Data Governance Guide for Beginners

In this concise data governance guide for beginners, we’ll look at the concept of data governance and how to implement it in your organization.

Organizations are collecting an enormous amount of data across multiple departments. This data needs to be organized to be able to extract value from it and drive business decisions. Not managing the data properly can cause process inefficiencies, data duplications, and can incur penalties for non-adherence to data regulatory compliances.

There nemuste a method to collect, de-silo, process, and manage internal and external data. This is where data governance comes in.

What Is Data Governance?

Data Governance is setting and following the required processes, systems, and guidelines to scale, organize, and use an organization’s data optimally. It is the accountability a business has toward customer data.

According to GartnerOpens a new window :

Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics.

Data governance consists of policies, processes, rules, and regulations guiding how an organization collects, stores, and manages data. Consistency makes it possible for organizations to act on the data.

While the terms – data management and data stewardship are often confused or interchanged with data governance, they’re not the same.

Data management pertains to implementing data architecture, tools, and processes to organize, use, and store data, whereas data governance can be considered the strategic aspect of data management.

Stewardship, on the other hand, focuses on ensuring data accuracy, documenting data sources, adding metadata, and resolving data-related issues.

Guiding Principles for Data Governance

The Data Governance InstituteOpens a new window has proposed the following eight data governance guiding principles to be adopted in data governance initiatives:

1. Integrity

Data governance stakeholders must exercise integrity and honesty when discussing data-related issues.

Being truthful and transparent also ensures that data collection practices are compliant with regulatory compliances such as GDPR.

2. Transparency

All data governance practices implemented at an organization should be transparent. This includes being open with stakeholders about data-related decisions, and how the organization is collecting and using internal and external data.

3. Auditability

Data-related processes pertaining to data governance will be auditable. The data collected will also be auditable to measure it against data standards and understand its utility for a specific purpose.

4. Accountability

Data governance will decide accountabilities for cross-functional data-related processes, roles, and policies. Accountability should be assigned to stakeholders across multiple teams to ensure that everyone gains access to data through standard data governance processes.

5. Stewardship

Data governance will define accountabilities for data stewards and stewardship activities to ensure that data-related rules and regulations are appropriately implemented and data is easily accessible to the relevant stakeholders.

6. Checks-and-Balances

Data governance will decentralize accountability to be widespread among multiple, related teams.

7. Standardization

Data governance will introduce standardization measures to make data usable for various use cases.

8. Change Management

Data governance will introduce change management policies to facilitate the structure and use of master data and metadata.

Implementing Data Governance

Here is a five-step framework to implement data governance initiatives:

Step 1: Set Goals for Data Governance

A good place to start with goals for your data governance program is to identify areas for improvement. Rather than going all-in, pick a couple of crucial areas that would significantly impact the organization in a short time. It could be to resolve data sources that generate duplicate data or make necessary changes in how the organization manages confidential user data.

Step 2: Streamline Data Availability

Depending on your goals, identify the data points you’d require and their sources. For example, if you want to merge duplicate customer data, the potential data sources could be the CRM, customer support, and marketing automation software.

Integrate these data sources with a central system to make data easily accessible.

Step 3: Define Data Governance Policies and Roles

The organization must designate stakeholders with relevant roles for data management, data administration, and data stewardship. The organization should foster collaboration between IT and concerning departments to ensure the success of data governance initiatives.

It’s mandatory to include policies for data access, usage, and integrity.

Step 4: Plan Your Implementation

Handling data can be a risky endeavor. Data loss, duplication, or breach can nix an organization’s data governance initiatives. Therefore risk mitigation activities should be planned accordingly.

Use the data governance guiding principles outlined above to craft your data governance program. Different organizations function differently, so be realistic about policy implementation. Pick departments that you can easily work with and expand as you progress.

Step 5: Establish a Feedback Mechanism

Data governance is a dynamic initiative and entails a culture of continuous improvement. You’re likely to walk into some hurdles or your goals may change as your data governance program matures. Regularly evaluate where you stand and decide if you need to correct your course or need to redefine the benchmarks.

In Closing

As organizations adopt the data-driven culture, they need to have a more robust approach towards how they manage the data. Data governance programs are effective to ensure data security, boost data quality, and cut down data management costs.