Sometimes you go to a conference that just crackles with the excited atmosphere that surrounds the moment that has come. When Houlihan Lokey were putting together their conference on data and analytics, which took place last week, I can well imagine that there was a conversation that went “we need to attract 150 at least so let’s invite 350“. We have all done it. And then comes a day when almost 300 of the invitees turn up, it’s standing room only in front of the coffee urn, and the room pulsates with conversation, networking, and commentary. So it was at the Mandarin Oriental in London last Wednesday, and there were other virtues as well. Working in panels of corporate leaders and entrepreneurs, a short conference with short sessions had real insight to offer. There is a lesson there for all of us still indeed to put on 3 day events – short and intensive and double track does leave a worry that one might have missed something as well as an appetite for more. 

After a Keynote by Phil Snow, the CEO of FactSet, the conference resolved into four panels covering insurance, research and IP, risk and compliance, and lastly a group of founders talking about their companies. And while companies like FactSet now take a fully integrated view of the marriage of the content and technology with data and analytics, it is also clear that companies in the sectors covered straggle across the entire spectrum from a few APIs and data feeds, right through to advanced algorithmic experimentation and  prototyped machine learning applications. And everywhere we spoke about what AI might mean to the business. But no where did we define what exactly that might mean, or demonstrate very tangibly  real examples of it in action. And this for me strengthens a prejudice. It is one thing to look back on the algorithms that we have been using for five years and refer to them in publicity as a “AI -driven service”, but quite another thing to produce creative and decision-making systems  capable of acting autonomously and creatively. 

Yet the buzz of conversation in tearoom was all about people wanting to take advantage of the technology breakthroughs and data availability, and wanting to invest in opportunistic new enterprises. This is much better than the other way round, of course: many of us remember the  period after the “dot com bust” when the money dried up and investors only wanted to look at historic cash flows. But as the data and analytics revolution presses forward further, there have to be satisfying opportunities to create real returns in a measurable timespan. I do not think this will be a problem but I do think that we have to expect disappointments after the exaggerated wave of expectations around AI and machine learning. And from conferences like this it is becoming clearer and clearer that workflow will remain a key focus. Creating longer and longer strands of work process robotics and using intelligent technology to provide decision-making support and  then improved decision-making itself seems likely. While RPA (robotic process automation) is making real inroads into clerical process, it is not yet either having an impact on nontrivial decision-making, or upon the business of bringing wider ranges of knowledge to address decision s normally made by that most fallible of qualities, human judgement. 

Looking back, there was another element that did not surface at Wednesday‘s fascinating event. Feedback is what improves machines and makes the development track accelerate. But as we build more and more feedback loops into these knowledge systems we learn more and more about the behaviour of customers, and the gaps between how people actually behave and what they say (or we think) they want, grow larger. The “exhaust data” resulting from usage  does not get much of a mention on these occasions. But if, for example, we looked at the field of scholarly communications and the research and IP markets, I could at least make the argument that content consumption at some point in the future will be the prerogative of machines only. The idea of researchers reading research articles or journals will become bizarre. There will simply be too much content in any one  discipline. The most important thing will be for machines to read, digest, understand and map the knowledge base, allowing researchers to position their own work in terms of the workflow of the domain. And one other piece of  information will then become vitally important. The researcher will need feedback  to know who has downloaded  his own findings, how they were rated, and whether other scholars’  knowledge maps matched his own. Great contextual data drawn from a wider and wider range of sources is fuelling the revolution in data and analytics. Great analysis of feedback data coming off these new solutions will drive the direction of travel.

None of this lies at the door of Houlihan Lokey. By providing a place for a heterodox group of investors and entrepreneurs to mingle and talk they do us all a favour, and in the process demonstrate just how hot the data and analytics field is at the present moment.