Article by Paula Newton and Maria Fonseca
In a recent Tedx talk by Martin Hilbert, the topic of the impermanent nature of Big Data was discussed. Interesting food for thought is added to the big data debate in this talk. Martin Hilbert is a lecturer at University of Cafornia, Davis that investigates a multidisciplinary approach to understanding the role of information, communication, and knowledge in the development of complex social systems. In the referred talk, Hilbert explains that the usual idea behind big data is to take lots and lots of information and to be able to synthesise that and use it in such a way that it is able to inform decisions. The goal of organisations is often to take data about us and serve it up so that it provides knowledge about us. However, Hilbert has a slightly different take on this than some. He argues that:
In a recent Tedx talk by Martin Hilbert, the topic of the impermanent nature of Big Data was discussed. Interesting food for thought is added to the big data debate in this talk. Martin Hilbert is a lecturer at University of Cafornia, Davis that investigates a multidisciplinary approach to understanding the role of information, communication, and knowledge in the development of complex social systems. In the referred talk, Hilbert explains that the usual idea behind big data is to take lots and lots of information and to be able to synthesise that and use it in such a way that it is able to inform decisions. The goal of organisations is often to take data about us and serve it up so that it provides knowledge about us. However, Hilbert has a slightly different take on this than some. He argues that:
In a recent Tedx talk by Martin Hilbert, the topic of the impermanent nature of Big Data was discussed. Interesting food for thought is added to the big data debate in this talk. Martin Hilbert is a lecturer at University of Cafornia, Davis that investigates a multidisciplinary approach to understanding the role of information, communication, and knowledge in the development of complex social systems. In the referred talk, Hilbert explains that the usual idea behind big data is to take lots and lots of information and to be able to synthesise that and use it in such a way that it is able to inform decisions. The goal of organisations is often to take data about us and serve it up so that it provides knowledge about us. However, Hilbert has a slightly different take on this than some. He argues that:
In a recent Tedx talk by Martin Hilbert, the topic of the impermanent nature of Big Data was discussed. Interesting food for thought is added to the big data debate in this talk. Martin Hilbert is a lecturer at University of Cafornia, Davis that investigates a multidisciplinary approach to understanding the role of information, communication, and knowledge in the development of complex social systems. In the referred talk, Hilbert explains that the usual idea behind big data is to take lots and lots of information and to be able to synthesise that and use it in such a way that it is able to inform decisions. The goal of organisations is often to take data about us and serve it up so that it provides knowledge about us. However, Hilbert has a slightly different take on this than some. He argues that:
In a recent Tedx talk by Martin Hilbert, the topic of the impermanent nature of Big Data was discussed. Interesting food for thought is added to the big data debate in this talk. Martin Hilbert is a lecturer at University of Cafornia, Davis that investigates a multidisciplinary approach to understanding the role of information, communication, and knowledge in the development of complex social systems. In the referred talk, Hilbert explains that the usual idea behind big data is to take lots and lots of information and to be able to synthesise that and use it in such a way that it is able to inform decisions. The goal of organisations is often to take data about us and serve it up so that it provides knowledge about us. However, Hilbert has a slightly different take on this than some. He argues that:
“Data analytics alone will not dominate the world in the future.”
It is argued that this is not down to technical issues really. Rather, data has to be used in a sensible and logical way. It is easy to show correlations between different metrics that are not really linked in any way at all if there is no logic used. Correlation and causation have to be determined. Big data helps with this by providing more and better data. Big data offers real opportunities for science to avoid problems with data that we may have seen in advance.
Big Data is past data
The limit to the power of data is argued to be that data is from the past, or at best, real time. This means that data can only really provide information from the past. If the future follows what happened in the past then that is great, but of course, that is not always the case. It is explained that if significant changes occur then past data is going to be fairly limited in helping to inform our understanding of the future. The problem is that people are complex and so while organisations can predict what we will do in the future; this might be changed by circumstances that happen to us. This means that data analytics on its own is not enough. For example, the development of some countries cannot predict the development of others. Theory can help to add new parameters, but it can still be difficult.
The digital revolution helps with developing theory as well as data, which is beneficial. By adapting theory it is possible to see what could happen, when combining this with data, but it is very hard to do this based on a past that was different. However, in the digital age it is argued that it is possible to simulate futures that never happened in the past. This can help with building theory. Theory is defined as a family of models.
