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.

How Data Science Beat Human Mind?

Piyanka Jain

So you’d do well to empower yourself with some data science prowess. How Data Science Beat Human Mind? was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Product Manager Salary Data in 2020

280 Group

This article details product manager salary data for general product manager jobs (unspecified type). Benchmark data also shows that VPs and Directors of Product are strongest in five specific skill sets: Strategy, Business Skills, Competitive Analysis, Pricing, and End of Life.

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


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.

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.

Cracking The Data Code – Mike Bugembe on The Product Experience

Mind the Product

Edwards Deming wrote, “In God we trust, all others must bring data.” – but we’re not always taught how best to use data. So you’re always better off collecting more data. How to use data properly. The ethics of data.

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 ».


The Emerging Data Trends For 2020


New trends, software and applications appear regularly and are continuously expanding the role that data plays in decision making and operations for business. What are the emerging analytical, big data and data management trends for 2020? Augmented Data Management.

Agile 56

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.

Schema Evolution Patterns

Speaker: Alex Rasmussen, CEO, Bits on Disk

If you want to make your development team squirm, ask them about database schema changes or API versioning. Most development teams struggle with changing database schemas and updating API versions without breaking existing code. Alex Rasmussen is an expert in helping teams through these struggles. His talk will examine database schema changes and API versioning as two instances of schema evolution: how your systems respond when the structure of your structured data changes.

How Accurate Is Your UX Research Data? By Martina Kuvalja

Mind the Product

In this ProductTank London talk, she explains how you should go about collecting data if you are part of a small team, and how to have meaningful conversations with the research members of your team. Also, is the right data being collected and is it valid?

UX 149

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


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


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.

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.

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 and Digital Businesses by Ali Gulez

Mind the Product

In this talk at ProductTank London, Ali Gulez, a data scientist at Trainline gives a lesson in data products and their impact on business. Ali takes us through the role of data science in an organization and dives deeper into the following key points: Data products.

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.

Here’s Why Every Product Manager Must Learn to Love Data


“Big data.” It’s one of Silicon Valley’s favorite and most annoying buzzwords, yet there’s no data shortage in sight as data companies continue sprouting (and growing) like weeds. Not being a “math person” or a data scientist is no longer a valid excuse to diss data.

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.

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.

Product Management Tips for Data Science Projects

Mironov Consulting

Data science has traditionally been an analysis-only endeavor: using historical statistics, user interaction trends, or AI machine learning to predict the impact of deterministically coded software changes. This is data science (DS) as an offline toolkit to make smarter decisions.

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.

Birst automates the creation of data warehouses in Snowflake

Birst BI

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead.

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?)

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 UX of Data


Generating data is easy. Data is often not accessible unless you can write code. People in non-technical roles rely on data every day to make decisions, develop ideas or measure success. She goes to her colleague Enzo who is a Data Analyst to ask for help. Data is hard.

UX 107

Flexible and secure Data-as-a-Service delivered today

Birst BI

How many duplicated, dirty data pipelines are running throughout your organization? To put it simply, by setting up DaaS, Birst offers a better way to democratize data without sacrificing security, governance, and control. DaaS is a core component of modern data architecture.

The ABCs of data: how to make sense of your company’s data as a product manager


In the age of data and analytics, we’ve come to believe that, with enough data, we can make smart decisions and become truly data-driven. But as the global data volume has gone from big to enormous, many businesses today find themselves thinking: What do we do with it?

Creating the "Right" Product Roadmap With Data

Speaker: Sunil Parekh, Head of Product Management, SimplyInsured

Data can be qualitative or quantitative, and comes from multiple sources: customer interviews, product usage & funnel analytics, company financial performance, and internal stakeholders. How do you use that data to create a product roadmap that is aligned with your organization’s business needs?

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.

Should You Be a Data Scientist or a Data Analyst?

PMLesson's Ace the PM Interview

If you’re looking to break into tech, you’ve seen the term “data science” thrown around. It’s no surprise HBR named Data Scientist the “sexiest job of the 21st century”; data is more valuable and more available than ever. Keep in mind that data careers are changing constantly.

How Can Intent Data Increase Sales Conversions?


Buyer intent data is probably the most underrated concept of our times. intent data


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.

UX 166

Selling Data and Decisions to your Team

Speaker: Cait Porte, SVP Product and Customer Experience, Zmags

Gathering support for a product feature or enhancement is a critical skill for Product Managers. Talking to customers, working with key stakeholders in the business and convincing development that a feature is necessary can be a daunting task. Join Product Management expert Cait Porte as she covers how to sell your ideas internally by leveraging data to drive decision making.