Don't let sensitive data become your Achilles heel

When was the last time a business process was successfully executed without utilising data? Businesses today are built on data, with customer data being some of the most valuable information that companies handle.

Customer data, containing personally identifiable information, is also among the most sensitive information many businesses have to manage today. There are rightfully numerous regulations, rules, and frameworks established to manage how sensitive information is handled, to prevent it from inadvertently being made public or falling into the wrong hands.

Safeguarding customer privacy and protecting the company’s reputation through data security are of utmost importance. However, enterprise security is inherently complex and challenging to implement comprehensively. A robust data governance framework is essential to maintain control over data and its management.

Digging into data governance

Broadly speaking, data governance encompasses a framework of principles, practices, and tools that assist businesses in managing the complete lifecycle of its data. It ensures that data-related activities align with the business strategy. A sound data strategy connects the demand for accessible, high-quality data with the desired outcomes of the business.

This becomes particularly crucial in the era of generative artificial intelligence (GenAI), where immense volumes of data are utilised to train AI models that power tools like ChatGPT. Now more than ever, good data governance is an essential component of conducting business, not only ensuring that high quality information is available to make informed decisions, but also that the business applications and processes driven by AI are reliable and accurate.

A pragmatic data governance strategy typically affords data teams enhanced data management, visibility, and the ability to audit data access patterns across their organisation. Consequently, such a strategy will generally outline who manages key business data, who authorises access to it, and who makes it available for auditing or other external reviews.

Within this framework, a data governance strategy is crucial for ensuring compliance with industry standards, including the protection of personally identifiable information (PII), as well as adherence to international regulations like the EU’s General Data Protection Regulation (GDPR).

Striking out on structure

Structuring data for effective governance has several challenges, largely due to the pervasive issue of data silos — pockets of data within an organisation that remain isolated because they were created or are used by separate solutions or distinct business systems.

The existence of data silos across various sources today, including data lakes, data warehouses, and databases, presents significant obstacles for governance teams. These silos impede the creation of a unified data view within an organisation, leading to inefficient data discovery processes.

Furthermore, many organisations accumulate vast quantities of unstructured data located both on-premises and in cloud storage platforms. A subset of this unstructured data is converted into structured tables within data warehouses for the purpose of business intelligence and analytics. However, the movement of data itself can further create more silos, scattering data across two or more systems. The absence of a consolidated view obstructs the discovery, access, and effective analysis of data and AI assets.

The challenges in achieving a unified view of organisational data are compounded when an organisation introduces real-time data. It doesn’t take long to lose track of what data is available or how data flows through multiple systems and streams. This is a significant business problem in an era where real-time products and services depend on such data.

The path to good governance

The good news is that businesses can start to take simple and straightforward steps to begin their journey toward data governance best practices. For instance, establishing executive oversight is a critical initial move in developing an effective data governance strategy, and for its adoption across various business lines.

Additionally, it is crucial to adopt asset management practices to catalogue key business data, assign data domains to the right data owners and stewards, and identify high-value use cases (with the data required) for their implementation. Outlining methods for monitoring and reporting on data security, usage, sharing, storage, deletion, and auditing is helpful, along with establishing metrics to quantify and assess the effectiveness of data governance.

Conducting a quick audit of existing systems and processes serves as a good starting point towards making a unified data view more readily achievable. Many legacy solutions available today are designed with storage-centric, batch-oriented workloads in mind — which hinder a company’s ability to meet the requirements of data governance in managing dynamic streaming data and event-driven architectures.

A fresh approach to governance, one built for data in motion, requires a new type of solution. This is where implementing a unified data governance solution with stream governance capabilities to deal with real-time data plays a big role. It helps companies to ensure that rules are in place to comply with regulatory requirements, while at the same time, safely protecting data.

Good for business, good for customers

Strictly speaking, operational excellence may not be the primary goal of data governance, yet it often emerges as a significant benefit. The cost of attaining a standard for data governance typically falls well below the value it creates for the business, even if this trade-off can vary across different business units or silos of the same company.

The establishment of a robust data governance framework, supported by an appropriate platform, results in significant benefits for a business. This framework not only ensures regulatory compliance but also facilitates the introduction and growth of data-intensive applications that enhance the organisation's value. These can include GenAI, data analytics, and a myriad of products and services dependent on analysing data in motion.

With these capabilities, companies can accelerate the development of real-time experiences that foster differentiation and elevate customer satisfaction, all while maintaining strict compliance standards. That is why modern data governance, built for data in motion, plays a crucial role in enabling collaboration and knowledge sharing that is essential for becoming an event-centric business. This is increasingly important in adapting to the continuously changing landscape of data regulations.

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