Structuring Data Initiatives for Work from Home Environments | Transforming Data with Intelligence - TDWI

Summary

Structuring Data Initiatives for Work from Home Environments

Given the increase in remote work, we offer three measures that can improve your data initiatives.

Structuring Data Initiatives for Work from Home Environments

Given the increase in remote work, we offer three measures that can improve your data initiatives.

By now, it’s clear that remote work is the new normal for office jobs. 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. Business teams replicated their work environment in their home offices -- analyzing data, improving business processes, monitoring the competition, and developing growth strategies.

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

Now, as the world emerges from the pandemic, data quality and data management have moved to the forefront for many organizations because the information business users access must be accurate, complete, and reliable, no matter where they access that data.

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

In the pre-pandemic world, data governance programs included in-person conversations about data, from routine governance meetings to casual conversations about data’s meaning, purpose, and quality levels. Data teams now rely on enterprise collaboration tools to meet their governance needs.

How can you streamline and improve data initiatives in a remote-work or hybrid-work environment?

Keep the Lines of Data Communication Open

Data governance has long been the model for opening up communication lines about data between business and IT. 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 often suffers. Collaborating on data initiatives now requires regular virtual meetings, instant messaging, and other collaboration tools. In lieu of in-person collaboration, more emphasis must be put on how data is captured and how it’s communicated throughout the organization.

To do this effectively, a modern approach to governance should prioritize not only data quality execution but also lineage tracking and data catalog curation. 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.

In tracking data lineage, a natural communication network emerges, in which data users can observe how and where the data is received -- and then begin to collaborate to define data sets, business rules, data processes, and quality standards. This network also serves as a way to maintain open communication lines around data quality and other aspects of managing data.

Use Cloud Technologies to Easily Transition Between Work Environments

As more data becomes available to users across an organization and more collaboration is done to govern this data, the cloud will become a business imperative to effectively store this information. Especially as employees work in multiple locations in a single week -- home versus office versus customer site -- they need to access data in real-time no matter where they are physically located.

Organizations that have begun investing in cloud-based architecture are already reaping the benefits. They can automate the collection, curation, and organization of data, and they are doing it cost-effectively through service providers that deploy this capability.

Considerations for Security Within the Work-from-Home Environment

It goes without saying that once data becomes more easily accessible, especially while users are working through multiple internet connections, data security is more critical than ever before. Above every other consideration, data compliance is imperative to ensure your data is protected.

Organizations can and should implement programs that increase data security, such as VPNs and multifactor authentication. Multifactor authentication is especially important today because of the proliferation of devices and the dramatic increase in remote access scenarios. Many organizations have shifted to a hybrid onsite and remote workforce, resulting in more remote connections. In addition, many have instituted “bring your own device” (BYOD) policies that present new challenges in terms of mainframe security.

Multifactor authentication has the benefit of being easy for end users (as well as simple and cost-effective to customize and administer) while remaining very effective as a means of authenticating users’ identities.

Summary

With our new way of work, there are several factors an organization should consider in its efforts to streamline its data initiatives. To make its remote workforce the most productive, an organization must keep its lines of communication open. It should consider the benefits of the cloud to ease access to data in multiple locations. In addition, security should never be neglected; because remote work has resulted in new work locations, data compliance is still vital in ensuring data is protected.

Structuring Data Initiatives for Work from Home Environments | Transforming Data with Intelligence - TDWI
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