Peter Bernus
email: p.bernus@griffith.edu.au

Ovidiu Noran
email: o.noran@griffith.edu.au

Data Rich -But Information Poor

Keywords: situation awareness, big data, decision model, situated reasoning

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The ability to gather very large amounts of data has preoccupied the research community and industry for quite some time, starting within Defence in the 1990s and gradually evolving in popularity so as to become a buzzword in the 2010s. 'Big data', the 'sensing enterprise', and similar concepts signal a new era in which one hopes to provide 'all necessary' decision-making information for management in the quality and 'freshness' required. Whoever succeeds in building such facility and use the data to derive decision making information efficiently and effectively, or produce new knowledge that was not attainable before, will 'win'. The hope is well founded, if one considers an analogy with the early successes of evidence-based medicine (Evidence Based Medicine Working Group, 1992;[START_REF] Pope | Resisting evidence: the study of evidence-based medicine as a contemporary social movement[END_REF] that transformed the way the medical profession decided what treatments to use. At the same time, it is necessary to define realistic expectations in order to avoid expensive mistakes; for example, even evidence-based medicine that relies on large scale data gathering through clinical trials and careful statistical analysis is showing signs of trouble [START_REF] Greenhalgh | Evidence based medicine: a movement in crisis?[END_REF], with the usefulness of the evidence gathered when it is applied in complex individual cases increasingly being questioned. Generally, it is becoming obvious that finding new ways to correctly interpret complex data in context is necessarya claim supported by many authors [START_REF] Hbr | From data to action (a Harvard Business Review Insight Center report[END_REF].

An obvious viewpoint is that when intending to use large amounts of gathered data to create useful decision making information, one must carefully consider the information needs of management and especially how the interpretation of data is influenced by context. The goal of this paper is to investigate and analyse, from a theoretical perspective, what missing links exist in order to put 'big data' to use apart from the obvious 'low hanging fruit' applications.

Current Issues in Big Data and Data Warehousing

The role of Management and Control (or Command and Control) is to make decisions on multiple levels, i.e.: Real time, Operational, Tactical and Strategic, often guided by various types of models. Along these lines, two notable systematic models of decision making (which is the essence of management and control) are the GRAI Grid [START_REF] Doumeingts | GRAI grid decisional modelling[END_REF]) and the Viable Systems Model or VSM [START_REF] Beer | Brain of the firm[END_REF]. These models are very similar and differ mainly in the details emphasised (Fig. 1). Fundamentally, these generic models identify management, command and control tasks and the information flow between them.

Fig. 1.

A simplified view of two equivalent decision making models: Viable Systems Model [START_REF] Beer | Brain of the firm[END_REF] and GRAI Grid [START_REF] Doumeingts | GRAI grid decisional modelling[END_REF] To make successful decisions, it is necessary to satisfy the information needs of the management functions as depicted in Fig. 1. The big data movement (discussed in Section 1) has a 'little brother': data warehousing (with its associated techniques), which made similar initial claims of creating meaningful insight for management.

Even though there exist many success stories, there were some notable failures of data warehousing to deliver on its promises, owing to similar causes (see below).

In data warehousing, the methodology suggested by its proponents was to take copies (snapshots) of operational databases (and other data repositories) and build an interface, based on which the data could be 'mined' (analysed) to find managementrelevant information. To build such a facility fast and in an affordable manner, the methodology suggested by [START_REF] Inmon | Building the Data Warehouse[END_REF], Kimball (1996) and others was to create the data warehouse out of existing databases and possibly transaction logs so as to gain management insight. In other words the aim was to create a narrative that is characterising the present or predicted future situation, which is essential for strategic decision making. Big data is no different, using traditional data analysis and machine learning techniques the protagonists derive useful interpretations, just on a larger scale than data warehouses can do, as data sources are larger and include multiple sources. However, both the big data and data warehousing movements (even though their scales are different) have in fact similar shortcomings, such as:  The associated methodologies give not enough weight to first understanding the fundamental information needs of the decision maker;  Very little is done to correlate internal-and external data sources to create useful information for decision making (i.e., relating the endogeneous and the exogeneous);  Insufficient effort was put into realising what data would be needed but was unavailable (for being able to draw useful inferences); Even the recent method to limit the amount of sensor data taken into account in situation assessmentwhile providing the facility to switch 'on or off' additional pre-stored sensor data sourcesis relying on the commander to pinpoint what data should be taken into account to possibly change the narrative;  If the above deficiency is identified, then the need for data that is not available, but is deemed necessary, may become the source of additional data collection tasks. However, this can inadvertently result in poor data quality (HBR, 2014, p47;Hazen et al, 2014, p78). This is because the essential but problematic Information Systems point of view of the data gathering task is ignored (i.e., how to avoid data quality problems due to data entry by humans who consider it a chore), in favor of only solving the easier to manage database / computing problems (how to use various algorithms to identify patterns in data, which problem is essentially technical in nature);  Very little has been done on transforming existing processes so that they would produce the necessary data as a by-product of the production (or service delivery) process, instead of requiring additional data entry (which was found to be the main source of data quality issues) [START_REF] Hazen | Data quality for datascience, predictive analytics, and bigdata in supply chain management: An introduction to the problem and suggestions for research and applications[END_REF];  There has been a propensity to disregard the context of the collected data, and thus creating the danger of situation mis-identification without even being aware of having committed this mistake [START_REF] Santanilla | What Can Digital Disease Detection Learn from (an External Revision to) Google Flu Trends?[END_REF]. The data warehouse movement largely concentrated on collecting and making available for analysis the internal data of the enterprise, while the big data movement, with its roots in the 1990s (e.g. the 'Dominant Battlefield Knowledge' movement [START_REF] Szafranski | A Theory of Information Warfare[END_REF]), concentrates on the ability (initially, of the military) to access an unprecedented amount of external data so as to gain advantage over adversaries -and indeed, specialised systems have been built and deployed in order to achieve this goal.

