The Chartio Blog

Expert advice on business intelligence to help drive data at your company.
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How to Use Smart Discounting in SaaS Sales

Discounts can be a necessary evil. You will probably have to give something to compel customer to act, so you should expect to have to deal with it at some point in your negotiation. So, you should have a consistent process for addressing discounts before you begin selling your product.

How to evolve your outbound email campaigns

The key to outbound marketing is keeping it simple and testing continually, according to Ryan Buckley the founder of content marketplace and email address locator Toofr. Ryan joined us recently to host a webinar titled Sales Outbounding: The Secret to Getting It Right.

What's the True Cost of Business Intelligence?

It's not easy to estimate the long-term costs of a business intelligence project. Even when you know the numbers, it’s difficult to estimate which options you’re going to need. Sometimes, it seems that the more time you spend talking to vendors about your BI project, the less you know about the total cost of getting it done. We can’t do all your homework for you, but we're written a white paper to introduce you to the unanticipated costs of implementing business intelligence, how to ask about them, estimate them, and mitigate their risks.

Data Munging Crowdsourced Information without SQL, Using Chartio's Data Stores

Chartio's great for analyzing and reporting on data on your company's systems, but what if you don't have all the data you need to complete your analysis?  

We recently held a joint demo with CrowdFlower to show how to crowdsource the information you need and analyze it in Chartio. 

In this post, I'm going to explain how we collected the data, and show you the analysis we performed that night.

Understanding Interleaved Sort Keys in Amazon Redshift, Part 2

Previously, we discussed the role of Amazon Redshift's sort keys and compared how both compound and interleaved keys work in theory. Throughout that post we used some dummy data and a set of Postgres queries in order to explore the Z-order curve and interleaved sorting without getting bogged down in implementation details. In this post, we will explore some of these implementation details, discuss a common tactic that can benefit from using compound and interleaved sort keys together, and run some benchmark queries against a data set with billions of rows. Throughout this post we will link to code that can be used to recreate our results.