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The Identity of the Contemporary Public Library

 | 
Margarita Pérez Pulido
, 
Maurizio Vivarelli

2. Models of Analysis, Measurement, Evaluation

Between Quantity and Quality: Big Data and the Value of Data Interpretation

Chiara Faggiolani

Texte intégral

Introduction

  • 1   Literature is exponentially growing. For example Nathan A. Rinne, Big Data, Big Libraries, Big Pr (...)

1The digital revolution has dramatically transformed the way in which information is produced, stored, and distributed. Today we have large amounts of data, of various types and of varying degrees of quality, that we find it hard to frame in predefined categories since it is very different from the data that we have used and interacted with so far: the so-called Big Data. Libraries, especially research libraries, are becoming more aware of what their strategic role can be in preserving and organising this explosive amount of information to make it universally available for research and future generations. It is not just a matter of building a new and costly infrastructure, but also of readjusting the library system as a function of new content: since now libriaries deal expecially with books, but in the future they will have to cope with all the available data1.

2Libraries can and are assuming a key role in making this information opinions more useful, visible, and accessible, such as creating taxonomies, metadata designing schemes, systematising and retrieval methods. The impact Big Data can have on libraries does not imply only a rethinking of the role librarians may play in classifying and presenting Big Data cleansed of their noise, but it also requires a reflection on the use of Big Data in the evaluation or research in the library to improve the effectiveness of their service or, more generally, the impact of libraries on society, exploiting the data deluge to make better decisions and optimise their performance. This paper focuses on this aspect by essentially addressing to two issues mentioned in the title: the former is methodological and relates to the way in which the concepts of quantity and quality change in the new paradigm of Big Data; the latter is related to the value data interpretation takes on in this changing landscape characterised by a flood of data.

From the evaluation to the research in libraries: the development of mixed methodology

3Before talking about Big Data, defining them and understanding how this concept can have an impact on evaluation activities in the library, it seems appropriate to take a step back and briefly review the history of measurement and evaluation practices in Italy in the last 30 years.

  • 2Cf. Quanto valgono le biblioteche pubbliche? Analisi della struttura e dei servizi delle bibliotec (...)

4A first reflection on measurement and evalutation began in Italy back in the Eighties, and yelded the first theoretical contributions during the Nineties. In this first phase the object of study was the investigation mainly revolving around the analysis of “data structure” and “activity data”. The former referred to the data relating to seats, space organisation, presence of equipment, reading places available, features, documents, or to all those aspects that were not the result of an activity but that made up the library “supply” that would the starting point to organise the service. The latter referred to the distribution of resources, purchases, attendance, consultations, members, loans, cultural activities, events, etc. .; it ultimately consisted in the effects of the services put in place by libraries that corresponded to the following questions: “How much is the library used?” and “What does the library do to increase this use?”2.

5From the methodological point of view, it was obvious that it was a matter of investigations that found a perfect tool in the library statistics and the ability to synthesise indicators.

  • 3Linee guida per la valutazione delle biblioteche universitarie, italian edition of Measuring Quali (...)
  • 4Giovanni Di Domenico, Progettare la user satisfaction: come la biblioteca efficace gestisce gli a (...)

6From the point of view of the investigation, if the first phase was characterised by the attention to data on the library structure, the second phase focused on the use: since 1994 it began to pay more attention to the problems linked to methods, because what had changed in the meantime, thanks to the knowledge gained on that subject, was also the size and scope of cognitive practices, matters of measurement and evaluation. The analysis started considering not only those data structure and activities, with the indicators developed in those years perfectly knew how to respond to, but also the use of services by users3. There is a growing awareness that there are volatile and intangible aspects of the service that should be taken into consideration, audited and measured4. It is at this stage that you can trace the birth of a first methodological reflection, recalling the debate “quantity versus quality” which has always characterised the methodology of social research, because in the evaluation of opinions with respect to service use, user’s needs are indeed suitable instruments in terms of information but also methodologically and scientifically sound. Today we are in the third stage of the library assessment, a phase characterised by the availability of different methods and tools of knowledge that follow an expansion of the research objects that are of interest (Fig. 1).

Fig. 1 – The three phases of library assessment

Fig. 1 – The three phases of library assessment
  • 5Alison Jane Pickard, Research Methods in Information, London, Facet, 2007.
  • 6   Data, information and knowledge are not synonymous. ITIL v3, the third version of the Information (...)

7This is why we prefer to use the term “search for the library”5: including not only those investigations that can be developed after the delivery/use of the service for evaluation purposes but also before, to design the best one.This expression refers the set of investigations that can be implemented “in” the library and more generally “for” the library in order to understand and explain the processes and generate real changes. We must firmly keep in practice the data-information-knowledge-decision chain6 (Fig. 2).

Fig. 2 – Chain data-information-knowledge for better decisions

Fig. 2 – Chain data-information-knowledge for better decisions
  • 7   See, by G. Di Domenico, L’impatto delle biblioteche accademiche: un progetto e un seminario, Roma (...)
  • 8Giampaolo Fabris, Societing. Il marketing nella società postmoderna, Milano, EGEA-Università Bocc (...)
  • 9C. Faggiolani - A. Galluzzi, L’identità percepita delle biblioteche: la biblioteconomia sociale e (...)