Big data without theory may be damaging because it can have the problem of locking a person or business into its own past. An example given is that political parties can latch onto the idea that you support a certain issue. This might strengthen your beliefs, and reinforce past patterns. This can make it difficult for you to change your patterns. You can get pigeon holed into a box. It can affect all kinds of aspects of your life. Sometimes this can be a good thing and can help with customer satisfaction in some cases. However, it can also prevent you from changing unhealthy behaviours.
Big data knows nothing about who you would like to be, only who you have been in the past, it is argued. Your free will to change is tied to your past behaviour. People consolidate that every day with their online behaviour, and you become a stereotype, digitally. This has the problem of creating self-fulfilling dependencies.
Real change needs to be driven differently since it is visions that will change situations, not data. Visions model futures that have never happened in the past. They are creative and cannot use data alone – this is insufficient. Hilbert argues that there is a limit to the abilities of data. Data will inform and ground our visions into reality but because it is from the past, our past does not define our future. Rather, our visions do, because they can change paradigms. We need out of the box thinking to be able to create a better future in a big data world. To think how of the box implies finding new ways to use data analytics.
Addressing big data through debate and conversations
Hilbert´s questions on how big data should be driven by vision, are similar to the ones formulated by Donald Farmer, a Business Intelligence Expert, and the Vice President of Product Management of Qlik, a BI software company that has pioneered new ways of transforming data into meaningful information. Farmer recently participated in the #drivenbdata Summit, an event about big data and analytics, that happens every year in London. In his talk, Farmer launched a thought-provoking question to the audience that set the stage for his philosophical take on big data and analytics:
“We are all “natural” analysts. What does that mean?”
Farmer mentioned how we live in a society that expects a high degree of literacy. In one generation, we have reached 85 percent of literacy worldwide. We are now able to instantly gather information and get information. But in terms of data we have not yet achieved data literacy. “We are still in the Middle Ages, when it comes to understanding data”, he said. It is important to improve data literacy in our organizations, so decision-making based on data increases dramatically. Regrettably that is not the case yet. Access to BI is still only at 20 percent, despite all the innovations. What this means is that there is just 20 percent of data-savvy people who have access to the decision-making process. Why? In Farmer’s opinion it is because the tools available are not “natural”. A good example of a natural tool is the iPad because it is intuitive and one can touch it to find their way through it. As he says “touching is trusting.”
According to Farmer, the way to solve the unnaturalness of data analytics is to embrace an understanding of the world in a whole new way. This should be shaped by three realities: “Impermanence, uncertainty and debate”. Impermanence and diversity are important because for users data is extremely diverse. Diversity is absolutely critical since all information artifacts display entropy, which means that everything that has happened is irrelevant. It is “past data” as Hilbert would argue. There is a permanent decay of information and a constant change in patterns. This leads us to the second reality: uncertainty. “Perfect data is equal to unicorn tears”, said Farmer in a powerful metaphor. Everything is continually decaying and changing and becoming impermanent, therefore one needs to develop the ability to respond to change. Analytics systems need to be looked at as dynamic since you cannot build a system and simply stick to it.
The way to deal with uncertainty should be through debate, as conversation helps people understand the world through various perspectives, depending on the observer and the questions posed. “Storytelling should prompt debate” says Farmer. “What is it do you see? What are your insights?” According to Farmer, conversations are the significant characteristics of the new economy. Through conversations, people discover what they know, share it with their colleagues and in that process create new knowledge.
Conversation and debate can be the “natural” analytics of people and the way to deal with big data to create the better visions to improve our future.
Paula Newton is a business writer, editor and management consultant with extensive experience writing and consulting for both start-ups and long established companies. She has ten years management and leadership experience gained at BSkyB in London and Viva Travel Guides in Quito, Ecuador, giving her a depth of insight into innovation in international business. With an MBA from the University of Hull and many years of experience running her own business consultancy, Paula’s background allows her to connect with a diverse range of clients, including cutting edge technology and web-based start-ups but also multinationals in need of assistance. Paula has played a defining role in shaping organizational strategy for a wide range of different organizations, including for-profit, NGOs and charities. Paula has also served on the Board of Directors for the South American Explorers Club in Quito, Ecuador.
