Toward the end of 2017, Harvard Business Review published its annual list of “Best-Performing CEOs in the World”, based on a number of HBR’s chief executive ranking metrics.
So why Iowa? It’s a question we get often from executives around the world. For such an innovative company of software architects, artificial intelligence engineers, and data SMEs, how and why do we do it from Iowa? The answer is quite strategic, and I'll go over four of the key reasons we choose to grow Kingland from Iowa.
Last week leading banks, broker dealers, and regulators gathered in New York for DTCC’s third annual Fintech Symposium. This is always a must attend event for Kingland as its incredibly well run and covers many of the emerging trends in the industry in an afternoon. DTCC’s perspective is critical as they sit at the cross roads of the sell side, buy side, and global regulatory environment, so if an issue is on the table, there’s a strong reason.
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"?
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.
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.
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.
"We need to buy more data," has been a presumptive, knee-jerk reaction to data management challenges over the past 30 years. Organizations spend millions of dollars gathering as much data as possible to fill their data-source gaps. The hope is to have enough data to fuel the insight needed to improve the customer experience. Yet, the cost and complexity of having the required number of data sources typically outweighs any organization's budget. Many times, the data quality is inaccurate and or becomes stale.
Kingland will be actively engaged at the 2017 Data Management Summit (DMS) this year on April 4 in New York City. This conference brings together the community of data management professionals working within the financial technology industry. With key topics focusing on data quality, data governance, regulations, data lineage, cognitive, and entity data / client onboarding, DMS 2017 is a great event to keep up with the latest best practice approaches to both keep your firm in compliance and drive greater efficiency in your data management efforts.