The Chartio Blog

Expert advice on business intelligence to help drive data at your company.
Sign up to get the latest data tips delivered to your inbox.

How Companies Can Use Data to Make Decisions

There are 2.5 Quintillion bytes of data are created every day and 90% of the world’s data today has been created in the last two years alone. The question is this: how can your company use your business data to make better decisions? In this blog post we examine how to extract the your data to answer key business questions. 

The Six Business Dashboards that are Core to Startups

Operating a startup can sometimes feel like walking through a minefield. At a moment’s notice, you might need to reverse your company’s course and make crucial changes to product, strategy and campaigns. With all these different moving parts, it's easy to get lost in the data. Rather than swimming in data, in this blog post we'll help you create six business dashboard to run your startup on. 

How to Create the Ultimate Marketing Dashboard

In our latest webinar, How to Create the Ultimate Live Marketing Dashboard, we teamed up with Treasure Data to show how live data integration is critical to the success of businesses. However, many businesses—whether they’re a Fortune 500, have a large data team or are an emerging startup—struggle to access their data in real-time.

Read more to learn how to create your ultimate Marketing dashboard. 

Operational Use Cases for Analytics

What are some great use cases for analytics? In this blog post we focus on two key areas: predictive and customer analytics, and how they can both be achieved through a Business Intelligence solution. We'll cover how to transform your data into actionable insights and drill down into specific use cases.

With these use cases, companies can move beyond pure reporting and perform analysis based on their data and increase their operational throughput.

The Difference Between Reporting and Analytics

What's the difference between reporting and analytics? Even though many people use the two terms interchangeably, please note that “reporting” and “analytics” are not synonymous. 

While both “reporting” and “analytics” collect data and information from the same data sources, you can’t have analytics without reporting. And without analytics, reporting doesn’t glean the powerful insights that leads to better business decision-making. Since this question comes up a lot, so we decided to decode the differences. 

Building a Data Stack: Treasure Data + Chartio

Today, we’re excited to announce our partnership with Treasure Data, the leading cloud platform to make all data connected, current, and easily accessible, together with Chartio to bring customers a new Live Business Intelligence Integration. By leveraging this integration, your company can quickly have a full-stack analytics solution, ultimately providing a better view into business insights.

Building a Data Stack: An Overview on Google BigQuery

Google BigQuery was initially created by developers as an internal tool called Dremel. Since launch, Google has made significant changes upon the internal tool and created a data warehouse that is used by both large enterprises as well as smaller companies. Google BigQuery takes full advantage of Google’s infrastructure and processing power and is an append-only system.

Whether you’re evaluating data warehouses for the first time, performing a competitive advantage or looking for a cloud-based solution, you can use this as a reference point. As a high-level analysis, we’re focusing around the key areas of performance, operations and cost, as we believe these are the crucial elements for your evaluation process.

Building a Data Stack: An Overview on Amazon Redshift

Since launching in February 2013, Amazon Redshift has been one of the fastest-growing Amazon Web Service (AWS) offerings. Since its launch, Amazon Redshift has added more than 130 significant features making it cloud-native data warehouse that’s designed for use alongside Business Intelligence tools. 

Whether you’re evaluating data warehouses for the first time, performing a competitive advantage or looking for a cloud-based solution, you can use this as a reference point. As a high-level analysis, we’re focusing around the key areas of performance, operations and cost, as we believe these are the crucial elements for your evaluation process.

An Overview on Amazon Redshift and Google BigQuery Security

As both Amazon Redshift and Google BigQuery are petabyte-scale, cloud-based data warehouses, they both possess their own set of parameters when tackling end-to-end security for their customers. Data warehouses require a flexible and powerful security infrastructure and operate with scalable requirements. Both Amazon Redshift and Google BigQuery (alongside their parent ecosystems) take security very seriously but handle it in different ways.

Operations and Maintenance: Amazon Redshift and Google BigQuery

With a cloud-based data warehouse, there’s no physical infrastructure to manage, allowing for a streamlined focus on analytics and insights, rather than hours of manual maintenance. But, like any system, every data warehouse needs to undergo maintenance for a tune up from time to time.

In this blog post, we’ll cover the crucial differences in how Amazon Redshift and Google BigQuery perform maintenance. For many companies, maintenance is a point of contention as it’s a leading indicator of overall data warehouse performance.

How to Load Data into Amazon Redshift or Google BigQuery

In evaluating a data warehouse, it’s important to consider how data loads from your database into the data warehouse. Second, it’s critical to also consider the speed, accessibility and latency once data is loaded into the data warehouse. 

