By Ibby Rahmani
Published on 2021年11月8日
In the previous blog, we discussed how organizations are pursuing data culture and why most are failing. But using the right tools can help them scale data-driven initiatives. In this blog we will discuss the top 3 challenges for organizations scaling self-service analytics, showing how Alation and Tableau address these challenges and deliver faster time to insight.
To recap: Here are the top 3 challenges presented by a growing data landscape (or the 3 abilities that are toughest to scale):
Ability to find and trust data
Ability to meet governance requirement at scale
Ability to share information and find existing work
Analysts spend countless hours searching for trustworthy data to create dashboards and perform analysis.
Yet today, business users confront a high volume of dashboards. Without context and origin of the underlying data, they struggle with which dashboard to trust and lack confidence in their decisions based on any dashboard. Users need to trace the origin of data, from individual Tableau worksheets, through embedded data sources in Tableau, to the original data table outside of Tableau
Basically, organizations need the right tools so business users can easily find, understand and trust datasets and dashboards.
Traditionally, self-service reporting analytics and data governance have been opposed. The goal of enabling more people to visualize and analyze data has interfered with the need to govern data (and prevent it from falling into the wrong hands).
The need for balance emerges. Too little governance can lead to disorganized analytics, where insights are difficult to trust. By contrast, excessive governance can lead to data so tightly controlled that insights are few and far between. A balanced approach to governance is critical to the success of a self-service analytics practice.
An ideal governance solution will help organizations govern data without depriving users of the agility and convenience of self-service analytics. Governance needs to be part of the entire user experience. As users evaluate data, they need context clues to understand:
If they can trust the data, (and if they can,)
Do they have permission to use the data?
How are they permitted to use it?
Collaboration is key to data-driven culture. An environment where no data stakeholders work in isolation is the norm for today. You are expected to work together, sharing knowledge and experience via a single platform. Yet collaborating at scale presents unique challenges.
Sharing knowledge across people, divisions, and regions is not easy. Once you have gleaned new insights from data, you should share your knowledge with the rest of the organization. Contextual knowledge of the data will guide other users on how to use it. This context communicates which data is complete, which is trustworthy, what a specific field name means, etc.
But getting people at all levels, from business users to analysts, to actively participate in data management activities is not easy. This is because data and teams typically work in silos. People need one platform where they can collaborate.
Together, Alation and Tableau enable you to make valuable business decisions quickly and easily. Joint users can quickly find trustworthy data, gain correct interpretations, identify the relevant dashboards that use the data, and draw self-service insights with confidence. Through deep integration, curation in Alation is surfaced so that viewers of Tableau Server see which sources of information are endorsed as safe-to-use. This helps them steer clear of inaccurate dashboards, and ultimately reach the right business decisions.
Alation facilitates enterprise-wide access to trusted data from a single platform. With Alation, you don’t have to spend hours trying to find the right data because you have metadata to guide you. Alation catalogs all metadata by connecting to a vast range of data sources used by Tableau. Alation search capabilities help you to quickly find, understand and trust reports (and their underlying data) within Tableau and beyond.
This means you can build on your colleagues’ workbooks on Tableau Server and avoid recreating work. You can come to Alation and ask the experts, “which workbook should I use? Where is it?What is the origin of the data in the workbook?” Plus you get helpful descriptions, relevant business glossary terms, and guidance on how to use the workbook.
Users can easily locate the origin of data with Alation. Lineage capabilities trace the origin of data, from individual Tableau worksheets through embedded data sources in Tableau servers, to the original data table residing outside of Tableau. Each data table has its Alation page, which allows curators to collaborate and provide information, such as ratings, reviews, and annotations about the data. With such curated metadata, you can be confident about the data table and hence the worksheets that build on it.
You can also feel confident you’re using business and data terms correctly. Alation integrates with the Tableau business glossary so you can see definitions and how other people use the same terms. This helps you to make informed decisions to find and select Tableau workbooks and dashboards for analysis and decision making.
Together Alation and Tableau deliver governance for insight that is simple and actionable for every customer. We extend those governance capabilities to give organizations the speed of self-service analytics with the trust in data that comes from consistent enforcement of data governance best practices.
Data governance must govern human behavior. In this way, governance at the point of consumption guides compliant behavior with trusted data. By crowdsourcing feedback, Alation curates the best data according to stewards and propagates it across the catalog to more end users.So, as you use data, you get quality, completeness, and expert advice in accordance with governance.
Curated data is trusted (and useful!) data. Data stewards use Alation to curate data that is automatically inventoried in the Alation Data Catalog. Using Alation’s curation features, stewards can describe the context of a table, explain meanings of each field, and confirm automatically recommended business semantics in order to capture the context of data.
With the Alation @ Tableau feature, Alation endorsements, warnings, and deprecations are directly surfaced in Tableau without users leaving their Tableau dashboards. Certification badges on Tableau data sources quickly reveal the work that data governors and stewards have done in Alation, speeding time to understanding and trust. Once you find a Tableau dashboard, you have intelligence about the dashboard courtesy of Alation. This means you can trust the data.
Share information and find work from a single platform
The Alation Data catalog facilitates easy collaboration through a single platform. It enables you to share your own wisdom, through wiki-like articles and conversations. And our tight integration with Tableau means you can build on your colleagues’ workbooks on Tableau Server and avoid recreating work. Importantly, you can collaborate at scale, with colleagues across time zones and departments.
Alation pages (about Tableau) helps you find existing dashboards and analysis. Users can search by object, specifying to look for BI objects, and identify the dashboards they want.
Tight integration with the business glossary across both platforms allows you to see the definition of a field and how other people use it. This helps you to make informed decisions to find and select Tableau workbooks and dashboards for analysis and decision making.
Alation and Tableau foster data culture by enabling confident data-driven decisions. Our seamless integration allows you to quickly discover the relevant, governed data you can trust. When you have trusted data on a platform build for collaboration, you can uncover insights and make smarter decisions more quickly. We empower people in organizations to access curated data sources certified by your data stewards. As a data leader, this means you can help analysts spend less time searching for accurate data and more time analyzing it.
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