Two issues become apparent when analysing the history of creating useful decision-making information, whether through data warehousing or big data analytics and the associated business intelligence processes:

1. On each decision-making level, we must correlate internal and external data;

2. With the opportunity to collect and access very large amounts of data, it becomes difficult to identify patterns that are useful for decision making (there are too many patterns that algorithms can identify)unless one uses heuristics (i.e., the result of prior learning) to discern what is relevant and what is not. Importantly, the measure of relevance changes with time and with the current interpretation of data.

Making Effective Decisions

Tasks that appear in each type and level of decision-making and the feedback that can be used to inform the filters used to selectively observe reality may be studied using a finer model that explains how successful decisions are made. This filter is part of the well-known Observe, Orient, Decide and Act (OODA) Loop devised by John Boyd [START_REF] Osinga | Science, strategy and war: The strategic theory of John Boyd[END_REF]) (see explanation below). It must be noted that on closer inspection, it turns out that OODA is not a strict loop because it is precisely the feedbacks inside the high level 'loop-like' structure that are responsible for learning and for decisions about the kind of filters necessary. Note that this 'loop' is often misunderstood to be a strict sequence of tasks (e.g. cf. Benson and Rotkoff's 'Goodbye OODA Loop' ( 2011)) when in fact it is actually an activity network featuring rich information flow among the OODA activities and the environment.

A brief review of Boyd's OODA 'loop' can be used to point to potential development directions for a 'big data methodology' for decision support. Accordingly, decisions can be made by the management / command & control system of an entity, in any domain of action and on any level or horizon of management (i.e., strategic, tactical, operational and real-time, performing four interrelated tasks:

 Observe (selectively perceive data [i.e., filter]measurement, sensors, repositories, real-time data streams, using existing sensors);  Orient (recognise and become aware of the situation);  Decide (retrieve existing-, or design / plan new patterns of behaviour);  Act (execute behaviour, of which the outcome can then be observed, etc.). According to Boyd and subsequent literature analysing Boyd's results [START_REF] Osinga | Science, strategy and war: The strategic theory of John Boyd[END_REF], to make the actions effective, decision makers must repeat their decision making loops faster than the opponents, so as to disrupt the opponent's loop. Note that the first caveat: one can only 'observe' using existing sensors. Since there is no chance to observe absolutely everything, how does one know that what is observed is relevant and contains (after analysis) all the necessary data, which then can be turned into useful situation awareness [START_REF] Lenders | Gaining an Edge in Cyberspace with Advanced Situational Awareness IEEE Security & Privacy[END_REF]? The likely answer is that one does not know a priori; it is through learning using past positive and negative experiences that a decision system approaches a capability level that is timely and effective in establishing situation awareness. This learning will (or has the potential to) result in decisions, which are able to identify capability gaps (and initiate capability improvement efforts). This is the task of self-reflective management, comparing the behaviour of the external world and its demands on the system (the future predicted action space) with the action space of the current system (including the current system's ability to sense, orient, decide and act). In this context, the 'action space' is the set of possible outcomes reachable using the system's current technical-, human-, information-and financial resources.

This learning loop depends on self-reflection and it is in itself an OODA loop analogous to the one discussed above, although the ingredients are different and closely associated with strategic management. The questions are: a) what to observe, b) how to orient to become situation aware and c) what is guiding the decision about what to do (within constraints and decision variables and the affordances of action) so as to finally be able to perform some move. The action space of this strategic loop consists of transformational actions (company re-missioning, change of identity, business model change, capability development, complete metamorphosis, etc.).

Essentially, such strategic self-reflection compares the current capabilities of the system to desired future capabilities, allowing management to decide whether to change the system's capabilities (including decision making capabilities), or to change the system's identity (re-missioning), or both. Note that management may also decide that part of the system will need to be decommissioned due to its inability to fully perform the system's mission. Such transformation is usually implemented using a separate programme or project within a so-called Plan-Do-Check-Act (PDCA) loop (Lawson, 2006, p102) (not discussed further as it is outside the scope of this paper).

The above analysis can be put to use in the following way: to achieve situation awareness, which is a condition of successful action, 'big data' (the collective technologies and methods of data analysis and predictive analytics) has the potential to deliver a wealth of domain-level facts and patterns that are relevant for decisionmaking, which were not available before. However, this data needs to be interpreted, which calls for a theory of situations ultimately resulting in a narrative of what is being identified or predicted; without such narrative, there is no true situation awareness, which can significantly limit the chances of successful action.

It is therefore argued that having the ability to gather, store and analyse large amounts of data using only algorithms is not a guarantee that the patterns thus found in data can be turned into useful information that forms the basis of effective decisionmaking, followed by appropriate action leading to measurable success.

The process works the other way around as well: when interpreting available data (however large the current data set may be), there can be multiple fitting narratives and unfortunately it is impossible to decide which one is correct. Appropriate means of reasoning with incomplete information could in this case identify a need for new data (or new types of data) that can resolve the ambiguity. Thus, supporting decisionmaking using 'big data' requires the collection of a second level of data, which is not about particular facts, but about the creation of a 'repertoire' of situation types, including facts that must be true, facts that must be not true, as well as constraints and rules of causes and effects matching these situation types. Such situation types can also be considered as models (or model 'archetypes') of the domain, which then can be matched against findings on the observed data level. Given the inherently perpetual changing nature of the world, these situation types are expected to evolve themselves; therefore, one should not imagine or aim to construct a facility that relies on a completely predefined ontology of situation types. Rather, there is a need for a facility that can continuously improve and extend this type of knowledge, including the development and learning of new types that are not a specialisation of some previously known type (to ensure that the 'world of situations' remains open, as described by Goranson and Cardier (2012)).

Using Big Data in Decision-making for System of Systems

The Dominant Battlefield Knowledge movement [START_REF] Szafranski | A Theory of Information Warfare[END_REF] pointed out quite early that in spite of the ability to deploy a large number of sensors, in order to achieve situation awareness data needs to get filtered based on 'relevance'. The word 'relevant' is in inverted commas, because it is precisely the possible situations of interest that dictate what data are or would be relevanthowever, one does not know a priory (and / or unambiguously) what situation one is actually in! Therefore, the continual narrative of the situation changes the data needs [START_REF] Madden | From Databases to Big Data[END_REF], as well as what needs to be filtered out and should be kept.

