 Felly, rhai'n ddweud i'w blaes, ac yn gŵr, mae'r bwysig data drwy'n dylunai. Felly mae'r bwysig data drwy'n dylunai, ond mae'n bwysig data drwy'n dyluniaeth Jagve Llywodraeth ond yn iawn chi'n ddillodol ei ddefnyddio sydd yn rhaid i fyfyrdd amdano nhw gallwn i'r drafod o drwy yma... Mae'n edrych chi'n ddal ac mae wedi'u dod oherwydd i fathrith oed. Mae'n ddweud yw'r cyffredig yw'r llwydd yw'r gweithio, a'r lle'r lle yn ddweud yn gwybod yma yn 10 bar. Felly mae'n gweithio'n gweithio'n diwrnod o'r drŷ, ond rwy'n gwybod, ond rwy'n gweithio'n ddweud. Felly rwy'n gweithio'n ddweud, ond mae'n gweithio'n ddweud, ond mae'n gweithio'n ddweud. Mae'r bolech yn ddweud. Mae'n cyfrifiadau amdano, ac rwy'n gweithio'n ddweud. I think there are some more serious takeaways from today and these are just things that I kind of have been noting down as we've gone through and they're the ones that I kind of remembered so apologies this is a very quick summary but I think firstly we don't want to get hung up on the big word it seems to me several of the speakers have said that and I think still there is well there's the thing I talked about earlier which is the confusion between big and open but I'm less worried about that now actually and I accept some of what Max was saying in that space which I think is quite interesting but I think there is potential confusion between big data and data that happens to be big and I don't think those two things are the same and I think some of what some of the speakers have been talking about really is data that happens to be big rather than big data I think the other thing is we don't want to focus exclusively on the technology or the infrastructure or whatever I mean a couple of speakers have said that's very much a problem we can grapple with I think the whole cultural change thing is much more interesting and we've heard a couple of aspects of that again Max's references to the kind of cultural inertia that has to be overcome in the public sector I thought was really interesting but we've also heard about the kind of cultural inertia around sharing of data and that kind of thing and it seems to me that if universities for example were to start making significant use of big data in the area of learning analytics and so on there would be similar cultural inertia to overcome there would be similar concerns on both sides from the provider side and the kind of user side so I think those are really interesting areas I think we need to develop a culture of being evidence based I mean in the opening keynote Rob referred to poor decisions being based on poor data and I think we need to get over that we need to become more evidence based and we need to understand that evidence we need to ask questions of our data that have efficiency or effectiveness implications we need to be asking the right questions and we need to understand the kinds of questions we need to ask in order to understand the kinds of technologies that we need to be putting in place to help us answer those questions and I'll come back again and this has come up several times we need to grow this pool of people who can undertake data analytics perform data analytics whether we call them data scientists whether we call them knowledge engineers but again one more word of warning about potential confusion here I think we've heard the label data scientist today referring to both people who understand how to do data analytics and to people who understand how to manage data in the long term the kind of more curatorial aspects of data management and so on and I think we need to be careful not to mix not to use that label in a confusing way for what are really two quite different roles it seems to me finally I think and I'm saying this really in terms of what universities need to think about I think we need to look for opportunities to exploit near real time data because I think there will be opportunities to do that we need to think about the ways we open up our data and get access to our data and ensure access to our data and again I'm relieved to hear that open data is a key component of big data I mean there are different things but I think there are you know max made a good point there about the open data being a driver towards big data and analytics and so on so with that I'm going to wrap up those are kind of the takeaway messages from my point of view I do just want to say a few thank yous so can we just thank again all the speakers I think it's been a great day we get into this horrible thing where we have to clap everyone but let's do it anyway can we just say thank you to Sadie who's at the back Sadie stand up a minute because Sadie's done all the hard leg work can we say thanks to the guys doing the streaming the guys at the venue the guys who've looked after people doing all the presentations with the mics and putting the slides up and all that kind of thing and then three and a go to Lisa who's looked after the web stream and so on and to Joe and Mike who've been running up and down with the microphones and finally then to you for being such a great audience thank you very much for coming can you please hand in your evaluation forms on the way out they are useful to us I mean if you haven't had a chance to sit there and fill it in either do that now or at least do it during the drinks reception but please preferably can you hand them in on the way out because they do help us plan future meetings can you keep your badges those of you that are coming to the drinks reception can you keep your badges for the drinks reception we'll collect them at the end of that so drinks reception is upstairs for those of you coming I'll see you upstairs in the Dorchester library for the rest of you thanks for coming goodbye