8If, in past years, we have developed effective tools to study customer satisfaction carefully, thus becoming a common practice in every library, today the focus has shifted on the subject of the social and economic impact of libraries7 and user evaluation. Libraries began to place a premium on the more psychological, emotional and affective levers that motivate the choices of use.Today we are aware that users can make choices by constantly changing and that there is no set of values or permanent demands8. Moreover, we are also aware that the data on real user satisfaction shall not only be meaningful, if upstream users’ needs and motivations have been investigated and identified, but also in this case this figure could be only partlially indicative. It is difficult to understand the perceived identity of the library that undermines the equation “effective library = library attendance = satisfaction”, it is the power of perception making it so unstable9.

  • 10Vincenzo Russo, Processi di costruzione del significato: il sistema cognitivo, in: Psicologia del (...)

9Researchers have started, therefore, to pay attention to this “new” object of investigation – i.e. perception, in order to understand it not as a passive moment where the user automatically receives information, but as an actual selection and actively constructive experience, starting from the stimuli in the environment, in close cooperation with the patterns, expectations, motivations shared by the user himself10.

  • 11A. Galluzzi, Libraries and Public Perception: a Comparative Analysis of the European Press, Oxfor (...)
  • 12C. Faggiolani, Posizionamento e missione della biblioteca. Un’indagine su quattro biblioteche del (...)

10There is a gap between what can be done in the interests of effective service and what is perceived by the user, because the truth is that we perceive things not as they are made but how we are made. We shall go back on this point further on. It is in this gap that we find a number of agents including memories, past experiences, media influence11, and other related factors – not the least – the characteristics of the reference context, that shape the library perceived12. This path shows that the premise for evaluation, or research in the library, have been in part redesigned.

  • 13G. Di Domenico, L’impatto delle biblioteche accademiche, p. 16.
  • 14Abbas Tashakkori - Charles Teddlie, Sage Handbook of Mixed Methods in Social & Behavioral Research(...)

11The point of view of the objects of investigation highlights the need to further analyse not only the issues related to service efficiency and effectiveness, and to user satisfaction, but also the effects and benefits that the library has on users themselves, and on the communities they belong to. On the community as a whole, these effects relate to knowledge, skills, people skills; intellectual and cultural development of individuals; their well-being; their prospects of studying, living and working13.From the methodological standpoint, there has been a shift from a mainly quantitative approach to a mixed approach (Mixed Research), through the integration of qualitative research. Although the history of social research methodology has always been marked by a dichotomy “quantity versus quality”, today it is clear that the two approaches can be used together, as demonstrated by the development of this “third methodological movement”14. The research conducted on a combination of qualitative and quantitative methods has been designated in the literature over time with different terms:

  • quantitative and qualitative methods15;
  • methodological triangulation16;
  • combined research17;
  • mixed methodology18: this is the most widely used and now, we could say, consolidated expression19.

12In our field, but also outside of it, we have come to speak of Mixed Methodoloy thanks to the growing popularity of qualitative research, that kind of research aimed at a greater and better understanding of the point of view of users, the people, with the purpose of involving them first hand in the assessment process.

3. The qualitative research in libraries: what does it offer more?

13Offering a universal definition of qualitative research is not easy because the expression takes on different connotations depending on the context in which the discipline is used, and the contexts are manifold. A keyword search on Google search engine is sufficient to account not only for the substantial amount of resources covering the subject, but also for their variety. Qualitative research is located, that is, at the intersection of a wide range of intellectual traditions and disciplines, and at the same time it is not entirely comprised by any of them. What characterises it is not the reference to a particular area of ​​study, but the reference to some basic indications when doing research: the central role played by subjects’ worldview in interpreting reality; generating materials through strategies sensitive to the contexts of everyday life; the desire for a holistic and profound understanding of the reality under examination. If it is true, then, that the scientific orientation of quality crosses fields and contexts belonging to very different studies, it is also true that they all share a common aspect: the need for a useful methodological approach to knowledge and an in-depth analysis of an object study considered complex and integrated in their sphere of activity. Among the possible definitions, here is the most cited one in the literature:

  • 20The Sage Handbook of Qualitative Research. 4th edition, edited by Norman Denzin, Yvonna Lincoln, (...)

Qualitative research is a situated activity that locates the observer in the world. Qualitative research consists of a set of interpretive, material practices that make the world visible. These practices transform the world. They turn the world into a series of representations, including fieldnotes, interviews, conversations, photographs, recordings, and memos to the self. At this level, qualitative research involves an interpretive, naturalistic approach to the world. This means that qualitative researchers study things in their natural settings, attempting to make sense of or interpret phenomena in terms of the meanings people bring to them20.

  • 21Mario Cardano, La ricerca qualitativa, Bologna, Il Mulino, 2011.
  • 22Chris Anderson, The Long Tail: Why the Future of Business Is Selling Less of More, New York, Hype (...)