In the real world of cooperative action (whether business, government or military), such as in collaborative networks (Camarinha-Matos et al, 2011) or virtual organisations created by them (both being socio-technical kinds of systems of systems (SoS)), decisions are not taken in a completely centralised way. The participants of a system are systems themselves, in control of their own resources; moreover, usually each participating system has its own system identity as well as multiple commitments at any given time (with one of these commitments being to belong to the SoS in question). The types of strategies that need to be used in such scenarios have recently been reviewed in the extensive state of the art report by the Committee on Integrating Humans, Machines and Networks [START_REF] Cihmn | A Global Review of Data-to-Decision Technologies (2014) Complex operational decision making in networked systems of humans and machines a multidisciplinary approach[END_REF], which calls for an interdisciplinary approach, similar e.g. to the collaborative networks research area (instead of relying on a purely computational viewpoint as a background discipline).

A SoS must be robust to cope with the situation when a participating system is not performing (e.g. becomes faulty, is destroyed, or otherwise unavailable, or due to communication channels being compromised, etc.). Successful SoS level decisionmaking must be framed as a cooperative conversation of information exchange and commitments, however with the added complexity that important systemic properties (e.g., availability) of the SoS need to be maintained, without being able to completely rely on the same property (i.e., availability) of individual participating systems.

To overcome this difficulty, the architecture of a successful SoS must be dynamically reconfigurable, so that the functional integrity of the SoS is preserved, including its mission fulfilment and its management and control. The robustness of the decision system is only achievable, if i) the decision function is built to cope with incomplete information (at least for a limited time), ii) the decision function can proactively provide guidance regarding its information needs to the contributing systems that 'observe', so as to resolve ambiguity or to replace information sources that became unavailable, iii) the allocation of the OODA loop functions to resources is dynamic -similar to how cloud computing can achieve required capacity, availability, scalability, and other desirable systemic properties (the 'ilities') [START_REF] Lehrig | Scalability, Elasticity, and Efficiency in Cloud Computing: a Systematic Literature Review of Definitions and Metrics Proceedings of the 11th Int ACM SIGSOFT QoSA[END_REF].

This self-awareness requirement for a SoS is in addition to the self-reflection requirement discussed (Section 3), as it requires operational (and real-time) reconfiguration, based on the need for timely and always available reliable narrative.

Although it is not possible to go into the technical details within the limits of this article, the theory that allows the two levels -situation theory and domain level theory(ies) to coexist is channel logic [START_REF] Barwise | Information Flow: The Logic of Distributed Systems[END_REF]. Mathematically, given the category of situations (that represent situation types) there exist a mapping between situation types that regulates the way complete lines of reasoning can be 'transplanted' from one situation type to another. This transplanting works as follows: when there exist a logic in a known situation type A, and the facts suggest that the situation is of a related type B, many but not all facts and inferences should also be valid in type B. As a result, if we have a known situation (of type A) with facts supporting this claim, and we only have scarce data about another situation of interest (of type B), channel logic allows us to deduce the need for data that can be used to 'fill in the details' about this second situation.

The mapping from one category to another is a morphism between categories, and can be implemented using functional programming techniques. The practical consequence is that the decision maker can use this (strictly formal) analogical reasoning to come to valid conclusions in an otherwise inaccessible domain (or if this is not possible, narrow down the need for specific data that can support a valid conclusion). This is a rudimentary explanation of the ability of the situation theoretic logic to infer that for decision making there is a need for specific unavailable data that can disambiguate the interpretation of data available at the time.

Conclusions and Further Work

The conceptual analytical work presented in this paper can be extended and used as the concept of a solution creating an ongoing situation awareness capability. All application domains (e.g. business, government, military, etc.) have typical situations and thus maintain a specific 'repertoires' of actions known to work. The knowledge of these situations can be acquired partially through education and partially through practical experience. The efficient use of these patterns depends on them being utilised fast, without too much explicit thought; in other words, efficient behaviour is typically based on the use of tacit skills and knowledge (irrespective of the fact that some of this knowledge would also be available in an explicit, formal form).

As pointed out, the effectiveness of the OODA loop is also dependent on its efficiency -hence, whoever does it better and faster will win. If, for example, one is not able to process the gathered information in a timely manner, the resulting action/s may become irrelevant, because one's adversary may have already acted and thus, changed the situation. Given the complexity of situations and the amount of data available, using data analytics in conjunction with situation recognition could dramatically speed up the loop, hence increasing the chance of success.

The technology for data analytics and predictive analytics is currently subject to substantial ongoing effort in research and in industry. The authors are observing the technical side of this movement, while concentrating their effort on the promising, however difficult to implement and mathematically challenging technology that is developed on the basis of situation theory (Goranson & Cardier, 2012). The main aim is to demonstrate the use of such technology to construct resilient Systems of Systems through dynamic management, command and control of a large number of cooperating participating agents.

Acknowledgements. The authors would like to acknowledge the research grant (Strategic Advancement of Methodologies and Models for Enterprise Architecture) provided by Architecture Services Pty Ltd (ASPL Australia) in supporting this work


Manfredi Vale

Marta Pantalone

Morena Bragagnolo

Aghetera Sas

Collaborative Perspective in Bio-economy Development: A Mixed Method Approach

Keywords: supply chains integration, economic input-output, life cycle analysis, sustainability, resilience

The hybrid Economic Input Output -Life Cycle Assessment (EIO-LCA) coupled with qualitative methods for consensus creation and participative policy building is a proposed methodology for dealing with complex and datarich contexts that have to be connected in order to construct a robust regional bioeconomic profile that informs policy makers and other stakeholders about socio-economic and ecological impacts -and suggest at the same time strategies for its validation and implementation. An important result of this work is that the process of data collection and systematisation also provided ample opportunity to be in touch with a wide and diverse range of actors that characterize the different supply chains, ranging from the primary (farming, aquatic resources, forest sector) to manufacturing and services-related companies. These contacts encouraged the establishment of a network exchanging knowledge on crucial aspects for the further development of bioeconomy.

