November, 2022

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What are Smart Contracts in Blockchain?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Source: Image by Gerd Altmann from Pixabay Smart contracts are blockchain-based computer programs that activate at predefined times. In most cases, they are used to eliminate the need for a third party during the execution of a contract, allowing all parties to […].

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7 enterprise data strategy trends

CIO Business Intelligence

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices. As with just about everything in IT, a data strategy must evolve over time to keep pace with evolving technologies, customers, markets, business needs and practices, regulations, and a virtually endless number of other priorities.

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IBM Builds on Analytics and BI Foundation

David Menninger's Analyst Perspectives

In today’s data-driven world, organizations need real-time access to up-to-date, high-quality data and analysis to keep pace with changing market dynamics and make better strategic decisions. By mining meaningful insights from enterprise data quickly, they gain a competitive advantage in the market. Yet, organizations face a multitude of challenges when transitioning into an analytics-driven enterprise.

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Systems Thinking and Data Science: a partnership or a competition?

Jen Stirrup

Information is pretty thin stuff, unless mixed with experience. – Clarence Day (1874–1935), American essayist. Why do organizations get stuck with their data? It is such a fundamental question. Often, this problem can be due to the organization concentrating solely on technology and data. However, organizations can be supported by a synergistic approach by integrating systems thinking with the data strategy and technical perspective.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionzing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs. Putting the right LLMOps process in place today will pay dividends tomorrow, enabling you to leverage the part of AI that constitutes your IP – your data – to build a defensible AI strategy for

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AI’s ‘SolarWinds Moment’ Will Occur; It’s Just a Matter of When

O'Reilly on Data

Major catastrophes can transform industries and cultures. The Johnstown Flood, the sinking of the Titanic, the explosion of the Hindenburg, the flawed response to Hurricane Katrina–each had a lasting impact. Even when catastrophes don’t kill large numbers of people, they often change how we think and behave. The financial collapse of 2008 led to tighter regulation of banks and financial institutions.

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How Much Math Do You Need in Data Science?

KDnuggets

There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.

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What Whirlpool’s CIO does to make its digital business models run end to end

CIO Business Intelligence

As a household name in household goods, with annual sales of $22 billion, Whirlpool has 54 manufacturing and tech research centers worldwide, and bursts with a portfolio that includes several familiar brands including KitchenAid, Maytag, Amana, Yummly, among others. The company employs 69,000 around the world as well and Danielle Brown, the company’s SVP and CIO, has a unique perspective on how best to lead the company’s digital transformation strategy.

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Mind the Gap Between Data and Analytics

David Menninger's Analyst Perspectives

If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.

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What to Look for in a Data-Savvy Fintech Marketing Agency

Smart Data Collective

Big data technology has changed the future of marketing in a multitude of ways. A growing number of organizations are leveraging big data to get higher ROIs from their organic and paid marketing campaigns. As a result, companies around the world spent over $52 billion on data-driven marketing solutions in 2021. The Fintech sector is among those most reliant on data-driven marketing.

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Scoring More Goals in Football with AI: Predicting the Likelihood of a Goal Based on On-the-Field Events

DataRobot Blog

Can artificial intelligence predict outcomes of a football (soccer) game? In a special project created to celebrate the world’s biggest football tournament, the DataRobot team set out to determine the likelihood of a team scoring a goal based on various on-the-field events. My Dad is a big football (soccer) fan. When I was growing up, he would take his three daughters to the home games of Maccabi Haifa, the leading football team in the Israeli league.

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Addressing Top Enterprise Challenges in Generative AI with DataRobot

The buzz around generative AI shows no sign of abating in the foreseeable future. Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments. Ultimately, the market will demand an extensive ecosystem, and tools will need to streamline data and model utilization and management across multiple environments.

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Introduction to Pandas for Data Science

KDnuggets

The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.

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How to Use DevOps Azure to Create CI and CD Pipelines?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction In this article, we will discuss DevOps, two phases of DevOps, its advantages, and why we need DevOps along with CI and CD Pipelines. Before DevOps, software development teams, quality assurance (QA) teams, security, and operations would test the code for several […].