14What more does it offer compared to traditional instruments? The main advantage of qualitative research in our opinion is its context sensitivity21, meant as the ability to grasp aspects of the context of decisive and influential reference relative to the analysis of the meanings and interpretation of the data collected.Qualitative surveys are much more open to the context: their great strength lies in the discovery of completely new and even unexpected connections. This is particularly significant in our sector: there are different empirical studies, in fact, showing that a bad perception of libraries often depends on the weaknesses in the context of reference, rather than on specific inefficiencies of the library. Understanding the dynamics that explain the profound social behaviour has become an increasingly challenging task. Infact, it no longer is enough to identify the so-called behaviour of “average” users: now it has become necessary to factor in their increasingly sophisticated characteristics, to get to identify in which “long tail”22 of the society they are located. This is because the increase in the heterogeneity of cultural society has led individuals to generate new demands, more and more specific, heterogeneous and articulated ones.For this reason, the major strengths of qualitative research in the library can be recognised in:

  • historical awareness, which means ability to contextualise certain expressions and examine historical facts relevant to the library phenomenon;
  • contextual awareness that can give an account of the variety of meanings that apparently homogeneous institutions like libraries can take, depending on the context, in the community where they are located23;
  • the ability to make the user the focus of research so as to be both the end that directs the actions in the library and the means of knowledge;
  • the ability to create new applications as well as produce valid responses, expanding critical sense and producing useful information to build a new identity the library needs to face the scenario of transformation and change affecting it.

4. Big Data revolution

15The amount of information produced every day is growing exponentially: smartphones, tablets and smartwatches, devices increasingly available to everyone, objects which are technologically very advanced, able to perceive their environment through sensors and share this information through an Internet connection. The same logic underlies social networks or e-commerce sites.

  • 24Among the Open Data made ​​available, for example, we can find economic data of local authorities, (...)
  • 25   The term ‘Internet of Things’ refers to a family of technologies whose purpose is to extend the c (...)

16There are many statistics updating us about the impressive rate of data produced every day worldwide, driven by digitisation, by the multiplication of devices available to users, collaboration 2.0, from the opening date based format Open Data24, the spread of sensors and devices IoT- Internet of Things25. When we use ICT applications in our everyday activities, we are more or less willingly leaving digital breadcrumbs which allow us to record individual and collective behaviour with unprecedented accuracy: desires, opinions, lifestyles, movements, relationships. We leave a trace on the social networks we participate in, each time we put in queries to search engines, in the tweets we send and receive. As our purchases keep track of our lifestyle, our social relations also leave a trace in the network on our telephone calls and emails, and in the links of the social networks we use. It is this abundance of data generated by users and not mediated by intermediaries which gives organizations the ability to target the specific needs of the various niches of consumers and users and, therefore, even in the face of a greater complexity of analysis, improve their supply of goods and services.

17But let us go step by step and before we get to address this, let us try to outline the evolution of the concept of Big Data in recent years. Every two days we now create as much information as we did from the dawn of civilisation up to 2003. That is how Eric Schmidt, Google CEO until 2011, described this phenomenon a few years ago, which is producing a real change paradigm in statistics and social research.The term began to circulate systematically in 2011 – as indicated by Google Trends26, the Google tool that shows the amount of searches made on specific keywords – to indicate a sector of the information technology market aiming at the efficient management of enormous information databases, or digital archives that contain such a quantity of data that they require new hardware and software solutions. A definition of this phenomenon, however, has not been formalised yet. The following is the most accredited:

  • 27Mark Beyer - Douglas Laney, The Importance of Big Data. A Definition, Stamford, CT, Gartner, 2012.

Big Data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making27.

  • 28   For further information Ian Ayres, Super Crunchers. New York: Random House, 2008; Chris Snijders (...)
  • 29Douglas Laney, 3D Data Management: Controlling Data Volume, Velocity and Variety, «Gartner. Applic (...)

18Today we have large amounts of data, of various types and of varying degrees of quality, that we find hard to frame in predefined categories, because they are very different from those we were used to and have interacted with so far. Large amounts of complex data that provide information on phenomena difficult to observe directly, but useful for the decision activity28. To indicate the problems and peculiarities that characterise Big Data, a three-dimensional model was initially developed, known as the 3V model (Fig. 3), and originally proposed by analyst Doug Laney in 2001, even before the term Big Data spread29.

Fig. 3 – Big Data: 3V model

Fig. 3 – Big Data: 3V model

19Volume, the same expression that Big Data points out, focuses on the quantity, that is, the overwhelming amount of data you have to deal with and in some ways it is the most easy to understand. According to classic quantitative science, it is the passage from megabytes (106 bytes) and gigabytes (109) up to terabytes (1012) to petabytes (1015), exabyte (1018), zettabyte (1021) up to reach the Yottabyte (1024). Usually when we talk about Big Data we do not give the actual size of the database because it is likely that what is meant today may seem surpassed tomorrow. A second characteristic that accompanies these data is their variety: data can be of any nature (structured, semi-structured or unstructured), resulting from heterogeneous sources and sometimes unconventional, such as text documents, images, audio, video, sensors. Insofar as libraries, think of users’ conversations on social networks, their interaction with OPAC, websites, applications, etc. Structured data are those already present in the database standard as the management of libraries. Unstructured data, on the contrary, include social media, blogs, wikis, e-mails, videos, photos, etc. All data that can easily reveal the mood of the user.A third feature is velocity, which refers to the increase in data loaded in real time and that, just as in real time, require testing, if they are to be transformed into valuable information with a data-driven view. One of the classic problems when designing databases is the access time to data.

  • 30Davide Bennato, Il computer come macroscopio. Big Data e approccio computazionale per comprendere (...)
  • 31Mark Van Rijmenam, Why the 3V’s Are not Sufficient to Describe Big Data, in the blog Datafloq. Co (...)

