The year 2020 brought an unexpected shift in the way businesses operate. Remote working swiftly became the new norm, prompting companies to adapt to managing their employees from a distance. With this transition, IT teams had to innovate to provide secure access to data for a dispersed workforce, while business teams endeavoured to replicate their familiar work environments within the confines of their home offices. This meant analyzing data, enhancing business processes, monitoring competitors, and devising growth strategies, all from the comfort of one’s own home.

While IT teams successfully overcame unique challenges, such as securing networks from cyberattacks and ensuring compliance with security regulations, companies also seized the opportunity to embrace digital transformation technologies and upgrade legacy systems. As the world gradually emerges from the pandemic, one essential factor now takes centre stage – data quality.

The Crucial Role of Data Quality

In today’s data-driven landscape, data quality is paramount for organisations. Ensuring that the information business users access is accurate, complete, and reliable is non-negotiable. When data quality falters, two undesirable outcomes emerge. Firstly, business users may unknowingly rely on inaccurate data, leading to unreliable insights and flawed decision-making. Alternatively, they might choose not to incorporate analytical insights from enterprise data into their strategies, leaving a treasure trove of valuable insights untapped.

Traditionally, data governance programs relied on in-person interactions to foster communication about data. This included routine governance meetings and casual conversations about data’s meaning, purpose, and quality. However, the remote work landscape necessitates a shift in this paradigm.

1. How Remote Work Alters Communication Around Data

Data governance has long been a model for opening up communication lines between business and IT regarding data. This involves assigning data owners, stewards, users, and subject matter experts who collaboratively define data sets, implement processes, establish access methods, set data quality baselines, and create data catalogues.

In a world where teams are dispersed, communication has taken a hit. Collaborating on data initiatives now relies heavily on virtual meetings, instant messaging, and other collaboration tools. As time goes on, data governance endeavours become increasingly challenging, and business users struggle to find and apply high-quality data to support data-driven tasks.

As our work environments evolve, so should the discipline of data governance. With information scattered across numerous cloud platforms, data centres, and home computer systems, it’s essential to incorporate new active data governance approaches and technologies to automate the collection, curation, and organization of data.

2. Adjusting Data Governance for a Remote Workforce

Effective data governance in our remote world necessitates a holistic approach. Modern governance should prioritize not only data quality execution but also lineage tracking and data catalogue curation.

By incorporating robust quality controls into data governance, organizations build trust among business users who are analyzing data from home. Additionally, by automating data lineage, companies uncover key details about data’s origins, its route across data systems, and any changes that occur along the way. When business users can track data’s origins, they can verify the source of information, uncover and resolve quality issues, and ultimately, trust their analytical outcomes.

Collaborating and sharing insights and feedback remotely is still essential. Data governance can help organizations establish an overarching digital communication network, dedicated to data. Data users collaborate through this network to define data sets, business rules, data processes, and quality standards. They can also use the network to have regular data governance meetings and maintain open communication lines around data quality and other aspects of managing data.

All information regarding data quality and data lineage is stored in a searchable digital data catalogue on the communications network. By also incorporating machine learning technologies in the catalogue, businesses can add automation to fine-tune business insights, making it easier for users to analyze data from home.

3. The Role of Machine Learning and Automation

Machine learning algorithms and automation technologies can eliminate some manual cataloguing tasks that take even longer in remote work environments. For example, when organizations collect metadata, automatically profiling and establishing semantic tags establishes key details to enable lineage mapping more efficiently.

From there, semantic tags and machine learning can recommend likely relationships and lineage diagrams. This enables users to confirm automated insights and focus on collaborating on linked business context. As a result, business users quickly discover known gaps to focus on addressing.

Additionally, machine learning algorithms combined with workflow management and a communication network empower users to surface insights about data access, ownership, or data quality issues based on historical user activity.

Leveraging a digital, business-intuitive data catalogue enables self-service analytics while operationalizing data governance with high-quality data.

4. Advancing Data Trust from Home

A data catalogue that organizes high-quality business data enables consistent understanding among data consumers anywhere in the organization to deliver data excellence. When business users have trustworthy, high-quality data to navigate remote work, they can:

• Uncover the impact of data on business processes and identify how data can improve those processes.
• Decipher data’s business meaning, uncover critical business intelligence and turn data assets into valuable insights.
• Quickly communicate with data owners and stewards if data quality or any other data questions arise.
• Trace exactly where data came from, where it’s going next, and any transformations to verify data sources.
• Trust the data they use is reliable, and accurate, and won’t generate faulty insights.

Data quality is paramount for building data trust and understanding. However, it takes users across the organization to collaborate to turn enterprise data into trustworthy, easily understandable business knowledge. Companies must complement their efforts with modern data governance technologies to streamline data initiatives, establish consistent remote communication, and build a high-quality, business-ready data catalogue.

Read the Precisely eBook Data Governance 101: Overcoming Data Governance Challenges and learn more about the challenges associated with data governance and how to operationalize solutions.

In conclusion, as remote work becomes the standard, adapting data governance and quality control strategies is vital. The evolution of the workplace demands a shift in how data is managed and communicated, with a strong focus on automation, machine learning, and a robust data catalogue. Embracing these changes ensures that organizations can continue to thrive in a remote work environment, harnessing the full potential of their data.

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