Blog > Data Governance > Four Considerations for Streamlining Data Initiatives in Work From Home Environments

Four Considerations for Streamlining Data Initiatives in Work From Home Environments

Authors Photo Eric Yau | May 9, 2022

By now, it’s clear that remote working is the new normal. Businesses have settled into their work from home practices , managing employees offsite and learning new ways to remain productive. IT teams have figured out how to provide secure access to data for a remote workforce. And business teams replicated their work environment in their home offices – analyzing data, improving business processes, monitoring the competition and streamlining data initiatives.

IT overcame unique challenges like securing their networks from attacks, protecting cloud applications and infrastructure, while ensuring compliance of security regulations. Companies adopted new digital transformation technologies and upgraded legacy systems.

As the world emerges from the pandemic, data quality is now at the forefront for many organizations. Subsequently, organizations must now ensure the information business users access is accurate, complete and reliable.

If accessed data is low-quality, one of two things happen. Either business users will utilize bad data, resulting in unreliable insights. Or users won’t even bother incorporating analytical insights from enterprise data into business strategies or decision-making which leaves valuable insights untapped.

In a traditional pre-pandemic world, data governance programs included in-person conversations around data. From routine governance meetings to casual conversations on the fly about data’s meaning, purpose and quality levels. Instead, they now rely on enterprise collaboration tools for streamlining data.

data governance challenges

1. How Remote Work Alters Communication Around Data

 Data governance has long been the model for opening up communication lines between business and IT about data. Data governance assigns data owners, stewards, users and subject matter experts. Together, they define data sets, implement processes, establish access methods, set data quality baselines and build data catalogs.

However, once teams are taken out of an office setting, communication has suffered. Collaborating on data initiatives now requires regular virtual meetings, instant messaging and other collaboration tools.

As more time passes, data governance endeavors become increasingly difficult 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 distributed across numerous cloud platforms, data centers and home computer systems, incorporating new data governance approaches and technologies to automate the collection, curation and organization of data is essential.

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Data Governance 101: Overcoming Data Governance Challenges

Learn more about the challenges associated with data governance and how to operationalize solutions.

2. Adjusting Data Governance for a Remote Workforce

 Effective data governance in our remote world requires companies to think more holistically about their data initiatives. A modern approach to governance should not only prioritize data quality execution but also lineage tracking and data catalog curation.

By incorporating robust quality controls into data governance, organizations build trust among business users who are analyzing data from home. Additionally, by ingesting and cataloging 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 are stored in a searchable digital data catalog on the communications network. By also incorporating machine learning technologies in the catalog, 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 cataloging 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 and streamline data.

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 and streamlining data.

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

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

4. Advancing Data Trust from Home

A data catalog that organizes high-quality business data enables consistent understanding among data consumers anywhere in the organization to develop meaningful intelligence. 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, 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 streamlining data initiatives, establish consistent remote communication and build a high-quality, business-ready data catalog.

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