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Human and AI Partnerships Are Integral to the Future of Customer Experience

CIO Business Intelligence

The age-old debate on technology versus human capability remains inconclusive. But in this time of artificial intelligence (AI), analytics, and cloud, we’re seeing more opportunities to think of how humans and machines can come together as a team, rather than acting against each other. From diagnosing diseases and delivering effortless customer experiences to understanding human preferences and providing new customer insights, the human and AI partnership is evolving — and more in sync than ever

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Cloud Computing Realities Part 3: Business Continuity

David Menninger's Analyst Perspectives

In my previous perspectives on cloud computing, I addressed some of the realities of cloud costs as well as hybrid and multi-cloud architectures. In the midst of the pandemic, my colleague, Mark Smith, authored a series of perspectives on considerations for business continuity in general, beginning with this look at some of the investments organizations must make to mitigate the risk of business disruptions.

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How to Deliver a Modern Data Experience Your Customers Will Love

In embedded analytics, keeping up with the pace of innovation is challenging. Download Qrvey's guide to ensure your analytics keep pace so you can solve your user's biggest challenges, delight them, and set your product apart from the competition. The guide outlines how to use embedded analytics to: Increase user satisfaction Go to market faster Create additional opportunities to monetize your product It also shares what to look for to ensure your embedded analytics are keeping up with the lates

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Why Big Data Is The Future Of Sales And Marketing

Smart Data Collective

Proper marketing and sales prospects play a huge role in improving the success rate of your business. The strategy can either be offline or digital. However, digital marketing has become the major focus of marketers across all industries, mainly due to how customers interact and engage with modern businesses. Seeing an opportunity and knowing how and when to take advantage of it defines the majority of where today’s marketers stand.

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The Evaporation of Privacy

TDAN

Have you ever read those little pieces of paper inserted into your bank statement, credit card statements, insurance bills, mutual fund statement, and all of your other statements and bills? We all get them. You know, those flimsy pieces of paper, printed in small type and written in convoluted English. I have started collecting them— […].

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If I Had To Start Learning Data Science Again, How Would I Do It?

KDnuggets

While different ways to learn Data Science for the first time exist, the approach that works for you should be based on how you learn best. One powerful method is to evolve your learning from simple practice into complex foundations, as outlined in this learning path recommended by a physicist who turned into a Data Scientist.

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Top 5 Interview Questions on Multi-modal Transformers

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Source: totaljobs.com Introduction Until recently, developing new, improved transformers specifically for a single modality was common practice. However, to tackle real-world tasks, there was a pressing need to develop multi-modal transformers models. Multi-modal transformers models are the type of models that employ the […].

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The Definitive Entity Resolution Buyer’s Guide

Are you thinking of adding enhanced data matching and relationship detection to your product or service? Do you need to know more about what to look for when assessing your options? The Senzing Entity Resolution Buyer’s Guide gives you step-by-step details about everything you should consider when evaluating entity resolution technologies. You’ll learn about use cases, technology and deployment options, top ten evaluation criteria and more.

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Why CIOs should invest in digital through economic headwinds

CIO Business Intelligence

During the opening keynote at the recent Gartner IT Symposium in Barcelona, Gartner analysts said that CIOs should look to its latest moniker, IT for sustainable growth , to drive business transformation by focusing on three key strategies: ‘revolutionary work’ to empower the workforce, ‘responsible investment’ to balance financial and sustainability objectives, and ‘resilient cybersecurity’ to support business outcomes “without constraining them”.

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Using ML and Dataiku to Make 2022 FIFA World Cup Predictions

Dataiku

It’s been four years since the last World Cup in 2018, when France won their second star on their crest. Even if you are not particularly interested in football, you might still find that World Cups are a fun moment where people share joy and excitement. I wanted to participate, but my knowledge would be limited to knowing Neymar and Kylian Mbappé's names.

