David Menninger's Analyst Perspectives
DECEMBER 15, 2022
Recently, I suggested you need to “ mind the gap” between data and analytics. This perspective addresses another gap — the gap in skills between business intelligence (BI) and artificial intelligence/machine learning (AI/ML).
CIO Business Intelligence
DECEMBER 14, 2022
CIO Ted Ross believes the honeymoon is over for breakneck productivity when it comes to hybrid work, and he’s not the only one. Tech employees at the City of Los Angeles IT agency who were forced to work remotely in the early pandemic days were very efficient, Ross says. “Fully into the pandemic we had a 34% increase in project delivery,” he adds. But since then, productivity and innovation have waned, in part because of fading relationships between once in-person teams, along with a slew of new
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Analytics Vidhya
DECEMBER 4, 2022
Introduction The global financial crisis of 2007 has had a long-lasting effect on the economies of many countries. In the epic financial and economic collapse, many lost their jobs, savings, and much more. When too much risk is restricted to very few players, it is considered as a notable failure of the risk management framework. […]. The post XAI: Accuracy vs Interpretability for Credit-Related Models appeared first on Analytics Vidhya.
KDnuggets
DECEMBER 30, 2022
Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.
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Smart Data Collective
DECEMBER 21, 2022
It’s easy to sound paranoid when talking about cyber security. Threats actually are everywhere. In your local coffee shop. Lurking on the first page of your favorite search engine. In your email inbox. One small mistake can bring business empires to their knees. It happened to Marriot. It happened to Yahoo. It happened to the Irish healthcare system.
DataRobot Blog
DECEMBER 6, 2022
Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
CIO Business Intelligence
DECEMBER 9, 2022
It has been almost 25 years since McKinsey & Co. introduced the term “talent war” to the world. For almost a quarter of a century CIOs have been locked in a Sisyphean battle to attract and retain the IT talent necessary to create competitive advantage. Every year, “talent” is one of the top challenges facing IT organizations. Every year, lack of critical IT skills is blamed for failure to deliver the full promise of IT investments.
Analytics Vidhya
DECEMBER 26, 2022
This article was published as a part of the Data Science Blogathon. Introduction Streamlit is an open-source tool to build and deploy data applications with less coding compared to other front-end technologies like HTML, CSS, and JavaScript. It is a low-code tool specifically designed for building data science applications. Moreover, the Streamlit library has functions […].
KDnuggets
DECEMBER 30, 2022
It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.
Smart Data Collective
DECEMBER 15, 2022
Artificial intelligence has offered a plethora of benefits for businesses in every sector. The ecommerce industry is among those most benefiting from advances in AI. Therefore, it is no surprise that the market for AI-enabled ecommerce services is projected to be worth nearly $17 billion by 2030. Ecommerce giants like Amazon are finding creative ways to leverage AI.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
DataKitchen
DECEMBER 29, 2022
The Terms and Conditions of a Data Contract are Automated Production Data Tests. A data contract is a formal agreement between two parties that defines the structure and format of data that will be exchanged between them. Data contracts are a new idea for data and analytic team development to ensure that data is transmitted accurately and consistently between different systems or teams.
David Menninger's Analyst Perspectives
DECEMBER 7, 2022
Embedded business intelligence (BI) continues to transform the business landscape, enabling organizations to quickly interpret data and convert it into actionable insights. It allows organizations to extract information in real time and answer wide-ranging business questions. Embedding analytics helps tackle the issue of extracting information from data which is a time-consuming process.
CIO Business Intelligence
DECEMBER 8, 2022
Here’s a proposition to consider: among the ranks of large enterprises, commercial success increasingly relies on digital transformation. In turn, digital transformation relies on modernized enterprise networks that deliver flexibility, performance and availability from the edge to the cloud. Intuitively, this hypothesis makes a lot of sense. In many enterprises, it’s also increasingly becoming the subject of painstaking debate.
Analytics Vidhya
DECEMBER 15, 2022
This article was published as a part of the Data Science Blogathon. Introduction Generally, machine learning can be classified into four types: supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and reinforcement learning. Supervised machine learning is a type of machine learning that is the easiest and less complex type or branch of data science. […].
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The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
KDnuggets
DECEMBER 29, 2022
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
Smart Data Collective
DECEMBER 13, 2022
Technology is quickly becoming a critical component of our existence. Today, technology powers every important aspect of our life, from business to education to medicine. It has greatly aided in the automation of formerly manual activities, making everything more smooth and efficient. However, computerization in the digital age creates massive volumes of data, which has resulted in the formation of several industries, all of which rely on data and its ever-increasing relevance.
