Using a Data Catalog To Build Your Enterprise Data Strategy

By Feng Niu

Published on February 20, 2020

Organizations collect and store data all the time; at the end of the day, the goal is for software and people to use the data in technical and business decisions. The mission of Alation is to make enterprise data more accessible and usable. Through our extensive experience of building the product and talking with customers, we’ve learned a few characteristics of our approach that make Alation a compelling platform for data accessibility and usability:

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Implement a Data Management Strategy

Making data more accessible and usable requires a strong data management strategy. Data catalogs are the main component of a data management strategy. Successful implementation of a data catalog leads to higher speed and quality of data analysis for the data users involved. When data users are enabled to produce quality data at a higher rate of speed, their overall attitude toward the process becomes more positive, leading to further growth.

Using a data catalog for data management helps data users avoid manually seeking out information and analytics from multiple databases. More time searching for data means mess time analyzing, which inevitably leads to loss of time and lack of productivity. Once the data management strategy has been implemented, the data catalog connects company wide cloud and on-premise data. This leads to faster searches, more accurate, and reliable analytic assets.

Processing data with and without a data catalog

Centralize Data for Enhanced Tribal Knowledge

If big data is a big elephant, individual users would be like blind men who have never encountered an elephant before trying to figure out what they have in front of them by touch. Each blind man feels a different part of the elephant’s body, but only one part, such as the side or the tusk, and they each come up with a wildly incomplete picture.

Everyone has their own mental models of what data there is and what are the best ways to use them. Each model is likely somewhat stale, incomplete, and inconsistent with each other. Although people do write and talk, the lack of a persistent and consistent medium for such technical knowledge often entails high-cost, high-latency, low-efficiency, and low-fidelity communications. In contrast, Alation centralizes an organization’s knowledge about data to address the above problems. In addition, we make knowledge sharing effortless by analyzing usage data and performing NLP over existing data documentation.

By centralizing data, the data catalog makes tribal knowledge possible, while simultaneously supporting governance, data security, and compliance across the enterprise. With the centralized system in place, those individual users gain common understanding about the accessible data, how it can be used, and what data sets are related to each other.

In a reasonably complex data environment, it is practically impossible for the user to bear such knowledge in her brain. One thing Alation strives to be is a brain extension for data users — we bring just-in-time knowledge and context when you need them. As an embodiment of this principle, Alation Compose is an app for SQL users with intelligent features such as Smart Suggest, Smart Preview, and Prescient Warnings.

Prioritize Data Quality

With the data management strategy in place and the data catalog driving efficiency from the centralization of businesswide data, prioritizing the highest quality data becomes the next big focus. This goal helps increase the usability of the businesses data and leads to further efficiency growths. Alation’s data catalog automatically groups data quality information such as metrics, reports, and lineage. While the data user is querying data and working on analysis, the catalog is also automatically showing quality flags, warnings, and reports. This helps individual data users understand data context and quality.

To ensure that the best data is being collected and stored, data should be reviewed often. Old data can lead to negative impacts on your streamlined data management strategy. This can negatively impact automations and analytics. To avoid these negative impacts, train data users to understand how to collect and input quality data vs. outdated poor data. Once data users understand common data practices within your organization, they can more easily identify irrelevant data from quality data.

Overview of data quality shown on a monitor

Self-Service Analytics Accessibility

To fully harness the benefits of more accessible data across a company, individual users need to be able to perform self-service analytics. Data users know how valuable data can be, but there are still many questions about the best way to use and define it. Where analytics were once closely managed, they are now more fluid, allowing for more data users to access and interact with data. This has caused questions about the security/trustworthiness of available data and whether the analysis has already been done before.

Thanks largely to more and more intelligent tools, such as data catalogs entering the industry, these questions can be limited. A strong data governance policy paired with a data catalog and data management strategy can lead to fewer questions about the validity of data. These strategies and tools can also lead to more trust within the team, which will inherently allow for more data accessibility. Once data becomes more accessible across different teams within the organization, more chances for innovation and big picture understanding can occur. This benefit alone can prevent multiple points of potential bottleneck across a company.

Implement Data Governance Support

Where self-service analytics strives to spur data creation and innovation, data governance guides creation and consumption. Accessibility and data literacy are growing more important for efficiency and business timelines. Data users need to be able to access and make informed decisions about the data they are analyzing or consuming. As accessibility needs grow, so does the need for data governance. We have learned from our customers that for data governance to succeed in driving the business, it must be an integrated part of the day-to-day activities of data consumers.

At Alation, we provide a platform that brings together business logics and data sources, data governors and data consumers.. The end result is effective data governance built on trustworthy analytics and collaboration.

With all of these steps in place, self-service analytics can properly work together with data governance in a capacity that supports the data user. If you want to learn more about how Alation can help your organization reach its data & analytics goals, schedule a demo today.

  • Implement a Data Management Strategy
  • Centralize Data for Enhanced Tribal Knowledge
  • Prioritize Data Quality
  • Self-Service Analytics Accessibility
  • Implement Data Governance Support
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