Data Driven Marketing — What it is, benefits and the future?

Data driven marketing

Data driven marketing is a process by which marketers gain insights and trends based on in-depth analysis informed by numbers. Big Data, machine learning, advanced algorithms and artificial intelligence are rapidly transforming the marketing industry. With data and analytical tools, brands can make more informed decisions about their digital strategy that will help give them a grip over their competition.

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War of the century: AI vs Humans (I)

Stormoopers

We are now living in a world where the ‘real’ and the ‘not so real’ are becoming so blurred. In the 18th century people couldn’t have dreamt of many things that we have today. And from the multitude of them in the last decade we created the most valuable and frightening in the same time: the Artificial Intelligence more common spelled AI. As much as we will take all the advantages we are still debating on a certain questions. How safe it will be with this let’s called machines? Can AI be trusted in the real world? Are we sure that this technology wouldn’t overpower humans? Will AI take our jobs?

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Lusha raises $40 million to surface sales lead contact info in your browser

Lusha platform

Lusha, a Tel Aviv-based startup developing a crowdsourced data community for business-to-business salespeople, today announced it has raised $40 million. The company plans to use the funds to further grow its sales platform as it looks to invest in R&D.

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AI in health care creates unique data challenges

The health care industry produces an enormous amount of data. An IDC study estimates the volume of health data created annually, which hit over 2,000 exabytes in 2020, will continue to grow at a 48% rate year over year. Accelerated by the passage of the U.S. Patient Protection and Affordable Care Act, which mandated that health care practitioners adopt electronic records, there’s now a wealth of digital information about patients, practices, and procedures where before there was none.

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Prediction 2021: The Year AI Became Normal

A clear pattern of growth has already emerged in AI: in 2018–19, the phase of experimentation became mature; in 2020, adoptions began in a serious way and suddenly, COVID-19 gave the business leaders an opportunity and impetus to push automation and AI. In 2021, the fallout from a second wave of COVID-19 will eventually become clear, starting with the rapid decline of many traditional, non-digital businesses. As the C-suite takes notice, following are the relevant trends I expect to emerge in 2021.

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BCBSA backs Lumiata’s $14M round to boost COVID-19 risk management using AI

AI Collage

The COVID-19 pandemic has been a pivotal time for advances in artificial intelligence as organizations use the technology to drive research, find new treatments and analyze patient data. San Mateo, California-based Lumiata is ramping up its efforts to use AI to help identify underwriting risks during the crisis. The company just got a big infusion of cash to help fuel its work.

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Which Data-Science skills are the most vital in 2021?

There are a lot of industry standard tools that any aspiring data scientist will certainly want to be familiar with. Experience with these tools is almost always presented as a requirement on job listings because they are likely the tools you will be working with in-house. At the very least, familiarity with the concepts presented by the tools will make them easier to utilize before you have gotten the chance to get experienced with them.

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Deploying Machine Learning into production: Don’t do Labs.

In my experience building analytics products at Best Buy Canada, applied data science projects rarely fail because of the science. They fail because the model couldn’t be integrated into existing systems and business operations. We have had sound models showing good accuracy, even with proofs of concept demonstrating value, yet still fail to get deployed into production. The challenge isn’t POCs, its scaling.

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An introductory Guide to variables and data types in Go

Golang tutorial

Hello there! So today we will be learning about Go variables and the different data types associated with Go. Just in case you are just started the Go language, you should read this. We would be using the Go Playground to learn and practice this example, and you can grab a pop-corn, this would be a long and interesting ride. Ready? Let hop in.

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What is Model complexity? Compare Linear Regression to Decision Trees to Random Forests

A machine learning model is a system that learns the relationship between the input (independent) features and the target (dependent) feature of a dataset to be useful in making predictions in the future. In this article, we are going to test the effectiveness of 3 popular models that vary in complexity.

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Data quality from First Principles

The right way to think about Data Quality, from Kimball and Uber’s points of view. If you’ve spent any amount of time in business intelligence, you would know that data quality is a perennial challenge. It never really goes away. For instance, how many times have you been in a meeting, and find that someone has to vouch for the numbers being presented?

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The evolution of Big Data compute platforms  – past, now and later

A journey into the evolution of Big Data Compute Platforms like Hadoop and Spark. Sharing my perspective on where we were, where we are and where we are headed. Over the past few years I have been part of a large number of Hadoop projects. Back in 2012–2016 the majority of our work was done using on-premises Hadoop infrastructure.

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OneTrust raises $300 million to automate data governance and compliance

OneTrust today announced it raised $300 million at a $5.1 billion valuation. The company says the round will be put toward product R&D as it looks to expand its sales, marketing, and engineering teams worldwide.

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Machine Learning in Baseball

This is an interesting problem to undertake because of the renaissance that has been taking place in baseball over the last two decades or so. Data has come to play a huge role in baseball and that means that patterns and statistics that were once considered fringe are now mainstream metrics. The home run is no longer king. The era of Moneyball has supplanted Longball.

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