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The Growing Importance of Data Management in the Digital Workplace

November 06, 2020 Information Management
David Roe
By David Roe

Ask enterprise leaders what data management is and you are likely to get a different answer from everyone you ask. This is particularly true given the move to remote work and the corresponding data management needs of the millions of computers now used for this new reality. However, there is a consensus on the fundamentals of data management, how it should work and what it should do. 

Data management, simply put, is the process by which an organization stores and distributes data around the digital workplace as well as figuring out the best and most compliant way to gather it. With the digital workplace built around applications that run off data or that enable workers to access data, data management and associated technologies are now one of the most important assets in an organization.

Employing data management enables more efficient access to data analytics that offer insights needed to improve business operations and identify opportunities for improvement. By establishing a better framework to access the wide swaths of data every business generates, companies can make more informed decisions and improve their ability to deliver valuable products and services to customers.

Data Management in the Remote Workplace

The pandemic has been an important reminder that organizational agility and speed are key to successfully navigating business disruption and market change. Effective data management is at the heart of this. Data is the engine that enables companies to see around corners, flex and adapt as needed, and run faster in order to scale up and seize the moment, especially in times of rapid change.

In our current remote work environment, where swivel chair and hallway conversations are no more, employee productivity, efficiency and time to value are prized more than ever, said Craig Stewart, chief technology officer at San Mateo, Calif.-based enterprise integration company SnapLogic.

As a result, many organizations are turning to low-code automation technologies to rid themselves of the repetitive, time intensive and low-value tasks that slow them down, thereby freeing themselves up to focus on more strategic projects that deliver fast business value.

“At the same time, the importance of data to organizations continues to grow, no longer prized only by data specialists but now a critical asset used on a regular basis by workers across each and every business function, from marketing to finance to HR,” he said.

There is another issue, too. It has become increasingly more challenging to distinguish an employee’s personal data from their work-related data given there’s no longer a clear delineation between work and home, said Joel Friedman, chief technology officer and head of software development at Chicago-based Aclaimant.

Due to the transition to remote work, data access restrictions have had to change to allow for access from remote locations. Bandwidth concerns for corporate applications are more important as users are no longer on corporate networks. All in all, these changes will benefit companies as they enable more secure and robust networks, systems and applications.

Related Article: How Edge Computing Will Transform the Digital Workplace

Securing Remote Data

As more data enters the organization, the security problem grows. Marcelo Lebre, chief technology officer at Remote, said the shift to remote work has forced companies to restructure their data management practices and employ stricter security policies to manage distributed teams across a digital workspace. As employees are more likely to use their personal devices without the added layer of security of a corporate firewall system, the risks of cyberattacks and data breaches have significantly increased.

Nowhere in the organization is this more true than in human resources. Handling and securing data properly has become an utmost priority for HR departments, which are now tasked with recruiting and onboarding new employees remotely. With confidential employee records being digitally transferred from personal computers, companies are placing greater emphasis on end-to-end encryption and multi-factor authentication tools as additional components of their HR data management procedures.

Businesses that have yet to facilitate training and update security policies for HR professionals within their company, significantly increase the risks of breaching data privacy laws and compliance requirements, which vary by state and country.

“With GDPR and CCPA seeking to extend guidelines on employee data privacy in the near future, compliance is now an important factor for companies with distributed workforces,” he said.

The Role of DataOps

Remote working has revealed the inconsistency and fragility of workflow processes in many data organizations. The data teams share a common objective: to create analytics for the internal or external customer, said Chris Bergh, CEO of Cambridge, Mass.-based DataKitchen. Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization and data governance. Each of the roles mentioned views the world through a preferred set of tools:

  1. Data center/IT: servers, storage, software
  2. Data science workflow: Kubeflow, Python
  3. Data engineering workflow: Airflow, ETL
  4. Data visualization and preparation: Tableau, Alteryx, self service tools
  5. Data governance/catalog (metadata management) workflow: Alation, Collibra, Wikis

Enterprises need to examine their end-to-end data operations and analytics creation workflow. Is it building up or tearing down the communication and relationships that are critical to the mission? Instead of allowing technology to be a barrier to teamwork, leading data organizations rely explicitly on automation of workflows to improve and facilitate communication and coordination between groups.

“In other words, they restructure data analytics pipelines as services or microservices that create a robust, transparent, efficient, repeatable analytics process that unifies all workflows,” Bergh said.

In the data analytics market this is referred to as DataOps. DataOps automates the workflow processes related to the creation, deployment, production, monitoring and governance of data analytics. Automation coordinates tasks, eliminating reliance on tribal knowledge and ad hoc communication between members of the data organization

“DataOps puts the entire data organization in a virtual space with structured workflows that enable analytics and data to be seamlessly handed from team to team,” Bergh said."DataOps automation makes it much easier for remote teams to coordinate tasks because the end-to-end data lifecycle is encapsulated in robust, repeatable processes that unify the entire data organization."

With DataOps, it doesn't matter where workers physically reside because the workflow orchestration integrates their work with other team members. It provides the structure and support to enable data teams to work remotely and together, producing analytic insights that shed light on the enterprise's most difficult challenges.

Related Article: Building Digital Resiliency for the Post-COVID World

Accommodating Remote Work

The increase in remote working has had a significant impact on data management as businesses can face many challenges when data is being generated from multiple offsite locations, said Eric McGee, senior network engineer at Houston-based TRG Datacenters.

Companies now must accommodate all the remote offices at employees’ homes and ensure they are included in the backup and data security plan. Businesses now must ask pressing questions like how data is being generated, which cloud platform is data being stored in, which physical devices are storing data, and whether all the data is being backed up.

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