Introduction

The concept of 'bioeconomy' is gathering momentum in European Union (EU) policy circles as a sustainable model of growth to reconcile continued wealth generation and employment with bio-based sustainable resources usage. Bio-based industries and bio-based products can represent a new development opportunity for European areas fighting with a still stagnating economy. The development of a bio-based economy is a long-term process that requires the involvement of many stakeholders on multiple levels and sectors [START_REF] Munda | Social multi-criteria evaluation: Methodological foundations and operational consequences[END_REF] and could have great impacts on local communities. According to the definition of the European Commission, the bioeconomy comprises those parts of the economy that use renewable biological resources from land and sea -such as crops, forests, fish, animals and micro-organisms -to produce food, materials and energy [START_REF]Innovating for Sustainable Growth. A Bioeconomy for Europe[END_REF]. Standing this wide definition, in order to strengthen its profile, it is necessary to bring into vision the main actors of the bioeconomy, primarily the companies that are taking action to achieve the objectives set by both the European Union and the United nations [3; 4; 5]. The bioeconomy is by and large an assemblage of regional activities that have their own regional character. For this reason, the choice of the most suitable strategy should be based on the involvement of all stakeholders [START_REF] Patel | Participatory scenario construction in land use analysis: An insight into the experiences created by stakeholder involvement in the Northern Mediterranean[END_REF], through participatory methods [START_REF] Lopolito | Modeling the bio-refinery industry in rural areas: A participatory approach for policy options comparison[END_REF], that brings stakeholders together and let them confront each other on specific issues, agree on common meanings, and foreshadow a strategy for action. These participatory processes should empower participants to have an impact on the local/regional context, making them active in the strategic planning in the areas where they live, supporting the policymaking process in accordance with politicians, technocrats, lobbies and civil society groups. The aim is to find an agreement on development scenarios and a general strategy to pursue common interests, breaking the 'vicious circle of non-participation' [START_REF] Sisto | Puzzling stakeholder views for long-term planning in the bio-economy: A back-casting application[END_REF]. The stakeholders are not always aware of their significant role in the co-creation of policies [START_REF] Odugbemi | Governance reform under real world conditions. Citizens, stakeholders, and voice[END_REF] and they need the support of methodologies that are able to promote their participation, encourage the interaction, and provide an operational synthesis of this process, leading to a strategy which could support the public decision-makers and policy-makers. The innovation beyond the state of the art of this project is to delineate and model the regional Bioeconomy with a hybrid Economic Input Output-Life Cycle Analysis (EIO-LCA) using statistical data of economic flows, conventional pollutants emissions, energy use, waste production of the 37 regional economic sectors (according to NACE1 classification) and integrate this information with sociological survey data collected during interviews and focus groups sessions.

The paper begins with a critical review of the literature on recent bio-economy impact-focused studies and on participative methods for policy design involving experts and diverse 'publics', thus suggesting that a mixed quantitative-qualitative approach is more appropriate for planning in highly complex contexts. The other sections present a synthetic description of methodologies and tools involved in this research, starting with an analysis of the baseline, as well as the main results on both disciplinary fields invoked, the economic-environmental modelling and the policy building through participative approaches. The concluding discussion recalls the importance of a mixed-method approach in order to better exploit its data richness and, at the same time, the importance of creating collaborative networks of different stakeholders in the field of bio-economy.

Literature Review

Bio-based products can include a diverse cross-section of items like enzymes; biobased end-use products; commodity, fine, and specialty chemicals; intermediates and polymers; feed, food and food additives; fuels; flavours and fragrances; pharmaceuticals; and bio-based energy. These products are woven into the economy in complex ways, making it difficult to compute the impact on the economy. Defining what will and will not be considered part of the core bioeconomy is important and has a great impact on the magnitude of various indicators of impact. For the bioeconomy to grow, many different segments of the economy must effectively interact. The connectedness of the bioeconomy can most easily be discussed in a supply chain framework. Supply chains are defined as all of the players involved in the movement of a product or service to the end customer. These networks transform natural resources into an end-use product, often via many different companies that add incremental value along the way [START_REF]Biobased economy indicators[END_REF][START_REF] Macchion | Improving innovation performance through environmental practices in the fashion industry: the moderating effect of internationalisation and the influence of collaboration[END_REF][START_REF] Macchion | Sustainability approaches within the fashion industry: the supplier perspective[END_REF].

Input-Output modelling is a linear modelling approach which involves the examination of the economic cycle of production by analysing the relationship between the flow of production inputs and resultant flow/destination of outputs in an economy. Input-output analysis has received attention also as a means for delineating the bioeconomy development in Europe and abroad, with a number of works attempting to gauge the impacts that bioeconomy is producing and will produce in economic, environmental and social terms [START_REF] Vagnani | Alla ricerca del valore della filiera vitivinicola: verso la formulazione di un modello di analisi[END_REF]. There are relatively few studies attempting a quantitative impact analysis of the existing and projected bioeconomy, also dealing with the problem of defining the scope, sectors and supply chains involved. An EU study [START_REF] Philippidis | Employing social accounting matrix multipliers to profile the bioeconomy in the EU member states: is there a structural pattern?[END_REF] has attempted an economy-wide quantitative assessment covering the full diversity of the European bioeconomy by employing social accounting matrices (SAMs) for each EU27 member encompassing a highly disaggregated treatment of traditional 'bio-based' agricultural and food activities, as well as additional identifiable bioeconomic activities from the national accounts data. The aim was to profile and assess comparative structural patterns both across bioeconomic sectors and EU Member States. The results indicate six clusters of EU member countries with homogeneous bioeconomy structures. Irish researchers applied an input-output framework to define the scope of the national bioeconomy [START_REF] Grealis | The Bio-Economy Input-Output Model: Development and Uses www[END_REF], thus establishing a Bio-Economy Input Output model (BIO) which can be used to analyse the linkages between the bio-economy sectors and the wider economy. Two major strategies have been developed in recent years to develop the Agri-Food and Ocean Economy sectors in Ireland: the Food Wise 2025 and the Harnessing our Ocean Wealth strategies. The report contains analytical studies utilising BIO to quantify, among other things, the impact of reaching Food Wise 2025 growth scenarios and Harnessing our Ocean Wealth 2020 targets. Another work prepared for the U.S. Department of Agriculture and the Congress [START_REF] Golden | An Economic Impact Analysis of the U.S. Biobased Products Industry: A Report to the Congress of the United States of America[END_REF] seeks to answer questions regarding the contributions of the bio-based products industry in the United States: the quantity of biobased products sold; the value of the biobased products; the quantity of jobs created; the quantity of petroleum displaced; other environmental benefits; and areas in which the use or manufacturing of biobased products could be more effectively used.