20Information technology has developed a number of strategies to reduce waiting times that database queries carry with them. In the case of Big Data, since the amount of information stored is actually huge, you need to approach the access problem in a completely different – parallel rather than serial – way, and with the technologies that are scalable linearly30. In this initial model based on 3V, over time other V’s were added to further characterise Big Data, thus reaching,according to some analysts, a new model characterised by a total of 7V’s31 (Fig. 4).

Fig. 4 – Big Data: 7V model

Fig. 4 – Big Data: 7V model
  • 32   IBM, What is Big Data? - Bringing Big Data to the Enterprise, 2013, <http://www-01.ibm.com/softwa (...)
  • 33   Oracle, Big Data for the Enterprise, An Oracle White Paper, June 2013, <http://www.oracle.com/us/ (...)
  • 34 John Gantz - David Reinsel, Extracting Value from Chaos, «IDC iView», June 2011, p. 1-12, <http:/ (...)
  • 35   This is a very widespread technique abroad that is catching up also in Italy, which consists in t (...)

21Some features were added, for example veracity32and value33: we cannot be certain that all data actually have an impact on the data-information-knowledge-decision chain, but we are aware that data, as the first link in the chain, represent the oil of the third millennium. The value refers to the ability to provide interesting and useful analysis that can predict future events and process them through advanced computing tools such as machine learning. The 4V model adds value to the 3V’s already examined, and it was used, for example, in 2011 by IDC analysts, according to whom «Big Data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis».34Veracity refers to one of the main problems: data must be reliable since theiruse is at the heart of delicate decisions and therefore must be properly cleaned. Variability refers to the fact that data flow and their meaning can quickly change, just think of the sentiment analysis35. Finally, visualization is not meant as the last step of the research process, but as a prerequisite to data analysis, useful to extract value from data. The display includes not only the realisation of ordinary graphics, such as cakes and histograms, but it requires the creation of complex graphs with many variables, though they must remain comprehensible.

  • 36Enrico Giovannini, Scegliere il futuro. Conoscenza e politica al tempo dei Big Data, Bologna, Il (...)

22Big Data, therefore, are the data being consciously supplied, in the hedonistic pleasure of wanting to tell others – in social networks, for instance – but also (and more often) unknowingly, in any of our online research and browsing, entry to sites and portals, downloads of data and documents. In practice, it has never been so easy and so cheap to produce and therefore to gain quantitative information, opinion polls and statistics. Companies and institutions in our country are still struggling to figure out how to value these important datasets and read the digital environment as a place of new opportunities36. But be careful: those who focus on the digital and think that it is essentially a technological change are wrong. What is changing is something more important and is precisely linked to the culture of applied research. Higher numbers do not necessarily produce greater insights. In this deluge of data, a new mentality regarding as their possible use will have to take shape: the ability to see relationships through different data seemingly difficult to assimilate will become more and more central in the future, so as to be able to ask the right questions each time using the various tools available.

5. Big Data process analysis

  • 37Challenges and Opportunities with Big Data. A community white paper developed by leading research (...)
  • 38   Ivi, p. 4.

23Before considering the merits of the relationship between Big Data and qualitative research, on which we will focus in the next paragraph, it seems useful to briefly summarise the process of managing and analysing Big Data, shown below (Fig. 5), consisting of 6 steps: (1) acquisition, (2) extraction, (3) integration, (4) analysis, (5) interpretation, (6) decision37. In the acquisition stage (1), you should select and possibly compress data, filter them in order to reduce the possible lack of accuracy, generating any metadata associated with the data (for example, how data were measured and acquired) and manage the origins of the data themselves. It emphasises the relationship with Big Data Challenges and opportunities: «Another important issue here is data provenance. Recording information about the data at its birth is not useful unless this information can be interpreted and carried along through the data analysis pipeline. For example, a processing error at one step can render subsequent analysis useless; with suitable provenance, we can easily identify all subsequent processing that dependent on this step. Thus we need research both into generating suitable metadata and into data systems that carry the provenance of data and its metadata through data analysis pipelines»38. Since the data obtained usually will not be already in the format required for analysis, during the extraction step (2) you should transform the data, normalise and clean them in order to improve their veracity, integrate (3) and handle any errors (for example, data partially missing that can be reconstructed). The steps of acquisition, mining and integration can be included in the more general “data preparation”, which is a crucial time in the management of Big Data. In the analysis phase (4), data are explored in order to extract information. This exploration requires the adoption of methods that differ from those traditionally used for the statistical analysis of small samples, and which include data mining, machine learning and visualisation. Having the ability to analyse Big Data is of limited value, unless users can understand the analysis. The subsequent interpretation phase (5) requires knowledge of the data scope of reference. Only knowledge of the context, origin of the data can help identify the pattern of interest. The same in-depth knowledge of the context of reference is also crucial in last phase of the decision (6) aimed at improvement. And it is on these last two aspects that we want to concentrate below.

Fig. 5 – Big Data Management and process analysis

Fig. 5 – Big Data Management and process analysis

6. The value of interpretation: qualitative research and Big Data

  • 39Viktor Mayer-Schönberger - Kenneth Cukier, Big Data. Una rivoluzione che trasformerà il nostro mo (...)
  • 40Chris Anderson, The End of Theory: the Data Deluge Makes the Scientific Method Obsolete, «Wired», (...)
  • 41John Timmer, Why the Cloud Cannot Obscure the Scientific Method, «Ars Technica», June 26, 2008, <(...)

