What Killed Silicon Valley Bank?
There have been almost as many opinions expressed about why Silicon Valley Bank (SVB) failed as there have been about ChatGPT and generative AI. Examples range from executive hubris and a lack of investment diversification to overly positive rating agency assessments and over-relaxation of protections implemented after the 2008 financial crisis. But a complete picture of events requires a broader and more data-centric perspective.
Beyond curiosity, seismic events such as the failure of one of the nation’s largest banks warrant the exploration of possible lessons for other business decision-makers. And if reporting by Bloomberg.com is accurate, many, if not most, of the possible causes of the SVB failure can be traced back to a lack of trustworthy data.
Just over a year before Silicon Valley Bank’s collapse threatened a generation of technology startups and their backers, the Federal Reserve Bank of San Francisco appointed a more senior team of examiners to assess the firm. They started calling out problem after problem. As the upgraded crew took over, it fired off a series of formal warnings to the bank’s leaders, pressing them to fix serious weaknesses in operations and technology, according to people with knowledge of the matter.
“Then late last year they flagged a critical problem: The bank needed to improve how it tracked interest-rate risks, one of the people said, an issue at the heart of its abrupt downfall this month.”
Lessons To Be Learned
It is credibly arguable that other examples of inadequate data accuracy and integrity contributed to SVB’s demise. The financial rating agencies, for example, were reportedly slow to respond to declines in the Bank’s fortunes. Some reports noted that the heads of business, financial, and model risk management all worked remotely. “Weaknesses in operations and technology” could have made data these decision-makers and others relied upon inaccurate, inconsistent, or out of date.
One key takeaway from the failure of SVB for any business leader is that solid business decisions are driven by the most accurate, complete, consistent, timely, and trustworthy data available. Another critical lesson is that modern, proven technologies and processes are absolutely necessary to provide the trustworthy “single version of the truth” every business decision-maker at every business needs. Given the explosive advent of generative AI and other low-code/no-code tools and technologies, incumbent risk management and mitigation solutions and processes will require modification and augmentation.
Debates over the “manner of death” at SVB will likely continue for some time. However, the “cause of death” clearly appears to have had its roots in a systemic lack of credible, trustworthy data upon which business leaders, regulators, and others could base their decisions and actions. This raises a third takeaway: risks to your data are risks to your business, up to and including its very survival. Your risk management and mitigation efforts must reflect this level of criticality if they are to successfully enable your enterprise’s agility, success, and sustainability.
What You Should Do Now
There are a few specific actions your enterprise can and should take to make the data that drives your business more consistently trustworthy. The order in which you and your colleagues do them is less important than getting them done as rapidly as possible, to avoid the harm untrustworthy data can cause.
Assess your data quality, now and frequently. You need an accurate and complete picture of the state of your data before you can take meaningful steps to improve its trustworthiness. Profile and validate what you have. Then highlight where improvements such as cleansing or elimination of multiple versions can be implemented, and schedule those implementations. And put processes into place that ensure data assessments are conducted regularly and as soon as possible after any significant changes or disruptions to your environment.
Follow your data. Data lineage is the ability to track your data from its origin through all its movements and transformations. Accurate, comprehensive, timely data lineage is essential to ensuring your enterprise decisions are based on trustworthy data. Audit trails and third-party applications and services can all help your enterprise to achieve and sustain data lineage success. Effective data lineage management can also ease and speed regulatory compliance.
Protect your data. You and your IT colleagues must ensure that data security and protection measures at your enterprise are sufficient now and in timely response to new risks and threats. This is especially important and challenging in the face of the explosive growth of low-code/no-code technologies such as generative AI. Your enterprise must be able to know whenever additions or changes to your environment take place and to ensure they do not degrade data quality or trustworthiness. This will require a combination of new and modified incumbent policies, processes, and technologies.
How Incisive Can Help
Incisive Software is focused on helping organizations build a strong foundation for success based on accurate and trustworthy data, especially in the face of new and growing risks spawned by generative AI and other low-code/no-code technologies. Incisive offers Incisive Analytics Essentials, a solution that enables you to gain managerial control over generative AI and other low-code/no-code deployments while making them available to authorized users. The Concourse platform, the heart of the Incisive solution, provides consolidated, comprehensive abilities to know what you have, know what changes, and effectively manage, protect, and trust your business-critical data across your entire enterprise.
To learn more about the risks associated with generative AI and other low-code/no-code technologies and more suggestions for mitigating those risks, please read “Generative AI: A Growing Risk to Enterprise Data.” To learn more about Incisive Analytics Essentials or to arrange a demo or free trial, visit https://www.incisive.com, email firstname.lastname@example.org, or call 408-660-3090.