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How to Generate Customer Success Analytics in Snowflake

As the distinction between data professionals and non-data professionals becomes smaller and smaller, the need for technology that bridges the gap between the two parties is crucial. The benefits of interacting with a data warehouse, especially with large amounts of data, are unquestionable, but as a peripheral member of the core technology team who might not be very technical, it is not always practical to generate SQL queries on the fly. 

This poses a problem, especially when departments such as sales, customer success, account management, etc., want the robust insights that could come from the vast amount of data that a company is storing, but they don’t necessarily know how to quickly gather these insights. 

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Oracle BI vs. Tableau: Which Business Intelligence Tool Is Better?

In the new normal as enterprises strengthen their efforts towards digitization, the need to use data to derive valuable insights for strategic decision-making has also risen. Precisely why the Business Intelligence market is seeing tremendous growth. According to a recent report the BI market is expected to reach USD 27870 Million by 2026. With this boom in the BI market, the number of Business Intelligence software has also increased. There are over 700+ BI tools listed on Capterra and close to 500 on G2 under embedded BI software and Data Visualization tools. In this article, we will provide a detailed comparison of the two most popular BI software in the market: Oracle BI and Tableau.

Oracle BI vs Tableau - A Neck to Neck Comparison

A large number of enterprises use either Oracle BI or Tableau for their BI requirements. The user list of both these BI software includes some of the biggest global brands. So, let us see what makes Oracle BI software and Tableau popular and how these compare when it comes to product features, pricing, developer experience, and more.

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How to Create Data Lineage With the Tableau GraphQL Metadata API

I love data. The ways it can be used to curate value and express relationships never ceases to amaze me. To this extent, visualizing data is often one of the most powerful ways to share insights and Tableau certainly is one of - if not the - most popular data visualization tools on the market. It's extremely simple for non-technical users to develop rich and meaningful graphs with a pretty intuitive UI and there are some really nice features under the hood that are used to speed up query performance when extracts are stored within Tableau.

My absolute favorite Tableau feature is that you can query your metadata using the same GraphQL API that Tableau itself uses. A portion of the metadata exposed includes the lineage for the fields, sheets, tables and data stores that exist within your Tableau Site. Exposing the metadata via an extensive API like this is a really forward thinking idea from the team behind Tableau. 

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Data Exploration and Data Preparation for Business Insights

What Is Data Exploration?

Data Exploration or Exploratory data analysis (EDA) provides a simple set of exploration tools that bring out the basic understanding of real-time data into data analytics. The outcomes of data exploration can be a powerful factor in understanding the structure of data, values distributions, and interrelationships. Data exploration can also be helpful for data scientists to gain proper insights into business data that was not easily seen previously.

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Build a Real-Time Data Visualization Dashboard With Couchbase Analytics and Tableau

Introduction

Couchbase Server is a hybrid NoSQL database that supports operational and analytical workloads. Couchbase Analytics in Couchbase Server 6.0 brings "NoETL for NoSQL," enabling users to run ad-hoc analytical queries on JSON data in their natural form — without the need for transformation or schema design — by leveraging a massively parallel processing (MPP) query engine.

Every enterprise has already invested in a visualization tool and therefore has a critical need to leverage existing investments. This includes not only tooling but also skillsets and training of business reporting and dash-boarding teams.

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Tableau + R: Back Your Data Visualizations With Statistical Testing

To speak bluntly, when it comes to its visualization capabilities, Tableau, while it appears so promising, astonishingly lacks in its ability to integrate seamlessly with statistical, hypothesis-driven testing. You may be let down constantly if you feel the need to not only visualize but compare your set of observations between groups on hard statistical grounds.

Hence, one must admit that there is still a strong value gap between visualization tools like Tableau, and pure statistical software such as Minitab, SPSS, SAS, and, of course, the humble yet tremendously powerful and open source workhorse, R.

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How to Transition From Excel Reports to Business Intelligence Tools

If you are one of those people manually creating reports using Excel, you know it can be overwhelming to meet the organizational expectations for quality, insights, and velocity. The business demands of twenty-first-century data analysis using twentieth-century tools are the root of too much pain and frustration. 

If you decided to do a little research on the latest and greatest alternatives, you quickly see how many tools exist to solve those challenges. Reviewing the options you think, “Hurray! This is going to be a snap. These tools make it seem so easy!” Next, you decide to take the plunge with a trial of a preferred tool like Tableau, Microsoft Power BI, Looker, Amazon QuickSight, or Google Data Studio. Don't have a tool picked out to trial yet? You can check out business intelligence software options on G2.