With all the excitement around the potentially transformational insights that can be derived from today’s modern business intelligence (BI) tools, the critical element of data governance is all too often overlooked. Data is a vital asset to any organization, and the way it is collected, organized, and managed should be a top priority. In this blog, we take a deep dive into what goes into a data governance program, and how Looker can help with implementing your strategy around it.

The longevity and success of any business depends on the quality of its data. Data governance strategy is at the core of any data-driven business and should be institutionalized as a critical business function, much like finance or human resources. When data is centralized and fully vetted, the insights derived from data can be trusted and leveraged for effective decision-making. Thanks to its unique architecture and capabilities, the Looker data platform provides control and automation—two vital underpinnings for any data governance strategy.

What Is Data Governance?

Let’s begin by taking a look at what we mean by data governance. In a nutshell, it’s a structured program that ensures adherence to data quality standards—holding people, processes, and technology accountable. A data governance program answers these questions:

  • What constitutes data?
  • Where and how is it collected, extracted, transformed, delivered, and used?
  • Who cares for and maintains which data?
  • Who owns systems, who owns data, and who stewards data?

A sound data governance strategy should:

  • Blend discovery, control, and automation to help business leaders determine who needs access to what data.
  • Determine the format of data and where it resides—in structured formats within applications or databases or in unstructured formats in documents and spreadsheets.
  • Ensure uncompromising security and compliance while meeting evolving business demands.

The Importance of Control and Automation: Who’s in Charge?

When it comes to building a data governance strategy, establishing control at the organizational level is a critical first step. Everyone needs to be working in unison—and that means all users are accessing a single source of trusted data, metrics are fully defined and agreed upon, and clear processes are in place. From our experience, we have found that grassroots data governance never works. Data governance needs to come from business leadership—from the top down.

To get control over data and set high data quality standards, you need data management in place. A data owner should be assigned—a subject matter expert or an executive—who vets the data and can assert that it is the single source of truth. Another key resource is a data steward who manages data, resolving discrepancies or historical differences in the data and ensuring that metrics are defined, locked down, and embraced company wide. A lack of control can introduce risks. Even with the best intentions, people may bring in data sets that are stale or have been blended with other data sets. This introduces variability and can skew decision-making and reporting, which is why data management is important.

To implement data governance successfully, automation is also essential. The less humans touch things, the less there is room for error.

Looker Provides a Single Source of Truth

Looker helps organizations control data and establish a single source of truth by design. Some BI tools allow users to pull in data from multiple spreadsheets and other sources and then create a dashboard. This can result in misleading and confusing output, such as spreadsheets—as an example—can go through many different iterations and touchpoints unmonitored and unmanaged. This makes it hard to determine the truth. Looker, on the other hand, is purpose-built for integration with a central data warehouse, where data has been fully consolidated and cleansed. While some see this as a flaw, we think it’s what makes Looker so robust—because users can only connect to governed data sources.

Looker Automates Metrics

Once the organization has defined metrics and everyone has signed off on them, it’s easy to incorporate them into Looker. Everything in Looker goes through the semantic layer—dashboards, queries, and reports—where metrics are defined and stored. Define your metrics once, and they propagate throughout all Looker output. And, if the definition of a metric changes, that change also ripples through. In contrast, with some other BI tools, if updating the definition of a metric is done in one place, the metric needs to be individually edited in every dashboard and report that references it.

Looker makes the process of defining metrics easy and highly scalable. One person defines them, and that serves the entire organization. And, best of all, this does not require a huge time commitment. The big benefit is that it forces companies to take a good, hard look at what do they truly mean when they say “sales revenue” or “margins.” Additionally, with Looker’s pop-up tooltips, which show the definition of metrics, there is total transparency. Again, it’s all about truth and trust through accountability and across-the-board consistency, all made possible through automation.

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Looker Enables Secure Access to Data

At certain organizations, some individuals are allowed access to certain numbers and others are not. For example, they may dictate that a salesperson should only view data for their region or the products they sell, while a sales manager needs to have access to national sales data across the entire product line. To support this level of control and for the sake of data security, Looker makes it really easy to establish role-based security. The solution can take attributes of each user and pass that information on to the database connection.

In addition to providing access at the role level, Looker allows access controls down to the row and column level. It processes a given query and makes the query string dynamic, so it only pulls back the data that’s relevant to the users. Because of these security controls, the person who’s looking at the data assets only sees what they’re supposed to be seeing.

Looker also offers compliance and security assurance. With new data privacy regulations like GDPR in place, improper storage of data—whether big data or not—can be a huge liability. Without a data governance program in place with tools like Looker that enable data consolidation and easy access, the ability to audit data is severely impaired.

Looker can assist with data provenance when given access to a data lineage table or historical record of data and its origins. This can help stakeholders trace how and why the data got to its current state within the organization. This information ensures authenticity and allows for that data to be reused. If the ability to trace data provenance is lacking, it’s more difficult to answer why, how, where, when, and by whom the data was produced. Without that information, data is much less trustworthy. With the Looker data platform placed on top of a centralized data warehouse or database, organizations can ensure that they are using trusted data while meeting compliance requirements and security policies.

Simpler Administration through Automation

Looker leverages its API to enable data stewards to perform multiple functions and to build once and reuse. Looker flips the time spent compiling data sets versus gaining insights. Instead of spending 75% to 90% on gathering and cleansing data and only 25% to 10% on data analysis—which is typical of most organizations—Looker flips those numbers so that teams can spend their valuable time where they should—gaining insights.

Accelerate Your Data Governance Program with Looker

With a data governance imperative and buy-in at the organizational level, you’re ready to get started on your program. By integrating key components of your data technology stack, Looker can vastly simplify the process and help you scale up through control and automation built into its architecture. Data governance will quickly become embedded into your data culture, making it easy for people at every level of your organization to extract valuable business insights and make informed decisions based on data they can trust and easily access.

Want to learn more about kickstarting your data governance program? Talk to one of our experts.


Josh Goldner
Josh is Analytic8’s Looker Practice Lead and is also a certified LookML Developer. Josh implements modern analytics solutions to help his clients get more value from their data. Josh is an avid outdoorsman and balances his professional work with hunting and teaching his coworkers how to fish.

Sean Costello After careers in proprietary trading and business valuation, Sean finally found his calling as a data analytics consultant. He graduated the University of Illinois with a degree in Engineering and is a certified Looker data analyst. In his free time, Sean enjoys working on cars, watching the Green Bay Packers, and exploring the local restaurants and pubs of Chicago.
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