March, 2019

Your Modern Business Guide To Data Analysis Methods And Techniques

datapine

In our data-rich age, understanding how to analyze and extract true meaning from the digital insights available to our business is one of the primary drivers of success. Despite the colossal volume of data we create every day, a mere 0.5%

All in the Data: Negative Attitudes and the Four Horsemen of the Data Apocalypse

TDAN

From time to time, TDAN.com polishes off oldie but goodie content. A regular columnist required time to address personal issues, so we are substituting one of my past columns in its place. I hope you have as good a time reading it as I had writing it back in October of 2017. While I was […].

IT 56

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Trending Sources

What Are the Business Benefits of Data Mining?

DataFloq

In today’s landscape, there’s nothing more important than valuable information. Companies receive information in the form of digital data or content, more commonly in the shape of performance and user metrics.

Bias-Busting with Diversity in Data

Rocket-Powered Data Science

Diversity in data is one of the three defining characteristics of big data — high data variety — along with high data volume and high velocity.

Gartner Report - Introducing DataOps Into Your Data Management Discipline

Data teams are increasingly under pressure to deliver data to support a range of consumers and use cases. DataOps techniques can address the data delivery challenges through a more agile and collaborative approach to building and managing data pipelines.

Trends in Data Management and Analytics

TDAN

Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or Data Lakes indicate that changes are under way.

BI Implementation Insights: Clear and Easy Starting Points

Sisense

Business intelligence implementation can seem like a daunting task at the outset. There are so many moving parts, needs, and requirements that finding the right starting point may feel like a shot in the dark. However, one of the most important aspects of running a successful business intelligence project is finding the right starting point. Clear starting points can help you launch new projects faster and acclimate to your platform’s tools.

More Trending

The Role of AI & ML in Network Monitoring

DataFloq

Network monitoring deals with detecting the slow components present in the network like overloaded or frozen servers, failed switches, failing routers, or any other problematic devices.

Automating ethics

O'Reilly on Data

Machines will need to make ethical decisions, and we will be responsible for those decisions. We are surrounded by systems that make ethical decisions: systems approving loans, trading stocks, forwarding news articles, recommending jail sentences, and much more.

The Importance Of Financial Reporting And Analysis: Your Essential Guide

datapine

“Vision without action is merely a dream. Action without vision just passes the time. Vision with action can change the world.” – Joel A. Barker. Financial analysis and reporting are one of the bedrocks of modern business.

11 Steps to Transition into Data Science (for Reporting / MIS / BI Professionals)

Analytics Vidhya

Introduction The rapid rise of data science as a professional field has lured in people from all backgrounds. Engineers, computer scientists, marketing and finance. The post 11 Steps to Transition into Data Science (for Reporting / MIS / BI Professionals) appeared first on Analytics Vidhya.

Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? In which format? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Can business people create reports via drag and drop? Can operations monitor what’s going on? Will the data lake scale when you have twice as much data? Is your data secure? In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently.

SAP Consolidates Its Analytics Efforts in The Cloud

David Menninger's Analyst Perspectives

I am happy to offer some insights on SAP drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements.

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10 things R can do that might surprise you

Simply Statistics

Over the last few weeks I’ve had a couple of interactions with folks from the computer science world who were pretty disparaging of the R programming language. A lot of the critism focused on perceived limitations of R to statistical analysis. It’s true, R does have a hugely comprehensive list of analysis packages on CRAN , Bioconductor , Neuroconductor , and ROpenSci as well as great package management.

7 metrics every sales manager must know and measure

Phocas

Sales managers need to be savvy and strategic to get ahead. Here's the 7 metrics every sales manager must know and measure. Job Role - Sales

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Lessons learned building natural language processing systems in health care

O'Reilly on Data

NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context. We’re in an exciting decade for natural language processing (NLP).

The Best Sales Forecasting Models for Weathering Your Goals

Every sales forecasting model has a different strength and predictability method. It’s recommended to test out which one is best for your team. This way, you’ll be able to further enhance – and optimize – your newly-developed pipeline. Your future sales forecast? Sunny skies (and success) are just ahead!

Seize The Power Of Customer Data Management – Best Practices

datapine

“Get closer than ever to your customers. So close that you tell them what they need well before they realize it themselves.” – Steve Jobs.

