“That team itself is on the business side of the enterprise, which has become the normal approach,” said Burns. “It is interesting also that it is largely composed of younger executives who are given quite a good deal of freedom to go off and pursue different avenues of potential opportunity.”
He points out also that there is a move to push those Big Data problems of data governance, how much and what data you can trust, back down to the data owners and bringing everybody in on the practical issues of data lineage, definitions and terms, the stewardship of the data up to the point of analysis and so on. Getting all of the data in from the many distributed data stores across a long established national bank with branches and divisions brings in all of the issues about data quality and technology.
“There is a shared feeling across people working in this area that we are in on the real beginning of an era of business analytics. There are challenges in the technology and the data management but there are also new era challenges in data protection. I find that whole growing field of concern with ethics in data management very interesting. There have been quite a few potential business cases or at least bright ideas where the answer is ‘No, we should not be going there’. It’s still very early days but for example in the DCU master’s course in data science those questions of law and ethics and their likely lines of development is one of the very first and guiding topics.”
IBM is a DCU partner in its master’s programme, which is typical of the many streams of involvement between the ICT sector and our third level and research institutions. A particularly noteworthy example is CeADAR, all of whose projects are industry nominated and supported. It now has over 40 ‘demonstrators’ or practical and usable data analytics tools in its portfolio after just 18 months in fully-fledged operation and funding. “The research proposals all come to us from our industry consortium partners,” says Centre director Dr Edward McDonnell. “The decisions on which to pursue are made by a joint committee according to our three research themes — Intelligent Analytic Interfaces, Data Management for Analytics and Advanced Analytics. The most important metric for us is the transfer of our research outputs back to industry.”
Quickfire projects
This consistent industry-led approach is unique for such an institution in its quickfire business approach. Projects last for just six months with tightly defined objectives and aim to produce practical, usable results, with up to 20 projects on the go simultaneously in the four participant universities. The range of smart technologies involved ranges from data visualisation to artificial intelligence and machine learning through text-to-speech/speech-to-text and machine translation.
The CeADAR projects are usable software tools mostly aimed at large volumes of unstructured data — Big Data. The results are on a par with or indeed indistinguishable from commercial products. IdentityMatch, for example, identifies persons of interest in social networks. That could be key influencers but is also applicable to searches for people with specific expertise or experience, in certain locations or business sectors.
Analytics and marketing is a well-established marriage and NudgeAlong is right in there. This is a recommendation engine that uses all of data about the specific customer — purchase history, outcomes of previous communications and preferred channels — all the way through personalised purchase incentives and their design to a final stage of personalised communications, generating actual message content.
TopicListener is another smart application that uses automatic speech recognition to enable tracking of topics across contact centre call traffic and online video transcript data. Developed for contact centres, it can be extended across other large volume speech audio domains. This is very clearly a technology with huge promise for the future.
Yet another and very practical tool is SmartSeg, which quite simply takes any customer database or set of and looks for segments with no prior assumptions. It finds natural groupings, directed only by the target number of segments the user sets. The tool then reports in plain English why it formed the segments. Some will be clear to any marketing team, like age and average spend and so on. But it has frequently come up with segments that were never thought of before, which can in turn be focussed on and segmented further. Then human experts can decide whether such an insight would have value if pursued further. This is data mining re-invented as a business tool for non-expert users.
Ireland is rapidly developing an analytics ecosystem, Dr Edward McDonnell, centre director, believes, across industry, research and third level courses, consultants and special projects and with a growing level of cross-fertilisation between the many strands. “Our work in CeADAR is towards the democratisation of data analytics, putting smart tools in the hands of non-technical experts to apply to their interests and responsibilities. But there is now a spectrum from that across to advanced analytics research, all cooperating to a high degree to advance quite literally a national competence and indeed excellence in data analytics. Big Data holds no fears, no issues for us.”
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