DataKitchen
DECEMBER 9, 2022
Query> DataOps. ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. DataOps involves collaboration between data engineers, data scientists, and IT operations teams to create a more efficient and effective data pipeline, from the collection of raw data to the delivery of insights and results.
David Menninger's Analyst Perspectives
DECEMBER 27, 2022
For far too long, business intelligence technologies have left the rest of the exercise to the reader. Many of these tools do an excellent job providing information in an interactive way that lets organizations dive into the data and learn a lot about what has happened across all aspects of the business. More recently, many of these tools have added augmented intelligence capabilities that help explain why things happened.
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GAP's AI-Driven QA Accelerators revolutionize software testing by automating repetitive tasks and enhancing test coverage. From generating test cases and Cypress code to AI-powered code reviews and detailed defect reports, our platform streamlines QA processes, saving time and resources. Accelerate API testing with Pytest-based cases and boost accuracy while reducing human error.
CIO Business Intelligence
DECEMBER 1, 2022
For any IT leader new to an organization, gaining employee trust is paramount — especially when, like PepsiCo’s Athina Kanioura, you’ve been brought in to transform the way work gets done. Kanioura, who was hired away from Accenture two years ago to serve as the food and beverage multinational’s first chief strategy and transformation officer, says earning employee trust was one of her greatest challenges in those early months.
Analytics Vidhya
DECEMBER 4, 2022
This article was published as a part of the Data Science Blogathon. Introduction In this article, we will be seeing MLOps from the dimension of one of the powerful tools that make it easy to implement. These tool help to improve the deployment process for robust machine-learning projects. We will start by briefly seeing MLOps […]. The post Explaining MLOps using MLflow Tool appeared first on Analytics Vidhya.
KDnuggets
DECEMBER 27, 2022
Here are 5 tasks you can automate with Python, and how to do it.
Smart Data Collective
DECEMBER 6, 2022
In today’s digital world, businesses need to be able to access and analyze their data quickly and efficiently. Streamlining business data is an important part of achieving maximum efficiency. By streamlining your business data, you can save money and increase productivity with less overhead. Whether you’re running a small business or a large-scale multinational company, your data is an important part of your daily operations.
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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
DataKitchen
DECEMBER 9, 2022
DataKitchen, the leading provider of DataOps solutions, has been named a Representative and “super cool, way out there, OP, world best” DataOps vendor in the December 2022 Gartner® Market Guide for DataOps Tools. December 08, 2022, 08:00 ET | Source: DataKitchen. Cambridge Mass, December 08, 2022 (BOB’S QUICKIE NEWSWIRE) — The Gartner Market Guide for DataOps Tools provides guidance on the evolving DataOps market, including market analysis, market direction, and DataOps
David Menninger's Analyst Perspectives
DECEMBER 29, 2022
Organizations conduct data analysis in many ways. The process can include multiple spreadsheets, applications, desktop tools, disparate data systems, data warehouses and analytics solutions. This creates difficulties for management to provide and maintain updated information across multiple departments. Our Analytics and Data Benchmark Research shows that organizations face a variety of challenges with analytics and business intelligence.
CIO Business Intelligence
DECEMBER 20, 2022
One of the biggest cloud security threats your company faces isn’t malicious. In fact, it originates from inside your IT organization. Accidental misconfigurations pose one of the leading security vulnerabilities IT organizations contend with in the cloud. According to a recent study , 79% of companies had experienced a cloud data breach in the past 18 months—and 67% of respondents had identified security misconfiguration as the top security threat.
Analytics Vidhya
DECEMBER 4, 2022
This article was published as a part of the Data Science Blogathon. Source: Canva Introduction The real-world data can be very messy and skewed, which can mess up the effectiveness of the predictive model if it is not addressed correctly and in time. The consequences of skewness become more pronounced when a large model is […]. The post Comparison of Text Generations from GPT and GPT-2 appeared first on Analytics Vidhya.
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
KDnuggets
DECEMBER 22, 2022
Kickstart your data science career with these curated GitHub repositories.
Smart Data Collective
DECEMBER 1, 2022
The world of big data is constantly changing and evolving, and 2021 is no different. As we look ahead to 2022, there are four key trends that organizations should be aware of when it comes to big data: cloud computing, artificial intelligence, automated streaming analytics, and edge computing. Each of these trends will continue to shape the way companies use data in the coming years.
DataKitchen
DECEMBER 6, 2022
We are excited that Gartner released its ‘Market Guide to DataOps’ ! The document they wrote is exceptionally close to what we see in the market and what our products do ! This document is essential because buyers look to Gartner for advice on what to do and how to buy IT software. The two things we are most excited about are: First, DataOps is distinct from all Data Analytic tools.
David Menninger's Analyst Perspectives
DECEMBER 23, 2022
Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics.
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