Dexibit’s CEO Angie Judge presented at Museums and the Web 2016 MWXX on The Data Divide, exploring power and influence in the museum. Watch the webinar or read Angie’s extended notes below.
Good morning everyone and thank you for having me here. My name is Angie Judge, from Dexibit, where we provide analytics for museums. We’re working at the very edge of how data science is shaping our cultural future and today I wanted to share with some of the thoughts that have been shaping our own strategy when we think about power and influence in a data driven future.
Like any research and development team, our thoughts are shaped by the world going on around us. For one of our development team, who builds predictive analytics for the stock market in his spare time, this is how he sees that world. We’ve always encouraged our tech team to moonlight, because you never know when one of these pet projects will come in handy one day. In this case, we’ve ended up using his stock sentiment algorithms as part of our analytics engine for museums. He’s a bit coy about his successes in share trading, though others playing this game, like Dr Senoguchi, an ex Bank of Japan official with a PhD in artificial intelligence, are getting success rates close to 70 percent. This is not new: share traders are increasingly reliant on algorithms to do their work for them. They’re not just using analytics to inform their decisions, but leaving them to make the calls, too. Most of them operate on easily digestible quantitative data, but they’re creeping up into harder territory.
And the reason why this is relevant is because the financial markets give us a fairly mature example of what could happen in our cultural future. With algorithms like these, and other developments in high frequency trading, power by analytics has become the norm rather than the exception. It has created a world of haves, and a much bigger world of have nots. And the financial markets articulate this digital divide so clearly, because this is literally a translation into the widening gap of rich versus poor at a time when economic inequality is at its highest in OECD countries since records began. Let alone the disparity between those who can afford to participate in the market versus those who are still struggling with putting food on the table, we see this gap heavily accentuated by those who can afford to amass significant computing power behind their efforts. In fact, it’s not until big news events – like what you see happening here, still relying on human analysis to interpret – that we can see the true extent of machine based trading, when these systems leave the market for a bit to see how people react, and we can see what we’re left with, the human component. This skinny part in the middle – is traditional trading. This is perhaps a pretty pronounced example, something called the herd effect, or a sign that more than 80% of the market power is controlled by a small few. Nanex labeled this event as disturbing liquidity, because the risk of that stampede effect can create market instability. For me, when I look at this picture, I see the data divide. And the question I want to ask today is: “How will this picture develop amongst museums over the next decade?”.
But before we go there, we need to step back a bit and answer the question of why that future is a trajectory for the cultural sector. And I think the answer for that lies in decision making. Because even the smartest team, working at full pace, with maximum resources available; is still limited by the fact there’s only 24 hours in a day, and only 7 days in the week. And as museums push ahead into a digital future, we need to find ways to power every decision of every day to be faster, better and more affordable. So we talk about the three types of decisions that are made in museums which could do with analytical assistance: efficiency, magnitude and volume.
1.Efficiency is often the starting block for incorporating analytics. We’ve run a survey these past few months, which revealed that 86% of museums are waiting for significant periods of time for decision makers to get access to information on what’s happening in the gallery today. And what’s worse, 66% of those museums are spending months manually administering that data. At the heavy end of the scale, around 15% were waiting a year or more with full time staff on the issue. Just on administration. Given our business is analytics, I would be the first person to promote the merits of data. It is an important investment in our future. But manual activities like this kind of administration – clicker counting, grappling with spreadsheets – this is not a value generating activity in itself. It is not adding experience for the visitor, or contributing to a preservation outcome. It is a means to an end and now, more than ever, when museum’s face tough fiscal challenges, we could all do with automation in this area, even if only for cost reduction.