Much like how cloud-based data warehouses revolutionized warehousing, a solution to data loading can be found through an ETL service via companies or in-house built tools. However, there are differences in loading data into Amazon Redshift and Google BigQuery. 

How Amazon Redshift and Google BigQuery Handle Provisioning

Data warehouses are architected to handle a large volume of data. In fact, many companies use data warehouses to store historical data going back at least three years—and this is a great practice when it comes to enriching information for a target persona or running product usage analysis.

As the overall volume of data grows, it’s not uncommon for a data warehouse to perform operational checks. Today, we’re analyzing data warehouse operations in four separate parts: provisioning, loading, maintenance and security. In this blog post, we'll cover provisioning.

Stay tuned as we continue to publish our full analysis on data warehouse operations between Amazon Redshift and Google BigQuery.

The Impact of Throughput and Concurrency on Your Data Warehouse

Interactive reporting is crucial for businesses. In enabling everyone to have access to data, it allows them to engage with information in new ways and ultimately, helps improve job performance.

As a data analyst, empowering business users with interactive reporting frees up your time to perform more sophisticated analyses. However, with the addition of users querying data, it can amount to performance pressures for a data warehouse.

Data warehousing performance is often measured by a multitude of factors and tests. In our ongoing analysis between Amazon Redshift and Google BigQuery, we're comparing how each handle throughput and concurrency. 

[Whitepaper] Introducing: What to Consider When Choosing Between Amazon Redshift and Google BigQuery

Data warehouses are not just for storing data, they must be architected to facilitate analytics and business reporting needs, handling complex analytical queries quickly without impacting  operational databases or other systems where the data was originally created.

Raw data on its own offers no insights and addresses few business goals, but by loading raw data into a data warehouse, it’s possible to facilitate data exploration, interactive reporting and data-informed decision-making for an entire organization.

Choosing a data warehouse that will help you analyze your data doesn’t have to be difficult. To help you make your data warehouse decision, particularly between Amazon Redshift and Google BigQuery, we’ve written a whitepaper titled “What to Consider When Choosing Between Amazon Redshift and Google BigQuery.

How Meteor Uses Segment Sources and Chartio to Drive Exponential Growth

During a recent webinar, Dan Ahmadi, Director of Growth at Meteor talked about how Meteor uses Segment Sources and Chartio to drive exponential growth. Meteor’s latest open-source project, Apollo, has generated 2.4 million Segment events per week, or four events per second, since its release earlier this year.

At an hourly rate, Meteor is pushing their Segment Sources event data into their Amazon Redshift data warehouse. Once the data has been processed through Amazon Redshift, Meteor can then instantly access and analyze their data within Chartio.

In this blog post, learn what types of data sources Meteor is using and how they create agile dashboards within Chartio. 

Using Data to Understand Metrics

People turn to data when they have questions and want answers. An integral part of a data analyst’s role is to use data to inform and influence the direction of your company by making sense of data and answering questions. Data is a vital instrument that has a major impact, especially if you get the results in front of high-level decision-makers.

By partnering across your company with teams ranging from Product to Sales to Marketing, you’ll be able to design A/B tests, run experiments, iterate on results and do the lion’s share of understanding patterns and trends in user behavior to find new opportunities for growth. However, to fully realize data’s power, you need to understand each department’s high-level metrics and run queries to answer those metrics.

Leveraging Your Data Warehouse as a Competitive Advantage

Today’s ultra-competitive business landscape requires companies to be agile and provide the best service on the market. Luckily, today’s leading companies are collecting more data than previous decades and those with the competitive advantage are using that data to beat the competition.

In this blog post, we’ll walk through the advantages in using a data warehouse for analysis and how data warehouses are a competitive advantage to the overall business.

When to Invest in a Data Warehouse

In our blog post Choosing the Right Database for Your Data Strategy, we provided a strategy in how to choose an operational database that will suit your company’s long-term needs. As an operational database is crucial for processing and storing transactional data, like product usage or contact information, data warehouses play an integral role in turning static data into insights.

How to Align Data with Business Strategies

Data plays a critical role in running a business. As a data analyst, you understand the importance of data. But sometimes, it can be difficult to turn your technical aptitude into business insights.

As data analysts evaluate user behavior through analytics tools, business users are interested in understanding the performance of their efforts—whether it’s Sales, Marketing or Customer Success.

Much like your company’s data, a data analyst cannot be siloed into his or her own world, running analyses separate from business conversations.