Demystifying the Data Governance Framework

The benefits of data governance

Setting up data governance for your enterprise offers multiple advantages: 

1. Trustworthy data. Multiple aspects of data governance result in a higher data quality, which increases its trustworthiness. From preventive tasks that mitigate the risks of data issues to validations and extensive testing that assert the data can be used. For example, when data scientists work with big data, they can move faster into the deployment of machine learning algorithms, if they know the incoming data has already been tested for automation prerequisites.

2. Better decision-making. Decision-making based on trustworthy data leads to better insights and understandings. Without a data governance framework set up to guarantee valid data, decisions are still made but are skewed towards errors and red herrings that stem from impoverished data.

3. Regulatory compliance. There are multiple regulations legislating data practices. One of the most famous ones is the European General Data Protection Regulation (GDPR). Adopting proper data governance results in regulatory compliance, which gives your organization peace of mind and legal support for your operations.

4. Clarity of operations. Data governance policies give you clarity of data operations - they make it clear which operational area you need to improve. They also assign delegated roles for various use cases. For example, if you need a subject matter expert for a new data set, the data governance policies specify who the data stewards are that can help guide you. 

5. Greater involvement across silos. Data governance policies involve data practitioners, business users, and management, making data the responsibility of several actors and therefore fostering greater involvement of stakeholders across silos.

6. And many more.

How to set up a data governance framework

What it means to have a successful data governance model will depend strongly on the regulatory requirements of your company.

A bank needing to adhere to Basel II will not have the same data governance framework as an e-commerce shop that needs to safeguard the data privacy of its customers under GDPR.

But irrespective of what regulations apply to your business processes, four pillars are part of a successful data governance framework.

1. Data security

Data security refers to all the preventive actions, guardrails, and corrective mechanisms set up to guarantee data protection. 

There are two streams to data security.

First, your company needs to have a day-to-day framework of how to manage data security. This often falls in the hands of the IT department or data engineering department. They set up rules surrounding data access. For example, they decide who can view data sets, who has credentials to access data in a data storage like a data lake or data warehouse, and how to revoke access to the data sources.

Second, there is a more nuanced and more regulated data governance practice for sensitive data. Data protection is especially concerned with protecting sensitive data as part of regulatory compliance (e.g. GDPR). A good data governance practice is to specify how an organization’s data can be used. For example, is customer profiling even allowed, or does it violate privacy rules?

2. People and roles

Effective data governance frameworks clarify who is in charge of data assets and data governance processes. 

At a minimum, a data governance framework needs to define who are the data owners and who are data stewards.

Data ownership refers to all the technical responsibilities for the production of data, its quality, and integrity. 

Data stewardship, on the other hand, refers to the business aspect of data governance. Data stewards act as translators and culture champions, helping non-technical subject matter experts access and understand data to improve their decision-making.

Depending on the size of your organization, you might need a more branched out data governance team, with a Master Data Management leadership role, to coordinate the data owners and data stewards across your business units. 

3. Data quality

The goal of data governance is to deliver high-quality data you can trust. High quality is both a technical and business term. 

On one hand, your technical ecosystem of tools needs to comply with high data standards. Data lineage tracing, monitoring for errors, alerts, and automated error corrections are just some of the technical checks and balances implemented in systems that guarantee high-quality data.

The classical minimal standards test is whether the data that was extracted from the data sources complies with the data integrity and referential constraints of your data model. 

More advanced companies also impose semantic tests to reflect the business rules within an organization. For example, they validate and sanitize business metrics before they are used in analytics to derive insights and make business decisions. 

4. Data democratization

Unless all your people can tap into data to make better decisions, your data governance framework is missing something. 

This is why data democratization is often one of the pillars of a good data governance strategy. 

Sharing data and understanding the data you work with is paramount to making good decisions. This is why the best data governance frameworks plan for a data catalog - a source of data definitions. It acts as a self-serve business glossary, that allows everyone to access and understand the data they work with.

The four pillars of a data governance framework act as guidance on what you should include in your data governance plan. 

But you have to look out for potential hurdles that might come your way.

The challenges to setting up data governance

Setting up data governance sounds good in theory, but it can be easily led astray in practice. 

There are two challenges you need to overcome before you can successfully deploy your data governance framework.

1. Relevancy

Let’s be honest, rarely anyone in your organization wants to optimize metadata management out of the blue and without it being tight to their business unit’s OKRs and KPIs.

The success of a data governance initiative will be highly correlated with its relevance to the organization’s needs. 

Smart data teams tie the data governance efforts to existing processes, such as:

1. Digitalization - set up security practices as you migrate your data elements from spreadsheets to online APIs.
2. Cloud migration - talk about quality checks as a way to improve on your previous onsite data storage that was migrated to the cloud.
3. Data culture - introduce the data catalog in your weekly or monthly town halls.

In other words, show how data governance fits into the wider data ecosystem of your organization. 

2. Depth and breadth of adoption

Data governance programs are often destined to fail from the get-go because of the data team leading the data governance initiative.

In many organizations, the IT or data department tries to take over the data management on their own without buy-in from senior leadership like C-suits.

But without the buy-in, they are doomed to have limited resources, unaligned work with company-wide goals, and often clashing priorities with other change management within the organization. 

As Takehiko (“Tak”) Nagumo, managing executive officer at Mitsubishi UFJ Research and Consulting (MURC) said: 

“Just like any other important matters, we need the board’s backing on data. Data’s existed for a long time, of course, but at the same time, this is a relatively new area. So a clear understanding among the board is the starting point of everything. We provide our board educational sessions, our directors ask questions, and all that further deepens their understanding.”

To set yourself up from the get-go it is crucial that:

1. You get buy-in from the most senior leadership in the company. If possible, make a senior leader the master data management lead, so they can champion data governance throughout the enterprise.
2. Pick the right data governance tools for the job. With the right tool, you can dive into the depths of your data architecture and make sure data governance practices reach every data set and every department working with data, irrespective of their business workflows. 

One such tool is Keboola.

How can Keboola help you implement your data governance framework faster?

Keboola is an end-to-end data operations platform that comes with in-built data governance tools, that help you automate a lot of the heavy lifting when implementing your data governance policies:

• Track data lineage and operational metadata, describing user activity, job activity, data flow, schema evolution, data pipeline performance, compliance with your security rules, etc. Keboola implements data governance by design, which offers you extensive people tracking and audit capabilities as well fingerprinting to comply with regulatory standards on one hand, and fully understanding the data lineage on the transaction - and event-level on the other hand.
• Deploy the Data Catalog to centralize and unify data definitions, hence increasing data understanding and accessibility across business departments. Unified definitions allow you to increase data quality as well, by disambiguating different interpretations of the same incoming data. 
• Guarantee best-in-class security standards out of the box. 
• Support different governance roles, by using granular access practices, which safeguard data safety and privacy, while empowering every user to get the data they need to do their best work. 

But Keboola is not just a tool for automating data governance. It is designed to automate and speed up all data operations, from ETL pipeline construction and maintenance to deploying machine learning models in production. 

We offer a no-questions-asked always-free tier. Try Keboola out and check for yourself what Keboola can do for you.