2.The next decision type we make is decisions of magnitude. Some of the calls we have to make in the museum have significant impacts, and not just financially. We’re talking about our ability to succeed in our strategic mandates, or our ability to serve the needs of our constituents. Often one of the first voices of dissents we hear when analytics is tabled at a museum is that a computer should never replace important decisions, which deserve curatorial opinion, and I’m not advocating that it should. Where data and algorithms can help us here is in seeing the wood from the trees. Making an informed call. Understanding influences and trends and finding new opportunities for innovation and improvement. It’s a blend – getting analytics to do the leg work for us, but leaving the final call to the human brain. On the flipside though, it’s also a matter of bringing a bit of a microscope to the conundrum of the hippo – when debate is unfairly dominated by the highest paid person’s opinion. A decision without a basis in fact is equally as flawed as a decision without context. Both are built on assumptions and guesswork.
3.And finally, we have decisions of volume. In the museum environment, this could be something such as making individual recommendations to a visitor. It’s probably not possible for us to assign an individual tour guide to follow each visitor around and check the museum’s records and the visitor’s file at each turn to evaluate what the visitor might enjoy based on what they’ve liked so far and how that compares with other visitors who have gone before them. But analytics can do that, in an uninvasive manner for thousands of visitors in each second. These are tiny decisions, but they’re based on big data sets, and given there is over a billion visits taken in the world’s 55,000 museums each year, there are literally billions of these decisions to be made.
And if we pull in our example from the financial market, we can see the broker using all three of these decision types, supported by analytics. In the first, our broker uses dashboards to pull in lots of data sources and free up more time with clients. In the second, our broker uses insight to make a more informed significant investment decision. And in the third, our broker uses high frequency trading algorithms to play voluminous decisions out in the market without having to employ an army to do so. This is the broker on the top side of the data divide, and this is the museum we should aim to be.
This is our world at Dexibit. Our technology is helping museums to make more efficient, insightful and personal decisions in a digital age. We are here because we believe data is one of the most exceptional horizons of the heritage world and that by looking at our collective past, we will find help our cultural future. I’ll include a few findings from our work later on, but here you can see how we’re doing this: Dexibit’s solution consists of a big data and analytics engine with an integration hub that senses onsite presence in the gallery, blends that with the digital touchpoints that complete visitor experience and then enriches this data with a cultural context. And most importantly of course, distilling all this information into real time, visual and personalised insight via a customisable dashboard to connect the entire museum staff.
Late last year, I wrote an article for the Centre for the Future of Museums together with the wonderful Dacia Massengill, and digital analytics expert Elena Villaespesa. We proposed that 2016 was the year of musedata. You might argue April is to early to call it, but my vote says that indeed it appears to have been just that. The 500 people who joined our arts analytics group on Facebook seem to think so, as do the daily Twitterrati with #musedata. When we surveyed museums on how likely they are to take steps towards data driven, the average say they are 76% likely to do something in this field this year. We’ve got a great panel coming up at AAM in Washington on what 2016 looks like and of course, here at Museums and the Web, musedata has been an incredibly strong theme – from Google Analytics Tune Up to the Big Data Session on this afternoon. But I think at Museums and the Web, we have a responsibility to look beyond 2016 as the year of musedata, and focus our sights on 2020. Or 2030. This year, sure we’ll be working on dashboards, and mashups, and great visualizations. But what’s next?
I think the first thing I want to achieve, even in this timeframe of 2016, is more a feeling than anything else. When digital innovation really sets in, it becomes part of the fabric of our every day lives, an extension of ourselves. Most of us feel that way now with things that were new and novel not too long ago – take video calling for example, or social media. I want analytics in the museum to be instinctual. Like when a museum’s board sit down and everyone pulls out their iPads to get an instant sense of what’s happened in the last 30 days, using this as the basis for their discussion. Or when a curatorial lead is walking the halls planning out a new exhibition and naturally reaches for their phone in their pocket to get a feel of how the space has performed for foot traffic in previous exhibitions. Or that moment when the staff enter the atrium in the morning and spot their dashboard up in lights and smile to themselves when they see visitation arrows trending upwards. And this point is more relevant for us in the cultural sector, than it is for stock traders or retail stores because we have such a high commitment to transparency. We have regulatory and moral obligations to report to governments, local authorities, donors, volunteers and our communities, and that can be both a blessing and a burden. And because we’re dealing often in public funds and endowments, we’ve also got a responsibility to prove our assumptions out before we act. We can’t just guess on what’s going to work, we have to know.
