This post is the work of John Randles, CEO of Siren
Moving from a Fintech centric view to other regulated industries
Now that I have settled in here at Siren, I’ve gotten to know something about the product, clients, partners markets and potential markets, and so I thought it’s an ideal time to share some thoughts about how Data Intelligence fits into the world. It’s important to know what it adds to the current world of Insight Engines, Business Intelligence and Data Science and what benefits can be leveraged across industries, particularly highly regulated markets.
The criticality of data in Life Sciences and Financial Services
As someone who has spent over 20 years in the Financial Service Data & Technology world, it has been really interesting to see the use of data in another very data centric industry like Life Sciences.
It has been really interesting to see an industry where the stakes are much higher. How, you might ask? Doesn’t the Financial Market manage trillions of dollars a day changing hands, manage massive risk and isn’t it the oil keeping the global economy going? Of course, this is true, and it does this with data at its heart.
“Death” is the number one in a list of “Adverse Events” of some synthetic Clinical Trial data we created for demoing and training recently. As I said, this was synthetic data and nobody died in the making of our demo but it is inspired from the data our customers handle every day. It brought home to me the importance of the work being done in Life Sciences and the criticality of data to the drug discovery, clinical trial and regulatory compliance process.
Not only is data at the heart of these two industries but these are two of the most highly regulated industries in the world. The US FDA are just as interested in Data Governance, Data Quality, Data Provenance as any of a myriad of Financial Regulators.
In small firms who run off a single database these are not big issues to implement. For large complex organisations in Financial Services and Life Sciences, these are monumental challenges. For example, the problems of data silos, processing core data across multiple internal and external systems, lack of data ownership, quality issues, cardinality mismatches and very complex products and client interactions are everyday challenges in both industries. To layer Governance, Quality and Provenance across a complex organisation and systems can seem like an overwhelming task.
Regulation and Data – Why regulators care so much about data
I was reading the FDA website recently and how it describes its approval process for drugs coming to market. Its process comprises of the following main steps:
- Analysis of the target conditions and available treatments
- Assessment of benefits and risks from clinical data
Contrast this with what the FSA in the UK says about their statutory objectives in Financial risk:
- Market confidence– maintaining confidence in the UK financial system;
- Financial stability– contributing to the protection and enhancement of stability of the UK financial system
- Consumer protection– securing the appropriate degree of protection for consumers; and
- The reduction of financial crime– reducing the extent to which it is possible for a regulated business to be used for a purpose connected with financial crime.
Both industries are concerned with managing risk while providing a critical product for society. When these services are provided well, they result in great outcomes. Miracle drugs come to market and can provide cures for the worst imaginable diseases. When financial services help people save for a great retirement they also provide a great necessity to a well-functioning society.
But both products can go horribly wrong and are tightly regulated for this reason. Catastrophic consequences are possible without the right controls in the supply of life saving drugs. Loss of life savings through badly regulated financial products can lead to dramatic change of personal circumstances and when this happens at scale it can lead to great uncertainty in society as a whole as we saw in the financial crisis. The stakes are high so the regulators need to be vigilant.
Regulatory bodies, in both of the mentioned cases, are highly sensitive to data. It is the main tool they have to validate that due care is been taken in operational process control. Governance, provenance, quality and control are all about ensuring that the appropriate risk is being measured and taken on behalf of the customer. Regulators need to see real data to show that operational processes are followed in the same way a financial audit needs all expenses correctly receipted. How else will they know if the regulation is being followed or not?
The role of Data Intelligence in Regulatory Compliance
Why is Data Intelligence a vital new tool in the fight to comply with the needs of regulators and the needs of delivering better products to the market? It comes down to the quality of compliance you want. Having good Data Intelligence in place can show any regulatory audit that you are on top of your game. There is bare compliance and then there is compliance as a competitive advantage. Adding real Data Intelligence can turn a chore into a real business advantage.
When we think of Data Intelligence at Siren we think of 3 things:
- Search across all data in every source (files, emails, web, databases, legacy systems etc.)
- Data Dashboards and Reports which are easy and intuitive to use
- Knowledge Graph representations of connections across disparate data sets, highlighting important relationships which just cannot be seen in other formats
In complex environments where data doesn’t fit nicely together, where there are multiple client databases with different client identifiers, different ways of defining compounds and molecules, different ways of identifying a futures contract and linking to underlying securities, Data Intelligence can help span these conflicting sources of data and start to make sense of them.
This, we believe, will be fundamental to the operational process control which all regulators want to see in their clients.
A great example of a regulation which can greatly benefit from Data Intelligence would be GDPR. The core of this regulation is knowing where the Personal Identifiable Information (PII) resides and how it relates to other data across the organization and outside it. Search, drill through and knowledge graphs of the relationship all help build a real world, usable picture of the data. This will be a long running event. We believe that post May 25th 2018 is when the industry will be looking at industrial strength solutions for GDPR, not the bare minimum.
Another regulation well suited to the strengths of Data Intelligence is CECL (Current Expected Credit Losses) due December 15th 2019. According to Deloitte “CECL promises to be one of the most significant accounting projects of the next five years”.
According to the leading Management Consulting firm Oliver Wyman “CECL will require the integration of a large number of risk and finance data elements across a broad range of business functions – a major challenge for most financial institutions today. Furthermore, unlike other loss forecasting applications, CECL results will be audited and Sarbanes-Oxley controls will be required now that the results directly affect public financial statements”.
At its core CECL is about having a more complete and up to date picture on credit risk than has ever been required in the past. This is something well suited to search, dashboards and knowledge graphs of Data Intelligence.
There are many other examples.
Data Intelligence is no longer just a great tool for the Secret Service Agencies of the world but is now an accessible set of capabilities which can be brought to bear on some of the most complex enterprise data challenges in the most regulated industries in the world.
Data Intelligence is a new tool in the world of Regulatory Compliance and we believe those who best adopt it will be able to use the strength of their compliance functions as a real differentiator in the market.
Knowing your data, how the data relates to other data, knowing the provenance and the quality of that data and who has access to it for whatever reason, is all part of having good data management practices in place.
And Data Intelligence will be at the heart of that challenge in the future.