ccoonnssuullttaanntt
cept note/Concept note
proof off ccoonnceptpro
ImplementatiioonnImplementatio
ssttrraatteegy
ailedDetailed
iimmppllementatioonn
plan
About the Authors
Shantanu Dubey is a Consultant within Cognizant Business Consulting’s Banking and Financial Services
Practice. He has over seven years of business and IT consulting experience in implementation of BASEL,
regulatory reporting, business intelligence and core banking solutions. Shantanu also has experience
working with leading banks on product development, business process optimization, business require-
ments management and gap analysis across various geographic locations. He holds a bachelor’s
degree in information and technology engineering from RGPV Bhopal, and a post-graduate diploma in
management from I2it Pune. Shantanu can be reached at Shantanu.dubey@cognizant.com.
Siddhartha Nainwani is a Consultant within Cognizant Business Consulting’s Banking and Financial
Services Practice. He has over seven years of business and IT consulting experience, working with
leading banks in business process management, business analysis and test management across various
geographic locations. Siddhartha holds a bachelor’s degree in engineering in information technology
from Shivaji University, Maharashtra, and a master’s degree in management from ICFAI Business School,
Mumbai. He can be reached at Siddhartha.Nainwani@cognizant.com.
Footnotes
1
For more information on Code Halos, please see our white paper, “Code Rules: A Playbook for Managing
at the Crossroads,” or our recently published book, Code Halos: How the Digital Lives of People, Things
and Organizations Are Changing the Rules of Business.
2
Wikipedia definition, http://en.wikipedia.org/wiki/Big_data.
3
“Three Reasons Why BI and Analytics Is The Top CFO Initiative,” Domo,
http://www.domo.com/learn/3-reasons-why-bi-analytics-is-the-top-cfo-initiative.
4
Anthony Duffy, “Unlocking the Potential of Big Data,” Banking Technology,
http://www.bankingtech.com/58812/unlocking-the-potential-of-big-data/.