By Susan Cook, Zaloni CEO

As COVID-19 disrupted economies around the world, many companies needed to quickly adapt their business models to stay competitive in the new pandemic reality. As a result, businesses have shifted priorities towards reducing costs, finding new revenue streams, mitigating risk, automating processes, and improving customer experience. In some cases, it accelerated the path to digital transformation to meet new digital requirements.

You hear the familiar adage that today "every company is a data company," and data is indeed at the core of these shifting priorities. It's essential during crises that companies are agile and can quickly pivot to leverage new and existing data for time-sensitive use cases. Created with disruption in mind, the data management discipline, DataOps, gives companies the ability to adjust operations during times of crisis quickly.

The Real-World Impact of DataOps During COVID-19

One example of how DataOps helped companies get ahead of their competitors during COVID-19 was in the banking industry. Specifically, the banks that quickly processed Payroll Protection Plan (PPP) loan applications. The CARES Act was enacted on March 27th, 2020, and banks began accepting PPP loans just a week later, starting on April 3rd. Many banks were unprepared and inundated with large, unmanageable volumes of complex data to process for the PPP loan applications. Bank of America alone received 60,000 PPP loan applications by 9 am on April 3rd.

The time-sensitive processing of these loans required an agile DataOps process that allowed banks to quickly ingest structured and unstructured data, ensure quality, enrich, and begin processing the submitted application and supporting forms. Additionally, due to stringent regulations, the banks needed to have the proper data governance to ensure that sensitive customer information was protected.

In addition to PPP loan applications, many banks needed to completely move their banking offerings from physical to digital as bank branches closed, hours were reduced during "shutdowns," and customers' preferences shifted towards online and mobile banking. This acceleration of digital transformation was more easily achievable with a solid DataOps foundation in place.

The banks that quickly processed and secured PPP loans or switched entire business processes and operations to digital and mobile banking were able to stay ahead of their competitors, increase customer satisfaction, and improve customer loyalty.

How DataOps Provides Data Agility and Competitive Advantage During Times of Crisis

The definition of DataOps can vary, but primarily the term is used to describe managing every step in your data supply chain from your data source to your data consumer to improve efficiency, ensure security and reduce costs. This emerging data management methodology combines agile development principles, made famous by DevOps, with operations management. DataOps is process-oriented and delivery-focused to process and deliver analytics-ready data to analysts and scientists quickly.

In addition to technology and processes, DataOps considers people as well. To implement it effectively, it requires extensible technologies that offer collaboration and self-service capabilities. The following are core capabilities of DataOps that provide data agility during times of crisis: 

• Automation to Improve Efficiency and Reduce Costs:  During times of crisis and economic uncertainty, reducing costs becomes a priority. DataOps reduces costs by automating and streamlining steps within the data supply chain to improve productivity and reduce the duplication of efforts that can be resource-intensive. Machine learning automates the identification of compromised data, makes recommendations, and executes data quality workflows to save time, improve data quality, and reduce manual error.
  

• Data Governance to Reduce Risk and Ensure Data Security: Customer demands for data privacy and regulations around customer data protection such as GDPR and CCPA increased the importance of data governance within IT organizations. During times of crisis, quickly adding, enriching, and integrating new data sources, especially customer or patient data, can create security and regulatory risk if proper governance controls are not in place. With DataOps, data governance is standardized across technologies and each step of the end-to-end supply chain providing full visibility, traceability, and control. Through automation and machine learning, personally identifiable and sensitive information can be identified and obfuscated quickly and reliably.
  

• Self-Service Data Access to Accelerate Analytics: Providing self-service access to data consumers such as data scientists and analysts is essential for analytics acceleration. Self-service allows data end-users to independently find, prepare, and start using quality, trusted data for analytics. This reduces the time it takes for an end-user to acquire data and diminishes the burden on IT.  Additionally, giving data analysts the ability to explore and discover data across the enterprise can lead to transformative business insights or uncover new business opportunities.
  

• Collaboration To Improve Productivity of Distributed Teams: Collaboration is one of the core pillars of the DataOps methodology, and it's become even more critical as teams work remotely during COVID-19. Data is siloed within business lines, and so is the information and knowledge about that data. For effective DataOps, companies should have collaboration built-into their data platforms for users to share and collaborate on metadata, data sets, data pipelines, models, and other data assets easily with users and teams. Collaboration improves data confidence and increases productivity.
  

How to Prepare for Future Crises

To prepare for future crises, consider modernizing your data architecture with technologies that support end-to-end DataOps. Below are my recommendations to make sure you are ready for unforeseen disruptions moving forward:

• Modernize your data architecture for agility and extensibility
• Adopt a DataOps methodology to optimize your data supply chain
• Leverage a collaborative, augmented data catalog to improve communication and productivity of distributed teams
• Provide self-service data access to reduce time to value in analytics
• Standardize governance across tools and systems to ensure security and regulatory compliance
  

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About the Author

Susan Cook is the CEO at Zaloni, with decades of experience in enterprise software sales, strategy, and consulting; specializing in Data and Analytics. A recognized technology leader, she has previously held executive leadership roles at IBM, Microstrategy, Oracle and other leading global technology organizations.