So it’s also the feeling of avoiding this. In Orbit here is a performance arts piece taken in New York, but we all know this feeling of being the hamster on a wheel. Prepping for the weekly management meeting, end of the month and reports are due, turn of the financial year and forget working on special projects, we’re filing our annual report. Buried in spreadsheets. Furiously emailing back and forth, then someone mucks up the version number. To achieve this future, our challenge is one of organizational change. It’s about using data as a communication tool. It’s about how we lead our teams. When we talk about the data divide, I believe that more than anything, this culture – the fabric of knowing – will separate the haves and the have nots. It’s not just a matter of efficiency, that the museums spending less time on data administration will be able to free more resources, but it’s a matter of mental bandwidth too. One museum has an FTE clicker counting, the other spends that time having conversations with visitors. One culture fosters innovation. The other stifles it. And so it goes on, to further accentuate the divide. So we when talk about power and influence in efficiency, it is the time and energy it enables us to invest in other, value generating directions. On one side of the divide, the analytics enabled museum will be able to leap of off the hamster wheel and break away from the pack. On the other side of the divide, the manual museum will still be ploughing away at their spreadsheets rather than thinking about the future. And I believe that will be a significant disadvantage.
All that talk about the future can get a bit academic, so I want to break it down to what this means today, to give you a real life example. And so here, we’re working with a large metropolitan art gallery, with annual visitation nearing half a million visitors and a collection of around 10,000 objects. There’s a lot going on at this gallery – free entry, ticketed events, souvenirs and hospitality, private events, membership programmes. Strategically, the gallery is looking at increasing retention for repeat visitation, deepening diversity, encouraging more engagement, refreshing digital channels – that’s a lot to deal with. There were a lot of spreadsheets. When we work with a client like this, we typically start our analysis with a question, which helps focus our investigation and avoids the ‘boil the ocean’ situation. And so, typical of any dashboarding project, our first question is – how does all of that complexity look at a glance? How can we see the whole lot, so that any staff member can pull out their mobile, or their tablet, login on their desktop or glance at a screen in the atrium and see how visitation, collection and venue compare, and explore how those comparisons trend over time. And this is what we created – now the signature first panels we present to any user logging on to Dexibit. We distilled all those key performance indicators into three key modules, and the top 30 points any museum senior leadership team wants to have on hand, and in the interests of user experience design, we tested this with a dozen different cultural institutions of all shapes and sizes. They wanted to see physical and digital visitation side by side – what we call an omnichannel approach that pulls in every digital channel – then environmental influence, the context of the state of the collection at a given time and a comprehensive overview of commercial performance. On one screen, no scrolling. And importantly, the plumbing behind this dashboard automatically connects it in near real time to all of those channels as those events are occurring. The museum can ’do more with less’ as we like to say, because all that time and effort spent worrying about administering data can be better directed someplace else. And it can act fast, as it’s not waiting until the end of the day, or the week, or the month, or God forbid, the year, to see what happened in the gallery today. We can reach into our pocket and see it, now.
And then as you can see here, from this basis we can delve into not just showing these data sets side by side, but blending them together to view the interrelationships. Here, we’re looking at the significant impact of weather on physical and digital visitation. Importantly, we’ve designed these dashboards so that everyone can understand them. More than that, each member of the gallery can personalize their own. Because they’re not just for the technical team, and they shouldn’t contain marketing jargon. Because to harness the power of data as a communication tool and avoid the data divide, data needs to be part of the language for everyone in the museum, not just select staff. Sure, there’s a level of digital confidence required to login to a website, but beyond that, we wanted to remove the fear and replace that with the fabric of knowing. Analytics can be a scary word, and really, it should be an accessible one.
