In today’s rapidly evolving data landscape, the efficient management of data is paramount. Much like oil, data possesses intrinsic value when handled meticulously. So, how can you refine your data to transform it into a true asset? These questions lay the foundation of Sarah Gadd’s recent presentation at the Airside Live virtual conference, which revolved around the construction of a framework for future data success.

“Most of you have heard the phrase, ‘Data is the new oil.’ But as we know with oil, it’s not valuable unless it’s refined and curated properly. So how do you think about refining your data, so it is a true asset?”

Sarah Gadd

For Sarah Gadd, who serves as the Head Of Semantic Tech, AI & Machine Intelligence at Credit Suisse, a global wealth management firm, data is a critical asset. Operating within a complex and heavily regulated data environment spanning 32 countries with dozens of regulations, it is imperative for the firm to maintain data security and compliance.

However, Credit Suisse’s approach to data success extends beyond mere data security. Gadd shared insights into the data strategy and data management framework devised by the Chief Data Officer (CDO) and their team, outlining their journey towards seamlessly integrating data across the organization.

Cultivating a Data-Driven Culture as the Cornerstone

The CDO group at Credit Suisse was established in 2017 with a mission to lay the groundwork for the organization’s future data success. While the introduction of an enterprise data governance tool in the same year and the development of a data management framework were significant milestones, the true essence of these initiatives lies in the culture surrounding data within a company.

“Having impeccable data and robust governance structures is vital, but their true worth is unlocked when individuals within the organization recognize the importance of data, understand how to access it, assess its quality, and are aware of the risks associated with erroneous data.”

Sarah Gadd

Aspects of a Data Culture

A robust data culture within an organization encompasses several aspects:

• Data as an Asset: Individuals working with data should easily discern its location, access methods, suitability for specific purposes, quality, associated risks, and points of contact for issue resolution.
• Ethical Data Handling: Ethical data practices should be ingrained in day-to-day operations, with data ethics becoming an integral part of the organization’s core values.
• Data-Driven Decision-Making: Consistency and repeatability should underpin data-driven decision-making processes. Both internal and external data sources should be utilized to predict future trends and guide strategic actions, fostering innovation in the form of new products, services, and business models.

“You can have the best data in the world, the best-governed data, fantastic systems, but unless the people really buy into it and understand how important data is, and know to go to the right strategic assets and [how] to use the enterprise systems, it’s not of value.” 

Sarah Gadd

Establishing such a data-driven culture is no small feat, regardless of the organization’s size. Even in the era of digital transformation, organizations must invest substantial effort and resources into cultivating a data-driven culture, particularly if they didn’t initially embrace a strategic data mindset and the necessary tools.

Building the Data Management Framework – Part 1

Credit Suisse embarked on its data management journey by forming the CDO group, implementing an enterprise data governance tool, and crafting a framework to govern data assets across the organization. This framework was structured around five key pillars:

1. Security: Ensuring data remains protected against unauthorized access, modification, destruction, or disclosure through various mechanisms, including access control, obfuscation, and anonymization.
2. Quality and Control: Measuring and maintaining data quality to support business processes and objectives. Implementing controls to facilitate issue identification and resolution while ensuring data quality.
3. Data Governance: Developing comprehensive data policies that define ownership, stewardship, and operational structures for managing data as a critical asset, with a strong focus on risk management.
4. Architecture: Designing and implementing an optimal data architecture to ensure data accessibility within appropriate systems, fostering usability.
5. Usage and Analytics: Enabling the extraction of business value from data through insights generated by business intelligence. These insights drive cost efficiencies, reduce risks, and stimulate revenue generation.

Delivering on the Framework – Part 2

Credit Suisse initially grappled with fragmented data scattered across various business units and corporate functions globally. Each entity possessed its data silos, spanning different data sources and platforms, from Oracle to Cloud to HDFS and Hadoop. The data team’s primary objective was to gain a comprehensive overview of key data assets and strategic data assets. This entailed:

• Enhancing Data Quality: By tracking and reporting on the quality of key data assets, both internally and for regulatory compliance.
• Simplifying Data Sourcing: Streamlining the process for data consumers to identify, locate, and access high-quality, strategic data through a catalog-driven standard data access framework.
• Industrializing Data Lifecycle Management: Managing, governing, and delivering data at scale through a consistent operating model, standardized tools, and clear roles and responsibilities.

Fostering the Data Culture – Part 3

The pivotal takeaway from Gadd’s presentation is the centrality of a data culture in Credit Suisse’s data strategy. Confidence in the reliability and quality of enterprise data is fundamental. Low-quality data erodes trust and leads to decentralized efforts to build alternative datasets, exacerbating inefficiencies and inconsistencies. Organizations that possess clean, trusted data stand out as industry leaders.

Building an enterprise-wide data-driven culture necessitates leadership from top stakeholders in the data office and information management teams. It demands years of concerted effort in establishing systems and accountability to ensure data trustworthiness. The executive board’s support was crucial in this endeavour.

To showcase the value of their efforts, Credit Suisse implemented governance around data and analytics projects, aligning them with strategic business initiatives. Transparency was a key element, achieved through the establishment of a global Analytics Community Forum, open to all levels and functions across the organization. This forum allowed sharing of use cases, ideas, and lessons learned, fostering trust and collaboration across the community.

“Everybody now enters their use cases, from ideation all the way through to production, so that people can search through that metadata if they’re starting on a new journey. If…I’ve got an idea, I can go see if somebody has already done that, maybe leverage it. Or…what lessons did they learn? So building up that trust across the community and that grassroots approach… is super important”

Sarah Gadd

An essential aspect of cultivating a data-driven culture involved providing data management education. Credit Suisse developed four distinct learning journeys within their online learning tool, catering to individuals ranging from Excel users to AI leaders, empowering them to self-educate and acquire new skills.

Selecting the right tools was a pivotal aspect. Credit Suisse sought to avoid introducing redundant tools while still promoting innovation. They evaluated new tools based on their potential to benefit the business, ensuring alignment with strategic objectives.

“[the Data Access Framework] has proven key to giving people access to the data they need, and to spin up sandboxes securely around data so people can get on with the actual use case they have and ask the questions they’re trying to solve for, versus spending months trying to locate the right data.”

Sarah Gadd

In summary, Credit Suisse’s Data Access Framework emerged as a key enabler, granting individuals access to data and secure sandboxes for their specific use cases. This approach minimized the time spent on data location and maximized the time spent on problem-solving and analysis, driving the organization towards data excellence.

Image from pxfuel