Over the past decade, we have transformed from a world in which institutions focused on the control and management of transactional information to the current state, which includes exponential volumes of crowdsourced and alternate data.
Key insights
- Organizations are building data literacy capacity so that data can ultimately be managed as an organizational asset, much like inventory and human resources
- Data is now derived from both direct and alternate sources and organizations must find ways to harness and leverage data they may not generate or directly control
- Rapid access to data-driven insights is a competitive advantage, but building internal capacity can be prohibitively expensive, so organizations are increasingly turning to external vendors to provide decentralized data storage and processing
In order to retain a competitive edge, radically different approaches to data management, utilization, and access are required. Increasingly, organizations are starting to consider and develop solutions using artificial intelligence (AI) and machine learning to help harness and leverage this intake of data and derive insights that help provide value to both themselves and their clients.
Panelists at RBC Investor & Treasury Services’ (RBC I&TS) recent Investor Forum discussed data management challenges and successes, where five broad themes emerged.1
1.The emergence of data ethics
Jennifer Stott, Senior Vice-President and Chief Data Officer at RBC, commented that “throughout the financial services world, ‘data ethics’ questions are arising about what we can do with data versus what we should do with data.”
One emerging theme is the concept that data generated by an individual can be wholly owned by that individual, added Nira Sivakumar, Partner and AI Strategy Leader, Deloitte Omnia. “In the European Union, for example, the introduction of the General Data Protection Regulation in 2018 focused on data protection and privacy for individual citizens meaning institutions are now faced with challenges about managing consent for the use of that data,” she said.
It raises a number of important considerations. “This is a challenging topic. Is there a notion of consent if I’ve published something on Twitter? What expectations do individuals have for the use and control of the content they’ve created on social media,” she questioned.
2.The rise of social media
From an institutional perspective, social media data is an external asset that can contribute to consumer insights in far different ways than internally held data. Social media feeds have become highly valuable in determining market sentiment and anticipating market movement and business opportunities. As a result, rapid assessment of social media data is a competitive advantage.
‘Data ethics’ questions are arising about what we can do with data versus what we should do with data
There is not necessarily consensus among legal or privacy professions about how or even whether social media data can be used, commented Sivakumar, “leaving aside questions about the quality and reliability of that data.”
In order to manage data effectively, we need baseline rules to determine where data comes from and who owns it, commented Craig Gatten, Vice-President, Client Reporting and Data Management, CI Investments Inc. “While this is an area of healthy debate, we still have a long way to go,” he added.
3. A growing need for data literacy
Data literacy, or the ability to derive meaningful information from data, is another pivot point, notes Stott. “Across organizations, we are building awareness and understanding of what data is, why data is important, how organizations can be accountable for the data they use, and the importance of securing authoritative data sources.”
The need for organizational data literacy stands in contrast to how other institutional assets are managed. While companies are adept in the management of their other assets, such as inventory and human resources, many are still evolving their management practices with respect to ‘data as an asset’.
Rapid access depends on exponentially growing processing power
“All participants in the data journey need to agree on how to assess, qualify, and manage the data they use to drive their business objectives forward. That’s when data becomes a true asset,” added Stott.
4.The role of organizational change management
An element of managing data that has been underestimated is the need for organizational change management, or “getting organizations to think differently about data,” Stott said.
Data is often “trapped” or locked in applications, but the valuable asset is the data, and not the application that allows organizations to use it. Reorienting organizational practices to unlock the potential of this data can require a top-to-bottom cultural shift that goes beyond new policies and procedures.
These changes pose challenges for organizations that are traditionally organized around highly structured data and transactional sources, said Gatten, “although we are starting to see some ‘quick wins.’ Helping stakeholders resolve data pain points can be a way to build a robust organizational data capacity," he noted.
5. Building a fit-for-purpose data infrastructure
As institutions grapple with data that has not been generated “within their four walls” and which they do not own, they are also relying on data infrastructures which again they neither own nor directly control.
Participants in the data journey need to agree on how to assess, qualify, and manage the data they use to drive their business objectives forward
Today’s data can provide access to key business-driving insights virtually instantaneously, but rapid access depends on exponentially growing processing power. As a result, organizations are now increasingly turning to cloud vendors to provide dynamic scalable data storage and support ‘compute for analytics’ workloads. Building the equivalent capacity internally, in contrast, would be prohibitively expensive.
The growth of external, cloud-based data storage and processing solutions means that organizations need to balance how data is stored and managed internally and externally, but also allows them “to dial consumption up and down as needed,” commented Gatten. “Today, we’ve moved to a full ecosystem of many different tools to address our system-wide data needs,” he said, “addressing where and how we store, access, and process data, both structured and unstructured.”
Data management is an evolving landscape, where new demands, disciplines, and resources require a re-evaluation of standard practices. As the scale of data management has increased, so has the sophistication required to handle new data sources and flows, creating opportunities for stewardship and growth.