Computer Vision Tutorial: A Step-by-Step Introduction to Image Segmentation Techniques (Part 1)

Analytics Vidhya

Introduction What’s the first thing you do when you’re attempting to cross the road? We typically look left and right, take stock of the. The post Computer Vision Tutorial: A Step-by-Step Introduction to Image Segmentation Techniques (Part 1) appeared first on Analytics Vidhya. Deep Learning Python clustering edge detection instance segmentation Mask R-CNN Segmentation Semantic Segmentation

Embedded Analytics Provide Meaningful Insights

David Menninger's Analyst Perspectives

Analytics and business intelligence (BI) play an instrumental role in enabling an organization’s business units and IT to utilize its data in both tactical and strategic ways to perform optimally.

CCO Melbourne Sponsorship Material

Corinium

Zendesk

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The North Star Playbook

Every product needs a North Star. In this guide, we will show you the metrics product managers need to tie product improvements to revenue impact. If you are looking for a more-focused, less-reactive way to work, this guide is for you.

Turn your customers into data advocates

Phocas

A key benefit of data analytics is the ability to understand the needs of your customers. High performing companies share data with customers to better understand how both can grow. Business Intelligence Basics Job Role - Sales Job Role - Executive

Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Take Complete Charge Of Customer Satisfaction Metrics – Customer Effort Score, NPS & Customer Satisfaction Score

datapine

“There’s a certain way of creating a service, hospitality, and experience that perpetuates people feeling like they matter.” ” – Julie Rice, entrepreneur, and investor. Today’s tech-savvy customers are driven by experiences.

A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text

Analytics Vidhya

Introduction I work on different Natural Language Processing (NLP) problems (the perks of being a data scientist!). Each NLP problem is a unique challenge in. The post A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text appeared first on Analytics Vidhya. NLP Python ELMo Natural language processing python word embedding

How to Choose the Best Embedded Analytics Solution to Modernize Your Application

If you are looking to modernize your application to improve competitiveness, then one of the quickest wins you can have is to embed sophisticated analytics that will wow your existing and prospective customers.

Evaluating Vendors’ Mobile Business Intelligence and Analytics

David Menninger's Analyst Perspectives

I am happy to share some insights gleaned from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements.

Snowflake : Executive's Guide to Alternative Data Analytics

Corinium

Investment managers are expected to spend more than $1.6 billion on alternative data sets in 2020. The sheer volume of data produced will necessitate a change in how businesses acquire, process, and use it.

Choose the right data analytics solution for your business

Phocas

Real-time data analysis delivers key business insights so people can create informed business strategies. Analytics can provide numerous benefits such as cost-efficiency, increased productivity, and keep you feeling in control.

Highlights from the Strata Data Conference in San Francisco 2019

O'Reilly on Data

Watch highlights from expert talks covering AI, machine learning, data analytics, and more. People from across the data world are coming together in San Francisco for the Strata Data Conference. Below you'll find links to highlights from the event.

The Rise of Embedded Self-Service Analytics

Speaker: Chris Von Simson & Nat Venkataraman

Can your users get the data and analytics they need without leaving your application? Watch this webinar with Chris von Simson of Dresner Advisory Services as he shares why user enablement has emerged as the theme in today’s embedded analytics landscape.

Take Advantage Of Mobile Dashboards – Examples & Selected Designs

datapine

We live in a mobile world. According to the statistics portal Statista , there are currently around 4.78 billion mobile device users worldwide.

Nuts and Bolts of Reinforcement Learning: Introduction to Temporal Difference (TD) Learning

Analytics Vidhya

Introduction Q-learning became a household name in data science when DeepMind came up with an algorithm that reached superhuman levels on ATARI games. It’s. The post Nuts and Bolts of Reinforcement Learning: Introduction to Temporal Difference (TD) Learning appeared first on Analytics Vidhya. Python Reinforcement Learning python

Get Your Analytics and Business Intelligence Any Time

David Menninger's Analyst Perspectives

For analytics to be effective, they need to be available to line-of-business personnel as needed in their normal course of conducting business, which today means providing rich mobile access to analytics through phones and tablets to support a mobile workforce seeking to conduct business in any location at any time.

Interview with Sriram Iyer @ CDAOI UK 2019

Corinium

Analytics Big Data

Machine Learning for Builders: Tools, Trends, and Truths

Speaker: Rob De Feo, Startup Advocate at Amazon Web Services

Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. But that doesn’t mean machine learning techniques are a perfect fit for every situation (yet). So how can a startup harness machine learning for its own set of unique problems and solutions, and does it require a warehouse filled with PhDs to pull it off?