24Looking at the future of research on the library, Big Data are an unavoidable issue, but it seems there should be a kind of reflection on how this approach should be integrated with other tools in use today, especially with qualitative research. The imperative approach with Big Data is in a nutshell to base decisions on data, not on intuition, nor experience, nor on a search led by a specific plan, whether quantitative or qualitative. Due to the huge amount of data, according to Big Data supporters, it is as if there was no need for any research design (Fig. 6): we no longer need a valid hypothesis on a phenomenon to begin to understand our world, we have no need of exploratory investigations. Data may reveal through an appropriate correlation analysis, the key aspects to intervene on. The results that would be obtained – supporters say – will be less affected by the prejudices and common sense, and we would certainly have them in a shorter time39.Big Data, according to what has been said, would seem to act on research questions even before the method. The question of the method is indeed secondary to that of the object. The Big Data approach to social research will analyse the way we live and interact with the world – proponents say – the society will leave at least part of his obsession for causality for simple correlations: we will no longer wonder the reasons of things but, in fact, only what the things are, if shown by the data. Big Data basically change the perspective from which we analyse problems, turning the search for causes in a search for connections. That is why this approach undermines – according to the supporters – the very concept of scientific method, as we know it today. Chris Anderson, Editor in Chief of «Wired» in 2008 said, «the data deluge makes the scientific method obsolete»40, sparking a lively debate41.

25Proponents/supporters argue that Big Data will see – as we are already witnessing – a continuous production of primary sources. We already have new tools of communication between the researcher and the object of investigation that allow us to create more points of contact between quantitative and qualitative research.

Fig. 6 – Comparison between quantitative – qualitative research - Big Data compared to the 4 phases of research

Quantitative research

Research design:

- fixed, close and structured

- distance from the object of study

Data collection:

- hard data

- statistical representativeness

Data analysis:

- use deductive methods

- descriptive statistics

- inferential statistics

Dissemination of the results:

- presentation with numerical summaries

- generalization

Qualitative research

Research design:

- flexible, open

- proximity to the object of study

Data collection:

- soft data

- theoretical sampling

Data analysis:

- use inductive reasoning

- analysis of the contents (texts) with or without CAQDAS

Dissemination of the results:

- presentation with narration

- depth of the obtained and contextualized results

Big Data

There is no research design. The questions arise from the analysis of data (Big Data analytics) and the display takes on a strategic importance.

26In general, as the telescope has enabled us to explore the universe and the microscope to discover bacteria, new techniques to collect and analyse huge amounts of data hold the very promising prospect of helping us see the world in new ways.

27As said, it seems evident that a change of dimension has also produced a change of state: a quantitative change has produced a qualitative change. The feeling is that this phenomenon, rather than posing a threat to social research, especially qualitative research, could represent a huge opportunity instead. Thanks to the flood of data we have at our disposal indeed an extraordinary amount of information, but this requires an even more extraordinary quality in knowing how to interpret them.

28Let us concentrate on the fifth stage of the process of managing and analysing Big Data, described in the previous paragraph: interpretation. Interpretation includes the ability to distinguish the truly relevant phenomena from the noise, accounting for the context and meaning. Is it not, perhaps, the prerogative of qualitative research? If we looked only at the numbers, forgetting the context, we would make a big mistake. Qualitative research is the best tool to map an uncertain territory. When libraries (like other organisations) want to further examine an unknown reality or phenomenon, they need a vision that Big Data alone cannot explain. They need insights and interpretations that qualitative research inevitably entails. When libraries want to build a strong relationship with their users they need to know their stories. Stories contain emotions, something that is very difficult to maintain and glimpse in the dataset. The numbers alone cannot seize them.

29It is difficult to represent only through algorithms the potential of a service, the courtesy and competence of librarians and how these issues impact on the meaning attributed to the attendance of the library over time. For all organisations that aim to have data-driven conversations with their public – including libraries –being able to apply a logic to these qualitative analytical models will be more important than ever in the future. Precisely because we are already in this deluge of data, we need to immerse ourselves in the substance that contextualises them and that allows us to make sense of them.

7. Conclusions: the future of research on libraries

30It seems evident that Big Data offer an inescapable perspective in view of a mixed approach towards which we ought to converge. In the future the mixed approach, which today is built on a new paradigm, that is the Big Data one, will have to include Big Data themselves (Fig. 7).

Fig. 7 – From a mixed approach of Big Data integration of Big Data in research

Fig. 7 – From a mixed approach of Big Data integration of Big Data in research

31For the variety of nature, Big Data lend themselves to being used in various sectors.

  • 42Andrew McAfee - Erik Brynjolfsson, Big Data: the Management Revolution, «Harvard Business Review», (...)
  • 43Giovanni Solimine, La biblioteca. Scenari, culture, pratiche di servizio, Roma-Bari, Laterza, 2004 (...)
  • 44Daniel Kahneman - Amos Tversky, Judgment Under Uncertainty: Heuristics and Biases, «Science», new (...)

