August, 2021

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How DataOps is Transforming Commercial Pharma Analytics

DataKitchen

DataOps has become an essential methodology in pharmaceutical enterprise data organizations, especially for commercial operations. Companies that implement it well derive significant competitive advantage from their superior ability to manage and create value from data. They will be able to produce high-quality, on-demand insight that consistently leads to successful business decisions.

Analytics 246
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Must know Pandas Functions for Machine Learning Journey

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Do you wish you could perform this function using Pandas. Well, there is a good possibility you can! For data scientists who use Python as their primary programming language, the Pandas package is a must-have data analysis tool. The Pandas package has everything […].

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Top 7 business intelligence trends for 2021

CIO Business Intelligence

Business intelligence is not an oxymoron like jumbo shrimp. It’s not dead. And it’s not being superseded by artificial intelligence.

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Understanding the Basics of Apache Spark RDD

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Hello readers! In this article, I am going to discuss one of the most essential parts of Apache Spark called RDD. Before getting into Spark RDD, I strongly recommend you to read my article, Understand the internal working of Apache Spark to get an overview of […]. The post Understanding the Basics of Apache Spark RDD appeared first on Analytics Vidhya.

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Optimizing The Modern Developer Experience with Coder

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have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth.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.

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When the Voice of the Customer Actually Talks

Rocket-Powered Data Science

For many years, organizations (mostly consumer-facing) have placed the “voice of the customer” (VoC) high on their priority list of top sources for customer intelligence. The goals of such activities are to improve customer service, customer interactions, customer engagement, and customer experience (CX) through just-in-time customer assistance, personalization, and loyalty-building activities.

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Sisense Earns Analytics Digital Innovation Award for 2021

David Menninger's Analyst Perspectives

The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.

Analytics 246

More Trending

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The Role of Model Governance in Machine Learning and Artificial Intelligence

Domino Data Lab

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. As such, model governance needs to be applied to each model for as long as it’s being used.

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10 Tips to Visualize Data Like a Pro

Juice Analytics

Have you nailed all the data visualization basics? Stuff like… ?You know pie charts are bad except in certain specific use cases; ?You can spot chartjunk from a mile away; ?You confidently pick the right kind of chart based on what you want to emphasize in the data; ?You use just the right amount of color to bring meaning, but not so much as to distract; ?

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Gradient Descent: Design Your First Machine Learning Model

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Building a simple Machine Learning model using Pytorch from scratch. Image by my great learning Introduction Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network. Training data helps the model learn […].

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How to Win New Business with External Data

TDAN

Increasingly, external data (alternative data, public data, open data – call it what you want) is being called the “secret sauce” of driving advanced analytics, developing machine learning and AI capabilities, enriching existing models, and delivering unrealized insights to every part of your organization. The difficulty in connecting to this data is top of mind for […].

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15 Modern Use Cases for Enterprise Business Intelligence

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?

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Improving Data Processing with Spark 3.0 & Delta Lake

Smart Data Collective

Collecting, processing, and carrying out analysis on streaming data , in industries such as ad-tech involves intense data engineering. The data generated daily is huge (100s of GB data) and requires a significant processing time to process the data for subsequent steps. Another challenge is the joining of datasets to derive insights. Each process on average has more than 10 datasets and an equal number of joins with multiple keys.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

Below is our fourth post (4 of 5) on combining data mesh with DataOps to foster innovation while addressing the challenges of a decentralized architecture. We’ve covered the basic ideas behind data mesh and some of the difficulties that must be managed. Below is a discussion of a data mesh implementation in the pharmaceutical space. For those embarking on the data mesh journey, it may be helpful to discuss a real-world example and the lessons learned from an actual data mesh implementation.

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Increase Analytics Influence: Leverage Predictive Metrics!

Occam's Razor

Almost all metrics you currently use have one common thread: They are almost all backward-looking. If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics. Predictive metrics! But first, let's take a small step back.

Metrics 143
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7 Ways to Improve Your Client Reporting

Juice Analytics

Presenting data to customers or clients is an opportunity: it is a chance to shape how they look at your solution, demonstrate the value you deliver, and guide them to better decisions. Unfortunately, a lot of client reporting is treated as a check-the-box activity. We wanted to share some examples of high impact reporting and the features that make them effective: 1.

Reporting 140
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8 Steps to Transformation at Speed & Scale – Your Guide to Deploying StratOps

📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. Prioritizes Outcomes : Focuses on concrete business outcomes from day one, rather than capabilities in isolation.

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Hyperparameter Tuning Of Neural Networks using Keras Tuner

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In neural networks we have lots of hyperparameters, it is. The post Hyperparameter Tuning Of Neural Networks using Keras Tuner appeared first on Analytics Vidhya.

