As the Consolidated Audit Trail (CAT) saga continues to unfold, there is continued uncertainty throughout the industry. When are the reporting deadlines? Are the specifications published and final? Are we reporting personally identifiable information or not? All of these questions are clearly on the table, being discussed by the SEC, SROs, and firms throughout the industry. As the weeks go by in 2018, we expect the answers to these questions to become clear.
Topics: Regulation and Data
For years and years, regulatory and legal requirements have created hundreds if not thousands of processes that require companies to capture information in documents or records. This information includes details about their customers, suppliers, market activity, and many other facets of their business. I don't know a company on the planet that really values all of this documentation. However, in the era of Artificial Intelligence and Natural Language Processing, we're discovering that all of this documentation may be a goldmine of data for these large enterprises.
In the last 30 days I've had many conversations with executives about cognitive, or artificial intelligence (AI) capabilities. I think we've made tremendous progress with our cognitive computing suite at Kingland over the last 7 years and it's fun to show people what we've done and where we're headed. The surprising thing to me, though, is the amount of skepticism I still hear from these leaders. When it comes to AI, there's almost a "too good to be true" cautiousness in the discussions. I think this is driven from the sheer volume of attention AI is getting as these leaders are asked daily to join the "AI Club". At the same time, though, I hear an incredible amount of optimism. Executives are thinking "maybe AI is one of the key investments I need to make to really make a difference." The efficiency, the automation, the chance to deliver something innovative for their company; all of these breed optimism.
I'd like to explore both sides of this skepticism / optimism coin. Let's look at one of the most popular questions I get: "So...is that really AI"?
Topics: Cognitive Computing
Executives are faced with an evolving challenge related to the "cognitive era." There is an unending, growing pressure from competitors, analysts, and vendors to invest in artificial intelligence (AI) and machine learning (ML), or "cognitive." Be wary. The cognitive trap is this: executives know there is tremendous potential with cognitive computing and they also know they have goals for their business for next year, so they simply combine them and assume cognitive MUST be the answer to their goals. My advice is don't fall into the trap of letting the "legend" of cognitive take over, but focus on value instead.
Topics: Cognitive Computing
Many enterprises are looking to platform strategies as a way to lower long-term maintenance costs while taking advantage of more software innovation. This term platform is a word we are all hearing more and more in the software world. But why Platform? Why now? I can't speak to all platform strategies, but I can explain why we at Kingland think platform is compelling.
Topics: AI for the Enterprise
Imagine these three characters really do walk into a bar together. They take a seat and begin talking about their priorities for the year. The banker commiserates about the latest regulatory deadline. The retailer talks about today's news about a top competitor. The accountant explains the challenges of ensuring there are no conflicts in taking on a new client. They order another round and continue the discussion.
The Consolidated Audit Trail (CAT) is coming. With specifications coming out this summer and compliance dates approaching quickly in 2018, broker dealers are now starting to put plans in place to report and comply with CAT. Some in the industry are calling the CAT “revolutionary," particularly due to the requirements for customer and account information reporting. Put simply, if there is a trade occurring in the US equity or options markets, the customer and account information related to those trades must be reported. This is the first time that this breadth of customer and account data will be reported throughout the industry – nearly 1,800 broker dealers, more than 100 million accounts, and an estimated 50 million individual and institutional customers. Why? To help the regulators monitor, understand, and mitigate market-manipulating events across customers, accounts, instruments, and venues.
Topics: Regulation and Data
Artificial intelligence (AI) is one of the most important software capabilities that the applications of the future will use. AI delivers capabilities that save time and can fundamentally improve the user experience from recommending next steps to simply automating tasks that are fairly repetitive. AI isn't science fiction, though. At Kingland, we believe you need real intelligence to do artificial intelligence well, particularly in the area of unstructured data.
Like many regulations, the Consolidated Audit Trail (CAT) requirements have been “baking” since first published by the SEC nearly six years ago. As others such as WatersTechnology journalist Dan DeFrancesco are covering, there is a lot of work ahead for the industry as 2017 marks the year that CAT becomes real. I’ve personally been working with regulators and industry members on CAT since 2013 and I think we can help firms understand some of the basics about CAT as well as some challenges that are likely to emerge.
What's your data worth? A few years ago I would hear this question come up fairly regularly in the financial services industry. Over the last number of years though, the topic of "valuing data" has been pretty quiet as many executives have been more focused on the pressures that new regulations and evolving market risks have placed on their data programs.
Earlier this year the Harvard Business Review published an article titled Do Know You Know What Your Company's Data is Worth? The authors introduce the concept enterprise value of data (EvD) and raise a variety of points, predominantly about how you can value data as an intangible asset or think about the impact data has on the company. They even take it to an extreme of assessing or valuing the impact on the business if the business didn't have access to its data.
Topics: entity data