The studies mentioned above highlight the data richness available on this matter and the need to capitalize on the wealth of information in order to promote policies effectively supporting the bioeconomy. The challenge to be grasped then becomes to "let the quantitative data speak" bringing it to the attention of those who, from different perspectives, work in the field of -or ar involved in -bioeconomy. The high uncertainty, of economic and regulatory nature, as well as the fact that it still mostly is a series of market niches, makes it necessary for the actors involved to identify a common glossary, a field of shared meanings that allows for the construction of alternative scenarios, in order to identify paths for collective action. Faced with the growing complexities of reality, the co-construction of bottom-up policies has taken hold in many fields [17; 18; 19; 20]. If planned and appropriately applied, participation can contribute to the design of policies and practices that are better suited to serving the needs of those concerned, that are perceived to be holistic and fair, accounting for a diversity of values and needs [START_REF] Gruszka | Framing the collaborative economy -Voices of contestation[END_REF]. These processes increase the legitimacy of decisions taken and contribute in saving time in the long path due to lower resistance among stakeholders.

Starting from the above literature analysis and state of the art, this paper aims to answer the following research questions:  RQ1 Identifying the sectors that make up the regional bio-economic mosaic;  RQ2 (By using the EIO-LCA model): measuring the extent of the difference -in terms of environmental and socio-economic impacts -between conventional and bio-economic sectors;  RQ3 (Starting from the living labs experience): building a network of inter-sectoral integration (combining agriculture, manufacturing and services) through a system of contractual guarantees that can be a model for developing the bioeconomy in Veneto region.

Methods

An Input-Output Analysis based approach for calculating environmental impacts of the economy that has become relatively widespread across the academic and institutional community around the themes of decision support systems is the Economic Input Output Analysis -Life cycle Assessment approach, based on Wassily Leontief's [START_REF] Leontief | Environmental Repercussion and the economic structure: an input output approach[END_REF] I-O method for environmental analysis. The approach defines the whole economy as the boundary of analysis and uses transactions between industry sectors, along with environmental emissions data to determine the environmental impacts throughout supply chains within the economy. To answer the research questions we developed a hybrid Economic Input Output table (EIO) of the regional bioeconomy in a life cycle (LCA) perspective to make it available for the environmental and socio-economic ex-ante and ex-post assessments of (regional) plans and programs. The Veneto economy was taken into consideration as a whole to get a clear view of the bioeconomy; then, in cooperation with Unioncamere del Veneto and Emilia-Romagna (Regional Unions of Chambers of Commerce) and IRPET (Tuscany Regional Institute for Economic Planning) we identified through the Regional Input-Output Table (IOT) the sectors and companies that are momentarily the main bearers of the development of bioeconomy. Given the fact that the bioeconomy is a broad field, we decided to study a representative sample of Veneto companies (2000) with at least one product certified as organic, trying to strengthen a more respectful agriculture and transformation of raw materials. Therefore on a macroeconomic scale it makes sense to analyze such economic input/outputs and the corresponding interactions among organic and conventional supply chains by considering at the same time the pressures they exert on the environment and the socioeconomic system as a whole.

The process continued with the construction of a regional version of the NAMEA 2 matrix (National Accounting Matrix including Environmental Accounts) for Veneto region (Italy) by disaggregating emissions based on a proxy variable defined as follows: number of employees per sector in Veneto Region compared to the total number of employees in the same sector in Italy. A second Matrix was derived from the regional Inventory of air emissions (INEMAR), 2013 version. This inventory estimates the municipal emissions of the main pollutants for each activity of the Corinair classification and type of fuel. Despite these figures can be considered more accurate than those obtained from the NAMEA data processing, they are aggregated according to categories not directly comparable with the classification of economic activities used to estimate the bioeconomy. The data available at INEMAR were processed to obtain results to be included in the NAMEA table. Three drivers of environmental pressures relate to: greenhouse categories (GWP); acidification (AP); ground-level ozone, while the 10 pollutants are: methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nitrous oxide (N2O), ammonia (NH3), non-methane volatile organic compounds (NMVOC), nitrogen oxides (NOx), particulate matter (PM10 and PM2,5) and sulphur oxides (SOx).

For the socio-economic analysis, we involved stakeholders in focus group and brainstorming sessions, on one hand, and in depth interviews, on the other. Both these moments were crucial for sharing knowledge with experts (entrepreneurs, farmers, NGO's members, researchers), to putting together the distinctive visions from different stakeholders and to drawing possible policy actions. A first source of data comes from 3 case studies of companies (in the fields of bio-plastics, bio-food, biomaterials for the construction industry). The aim of these in-depth interviews was to analyse the characteristics of the 'bio-company', highlighting the economic and cultural context in which it operates. A second source of data comes from a Living Lab (LL) experience. Living Labs are open innovation platforms described as "functional regions where stakeholders have formed a Public-Private-Partnership of firms, public agencies, universities, institutes and people, all collaborating for creation, prototyping, validating and testing of new services, products and systems in real-life contexts" [23; 24]. One precondition of LL activities is that the innovation processes are strongly connected to real-world contexts, including exploration, experimentation, co-creation and evaluation with consumers in real life environments.

