The Chartio Blog

Expert advice on business intelligence to help drive data at your company.
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The Evaluation Action Plan for Self-Service Business Intelligence

Evaluating a Self-Service Business Intelligence solution can be a daunting task, even for the most seasoned decision-maker. In our research for our white paper on the subject, we polled a handful of BI buyers at various stages in the process and have come to one conclusion: start the process by evaluating based on your business needs. Read on as we provide a framework around the evaluation process and break the evaluation journey into a few steps. 

The Business Benefits of Business Intelligence

85% of business leaders believe big data will dramatically change the way they do business, In this blog post, we'll examine the business benefits of implementing a Self-Service Business Intelligence solution that enables everyone--from an analyst to a business user--to explore their data. 

The Five False Fears of Democratized Data

For many companies, data belongs to only those who are hyper literate in data. Meaning, data teams own data and no one else has access to explore it. There’s been a fear in giving business users access to the data. Over the years of talking with hundreds of prospective customers, I’ve identified five main fears holding companies back from democratizing their data. In this post I name each fear and describe why each are baseless.

[White Paper] The Business Buyer’s Guide to Self-Service Business Intelligence

By 2018, Gartner predicts that most business users and analysts in organizations will have access to self-service tools to prepare data for analysis as part of the shift to deploying modern BI platforms. With this prediction in mind, it can be difficult to evaluate a solution that meets all your requirements. 

We're excited to introduce our latest whitepaper, The Business Buyer's Guide to Self-Service BI, which streamlines the evaluation process for you. Download this whitepaper to learn: 

  • The state of self-service Business Intelligence
  • The business benefits in implementing an organization-wide BI solution
  • An evaluation action plan for choosing a BI solution that fits your needs
  • A list of leading self-service BI vendors

Creating a Powerful Data Analytics Stack with Blendo + Chartio

Data is a crucial part of your business, no matter the size or purpose of your company. To make data-driven business decisions, you’ll need to combine various data sources from disparate parts of the company. In order to bring all your data into one place, you need to make a large effort to build its data and analytics infrastructure.

Today, we're excited to announce our partnership with Blendo, a self-service data platform that enables you to manage the data from disparate sources and send it to your BI solution of choice. 

Changing Your Company’s BI Mindset to Achieve Success

At its core, BI analyzes massive amounts of data coming from disparate sources like Salesforce, MySQL, Amazon Redshift, Google Analytics, etc, and come together to provide insights to business decision-makers. For businesses to succeed today, you need to put BI in the hands of business users. And for many companies, this involves a shift in their mindset. Read our take on the debate as we discuss the three ways in which adopting BI involves a company-wide mindshift (for the better).

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.

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