Every year, enterprises worldwide waste millions of dollars on failed data initiatives. While there are a number of factors that contribute to this, data governance is often cited as a major hurdle. That’s because too many organizations view data governance as a monolithic undertaking that can only be achieved using a top-down approach. But that line of thinking is outdated and doesn’t align with the needs of the modern enterprise.
Here are three reasons top-down data governance is failing, and what you can do about it.
Reason 1: Waterfall culture
The first reason top-down data governance fails is because of the “waterfall” approach to policy management. Using this model, all decisions are made by a small group of individuals. Their job is to plan out every detail of the governance program and then cascade policies down to practitioners.
As Tomasz Tunguz describes in his book, “Winning with Data,” there are several problems with this methodology:
These programs lack agility and resilience due to bottlenecks at the data producer/consumer threshold
Data teams spend more time navigating bureaucracy than working with data, which negatively impacts ROI
In most organizations, those who try to understand the availability and use cases of data assets encounter inefficiencies, partial answers, and confusing systems
When data work isn’t transparent, people don’t trust it, leading to data brawls, a.k.a. people showing up with different versions of the same analysis and arguing over the method by which they got there
Limiting collaboration and access means missing out on new ideas and opportunities for innovation
The hard truth is that it’s impossible to build data-driven cultures under waterfalls. In fact, 69% of data engineers say their current governance program makes their jobs more difficult. That’s because they didn’t have a voice in developing the program, even though they play a significant role managing the data.
You won’t gain adoption within your organization if you don’t bring your community along for the ride.
Reason 2: Only focused on risk avoidance and compliance
Another reason top-down governance programs fail is because they only focus on risk avoidance and compliance. Although data protection is a critical component of your data governance strategy, it should not be the only consideration. Data governance should holistically address the entire data and analytics process, enabling safe, efficient, and reliable project collaboration.
Locking down all access to data is counterproductive. It slows down your team and keeps them away from valuable, revenue-driving insights. Data producers can’t keep up with never ending ad-hoc requests, and data consumers are frustrated with the delays in getting what they need.
Reason 3: Cumbersome processes
One way to think of data governance is like the brakes on a car. Brakes aren’t there so you can drive slow. Brakes actually help you drive fast, safely. But top-down data governance is like driving with the parking brake on – you can’t get up to speed, and there’s a lot of friction when you try.
This friction can be attributed to the cumbersome processes – or road blocks – required for accessing data. Submitting TPS reports, emailing stewards, and queuing for data access are time-consuming activities at best, and often unnecessary given the proliferation of modern data catalogs that automate many of these processes.
Data governance should help accelerate safe access to data, not hinder it.
Why Agile Data Governance is different – and better
Unlike top-down data governance strategies that seek to control and parametrize every aspect of data access, Agile Data Governance empowers all stakeholders to participate in an inclusive data and analytics process, aiming to increase productivity in a safe, consistent, and auditable way.
It adapts the best practices of Agile and Open software development to data and analytics, iteratively capturing knowledge as data producers and consumers work together so everyone can benefit.
Today organizations and agencies in nearly every industry are adopting Agile Data Governance practices to drive faster and more accurate business insights, reduce redundant and inefficient work, increase reuse of data products, and build thriving data cultures. It is also being leveraged by practitioners of data mesh, who view it as the right framework for domain-driven governance and data-as-a product.
How to implement Agile Data Governance in your organization
While that all sounds great in theory, what does it look like in practice? And how do you implement Agile Data Governance in the enterprise?
The Agile Data Governance Playbook covers everything you need to know to get started, including:
Tips for achieving buy-in for cultural change
Why you need a governance committee and how to select an executive sponsor
Advice on selecting the principles that will act as your governance north star
How to identify and assign data stewards and ownership
Guidance on running your first Agile Data Governance sprint and prioritizing use cases
Download it now to learn how to implement Agile Data Governance in your organization.
