Data Literacy 101

Piyanka Jain

One of these key focus areas is making the entire organization Data Literate. He believes developing Data Literacy across the organization would ultimately support the remaining three?—?customer-first We develop a year-long partnership of infusing data in the DNA of the organization.

3 Musts For Building Data Literacy

Piyanka Jain

A few months into his new role as Data Analytics leader, Alan and his team clearly saw the lack of data-driven thinking across the… Continue reading on Towards Data Science ». data-culture data-literacy corporate-culture big-data data-science

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Data-Driven Blunders and how to Avoid Them

Mind the Product

“We are a data-driven company”. And, while the logic behind a data-driven approach is undeniable, too often the expectations that come with it aren’t met. Let’s be clear, good tracking and hypotheses validation with data is essential for any product manager.

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Is programming a must for applying Data Science?

Piyanka Jain

Do you need to be able to code in R, Python, or any other programming language to put Data Science to work for you? analytics careers machine-learning business data-scienceThe answer is NO! Continue reading on DataSeries ».

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Make an Impact with Analytics and Journey Maps

Speaker: Kirui K. K., Co-founder and CEO of Tanasuk Africa

Talking About Data Science by Jenessa Lancaster

Mind the Product

Jenessa Lancaster is a data scientist at Ocado Technology. In this ProductTank London talk, she discusses the importance of communicating data concepts to non-specialist audiences. We live in promising times for data science in business development. Communicating Data Science.

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Data in the News: How Big Data Can Make Food Safer to Eat

Indicative

The interconnectivity of tech devices and their capacity to process data continues to improve. It’s undeniable proof of a large amount of data on the web that can be used to evolve different industries and enhance consumer relationships with goods and services.

Data Science Consulting Is A SCAM!

Piyanka Jain

I run a Data Science Consulting company, and I say analytics consulting is a SCAM! In November 2017, Gartner released a report stating that 60% of big data projects fail. A Fortune Knowledge Group survey revealed that most of the 85% of the failure that Gartner reported were likely a result of executives’ tendency to trust their gut rather than data. I am passionate about using data to build better products and create amazing customer experiences.

Data Science Consulting Is A SCAM!

Piyanka Jain

I run a Data Science Consulting company, and I say analytics consulting is a SCAM! In November 2017, Gartner released a report stating that 60% of big data projects fail. A Fortune Knowledge Group survey revealed that most of the 85% of the failure that Gartner reported were likely a result of executives’ tendency to trust their gut rather than data. I am passionate about using data to build better products and create amazing customer experiences.

Top 5 Reasons For Data Science Project Failure

Piyanka Jain

Whether you are a seasoned data scientist or a business executive with a significant investment in analytics, chances are you’ve seen the… Continue reading on DataSeries ». data-science data-literacy

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

MONETIZING ANALYTICS FEATURES: Why Data Visualizations. Data Visualizations Have Gone From Rare to Ubiquitous 1 If DataViz Is Old News, What’s the Future of Analytics? Ubiquitous Five years ago, data visualizations were a powerful way to diferentiate a software.

Are You Data-driven, Data-informed or Data-inspired?

Amplitude

Have you noticed recently an increase in the usage of the terms ‘data-driven’, ‘data-informed’, and ‘data-inspired’ around your office? What does data-inspired actually mean and how is it different from being data-informed? data-inspired means trendspotting.

Product Manager Salary Data in 2019

280 Group

This article draws on multiple sources to provide Product Manager salary data. Trulia data shows the same average of $113k, but also indicates that the average for a Product Manager in the San Francisco area is $140k.

Why aren’t customer analytics companies letting you connect to your data?

Indicative

Data Storage Has Changed. The data landscape has transformed dramatically over the past few years and major players in the analytics space are failing to keep up. The market has moved to a place where every company, big and small, is able to afford their own data warehouse in the cloud.

Why Data is key in Building a Company That Learns

Mind the Product

The Data / Information / Knowledge Paradox (Drawn From the DIKW Pyramid ). Data : data is feedback from your users, your market, or your product. Data is an input , and as such has no real value in itself, it tells you what is happening. Information: information is the output of data, after analysis. As such, the goal of data analysis is to find out why something is happening. In that sense, the gap from data to information is technical.

Your Post-Launch Toolkit for Understanding Your Users

Speaker: Brittney Gwynn, former Director of Product, Simple Health

Data Science and Digital Businesses by Ali Gulez

Mind the Product

Machine learning data products can create significant value for businesses and their customers. For example, data products can enable customers to engage with more relevant content, facilitating and supporting repeat custom to the business.

Are you a Data Science hero?—?aka BADIRist?

Piyanka Jain

Are you a Data Science hero?—?aka One day we started talking about Sherlock and what it would look like if Sherlock transitioned his career to become an analyst or data scientist? These days huge amounts of data?—?“Big Big Data”?—?overwhelm Are you a Data Science hero?—?aka

BADIR?—?The Antidote to Low ROI from Data Science Projects

Piyanka Jain

The Antidote to Low ROI from Data Science Projects [link] Data Science, Machine Learning, Robotics, and AI?—?these One would think with such large investment, these organizations must be reaping a lot of value out of data science. This means that a mere 2% of all completed data science projects meet the required level of satisfaction. Aryng’s SWAT Data Science team uses the same framework, which has resulted in a tremendous success rate for the projects we have done.

6 Factors You Need to Consider When Selecting a Data Warehouse

Indicative

At Indicative, we pride ourselves on our data warehouse integration, a feature that is completely unique to our product. For those new to data analytics, this may be the first time you’re exploring data warehouse options. What is a data warehouse?