One of the most important things that we’re doing for that analytics voice in this sector is to enrich it. There are a lot of toolsets out in the general market, but there a number of pillars unique to the museum which need to be considered in the architectural approach and they bring a different language to the data. There’s also a number of lenses which need to be applied to the raw data at an algorithm level that are sector specific too. In either case, I’m not talking about an instrumentation level – we still need Google Analytics to record what’s happening on our website for example. I’m talking about what we do with that data next. For example, if we were thinking about a retail store, one of their strategic imperatives might be to driven traffic from instore to online, because it’s cheaper to service a customer via ecommerce. That assumption is not true for a museum. The relationship between onsite presence and digital channels is a more delicate and complementing balance. And if we explore this theme even further, the subtle differences become significant:
1.When we consider the setting of a visitor’s position instore or online, we’re dealing with a collection, not an inventory – the commercial transaction for a visitor and an artifact is many times removed. We’re not managing stock and necessarily trying to optimize the ones with the best profit margins or too much inventory in the back room.
2.We don’t want to hurry people through the conveyor belt, we want them to linger and engage on a deeper level – seeing a person spending a lot of time in one place isn’t an alarm bell that the instore service is lacking or that we’ve presented them with too many options. It could be a sign they’re in fact completely ingrained in enjoying an exhibition and we should leave them to it. Getting an idea of how they’re engaging with their digital channels on the ground will help us bettter understand that picture.
3.The same is true for dissent – negative sentiment isn’t necessarily a bad thing if we’re trying to foster a debate and critical thinking in our communities. To a retail store, that would spell bad news. Granted, in both situations, what they said is as important as the fact that they said it.
Harnessing the DNA of what makes a museum, it’s musedata, is an important challenge for us as a sector. We have to use this opportunity to forge our own future, rather than trying to borrow from a wardrobe that doesn’t really fit. In my mind this is a critical success factor to the data divide.
And here’s another one: insight. Those lightbulb moments. Once we get to that feeling of having data as part of the daily fabric of our lives, we also have to get to that point where that data is helping us learn new things and forge new advancements. It’s like that moment when you’re on vacation in a foreign city, searching for a great place to eat. You pull out your phone, do a bit of Googling, pull up Trip Advisor to validate your choice, tweet them your booking, switch to maps to get directions, and then post your plate selfie onto Facebook. That whole journey wasn’t just about the digital experience being right there in your pocket, but being insanely useful. It got you to where you were going. To the point where it makes you stop and scratch your head and wonder how we used to live like cavemen 10 years ago. At Dexibit, we obsessively search for these sorts of ‘uh-huh’ moments. For us, they are usually when we can show a team something about their museum that they didn’t know before, or a completely different way of looking at an old problem, or capture data previously undetectable.
And one of the reasons why these lightbulbs are so important is because most of what we measure is visitor engagement behavior, which is notoriously complex to measure. A museum is fundamentally different to a retail store, because we can’t just point at the cash register, and say, well great, we made $40,312.49 today, and we’ll call that a good day. We have to measure eyeballs. And the museum visitor doesn’t necessarily have a single shopping basket with a bunch of barcoded groceries, or shoes, or skin care. At the moment, there’s no one place where they put their ticket, their hospitality experience, their souvenirs, their content enquiries, their interests, their sentiment… The data relationship we have with each visitor and the commercial touchpoints of that are hard. And we might have over a million visitors to deal with in a single site. And of course these are still very human decisions, especially in museums. David Brooks wrote an excellent article in the New York Times about what data can’t do, and these things are so very relevant to our field, like:
1.It’s not good at social. We can measure how long a visitor spent in a space, how often they came back, how many points of connect they had, even their sentiment, but still it wouldn’t beat a conversation with that individual.
