Data usage has gained prominence and relevancy in recent years, thanks to the increasing importance of big data and BI adoption.

In this age of overwhelming data, rapidly advancing technology, and cutting-edge analytics, data governance plays a vital role in every organization. From setting rules and regulations for data management to measuring the quality of data and defining data resolution processes, the benefits of following data governance best practices cannot be ignored.

The total amount of data created, captured, copied, and consumed globally is forecasted to reach 97 zettabytes in 2022. With such overwhelming amounts of data churned out daily—alongside ever-changing data regulations implemented globally—data governance is a course that every organization must learn to navigate.

Developing a successful data governance program to accelerate data-driven digital transformation is no small task. Without proper planning and ownership of data governance, implementation efforts will fall flat. Yet even the biggest data governance challenges can be skillfully managed with the right framework, people, and practices.

What is Data Governance?

Data governance is a collection of processes, policies, roles, standards, and metrics that are needed to protect data assets to guarantee trustworthy, complete, and secure corporate data. Data governance defines who has control and authority over data assets and how those data assets are used within the organization.

The Data Management Association (DAMA) describes data governance as the “collection of practices and processes which help to ensure the formal management of data assets within an organization.”

A well-designed data governance program requires the direct attention of the CEO, with a governance structure that involves the C-Suite. This program also includes the following individuals and teams:

• • The data governance team reviews and approves all governance measures.
  • The steering committee monitors data governance decisions and implementation of the data governance framework.
  • A group of data stewards drives data management projects forward.
  • Executives and other representatives within the organizations take part in the decision-making process, in addition to the IT and data management teams.

4 Data Governance Challenges for Organizations

So, what are the most common challenges that affect the development and deployment of a successful data governance program? We explore each one of them in detail below.

1. Data Silos with No Single Source of Truth

Countless organizations struggle with data silos. Why? Because different departments within the organization collect their own data but fail to share it with other divisions. The main reason for this is that often datasets are locked away and only accessible to certain teams.

Similarly, different departments follow separate methodologies, priorities, and agendas in their operations. Teams may not know certain data is available and the potential value that data holds. This disconnect leads to inconsistent data sets and even more problems down the road.

The C-suite has a vital role in crafting a data governance strategy that ensures different departments collaborate and utilize information from different channels. The outcome is a dedicated data governance framework that unlocks datasets and breaks down data silos—that then empowers teams to create, maintain, and govern a single source of truth.

2. Lack of Trust and Ownership of The Data

Data quality is the key to actionable business insights, reliable reporting, and better results. Accurate, reliable, and consistent data is vital to building data trust and encouraging users to be innovative and discover new ways to turn data into value. When stakeholders distrust data, it negatively influences the process of reporting and analytics.

Being PCI compliant is incredibly important if you are regularly dealing with credit cards and financial data. Security breaches are very costly for organizations, so you don’t want to overlook compliance with all of these regulations as a major part of taking ownership of the data.

Another major roadblock in the data governance process is the problem of shadow IT. This is where development occurs either in-house or through an outsourced channel without the supervision and governance of the IT team.

If this occurs, there is no ownership or control of the data by the assurance functions. Similarly, due to a lack of defined data governance roles (data owner, data steward, etc.), the accountability and handling of data governance are somewhat misplaced.

In this case, the security and assurance teams lose visibility of the data asset. These teams don’t know what data is being processed—if it’s processed as per the law guidelines—where and how it’s stored, and whether it’s being used for a secondary purpose altogether.

When organizations have dedicated data owners who have full visibility into data assets, this paves the way for collaboration and process improvements.

3. Poor Leadership

Non-implementation and execution of new standards and policies require great leadership. Frequently, teams responsible for data governance implementation don’t understand which direction they need to go because they don’t have a clear plan to follow. Many organizations lack a Chief Data Officer (CDO) to manage data and information, a role that is becoming increasingly critical.

During the development of a data governance structure, the implementation process and its effects should be reviewed regularly, so that policies, delivery, structure, and execution are clear to everyone in the organization.

The active engagement of stakeholders also needs a cultural change, especially when it comes to individual attitudes regarding data governance success. While most organizations have a data office in place, most leaders lack an assessment of the attitudes of individuals in the C-level towards the success of data management and data governance.

If their attitude of data governance is negative, there may be reduced accountability, which will certainly impact data owners within various divisions.

4. Misallocation of Resources

Investing in data governance means that you are investing in the business. Although data governance is typically focused on risk management, there is also a major value-add for the business itself when data is governed well.

Data that is not ready for analysis or is of poor quality is a liability…not an asset for your company. If your data isn’t ready for analysis or well maintained, your data won’t be as accurate and it will end up costing your organization in the long run.

Organizations have vast amounts of data about clients, customers, patients, suppliers, employees, and more. The best way to go about a data governance program is to start with a thorough evaluation that allows stakeholders to create strategies based on the most pressing needs and the biggest opportunities for growth.

When you have a centralized data governance program, teams easily connect data lineage to business processes. Everyone understands their roles in data governance and how their use of data aligns with the organization’s vision.

The Collectiv team is ready to help you identify and overcome your top data governance challenges. Reach out to learn about relevant programs.