32Consider, for instance, the commercial sector, where we have examples of Big Data use for the recommendation of customised purchase suggestions: Amazon uses for this purpose all the data from users, from their previous purchases, products browsed42. In order to approach Big Data in this way, we need first and foremost a cultural change in the library: it is not just about the tools to use when researching, but it expresses an urgency especially on the ability to creatively identify the issues on which we can develop new investigations, in order to advance the understanding of the problems concerning the library. Qualitative research allows us to investigate the effects of attending a library in the life of the single individual and the impact on the community: all of this through the acquisition of the user’s point of view. All that through listening. The culture of listening is part of the “culture of the library” alongwith the culture service, soul and breath of the library, the organisational culture, the culture of results, the quality culture, the culture of communication, seen, in fact, also as a capacity for opening up and listening to users43.After all, librarians have always based their activities on the public, learning as much as possible, seeking common traits and differences between people in order to provide a more tailored service. In order to produce conclusions that transform raw data into new knowledge, in order to use this knowledge to make decisions, the librarian, while doing research, must take effective and rigorous analytical processes in all stages of the investigation process. Librarian researchers cannot merely rely on common sense and cannot make decisions according to the “common man”, which means, for example, using the so-called “heuristics” based on experience mental shortcuts44, they cannot read data thinking that they want to tell us what we believe is right or not, perceiving reality not for what it is but in the way we are made.

  • 45  Cristiana Bambini - Tatiana Wakefield, La biblioteca diventa social, Milano, Editrice Bibliografic (...)
  • 46  <https://tweetreach.com. The tool, which allows you to monitor the reach generated by each tweet, (...)
  • 47Alberto Petrucciani, Dino Campana alla Biblioteca di Ginevra, «Biblioteche oggi», 32, 2014, 8, p. (...)

33Libraries cannot behave like any individual: they cannot be based on perception. We cannot be satisfied with comfortable data, the kind that we have more easily at our disposal, because these data today are no longer enough. Libraries are sitting on a untapped informational geyser: just trivially think of the opportunities offered by social networks. Facebook provides site operators, a critical tool to learn about and better manage their users: Insights. Data on this dashboard can also be exported. Sections – Overview, Page, Post, People – offer a very detailed summary of the most important statistical data ranging from the number of likes, the demographic composition of users, from the detail of visits to the reach (reach) and engagment (report) that posts created45. For the measurement of Twitter there are some interesting paid applications including Tweetreach46, useful to know the impact and scope of our tweets. But not only.Think of the informative scope of the extraordinary disclosure of documents preserved in the archives of libraries that testifies the use people – maybe even important people – make of it, and of how important this documentation is for the history of culture, for example47.

  • 48  C. Faggiolani, La ricerca qualitativa per le biblioteche; C. Faggiolani – G. Solimine, Biblioteche (...)

34These examples once again emphasise that research in the future must not only focus on users and the activities they perform individually (their satisfaction with the service, etc.), but also on the relationships between users themselves and the correlations between what is done in the library with what is done outside. This transformation of the concept of library research is happening against a background where a discipline– social librarianship48– is in turn changing, whose vision is to serve the community, help people live better, increase social welfare, offer every day the tools to know and understand reality.

Notes

1   Literature is exponentially growing. For example Nathan A. Rinne, Big Data, Big Libraries, Big Problems?: the 2014 LibTech Anti-talk? 2014, Library Technology Conference. Macalester College, St. Paul, MN. Mar. 2014. Available at: <http://works.bepress.com/nathan_rinne/4> ; Amy Affelt, The Accidental Data Scientist: Big Data Applications and Opportunities for Librarians and Information Professionals, Medford, New Jersey, Information Today Inc., 2015. In Italy there is a lack of organic studies on the subject.

2 Cf. Quanto valgono le biblioteche pubbliche? Analisi della struttura e dei servizi delle biblioteche di base in Italia, Roma, Associazione italiana biblioteche, 1994. Report of the research “Efficiency and quality of basic services in libraries”, conducted by the National Commission AIB “Public libraries” and the Working Group “Management and Evaluation”. Coordination group and direction of research: Giovanni Solimine; working group: Sergio Conti, Dario D’Alessandro, Raffaele De Magistris, Pasquale Mascia, Vincenzo Santoro.

3 Linee guida per la valutazione delle biblioteche universitarie, italian edition of Measuring Quality, a cura della Commissione nazionale Università Ricerca, Roma, Associazione italiana biblioteche, 1999; Linee guida per la valutazione delle biblioteche pubbliche italiane. Misure, indicatori, valori di riferimento, Roma, Associazione italiana biblioteche, 2000.

4Giovanni Di Domenico, Progettare la user satisfaction: come la biblioteca efficace gestisce gli aspetti immateriali del servizio, «Biblioteche oggi», 14, 1996, 9, p. 52-65.

5Alison Jane Pickard, Research Methods in Information, London, Facet, 2007.

6   Data, information and knowledge are not synonymous. ITIL v3, the third version of the Information Technology Infrastructure Library, a globally recognised collection of best practices for managing information technology (IT), defines them as follows: the data are a set of facts originated from individual events (also called “raw” or “raw data” ); information derived from the data, fitted with the suitable interpretive context; knowledge descends from the information enriched with different elements: analysis, experience, ideas, values ​​and judgments, tacit or explicit, personal or institutional, coming from within the organisation or outside of it.

7   See, by G. Di Domenico, L’impatto delle biblioteche accademiche: un progetto e un seminario, Roma, Associazione italiana biblioteche, 2014, and L’impatto delle biblioteche pubbliche: obiettivi, modelli e risultati di un progetto valutativo, Roma, Associazione italiana biblioteche, 2012.

8Giampaolo Fabris, Societing. Il marketing nella società postmoderna, Milano, EGEA-Università Bocconi, 2009.