2.Data struggles with big problems. We still need leadership to break down a complex situation into the right approach and ask a starter question, or we’d get lost. And we still need a contextual decision to validate whether to act on the analytics.
3.And, as David puts it so beautifully, it favors memes over masterpieces. That latest exhibition might be hugely popular with visitors, but will it stand the test of time? Is it educationally relevant? Does the data allow for our diversity objectives, or is it imparting a natural bias? To what degree should the museum deal in digital pop culture?
But despite its shortcomings, I’d rather make a decision with data than without it, any day of the week. And the difference here when we talk about the data divide is the museum can make a 10% improvement through sweat and hard work, versus the museum with these kind of lightbulbs that can make a 10x improvement through advanced insight. So coming back down to an example of what that means in the gallery, earlier this year we worked with a smaller, regional and remote museum. This one would be pushing to get 100,000 visitors, but their entire community’s economy is dependent on the museum pulling in those numbers, so the pressure on this team is immense. And to top it all off, they don’t receive any public funding, so they have to balance those expectations from their local stakeholders with the need to achieve a return on investment at an individual visit level. And of course, like many museums, they’re dealing with competing tourist attractions and a hangover from the global financial crisis. And so once again, we ask a question. It can be an awkward balance between ticket price and visitor satisfaction, and in an independent museum, that’s an especially sensitive balance with an entire community economy watching over your shoulder. nd so we looked at all sorts of datasets, to determine how visitor satisfaction was influenced by engagement and how engagement was determined by behavior.
One of the really exciting things we’ve been able to do is to start combining these datasets to reveal completely new clusters in how we understand visitors. Previously, the museum has been a bit of a black box. We might spare some budget towards market surveying and try to get a read of our demographic profile, but that only tells us who is visiting, and not how they’re experiencing the museum. Using clustering algorithms, we’ve been able to find a way to separate visitors into completely new behavioral groupings, based on a unique combination of where people went, want they did there and how that journey unfolded. Those clusters might look completely different on a week day, say when we’ve got school groups coming through, than a weekend, when our audience is more family based. Or be heavily influenced in this case by large volumes of tour buses pulling into town. And what we found was that visitors who felt that weren’t getting value for money at the museum were only seeing half of it. They were getting half way through, thinking they had reached the end, missing the exhibits with the highest levels of engagements and feeling unfulfilled. This experience was a significant centroid. The biggest improvement this museum could make, in all the complexities given its place in the community, was a wayfinding one. That was a lightbulb moment. The museum needed to ask the question, and act on the answer. But Dexibit helped us detect the data, analyze it using culturally specific algorithms and visually turn it into insight.
So if we can do that in 2016, what might 2030 look like? One of the most important things I believe we can achieve is to look more like a network. Our founding team come from a telco background – so for us this is how we naturally see the cultural sector. We’re used to looking at communications networks of fibre and cabinets and towers and understanding capacity and connectivity and over the top plays. So when we look at the cultural sector, we see networks of artifacts, linked content, networked galleries within a building, multi tenant venues and of course a single visitor identity travelling between them. And we’re interested in the dynamics of how a network behaves, like the concept of museum interconnect. Much like how AT&T and Verizon might need to roam a service over each other’s network, a museum might need to host a visitor experience as part of a city fan pass, or pass over a touring exhibition, or exchange an accessioned object. And the data underpinning this needs to support not only that handshake, but a God’s eye view of the network as a whole and support a seamless individual experience. As an ecosystem, we’re more connected and more collaborative – think coopetition rather than competition – and these drivers will help the sector as a whole harness the power of the right side of the data divide. This speaks to our competitive strength as a sector. Sak’s aren’t going to recognize that one of their shoppers loved their latest Calvin Klein blazer purchase and recommend the next time they’re at Macy’s, they could find a great pair of shoes to complete their outfit. Coke and Pepsi might not want to share information on how their products perform side by side at Walmart. McDonalds won’t recognise your Burger King loyalty. But this is exactly the kind of thing that two museums can do. It’s the reason why we’re proposing a crowd sourced creative commons Digital Cultural Framework to describe the process hierarchy, systems architecture and data models to achieve this, and you can find more information on this project our website at dexibit.com.
