By Jean S. Bozman
Chief Data Officers (CDOs) have a weighty responsibility: they are “on point” to find the actionable insights and data trends from analysis of data lakes, data repositories and virtual “seas” of data flowing across their large organizations.
Data silos, different data formats – and organizational changes combining disparate data systems – make the CDO’s tasks challenging. Regulations have added to the CDO’s to-do list, including the EEU’s General Data Protection Regulation (GDPR), applied worldwide; the Health Insurance Portability and Accountability Act (HIPAA) in the health-care sector in the U.S. and region-specific financial regulations in the Americas, EMEA and Asia/Pacific.
How do CDOs approach these challenges? How do they solve thorny data standardization issues across their organizations and businesses? Not easily, but the work can be approached via better software tools, better business-wide data policies and IT best practices for data protection and analytics.
CDOs plan to leverage AI and machine learning algorithms to mine large data-stores more effectively and efficiently. This approach “opens up” analytics for use by the entire business – breaking down data silos that have grown up inside enterprise data centers. The CDO role will likely expand, over time, as it takes responsibility for the organization’s data strategy – and its ability to share data across business units and to mine it for actionable data insights. CDOs are responsible for ensuring that data is accurate, governed and managed, and they are increasingly tasked with leading the analytics efforts across their organization.
IBM CDO Conference
IBM invited dozens of CDOs to meet, in a San Francisco conference (LINK), to share best practices for building consistent data platforms across their organization.
IBM’s own data practices have served as a model for this approach to enterprise-wide data gathering, data storage, data management and analytics.
The aim of this approach is to make data ready for data analytics and AI/ML software. Business units throughout an organization should support data-consistency initiatives to make them more effective. Among the IBM executives who explained IBM’s own approach to the CDO role were Dr. Inderpal Bhandari, IBM’s Global Chief Data Officer; Beth Smith, general manager of IBM Watson, Data and AI; and Bob Lord, IBM’s Chief Digital Officer.
IBM is offering its own strategy as a model for other organizations looking to leverage data as an important corporate asset – and to conduct data analysis more effectively. Software and services can be used to forge end-to-end consistent data strategies that span data centers, public clouds, and hybrid clouds.
IBM also highlighted its own offerings, including IBM Watson and IBM Cloud Private for Data, which supports seamless movement of workloads between private clouds and public clouds, protecting data assets that are housed inside the enterprise data center privacy perimeter. We should also note that IBM’s worldwide Global Services business has also contributed to the best-practices knowledge embedded in IBM’s AI/ML/Cognitive offerings.
It’s been clear for several years now that IBM is reinventing itself, applying best practices to running its own business, and placing many of its infrastructure solutions in the context of broader solutions for cognitive computing. In recent years, IBM has redefined its business units to reflect the importance of AI, machine learning and cognitive computing to its traditional product-defined server, storage and software businesses. Now, it is bringing a portfolio of products and services to customers looking to leverage AI/ML and cognitive technologies for data analytics and data-management purposes.
The Role of Best Practices
Best practices are essential to gathering data worldwide – no matter what organization you’re in, and to modeling those best practices to others. In his role as CDO, Dr. Bhandari and his group have applied these methods and techniques across IBM. His rules of the road for his own operation include:
- Develop a clear data strategy
- Execute enterprise-wide governance and management systems
- Become the central data source and the AI framework for IBM
- Build deep data and analytics partnerships
- Develop and scale our talent in this area
High-profile companies and government organizations attending the conference, agreed with these priorities. Speakers included: USAA, an insurance company for U.S. military veterans and their families; the Department of Defense (DoD), healthcare group Kaiser Permanente, and the Swiss Re insurance company. Also represented were banks and financial institutions (Wells Fargo, Bank of the West and ING financial services); utilities (PG&E) and manufacturing concerns (McKesson), among others
Data gathering and storing data in normalized data formats are important strategies for CDOs. But listening to – and supporting – many forms of external governance is just as important. Government compliance, data protection policies, and data-security – all are vital to supporting a large-scale data platform. European GDPR standards, in effect since May 25, 2018, has shown the importance of paying attention to corporate-wide data management. If compliance is not proven, then fines of up to 4% of annual revenue will be imposed, impacting business operations; some fines have already been announced by the EEU to show GDPR is enforceable.
Changing Role of the CDO
Building more consistent data storage platforms will accelerate data analysis inside an organization’s business units – and provide actionable insights for CXO executives charged with adapting to changing business conditions. Another challenge for CDOs: scaling up the data analytics and AI activities across the enterprise, while leveraging the hybrid clouds that support data-processing across many customer companies.
Managing data across a vast organization calls on special skills—and careful planning. CDOS must set cross-organizational goals, including:
- Making better business decisions, based on deep analysis of the transactional data that was gathered by your ongoing business.
- Breaking down data silos across the organization, and making access to the data, and meta-data easier.
- Managing data from traditional workloads (enterprise applications and relational databases) and new ones (video streaming and NoSQL databases).
- Opening up data access to non-IT personnel inside the business units, providing access to data engineers, data scientists and business planners.
- Monetizing an organization’s transactional data, and reinventing business processes, turning corporate data into new sources of revenue and profits.
Corporate Cultures Are Changing
Key to making data initiatives work is the recognition that corporate cultures – the recognized norms of business unit roles and responsibilities – must change. Data assets span the entire organization, fed by hybrid clouds and end-to-end processes like blockchain and supply-chain logistics.
New ways of analyzing, protecting and managing data are leading to redefinition of many job roles (e.g., DevOps, data scientists and CDOs). Traditional roles kept systems running in the enterprise data center operational model. Now, new roles provide access and data-management by changing the IT organization’s culture and expanding it to create roles that span traditional boundaries.
By making the transition to more effective data analysis, customers have many goals. Among these are improving IT flexibility, leading to improved business agility; improving customer service; speeding up IT processing to find actionable data; and, in the process, monetizing the business value of new and existing data assets. As the emerging role of chief data officer (CDO) takes hold, these approaches to leveraging the inherent strengths in corporate data will turn into new business assets.