How Focus on Data Governance Helps You Outperform Your Competition

The quality of data is key to making informed decisions and, consequently, the success of an organization. But organization’s data assets are valuable only when valid and utilized properly. Often, poor-quality data ends up hampering an organization’s potential and competitive advantage.

To that end, data governance defines the principles ensuring an organization identifies quality data. Here’s how:

• It describes the processes, roles, responsibilities, and metrics to ensure an enterprise’s accountability and ownership of data assets.
• It delineates the course of action, the choice of data, the defined parameters, and the methods used.

All in all, robust data governance enables faster and wiser decisions. The following are the essential factors that serve as the key components for effective data governance:

• Data Quality
• Data Management
• Data Software Tools
• Data Security
• Data Compliance

Data quality largely depends on how it is collected, planned, analyzed, and processed. Most data analytics leaders need to assess, measure, and monitor their data and its analytics. Only organizations with data governance measures can focus on and achieve compliance-oriented goals.

That said, optimizing data quality is the first step toward data governance. Once an organization has established a culture of sharing, tracking, and standardizing information, other aspects like data management and software tools can be considered.

Data management is essentially the execution of data governance strategies. It sets forth the responsibility for implementing standards and policies for data governance. Typical tasks in data management include:

• Creating role-based access to data by creating access levels
• Implementing data governance policy rules
• Establishing and maintaining the data security of the data owned by the organization.
• Adopting appropriate measures to minimize risks associated with storing sensitive data.
• Creating a master data management system that gives a single data view across the enterprise.

Data management is key to data inventory; it lets organizations have methods and strategies for accessing, integrating, storing, transferring, and preparing data for analytics.

In fact, effective data governance is a direct product of data management maturity. It resolves questions like – what data an organization should manage and where should it be stored. Should it be on-premises or on a third-party cloud?

Data has to be taken care of properly and kept safe and secure. And this is made possible by using the right software tools that can be relied upon as solutions. An example would be using third-party services, especially when storing data in the cloud or on-premise devices.

Appropriate software solution tools help organizations maintain a consistent set of policies, processes, and data ownership. The process lets them monitor, manage, and control data movement effectively.

Moreover, these solutions help users establish guidelines, rules, and accountability measures that ensure standard data quality. Data governance tools also provide recommendations to help increase efficiency and streamline processes.

Countless malware threats and ransomware attacks affect businesses globally, costing organizations billions of dollars. Studies suggest that such attacks result in companies paying an average remediation cost of more than $1.8 million for every security breach.

A large amount of data at disposal can further complicate matters. For organizations with legacy platforms having rich data sources, these tend to create siloed information making it difficult to identify the source. The silos are often exported to the organization’s database and duplicated to combine data with other existing siloed data.

This makes it even harder to know the data source and its destination!

Favourably, data governance defines an organization’s data management rules and prevents potential leaks of sensitive business information/customer data from falling into the wrong hands.

The governance process analyzes confidential data and sets systematic architecture traceability. It lets the concerned team determine the data source, establish its route, identify those who have accessed it, and trace its use.

Data governance and compliance work hand in hand. Data grows and matures quickly, to the extent that data compliance has to be addressed and taken care of by organizations. They have to be compliant with what regulations in the government they belong to.

While the European Union has GDPR (General Data Protection Regulation), the US constitutes the well-known:

• PCI DSS (Payment Card Industry Data Security Standard)
• HIPAA (Health Insurance Portability and Accountability Act)
• SOX ACT or Sarbanes–Oxley Act of 2002 (the Public Company Accounting Reform and Investor Protection Act).

These regulations require organizations to trace their data from the source up to its obsolescence, identify who has access to it, and know-how and where it is used. Effective compliance can only be realized with a holistic approach to your data governance strategy.