Craig Kerstiens runs the Ecosystem group at Heroku which comprises their add-ons marketplace, core languages and API. He’s a well-known content contributor to the Postgres community. Craig’s originally from Alabama, so just ask him about BBQ, but has been a Bay Area resident for nine years. Chartio's AJ Welch interviewed Craig for Off the Charts.
Earlier this month, Chartio hosted a roundtable on best practices for Amazon Redshift. Since many of our customers store their data on Redshift, we wanted to bring together a panel of people with expertise in using Redshift for business intelligence applications. We were able to assemble a great team of panelists from AWS, Clever, and Lumosity who were able to give in-depth answers to our questions on Redshift best practices.
Before you try any BI product write out ten questions that you should be able to answer using your data. Answer each of them using any product you’re evaluating. Then, ask yourself the three questions that really matter.
This post will explore the traditional Customer Lifetime Value model, the variables that go into it, and the assumptions they make about the future.
When products have a long buying cycle, simple tracking can’t be used. Marketers need visibility into the buying process to help them make decisions.
As a key element of modern marketing, a solid content marketing strategy includes increasing search engine rankings to get found by prospective customers.
Best practices aren’t created equal. They are only "best" if the practices are profitable for your company. Don’t assume that great strategies work well in all circumstances.
It’s happening. Machines are taking over. Algorithmic trading took over Wall Street, and now, the same thing is happening in the world of marketing.
Agile Business Intelligence is a new way to think about the process of building and managing BI. In the last five years, technologies and practices have matured, so that it is now possible to deliver business intelligence systems faster, at lower cost, and with greater control by end users.
It’s getting harder to identify and convert prospects with traditional targeting methods. Dark pools of low information web browsers are now navigating under the radar and out of reach of advertisers. In some cases, digital advertising industry estimates suggest, audience reach has been cut in half.
We get a lot of questions from users who want to use Chartio to push their data harder. For those people, we're introducing Data Boot Camp, a introduction to data analysis using Chartio.
Today, Chartio is announcing Data Stores, a better way to store and transform data. We've always been at the forefront of agile business intelligence, and Data Stores makes Chartio even more agile.
We asked ourselves, 'What's the best way to measure how happy your customers are?' and investigated what other companies are doing. After some investigation, we settled on using a Net Promoter Score to measure customer satisfaction and explored more on NPS best practices.
Even after you've decided that you need a business intelligence system, it's easy to get lost in questions about data storage. How is my data stored, and how should it be? What kind of data storage does my business intelligence tool need? What's a data warehouse? Do I need one? If so, what kind of data warehouse do I need?
How happy are your customers? What can you do to make them happier? It's harder to answer those simple questions than you might expect. We recently decided to survey our happiest and least happy customers to find out how to improve Chartio and monitor our progress.
This blog post covers some common comparison operators in SQL and how to use them with the WHERE clause.
Chartio recently had the opportunity to analyze the data of Watsi, an innovative nonprofit based in San Francisco. And what we learned changed the way we thought about the economics of charity.
To combine tables you will have to JOIN them. This tutorial walks you through the steps to create a JOIN in SQL. In this example, we will answer the question - which customers have made the most purchases?
Our users love the Chartio dashboards they've been creating and sharing with their teams, and they've been looking for a way to give their customers access to a view of their data using Chartio. Today, we're announcing embedding - Chartio's solution for sharing data outside your company.
Chartio has added a bucketing step in the Data Pipeline, which divides the values from a column into a series of ranges, and then counts how many values fall within each range. You can use this step to create a histogram, which can be helpful in visualizing the distribution of data.