9C. Faggiolani - A. Galluzzi, L’identità percepita delle biblioteche: la biblioteconomia sociale e i suoi presupposti, «Bibliotime», 2015, 1, <http://www.aib.it/aib/sezioni/emr/bibtime/num-xviii-1/galluzzi.htm>.

10Vincenzo Russo, Processi di costruzione del significato: il sistema cognitivo, in: Psicologia del consumatore. Consumi e costruzioni del significato, a cura di Giovanni Siri, Milano, Mc Graw-Hill, 2004, p. 3-63.

11A. Galluzzi, Libraries and Public Perception: a Comparative Analysis of the European Press, Oxford, Chandos Publishing, 2014.

12C. Faggiolani, Posizionamento e missione della biblioteca. Un’indagine su quattro biblioteche del Sistema bibliotecario comunale di Perugia, Roma, Associazione italiana biblioteche, 2013; I nuovi volti della biblioteca pubblica. Tra cultura e accoglienza, a cura di Maurizio Bergamaschi, Milano, Franco Angeli, 2015.

13G. Di Domenico, L’impatto delle biblioteche accademiche, p. 16.

14Abbas Tashakkori - Charles Teddlie, Sage Handbook of Mixed Methods in Social & Behavioral Research.  2nd ed., Los Angeles, Sage Publications, 2010.

15Nigel Fielding - Jane Fielding, Linking Data. The Articulation of Qualitative and Quantitative Methods in Social Research, London, Sage Publications, 1986.

16Janice M. Morse, Approches to Qualitative-quantitative Methodological Triangulation, «Nursing Research», 40, 1991, 2, p. 120-123.

17John W. Creswell, Research Design: Qualitative & Quantitative and Mixed Methods Approaches, London, Sage Publications, 1994.

18Abbas Tashakkori - Charles Teddlie, Mixed Methodology: Combining Qualitative and Quantitative Approaches, Thousand Oacks, Sage Publications, 1998.

19   This approach to social research is now widely shared in Anglo-Saxon countries, as evidenced by the proliferation of journals dedicated to it, as «Journal of Mixed Methods Research» (<http://mmr.sagepub.com/>), «Australian International Journal of Multiple Research Approaches» (<http://mra.e-contentmanagement.com/>) and important textbooks like the afore-mentioned one by Tashakkori and Teddlie. Even in Italy in recent years we are witnessing a greater consolidation of this methodological approach, see for example Fulvia Ortalda, Metodi misti di ricerca. Applicazioni alle scienze umane e sociali, Roma, Carocci, 2013.

20The Sage Handbook of Qualitative Research. 4th edition, edited by Norman Denzin, Yvonna Lincoln, London, Sage, 2011, p. 3.

21Mario Cardano, La ricerca qualitativa, Bologna, Il Mulino, 2011.

22Chris Anderson, The Long Tail: Why the Future of Business Is Selling Less of More, New York, Hyperion Books, 2006.

23C. Faggiolani, La ricerca qualitativa per le biblioteche. Verso la biblioteconomia sociale, Milano, Editrice Bibliografica, 2012, p. 227.

24 Among the Open Data made ​​available, for example, we can find economic data of local authorities, those on the progress of urban traffic, geographic institutional datasets etc. They are all information with high added value, that can be used not only for the historical reading of social phenomena and increase the degree of transparency of public administration, but also to design new services. The Open Data portal of the Italian public administrations (http://www.dati.gov.it was created to allow citizens, developers, businesses, trade associations and public authorities to benefit of the same public administration information assets in the most simple and intuitive way.

25   The term ‘Internet of Things’ refers to a family of technologies whose purpose is to extend the capabilities of natively networked devices to any object. Objects can act as sensors, producing information about themselves or the surrounding environment. In this scenario, everyday objects (for example, alarm clocks, washing machines, sneakers, cars) will be connected to the Internet and will be able to produce data to process and analyse. It is estimated that in 2020 there will be 40 billion devices in use, or about 10 devices per person on Earth, and 44 zettabyte (ZB 1=1021 bytes) of data to process. See http://www.dailyinfographic.com/wp-content/uploads/2014/03/Xively_Infographic-2-1.jpg.

26   <http://www.google.it/trends/?hl=it>.

27 Mark Beyer - Douglas Laney, The Importance of Big Data. A Definition, Stamford, CT, Gartner, 2012.

28   For further information Ian Ayres, Super Crunchers. New York: Random House, 2008; Chris Snijders - Uwe Matzat - Ulf-Dietrich Reips, “Big Data”: Big Gaps of Knowledge in the Field of Internet Science, «International Journal of Internet Science», 7, 2012, 7, p. 1–5, < http://www.ijis.net/ijis7_1/ijis7_1_editorial.pdf >.

29 Douglas Laney, 3D Data Management: Controlling Data Volume, Velocity and Variety, «Gartner. Application Delivery Strategies», February 6, 2001, n. 949, <http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf>.

30Davide Bennato, Il computer come macroscopio. Big Data e approccio computazionale per comprendere i cambiamenti sociali e culturali, Milano, Franco Angeli, 2015.

31Mark Van Rijmenam, Why the 3V’s Are not Sufficient to Describe Big Data, in the blog Datafloq. Connecting Data with People, 2014, <http://floq.to/5Yai6>.

32   IBM, What is Big Data? - Bringing Big Data to the Enterprise, 2013, <http://www-01.ibm.com/software/data/bigdata/, July 2013>.

