If you’re whirling around in the 1.0 world, you might be a fan of measuring dials (dials per hour reps are doing). It's a useful metric when you’re training new reps and one that measures activity. But once a rep ramps up their pipelines, are you still using dials as a way to track results? What about number of appointments? Is the appointment metric important to you? Does that really give you the data you need to more accurately forecast opportunities?
Now that we have covered best practices for creating a framework for your dashboard, this last post will cover how to make your dashboard a beautiful visualization.
Now that we have established the purpose of the dashboard, start thinking about how the dashboard’s layout will best serve the audience and provide value. This is the first step toward thinking about how the dashboard looks and how it will function.
Your business intelligence dashboard is the portal to your most important business metrics and insights. It can be the daily gateway into your team's activities, the scoreboard for your organizational goals, or a way for your company’s stakeholders to find new insights.
Chris Winslett, product manager at Compose, joined Chartio's AJ Welch for a webinar on why a company with deep roots in NoSQL chose PostgreSQL for its analytics database. AJ followed up with a live demonstration of how to use MoSQL to build a Postgres database from your MongoDB data.
It’s critical to remember that data quality is a continual process that requires diligence from everyone in your organization. Use our steps to maintain data quality as a pillar of your data management system.
Hubspot does a good job of managing marketing lists. It's extraordinarily flexible in defining email lists for your promotions and nurturing campaigns. But if you want to test a list or a promotion, there's no way inside of Hubspot to select a portion of your list (say, 10%) and either send the remainder your standard email or reserve it for other promotions in the future
As you manage your data, make sure that the causes of errors are not only detected and corrected, but the process of collecting and managing the data is improved to prevent future errors and maintain a sound data system.
Chartio's customers are big users of Amazon Redshift as a data source, so we were pleased to host Tina Adams, product manager of the Redshift team at our most recent customer meetup. Tina introduced us to user-defined functions in Redshift, reviewed some of their best practices, and took questions from the audience.
To be successful at running a business you need to use data to make important decisions. You go through a continual process of collecting, updating, and creating data in order to have the insights that help you grow and succeed. The quality of the data your company uses is essential to the reliability of your business analytics and business intelligence.
Over the past five years or so, I’ve noticed the perception that relational databases are only good at descriptive statistics (count, sum, avg, etc.) on medium sized structured data sets. In other words, SQL just doesn’t work for inferential, predictive or causal analysis on larger or unstructured data sets. Although this may have been true five years ago, it’s a lot less true today.
We’re excited to announce that in our first year of participation in the annual BARC survey of BI users, Chartio was the top-ranked tool in four KPI’s and a leader 15 others in its peer group in the fourteenth annual BARC survey of BI users.
Though analytics and reporting have always been a central part of sales, this increased access to data doesn’t always result in an increase in sales performance. Even with data readily available, strategy and tactical knowledge can be a challenge for sales managers.
Of all the things one could do to ensure the success of a sales organization, identifying, tracking, understanding, and incentivizing reps with sales metrics is perhaps one of the most important.
Business intelligence exists to make people better at their jobs. Although the value of being data driven is indisputable, business intelligence is not an end in itself. Making decisions based on data – facts, trends, and likely outcomes – is a much better approach than intuition, gut feeling or emotion. Better decisions lead to better performance, results, and achievement. Business intelligence is a means to that end.
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.
The key to outbound marketing is keeping it simple and testing continually, according to Ryan Buckley the founder of content marketplace Scripted.com and email address locator Toofr. Ryan joined us recently to host a webinar titled Sales Outbounding: The Secret to Getting It Right.
The Lambda Architecture is a data processing architecture that has been getting a lot of attention lately. By using two parallel systems, we can blend relatively high-latency batch views of large data sets with small, low-latency real-time data sets.
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.
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.
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.