There is a moment in the life of every start-up when the entrepreneur realises that what he is selling is not what people are buying. Over 35 years ago this thought stopped me in my tracks outside of a solicitor’s office in a small Hampshire town in the New Forest. I was running the innovative legal retrieval start-up called Eurolex for the Thomson Corporation, and my visit to this small legal practice was part of a programme to capture as much baffling insight as I could from early customers of the service. I was selling them enhanced computerised legal information retrieval, more effective than human enquiry, covering a wide range of sources , far bigger than their own library resources and demonstrating modern technology in the law practice. How could I fail? And how was that  people were not buying my better mousetrap in the quantities that my five year plan required?

So I told the senior partner of this  law practice that his partnership really needed  my services. I sold hard on innovation and painted a picture of them being able to outstrip big city rivals with smart research covering far more sources than you would normally expect in a small country practice. He was unimpressed. He said that his current manual research activity was “good enough“. He said he was attracted by my trial offer for totally different reasons. He had no more room for books and people in his offices and would have to expensively relocate to expand the practice. He said my trial terms made it cheaper to use me than add more people. He said my billing system was compatible with his and he could simply download time and cost into his invoicing from my service. He was, he made it clear, almost totally uninterested in improved legal information retrieval,  but he would definitely buy what I was selling. I sat in the car park for almost an hour afterwards trying to rethink what we were doing as a business and retrofitting what we had invented into the way lawyers actually  worked.

I have had the great privilege to be able to meet and talk with many companies who innovate. But I have noticed with interest when they start to realise what their customers are buying as distinct from what they themselves are selling. Last month I had the pleasure of talking to my friends at Morressier (  , the Berlin-based service that gathers up all the information around posters and conferences as indicators of research group activity in scholarly communications. I realise that when I first met them I saw them as a collector of data that has been neglected and not curated. Now I see them as a way of judging the progress of a research project, through its interim activities and ability to describe its early results and objectives . Given the time pressures of scholarly research in a number of scientific disciplines, getting early indicators of potential and being able to gauge how close to completion key projects have reached becomes a high value component of predictive analysis of research outcomes. In my lifetime we have moved from taking over two years to publish a research report at the conclusion of a project to anticipating its likely outcomes at various stages of research  development. Now that Morressier have the data they can begin to apply the analysis. Combine that analysis with all the data about actual findings and you have a treasure trove of analytic feedback for funders, governments, research institutions, universities and research programmes. This company now becomes a potential powerhouse of trend research , alerting services, competitive analysis and consultancy , especially in fields that impact pharma , food science , agriculture , climatology, and any sector where governments and markets seek the earliest indicators of where to place the next bet. 

And  I have very similar feelings about another important growth point , .This Amsterdam-based start up began, to my mind at least, as a technology-centric, as distinct from a data-centric, project built fundamentally on blockchain technology. Today, as it embarks on its next funding round, the impact of work which it is doing with major players like Springer Nature, is beginning to show.   Real  contact with intermediaries and end users has shown them how the market in information is being framed by concern about impact and dissemination – who downloaded the document , who read it , who passed it to whom? – and what its provenance was – can you trace it to a legitimate source , is it fake news etc? . In the face of this , the company has become a Track and Trace player, using technologies like data ledger and document tracking to meet these needs. This marks a real shift for those who can recall times when the word “ metadata “ was always followed by a discussion on discovery . Now it is more often followed by a discussion on impact and dissemination . have found a rich new seam of need to exploit , which should make their funding round straightforward  

These two young companies are united in their discovery of addressable need . And by something else . They both respond to markets where the need for speed and certainty in information undercuts still prevalent thinking derived from the world of printed journals about acceptable timing and measurement of effect . While scholarly communications was quick to “ go digital” it has been slow to “ think digital” . These two companies , whose work could flourish as well in any other vertical sector of information markets, are indicators of more profound change in scholarly communications as well .

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.

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