In the LL experience we involved all people who, at the local/regional level, felt themselves committed to the improvement of the bio-economy business as a whole. All people were recruited from participants to a European Union's Horizon 2020 coordination and support action (Bio-STEP) aimed at engaging citizens and various stakeholders in discussions about the future development of Europe's bioeconomy. The group was then expanded according to a snowball sampling procedures. The role of all these actors is significant not only when describing the problem, but also when pointing out for solutions and conditions to develop shared visions. Sadly, it appeared very difficult to get policy-makers involved. These participants were recruited first by phone, then through a one-to-one interview, and when interested, they received an email with a formal invitation to participate in focus group sessions or more in-depth interviews. The aim of this part of the study was to outline a set of policies that can where the input of resources and output of emissions and pollutants are expressed in physical units.

enhance best practices in order to coordinate and improve the effectiveness of bioeconomic actions. We collected the voices of different stakeholders and let them dialogue on different topics: the potential of networking with other bio-economic actors, the critical issues to be addressed in the bio business sector, the willingness to cooperate with other conventional and bio actors.

Results

In the case of Veneto Region for the year 2012 the IOT is a square matrix of 37x37 sectors representing how the various economic sectors are dependent on each other because in the columns and rows there are, respectively, the monetary values of their inputs and outputs. We notice that, despite the sector Crop and animal production, forestry, hunting and related services consists of more than half of the companies identified as contributors to the Venetian bioeconomy, it represents one of the sectors with less information available: hardly ever these are capital companies and then budget information is not available on the databases available so far. By coupling the Economic Input Output (EI-O) table with the RAMEA (Regional Accounting Matrix including Environmental Accounts) it has been possible to extend the previously existing knowledge about direct and indirect environmental impacts: the ratio of value added for the whole economy compared with that of each of the bioeconomic sectors comprised in our sample of some 2000 companies has been used to estimate the impacts associated with bioeconomy in a life cycle perspective (LCA). Moreover, it is possible to further separate the contribution of final demand by distinguishing e.g. the internal aggregated one from that generated by the exports. By carefully screening the results of this combination of economic and environmental information, it is possible to select out of the 37 "canonical" IO industry categories, those economic sectors that at this stage (data refers to year 2012) a) are/were referable to bioeconomy and b) showed significant potential impacts -associated with the emissions to the atmosphere -on one or more standard impact categories. Some overestimation is possible under e.g. the NACE 'Manufacture of food products, beverages etc.' where the relatively high ratio of bioeconomic activities on the total is due to the inclusion in the calculations of conglomerated realities of large-scale distribution retailers with their massive turnovers, despite the transition to organic is rapid but still partial indeed. Other sectors of interest for the regional bioeconomy are obviously 'Crop and animal production' (despite the very low added value, which still is one of the issues of the "average" agricultural supply chain in Italy and Veneto), the 'Manufacture of coke and oil' (similar argument to the one concerning retail chains) -that is partially converting to renewables -'Rubber and plastic products', 'Wholesale and retail trade', 'Accommodation and food service activities'. The same applies to the export quota of demand. By referring to e.g. the manufacture of food products, the pressure exerted from the export quota in terms of direct and indirect emissions is on average higher for an order of magnitude when compared with emissions produced by the internal regional demand. This is not strange for highly internationalized economies as the Veneto one 3 . When moving from emissions to the associated potential impacts through dedicated LCA software, it is possible to quantitatively measure the proportion of the Venetian bioeconomy as an aggregate of multiple supply chains compared with the regional economy as regards to Global warming, Acidification, Nutrient Enrichment and Photochemical Ozone Formation (see figure 1). Definitely our sample of 2000 companies when compared with the total number of companies in Veneto represents 0.4%. The data obtained from this first estimate shows that the bioeconomy in Veneto has an interesting growth potential, representing, in terms of added value, 2.86% of the regional total.

So, what are the policies that, according to stakeholders, can help the increase of Veneto's bio-economic sector?

The in-depth interviews and case studies on bio factories have allowed us to outline the economic and cultural context within which bio activity in Veneto is developing. These markets are "niches", populated by a few or very few actors 4 (at national, European and international level). In this niches, because of the low number of actors and the strong ethical instances of some key players, the competition assumes a peculiar characteristic: it becomes "Coopetition", a hybrid between cooperation and competition that brings to light the positive results of the first concept while not losing the characteristics of the latter. Being very few, these companies may not be subject to the "mors tua, vita mea" rule, which regulates the conventional markets, but, while not abandoning the logic of profit, they can adopt ethical behaviors by which all can implement their profits minimizing losses (win-win logic).

The adoption of this logic has enabled companies, once they enter their market niche, to raise its standards. Rigorous controls on raw materials and production processes, with the aim of certifying their product as organic or their processes as sustainable, have introduced virtuous logics between competitors, creating a "standard upgrade process" aimed at giving the consumer more trustworthy products. It is the consumer's trust in the product, which allows the company to remain on the market, even though the price of their product is even three times higher than the conventional product. According to entrepreneurs, such trust is building up by taking care of the "moral" integrity of the company and its mission.

The possibility of building a cooperative network between the different actors in the field of bio-economy (not only businessmen, but also researchers, cultural associations, professionals) was assessed with the group work in the Living Lab sessions. One thing, in fact, is to analyze the single bio-realities individually considered, and another to analyze the possibility that they work together. The first hurdle has proved to be eminently cultural in a territory, such as the Italian north-east, made of small-medium businesses, often family-run.

