Alation + Tableau: Fostering Data Culture through Trust and Transparency: Part 1

By Ibby Rahmani

Published on 2021年11月3日

Alation + Tableau: Fostering Data Culture through Trust and Transparency - Part 1

To be competitive, organizations are trying to create a data culture. But what IS a data culture? A data culture empowers everyone in your organization with data-driven insights, which enable you to easily collaborate around shared goals to improve your organization.

Alation and Tableau are instrumental in helping organizations create a data-driven culture by surfacing trusted data. Alation complements Tableau to empower everyone in an organization to answer questions with data – increasing the productivity, and enabling data stewardship across the organization.

But creating a data culture is not easy. Companies spend billions of dollars to become data-driven, yet only 8% of organizations are successful in scaling analytics1 to get value from their data. This is because most have failed to focus on the three pillars needed to be data-driven: people, culture, and the right technology.

This is a 2-part blog. In this blog, we will discuss how the right technologies support a self-service analytics environment. In part two, we’ll unpack the core challenges of managing such an environment. With the right technologies, you can find data, trust data, collaborate on data, and use data —not just intuition — to make decisions quickly.

Alation + Tableau: Speeding Time-to-Insight

With Alation and Tableau, organizations increase confidence in data. Together these technologies empower people in organizations to access curated data sources that are certified by trusted data stewards.

The result? Analysts spend less time searching for the right data or verifying its authority. They have more time to analyze data, gleaning insights and creating valuable visual assets.

For analysts and other data experts, gleaning insights from data is incredibly time consuming. In fact, time-to-insight has been a big problem because it requires many steps.

As an example, let’s consider how a typical business user interacts with data without the right technology:

She first asks an analyst for it. However, the analyst doesn’t know where to find the data. The data, in most cases, is buried in a silo.
After finding data that may be useful, the analyst has to research how to interpret the data.
Finally, the analyst must learn the origin of the data to trust it. (This could take anywhere from days to weeks!)
After that the analyst will prep the data in Tableau. Then he will perform analysis and finally present the data to the business user.

Now let’s consider the same scenario, but this time with Alation. The time it takes to find, understand, and trust the data is shortened dramatically. As a result, the analyst can focus his time on performing analysis in Tableau. In this way, Alation increases the productivity of both the analyst and the business user by accelerating time-to-insight.

Our Joint Solution: How Customers Benefit

How do people understand data and trust it? Metadata (or data about data) is key. Alation helps you understand your data assets through valuable metadata. When users can find, understand, and trust available data assets, they can jumpstart the analysis process.

In fact,“The Alation Data Catalog for Tableau has enabled companies like GoDaddy and MercadoLibre to build data cultures that embrace self-service analytics as a foundation for decision making.” (The Alation Data Catalog Drives 2x The Return from Self-Service Analytics Initiatives).

With Alation, customers no longer spend weeks or months hunting for data. In fact, many have reduced the time it takes to find the right data — from months down to minutes. This is important because search & discovery is the most challenging and time-consuming part of the end-to-end analytic process.

Challenges of Self-service Analytics

Self-service environments allow everyone to explore data and extract unique insights. This relieves IT of massive pressure, allowing them to welcome people to trusted data and guide them (instead of policing data access). However, as organizations continue to amass tremendous amounts of data, self-service analytics become hard to scale.