5 Early Indicators Your Embedded Analytics Will Fail

thrilled to finally visualize their data. They ask to explore data on their own, create and. share analysis, and connect new data sources to the. requests for new and more complex data visualizations, the ability to customize dashboards, and real-time.

Three Tools for Data-Driven Product Management

ProductCraft

Read more » The post Three Tools for Data-Driven Product Management appeared first on ProductCraft by Pendo. Best Practices Data NPS PM Role User ResearchAs a product manager, I deal with three important challenges: Increasing user adoption.

An Engineer’s Journey To Become A Data Scientist

Piyanka Jain

She had always enjoyed data and patterns. She finished her hands-on data science coursework in business analytics, A/B testing, and predictive analytics. She now knew experientially how powerful data science can be. career-paths career-change data-science careers career-advice

Why you’re better off exporting your data to Redshift Spectrum, instead of Redshift

Mixpanel

I’m a Software Engineer at Mixpanel, working on our data export pipeline. My focus is on making it as easy as possible to send the data you collect in Mixpanel, to your destination of choice. This pipeline sends your data to Redshift Spectrum, which is different than Redshift.

Data-Driven Product Management: How To Become an Insight-Driven PM

280 Group

This article discusses how data-driven product management can help you use the right data, to uncover the right insights, and ultimately build the right product. The Buzz Around Data-Driven PM. There’s been a lot of talk lately about “Data-Driven Decision Making” (DDDM).

Why “Build or Buy?” Is the Wrong Question for Analytics

commit to staffing significant resources in development, support, and keeping up with advances in data. Architecting (and Re-Architecting) So Everything Works Together: If the component you choose to bind data doesn’t work. anyone to analyze data, share insights, and make.

Unlocking Data Science to Build the Future of Work by Mike Hyde

Mind the Product

Mike Hyde leads data science and data engineering for Workplace, Facebook’s new enterprise product for company connectivity. He is passionate about using data and insights to create innovative company cultures, so he spoke at ProductTank London about data for growth.

Why you Need Quantitative AND Qualitative Data

Mind the Product

Qualitative versus quantitative data: we’ve all been involved in a conversation debating their respective merits at some point in our careers. So which type of data is better? Another angle is the softness and hardness of the data. We’ll need more soft data to find the answer.

The Fundamentals of Building Better Data Products

Mind the Product

Data is the world’s most valuable resource, according to The Economist, and the companies that primarily deal in data – Google, Amazon, Facebook and the like – are among the most valuable in the world. Data Moves to the Centre of the Value Proposition.

Build a Customer Data Tracking Plan in 3 Easy Steps

Indicative

Building a data tracking plan in order to monitor and analyze your customer journey is at the root of customer analysis. Customizing a data tracking plan can be broken down into these three key steps: Identify Key Use Cases. Build a Data Tracking Model.

How King Crushes New Product Development using Data-Driven Insights

Speaker: Ian Thompson, Head of Business Intelligence at King, and Zara Wells, Strategic Customer Success Manager at Looker

Product Managers looking to leverage data to make informed product design decisions can learn a lot from renowned gaming company King, maker of Candy Crush and many other games - even if their product has seemingly no overlap with games. Don't miss King’s data expert (dare we say king?)

Inside the Mind and Methodology of a Data Scientist

Birst BI

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. To solve practical decision problems, the Data Scientist typically uses combinations of these methods.

What AI Means to a Data Scientist

Birst BI

However, there are simply not enough data scientists in the world to deliver on the AI potential. Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics.

Top Skills Required in every Data Scientist and Data Analyst

DemandMatrix

Different organizations may have different ways to define the core job roles and key responsibility areas for their big data roles. While the roles of a Data Scientist and a Data Analyst may sound very alike, there are some core differentiators too.

The pitfalls of being data-driven

Mixpanel

Speaking about the rollout in 2010, Facebook executive Adam Mosseri said his team stuck to their guns because their experience and intuition told them that it was a good strategy – even in the absence of supporting data, and with a backlash from users. Data is not the only variable.

How to Package and Price Embedded Analytics

customers absolutely need advanced capabilities like embedded self-service and the means to pull new data sources into the. and the data feeding them—as well as trigger both. rely on—enabling anyone to analyze data when and where. HOW TO PACKAGE & PRICE EMBEDDED ANALYTICS.

Why Data Science and UX Research Teams are Better Together

Mind the Product

Chris shares real-world examples and lessons learned as User Testing established its Product Insights team, a team which is made up of UX researchers, data scientists and data engineers. Most companies’ user research and data science teams work separately.

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Top 5 FAQ On Mastering Data Science And Making Your Career Transition

Piyanka Jain

Ironically… Continue reading on Towards Data Science ». data-science analytics education business careersRecently I have been mentoring fresh graduates of the Masters in Business Analytics program from a historic Virginia school.

3 Ways to Enlist Your Usage Data to Drive Product Development

Mind the Product

Having reliable data available in real-time allows you to quickly and accurately sort through what is a valid concern and what is not, keep your team on task, and let data-driven product development reign. Let’s dig into three examples and look at how usage data can help. With usage data, you can quickly get a sense of just how pervasive the bug is, and what sort of effort needs to go into fixing it. Usage Data Sheds Light.

What 2 Men And a Runner Taught Me About Data Science and Fraud Modeling

Piyanka Jain

I decided to go for a short run on a neighboring track… Continue reading on Towards Data Science ». machine-learning data-science businessAs I was wrapping up my work last evening, darkness started to loom outside.

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Creating the "Right" Product Roadmap With Data

Speaker: Sunil Parekh, Head of Product Management, SimplyInsured