The group work between different stakeholders has highlighted the need to implement policies: 1. for the creation of a network of bio actors [e.g. a) Provide a common glossary to match the language with which reference is made to the activities carried out or b) adopt an ethical code that protects patents and original ideas and prevents opportunistic behaviors]; 2. for the consolidation of relationships with other actors [e.g. a) with the university research, in order to create dialogue between the world of production and the world of scientific research and to optimize and finalize the resources in programs that have then possible productive effects (systems, biomaterials, crops) or b) for vertical and horizontal integration of the supply chain, with the aim of building certified chains from the producer to the consumer. It is, in this sense, to create the background for dialogue among niche markets and to ensure that individual actors are no longer "dwarfs" in a giant world (the 'conventional' one), but part of a cooperative network getting more and more resilient. The restructuring of the bio-economic sectors, in fact, moves the "power" on who, normally, does not have it: small producers (farmers, breeders), consumers, the civil society and their networks.

Conclusions

This research project has allowed to more reliably gauge the value of bioeconomy -a complex and data-rich aggregation of market niches -beyond its current economic and monetary weight in the Veneto region. The theme of the higher value of bioproducts and bio-supply chains has emerged, as well as the ongoing process of rebalancing supply chains and giving more power to historically disadvantaged stakeholders.

From an economic point of view, the adoption of a mixed approach has highlighted the sectors linked to bio-economy as showing higher value of production, higher added value and lower staff turnover, but at the same time they still account for only 0.4% of the total number of companies in the region and in many cases they are converting imported raw materials -from other regions or from the rest of the worldbecause regional production does not meet their demand.

From an environmental point of view, according to the first outcomes of the EIO-LCA analysis, there is evidence -across some sample bioeconomic supply chains -of reduced environmental impacts; however, further analyses are required to confirm those insights, both at the sectors' and system scale.

From a sociologic point of view, it has emerged that the true drivers of change are relatively small economic actors (both on the demand and supply side) capable of raising standards across the entire industry and promoting at the same time its resilience. Within these market niches, those actors are capable of imposing their "regulatory discipline" to other actors, going beyond their own boundaries and influencing the whole sector. These 'little ones' -being a cultural and knowledgeintensive avant-garde -demonstrate the link between culture, ethical instances, coopetition capacity, networking potential, increased resilience, win-win logic. Culture has in fact demonstrated to be the hidden engine behind this change of paradigm towards bioeconomy: familiar and entrepreneurial culture often dates back to the last century in Italy, with a very clear distinction between family and business, being still today the main prerequisite towards business managerialisation and development in this geographic area. Another important driver of change are consumers who, with their behaviours in terms of preferences and demand, affect and redirect the market, thus indicating in advance to the regulators the direction of future policies. Conversely, politicians are perhaps those less capable of vision and innovation among the various stakeholders, and tend to hardly chase the progress of others.

All of the above boils down to a very first attempt of defining an operational/managerial model which moves from the academic interaction of sciences and humanities -having realized that none of the two disciplinary fields can be exhaustive by itself -towards a hybrid approach to big (and diversified) data management that allows for the definition of a measurable link between knowledgebased and aware business management, increasing environmental performance -at the product and process scale -and a corresponding improvement of socioeconomic performance.

Fig. 1 .

 1 Fig. 1. Main impact categories associated with the emissions of conventional vs bio-based rubber and plastics production (logarithmic scale; ordered: values in thousands).

The NAMEA consists of the framework of National Accounts with the supply and use of goods and services expressed in monetary units linked with integrated environmental accounts

From the case study on bio-building factory emerges that in this field the analyzed enterprise is unique in its kind in Italy by the type of product proposed and, at international level, is one of the six existing in the field. Similar numbers are also found in relation to the production of bioplastic materials.


Salla Paajanen

Katri Valkokari
email: valkokari@vtt.fi

Anna Aminoff
email: annaaminoff@vtt.fi

{salla Paajanen

Katri

The Opportunities of Big Data Analytics in Supply Market Intelligence

Keywords: Supply Market Intelligence, Big Data Analytics, Strategic Collaboration, Data-Rich Systems, Collaborative Big Data Intelligence

Firms need comprehensive knowledge and understanding of the opportunities available in the market by creating supply market intelligence (SMI). SMI can facilitate in finding the best partners and combination of capabilities within collaborative networks (CN). However, despite of its evident managerial relevance, SMI is still little researched topic. Simultaneously, big data analytics (BDA) has developed rapidly, becoming vital for businesses across industries. The objective of this paper is recognizing the importance of SMI and opportunities of BDA through qualitative research. The data derives from two focus group discussions of 75 purchasing professionals and six qualitative interviews of BDA experts. This research contributes to our understanding of the opportunities of BDA in creating systematic SMI to reinforce strategic collaboration, and to the understanding of knowledge as a strategic resource for forming strategic CN. Collaborative big data intelligence creates value through, for instance, creating transparency in business processes and discovering market changes.

Introduction

Many firms have developed both horizontal and vertical collaboration networks to respond to the increasing global competition. Particularly in technology based industries, firms' competitiveness depends much on the complementary services and on the entire business ecosystem they are embedded in. Fast developing technology and often very short business opportunity windows create need for fast adaptation to changes in dynamic networked markets, and thus, there is a need for continuous exploration of information about the advancement of technologies and changes in the business environment. Companies need comprehensive knowledge and understanding of the opportunities available in the market to find the best partners and combination of resources to the collaborative networks (CN) [START_REF] Weele | The Future of Purchasing and Supply Management Research: About Relevance and Rigor[END_REF].

Dynamic sourcing strategies require intelligent actions through collaborative activities, thus supply management has a vital role in understanding the supply environment [START_REF] Zsidisin | Examining supply market scanning and internal communication climate as facilitators of supply chain integration[END_REF]. In purchasing and supply management (PSM) literature, the concept of supply market intelligence (SMI) has been introduced to answer this call. SMI is defined as "the ability to develop deep insights into key supplier market characteristics, including emerging technologies, price and cost trends, mergers and acquisitions (M&A), capacity requirements, quality and delivery performance, and other key supplier capabilities that form the basis for sound strategic sourcing" [START_REF] Handfield | An organizational entrepreneurship model of supply management integration and performance outcomes[END_REF]. SMI facilitates in selecting partners, making good contracts and developing collaborative business models [START_REF] Iloranta | kirjoittaja: Hankintojen johtaminen : ostamisesta toimittajamarkkinoiden hallintaan[END_REF]. Data about potential partners and supply markets are available in many forms and sources, but the challenge is to identify, extract, analyze, present and use the data in processes [START_REF] Lorentz | Situation awareness as a building block of purchasing and supply management capability[END_REF]. Collaborative approach is needed for utilizing the opportunities of big data analytics (BDA) in SMI.