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Data Analytics Solves Manufacturing Marketing Agency Challenges

Smart Data Collective

Data analytics is unquestionably one of the most disruptive technologies impacting the manufacturing sector. Manufacturers are projected to spend nearly $10 billion on analytics by the end of the year. Data analytics can solve many of the biggest challenges that manufacturers face. One of the most significant benefits of leveraging analytics in manufacturing is with marketing optimization and automation.

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Snowflake: Data Ingestion Using Snowpipe and AWS Glue

BizAcuity

Introduction. In today’s world that is largely data-driven, organizations depend on data for their success and survival, and therefore need robust, scalable data architecture to handle their data needs. This typically requires a data warehouse for analytics needs that is able to ingest and handle real time data of huge volumes. Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts allowing secure data sharing across the organizatio

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How Intent Data Helps Marketers Convert A-List Accounts

One of the biggest challenges for any B2B marketer is understanding your prospects’ next move — who is most likely to buy and when. Without these insights, marketing campaigns can feel more like guesswork, with high investment and little return. We’re here to tell you there’s a better way. By tracking buyers’ digital footprints and online activity, such as website visits, product reviews, and spikes in content consumption, you can engage prospects with a message that really resonates.

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9 Skills You Need to Become a Data Engineer

KDnuggets

A data engineer is a fast-growing profession with amazing challenges and rewards. Which skills do you need to become a data engineer? In this post, we’ll take a look at both hard and soft skills.

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Comprehensive Guide for Interview Questions on Transfer Learning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Source: Canva Introduction Competitive Deep Learning models rely on a wealth of training data, computing resources, and time. However, there are many tasks for which we don’t have enough labeled data at our disposal. Moreover, the need for running deep learning models on […].

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Carhartt turns to data under new CIO

CIO Business Intelligence

Carhartt’s signature workwear is near ubiquitous, and its continuing presence on factory floors and at skate parks alike is fueled in part thanks to an ongoing digital transformation that is advancing the 133-year-old Midwest company’s operations to make the most of advanced digital technologies, including the cloud, data analytics, and AI. The company, which operates four factories in Kentucky and Tennessee and designs all its products at its Dearborn, Mich., headquarters, began its digital tra

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Succeed With AI at Scale With These New Year’s Resolutions Tips

Dataiku

We’re t-minus five weeks away from January 1. Which means t-minus five weeks away from New Year’s Day, when conversations and social media feeds will be inundated with talks of resolutions — losing weight, exercising more, reducing screen time, getting that promotion, putting more money aside for savings, starting a meditation practice — you get the idea.

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The Essential Guide to Analytic Applications

Embedding dashboards, reports and analytics in your application presents unique opportunities and poses unique challenges. We interviewed 16 experts across business intelligence, UI/UX, security and more to find out what it takes to build an application with analytics at its core. No matter where you are in your analytics journey, you will learn about emerging trends and gather best practices from product experts.

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Is Artificial Intelligence Setting A New Standard For Web Design?

Smart Data Collective

Artificial intelligence is playing an important role in modern creative professions. There are a lot of reasons a growing number of companies are turning to AI technology. One poll showed that 61% of companies found that AI and machine learning were their best data investments. One of the industries that is evolving by adopting new AI tools in web design.

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Doing More with Less: 5 Ways Leading Organizations Maximize the Value of their Data

Teradata

"Doing more with less” is a familiar refrain echoing through the halls of many organizations. To answer this call, businesses are searching for efficiency gains & turning to data to unlock savings.

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3 Useful Python Automation Scripts

KDnuggets

The post highlights three useful applications of using python to automate simple desktop tasks. Stay tuned till the end of the post to find the reference for a bonus resource.

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Hierarchical Clustering in Machine Learning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Hierarchical clustering is one of the most famous clustering techniques used in unsupervised machine learning. K-means and hierarchical clustering are the two most popular and effective clustering algorithms. The working mechanism they apply in the backend allows them to provide such a […].

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. They're often developing using prompting, Retrieval Augmented Generation (RAG), and fine-tuning (up to and including Reinforcement Learning with Human Feedback (RLHF)), typically in that order. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are le