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Big Data Modeling Improves Business Intelligence

TDAN

Through big data modeling, data-driven organizations can better understand and manage the complexities of big data, improve business intelligence (BI), and enable organizations to benefit from actionable insight. Big data modeling is an extension of data modeling, a practice adopted by many areas of Information Technology (IT), used to better understand enterprise data resources.

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Artificial Intelligence for eCommerce: A Closer Look

Smart Data Collective

Artificial intelligence is one of the fastest-growing technologies. It grew 270% in just four years since more companies started noticing its benefits and adopted this technology. eCommerce businesses are no exception. Over 50 percent of them use AI for various purposes, like creating personalized services and automation. This article will share the basic concept of AI, including its definition, types, and current trends.

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Accelerating Drug Discovery and Development with DataOps

DataKitchen

A drug company tests 50,000 molecules and spends a billion dollars or more to find a single safe and effective medicine that addresses a substantial market. Figure 1 shows the 15-year cycle from screening to government agency approval and phase IV trials. Drug companies desperately look for ways to compress this lengthy time frame and to demonstrate the competitive advantage of their intellectual property.

Testing 246
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Marketing Operations in 2025: A New Framework for Success

Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com

Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.

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What’s the Link Between AI Governance, MLOps, and Responsible AI? Scaling AI

Dataiku

We are taking a break on the AI regulation series to take a step back and clarify the connection between AI Governance, MLOps, and Responsible AI. These three concepts fall off the tip of the tongues of industry practitioners, researchers, and regulators alike, all without having clearly defined connections or boundaries. At Dataiku , we believe that these concepts are central pieces of being able to scale with AI.

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3 Reasons Why Most Digital Transformation Initiatives Continue To Fail

BA Learnings

Are you thinking about initiating a digital transformation campaign in your company? If so, you may be worried about what could go wrong, and the challenges that possibly lie ahead. You’ve probably heard about the many unsuccessful stories, and you’re right to be concerned. Boston Consulting Group conducted a digital transformation study in 2020 to find out why such projects are missing the mark.

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Effective Data Visualization Techniques in Data Science Using Python

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Data Visualization Data Visualization techniques involve the generation of graphical or. The post Effective Data Visualization Techniques in Data Science Using Python appeared first on Analytics Vidhya.

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Differences Between Data Management and Data Governance

TDAN

A common misconception among c-level executives is that governance and management of data is the same thing other than in capital letters. Yet, there are some crucial differences between these two terms that should be understood before working on a project or implementing a program. Below, we will explore the main differences between Data Management […].

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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. In this article, we will explore both, unfold their key differences and discuss their usage in the context of an organization. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources.

Data Lake 140
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Addressing Data Mesh Technical Challenges with DataOps

DataKitchen

Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. Let’s take a look at some technical aspects of data mesh so we can work our way towards a pharmaceutical industry application example. .

Testing 246
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Data Exploration with Pandas Profiler and D-Tale

Domino Data Lab

We all have heard how data is the new oil. I always say that if that is the case, we need to go through some refinement process before that raw oil is converted into useful products. For data, this refinement includes doing some cleaning and manipulations that provide a better understanding of the information that we are dealing with. In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase.

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A ‘Fresh Squeeze on Data’ to Help Children Learn about Data, AI and Machine Learning

Cloudera

Dear Parents and Educators and Friends of Cloudera, If you are reading this blog, you know us at Cloudera as a group of self-described data geeks and data analysts. We believe data drives better decisions and moves businesses forward and for us, that’s exciting. We are innovating and helping Fortune 500 transform and grow because they can make better data-driven decisions at the accelerated pace we live and work in today.

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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.

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Let’s Understand All About Data Wrangling!

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Data- a world-changing gamer is a key component for all. The post Let’s Understand All About Data Wrangling! appeared first on Analytics Vidhya.

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Dataiku Series E: Unleashing Everyday AI

Dataiku

“AI is starting to deliver on its potential” — it feels like years that people have been singing variations of this refrain, doesn’t it? And while organizations have come a long way with AI, I don’t think we’ve even scratched the surface when it comes to business potential with and value from AI. That’s why today, we’re proud to announce a $400 million Series E funding round that will allow Dataiku to unleash Everyday AI within exponentially more organizations around the world.

IT 122
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5 Advanced Identity Theft Protection Tips in the Big Data Era

Smart Data Collective

There are a lot of great things about living in an era governed by big data. Big data creates a lot of new opportunities in business and our personal lives. However, there are also downsides to the sudden influx of data in the 21st Century. One major concern is that big data has made identity theft risks more significant. Addressing the Risk of Identity Theft in a World Increasingly Dependent on Big Data.

Big Data 140
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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. With an architecture comprised of numerous domains, enterprises need to manage order-of-operations issues, inter-domain communication, and shared services like environment creation and meta-orchestration. A DataOps superstructure provides the foundation to address the many challenges inherent in operating a group of interdependent domains.

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The Cloud Development Environment Adoption Report

Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.