But the final frontier is truly what happens at an individual level, not an aggregate one. Nancy Proctor once described it as tipping the curatorial pyramid on its head – rather than having a favored few decide a mass market message for the many, we can have a one to one conversation with the visitor, and they with each other. After all, most of what we do in a museum is about supporting those intimate moments when a visitor truly connects with the message to find deep meaning. Our big data challenge recognizing every visitor’s every visit is as unique as their fingerprint. Once we have the efficiency to do more with less, and the insight of those lightbulb moments to make dramatic leaps forward, we need to take pause to consider how we’re sharing those advancements with our visitors. I think this comes back to the essence of the data divide, and our role in cultural within society. As a sector, we have played a very important role in breaking down the barriers of the digital divide – by being part of the solution. We’ve opened access to connectivity and digital experience. For a disadvantaged part of our community, often our doors are one of the few places those individuals can put themselves on an even par with the rest of society. We need to do the same with data. We need to not only open access to data, but help our constituents harness it. It’s not just about given them control over their privacy, though surely that is important to, but sharing with them the feeling of what it’s like to have the analytics work for you, not on you. In the museum, analytics working for the visitor can help them achieve better educational and entertaining outcomes, share more experiences with their friends and families and find their voice.
I have a lot of admiration for the architects of this industry who have already made great in roads towards this. Cooper Hewitt (and I understand Micah Walter is talking on this later today) have broken new ground with the Pen, in being able to not only collect new data on the visit itself and the ability to share that with the visitor, but in creating an entirely new experience in doing so. The Dallas Museum of Art have done an amazing job of beginning to understand how each visit sits in the lifetime of the visitor, and how that picture of engagement begins to influence repeat visitation, then how it can be influenced with loyalty. We have started experimenting in this space by traversing the anonymous to the identifiable and we’d be very interested to talk with museums who see themselves headed in a similar direction. This idea of understanding the dynamics between a visitor’s identity – cross venue – and the picture of each visit, and how that data can be used for the venue and the visitor to deliver meaningful opportunities for engagement are where we’re headed next. This is early days for the sector, and we might look towards examples like Netflix, or the advancements being made with analytics in personalized education and precision healthcare, but really the world as a whole is just beginning to scratch the surface here of what’s possible with predictive analytics. It won’t be long before visitors expect as standard an agnostic venue identity to provide offers, recommendations and loyalty based on their cross channel engagements and onsite behavior. We have to ask questions like whether Blockchain has a role to play in recording these transactions. This is a very different proposition to going out and buying a CRM system, or deciding to upgrade our ticketing. So that when that visitor might not even be thinking about their next visit, the museum is already planting the seed, finding new experiences based on their previous journey, preferences and the similar enjoyment of others like them how they can best spend their time, rewarding them for doing so and presenting additional opportunities to complement it all. In an age where the threat of falling visitation is strong enough to keep us all awake at night, it’s imperative that we get there as fast as possible.