33   Oracle, Big Data for the Enterprise, An Oracle White Paper, June 2013, <http://www.oracle.com/us/products/database/big-data-for-enterprise-519135.pdf >.

34 John Gantz - David Reinsel, Extracting Value from Chaos, «IDC iView», June 2011, p. 1-12, <http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf >.

35   This is a very widespread technique abroad that is catching up also in Italy, which consists in the application of data mining in social networks. It is a method that allows you to collect and analyse real-time reactions of members or any tendency of a phenomenon. An accurate tool to detect and listen to online conversations by providing an interpretation of the phenomenon of the subject of study through the analysis of tone, positive or negative opinion, intensity of such opinion, emotion with which this was expressed and relevance. Cf. Andrea Ceron - Luigi Curini - Stefano Iacus, Social Media e Sentiment Analysis. L’evoluzione dei fenomeni sociali attraverso la Rete, Milano, Springer, 2013.

36Enrico Giovannini, Scegliere il futuro. Conoscenza e politica al tempo dei Big Data, Bologna, Il Mulino, 2014.

37Challenges and Opportunities with Big Data. A community white paper developed by leading researchers across the United States, 2012, < http://www.purdue.edu/discoverypark/cyber/assets/pdfs/BigDataWhitePaper.pdf>.

38   Ivi, p. 4.

39Viktor Mayer-Schönberger - Kenneth Cukier, Big Data. Una rivoluzione che trasformerà il nostro modo di vivere e già minaccia la nostra libertà, Milano, Garzanti, 2013, p. 80 (Big Data: a Revolution That Will Transform How We Live, Work and Think, 2013).

40Chris Anderson, The End of Theory: the Data Deluge Makes the Scientific Method Obsolete, «Wired», 2008, 7, p. 107-108, <http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory>.

41John Timmer, Why the Cloud Cannot Obscure the Scientific Method, «Ars Technica», June 26, 2008, <http://arstechnica.com/old/content/2008/06/why-the-cloud-cannot-obscure-thescientific-method.ars> ; David Bollier, The Power and Peril of Big Data, Washington D.C., The Aspen Institute, 2010, <http://bollier.org/sites/ default/files/aspen_reports/InfoTech09.pdf>.

42Andrew McAfee - Erik Brynjolfsson, Big Data: the Management Revolution, «Harvard Business Review», October 2012, p. 59-68, <http://ai.arizona.edu/mis510/other/Big Data - The Management Revolution.pdf >.

43Giovanni Solimine, La biblioteca. Scenari, culture, pratiche di servizio, Roma-Bari, Laterza, 2004, p. 190-199.

44Daniel Kahneman - Amos Tversky, Judgment Under Uncertainty: Heuristics and Biases, «Science», new series, 185, 1974, p. 1124 -1131.

45  Cristiana Bambini - Tatiana Wakefield, La biblioteca diventa social, Milano, Editrice Bibliografica, 2014, p. 24-25.

46  <https://tweetreach.com>. The tool, which allows you to monitor the reach generated by each tweet, is certainly a very appropriate if you are interested in identifying the effectiveness of your tweets based on the number of users that they can reach.

47Alberto Petrucciani, Dino Campana alla Biblioteca di Ginevra, «Biblioteche oggi», 32, 2014, 8, p. 4-9.

48  C. Faggiolani, La ricerca qualitativa per le biblioteche; C. Faggiolani – G. Solimine, Biblioteche moltiplicatrici di welfare e biblioteconomia sociale, in: Biblioteche in cerca di alleati. Oltre la cooperazione, verso nuove strategie di condivisione. Atti del Convegno delle Stelline, Milano 14-15 marzo 2013, a cura di Massimo Belotti, Milano, Editrice Bibliografica, 2013, p. 47 -57; G. Di Domenico, Conoscenza, cittadinanza, sviluppo: appunti sulla biblioteca pubblica come servizio sociale, «AIB studi», 53, 2013, 1, p. 13-25; G. Solimine, Nuovi appunti sulla interpretazione della biblioteca pubblica, «AIB studi», 53, 2013, 3, p. 261-271; Maurizio Vivarelli, Specie di spazi. Alcune riflessioni su osservazione e interpretazione della biblioteca pubblica contemporanea, «AIB studi», 54, 2014, 2/3, p. 181-199; C. Faggiolani – A. Galluzzi, L’identità percepita delle biblioteche: la biblioteconomia sociale e i suoi presupposti.

Table des illustrations

Titre Fig. 1 – The three phases of library assessment
URL http://books.openedition.org/ledizioni/docannexe/image/3616/img-1.jpg
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Titre Fig. 2 – Chain data-information-knowledge for better decisions
URL http://books.openedition.org/ledizioni/docannexe/image/3616/img-2.jpg
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Titre Fig. 3 – Big Data: 3V model
URL http://books.openedition.org/ledizioni/docannexe/image/3616/img-3.jpg
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Titre Fig. 4 – Big Data: 7V model
URL http://books.openedition.org/ledizioni/docannexe/image/3616/img-4.jpg
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Titre Fig. 5 – Big Data Management and process analysis
URL http://books.openedition.org/ledizioni/docannexe/image/3616/img-5.jpg
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Titre Fig. 7 – From a mixed approach of Big Data integration of Big Data in research
URL http://books.openedition.org/ledizioni/docannexe/image/3616/img-6.jpg
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