And so it seems, the challenge of the data divide in the coming years is really a leadership one. Naturally, as with any digital adoption worth pursuing, we have find that change in ourselves. With analytics, this involves a shift from a gut feel management style to data driven decision making. For many teams, that might place a higher degree of transparency on managing key performance indicators\and enabling these through the digital strategy. For those that are already pursuing agile management techniques, lean sigma continuous improvement or user experience lead design innovation, this will be a fairly natural fit. Within this change, we need to manage the uptake of digital adoption amongst the entire team. At Dexibit, we place so much importance on this underpinning principle that we’ve begun hosting Digital Academy Days to help uplift digital confidence amongst all the entire museum staff, and to make that kind of training more digestible for smaller museums too. But in saying that, it is also our challenge to take the technical complexity out of analytics, and present users from throughout the museum with meaningful, accessible, visual insight. Either way, we need to recognize that for analytics to be effective, it can’t be trapped in our digital departments. That speaks to the fact that museum management is a team sport. Most key processes – take, formulating a new exhibition for example – cross interdepartmental lines. It involves planning, curatorial, collection, experience, digital, facilities, marketing, front of house. We need to see data in the same way, as a collaborative communication tool. And the same is true for outside the building. To begin harnessing the opportunities of being on the right side of the data divide, we have to start the conversation – such as with MCN’s data and insights special interest group – in the opportunities for benchmarking, interconnect and more. That conversation needs to include international representation from all segments of the cultural sector, including allowances to raise the voice for smaller venue and especially for regional museums. Just as we don’t want the cultural sector to end up on the wrong side of the data divide, we don’t want the divide to happen within the sector itself, either.
If all that sounds like a big challenge, it is because this is one. But we are all so very fortunate to be here, now, able to help shape this future. This is important work. One of my fellow New Zealanders, physicist Sean Gourley, explains that all of this comes down to the fact it takes a human 0.65 second to make an informed decision. Apparently you can measure this by conducting an experiment with the world’s top chess pros to see how quickly their brains can identify whether they are in check or not. At the end of the day, there’s a limit on how fast we humans can think. Sean, calls that ‘the smallest atomic unit of strategic thought’. And there’s a big reward in being able to think faster. And when there’s a risk reward play in a market, you end up with a divide. Sean’s had a pretty incredible career. After working at NASA as a nano scientist, he turned his energy to data science and predictive analytics in war. He ended up with a larger data set on terrorist activity than the US military. So he knows a thing or to about the power of being the guy sitting on all the data. Sean reduces it all down to the point that we have a personal economic choice over whether we are the product of algorithms, or the owner. Will we be the master, or the slave? Because without taking an active step towards owning our future, we’re letting our future own us, rather than the other way around.
We all know in the world of predictive analytics we have to look to our past to help determine that future. So let’s do that now. This is, essentially, the haves and have nots. The digital divide – here it’s represented by looking at IP addresses per head of population. Either I can afford an Internet connection, a nice computer and some decent know how. Or, I can get left behind. Competition strikes. The world moves at a faster pace than I can keep up with. The gap widens. Analytics is going to accentuate this. As the share market shows, to Sean’s point, we can either be its master or its slave. Which side will the cultural sector end up on? What about your museum? Are we going to be up here, owning our data, leveraging intelligence, gaining differentiation… Or end up on the flip side, down here, with the have nots. Power? Or product.
So that is just a bit on how our team at Dexibit see analytics shaping our cultural future. But I don’t want the conversation to end here. If you haven’t already, please feel welcome to join those 500 others who are as passionate about analytics as we are in the Arts Analytics Support Group on Facebook, or jump on Twitter with #musedata to share your views. Or if you’d like a little reading for the plane ride home, grab our whitepaper on data driven insight in the museum from dexibit.com. But more than that, we’d like you to reach out. We are here to have a conversation – after the session, on our stand here at the conference or reach out and we’ll make a time together. We want to hear your feedback, to answer questions – and we’d like to know where you’d like us to head next. There is still a lot of work to be done and we very much want to develop in partnership with the industry. For those that are ready to get started on the analytics journey, or if you’re already somewhat down that path, we have a free trial available and can discuss pilot projects or consult for a specific proposal. Most importantly, we’re looking for beta partners in the next 30 days to jointly explore some of the new algorithm research I mentioned, so if analytics is going to be a significant part of your digital strategy going forward, I would encourage you to make contact soon so we can begin working together.
Once again, thank you for having me here today, and thank you for joining the musedata conversation. Now let’s go forth and conquer that data divide.