Recently, development of data management technologies and analytics enables leveraging big data in innovative ways [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. Big data refers to the capability to process and utilize data that has features of volume, variety, velocity (3Vs) and other granular and complex properties that distinguish big data from data in relational databases [START_REF] Chen | Data-intensive applications, challenges, techniques and technologies: A survey on Big Data[END_REF]. BDA consists of processes for identifying new insights that have potential to provide economic value. Fast development of BDA has left little time for maturing discourse and research. Conversely, previous approaches to CN were embarrassed by scarcity of data, and thus they need to be revisited. Therefore, in order to harness the potential of BDA in SMI, the main research question is: What are the opportunities of BDA in creating systematic SMI to reinforce strategic collaboration? Questions that help in answering the main research question are: What is the importance of SMI in creating competitive advantage across collaboration networks?, and How can BDA be utilized for SMI? This paper represents empirical results of two focus group discussions with totally 75 company managers and interviews of six big data experts. The focus group discussions provide broad viewpoints from purchasing professionals, whereas the big data experts bring valuable knowledge of the opportunities of BDA.

This paper is organized as follows. In the second section, we shortly discuss on literature of CN and then review the current literature base on SMI and BDA, in order to understand the current academic and managerial knowledge. In the third section, we describe the research methods of focus groups and qualitative interviews as our empirical research approach. The fifth section describes results from the conducted qualitative research. Discussion and conclusions recapitulate this paper.

Literature Review of SMI and BDA

Collaborative Networks

A digital, hyperconnected economy creates a specific and unique form of value creation wherein the firm and its partners generate value for various users in the networked market [START_REF] Zott | Business Model Design: An Activity System Perspective[END_REF]. Therefore, organizations frequently form various types of partnerships, such as CN and virtual organization (VO), in order to share knowledge, skills and resources to seize market opportunities, create innovative products, and provide value-added services together with partners. [START_REF] Afsarmanesh | Collaborative Networks and Their Breeding Environments[END_REF] For effective collaboration, new models are required in the era of data-rich world and for instance the concept of collaborative business ecosystem (CBE) has been highlighted [START_REF] Graça | The Need of Performance Indicators for Collaborative Business Ecosystems[END_REF]. Depending on the type of required support and objectives, these CN with different configurations and purposes are often divided to [START_REF] Oliveira | Competence Matching in Collaborative Consortia for Service-Enhanced Products[END_REF]: 1) Long Term-Strategic Network (i.e. Virtual breeding environments) and 2) Goal-Oriented Network (i.e. VO). In this paper, we focus on supply networks and markets as long-term strategic networks (supply base). Thus, we aim to complement the view by deepening understanding on development of new strategic collaboration networks (supply markets). The full potential of data-rich world can be captured in collaboration with external actors, i.e. CN, as access to and integration of third-party big data sources is required to explore changes in networked business environment.

SMI in Present Research

SMI includes recognizing and understanding supply market competition, dependencies and dynamics in global supplier corporate hierarchies, networks and linkages. SMI as a concept is often mentioned in PSM research. It is suggested to be an enabler for developing sourcing strategies [START_REF] Handley | Mediated power and outsourcing relationships[END_REF], interpreting supplier behavior and making supply management decisions [START_REF] Kaufmann | Debiasing Strategies in Supply Management Decision-Making[END_REF], as well as selecting and committing to suppliers [START_REF] Swink | On the benefits and risks of focused commitment to suppliers[END_REF]. SMI has an important role in securing availability from risky supply markets, in developing (new) strategic collaboration networks and in supply management and category strategy development as well as other strategic business decisions [START_REF] Handfield | Organizational Structure and Application of Supply Market Intelligence[END_REF]. Interestingly, 'breakthrough scanning' of the environment for innovations results in higher technical proficiency of the customer company and in increased knowledge sharing with suppliers [START_REF] Cousins | Breakthrough Scanning , Supplier Knowledge Exchange and New Product Development Performance[END_REF]. SMI is linked with higher levels of internal integration, as the resulting 'valuable information increases the inclusion and integration of supply management in the organization's activities' [START_REF] Zsidisin | Examining supply market scanning and internal communication climate as facilitators of supply chain integration[END_REF].

Although the importance of SMI is generally accepted in the literature [START_REF] Handfield | Organizational Structure and Application of Supply Market Intelligence[END_REF], the research is scattered, and our understanding of capturing the potential of SMI in creating strategic networks and competitive edge is still limited. Data are worthless without context and understanding, thus an intelligence hierarchy is applied (Fig. 1). Fig. 1. Data, information, knowledge and intelligence in SMI (Source: [START_REF] Handfield | An organizational entrepreneurship model of supply management integration and performance outcomes[END_REF]; [START_REF] Rowley | The wisdom hierarchy: representations of the DIKW hierarchy[END_REF])

Intelligence as the highest level refers to the capacity to acquire knowledge to facilitate actions [START_REF] Goertzel | Artificial General Intelligence[END_REF]. Data and information can be called pre-knowledge that are needed for knowledge creation [START_REF] Erickson | Intelligence in Action[END_REF]. Intelligence from the supply base and supply markets is generated when pre-knowledge is collected and analyzed to form actionable conclusions that affect a company's ability to strategically locate, secure, and manage sources of supply [START_REF] Jones | Supply Market Intelligence for Procurement Professionals: Research, Process[END_REF]. Active collaboration is needed during the process of transforming data into intelligence on a timely and precise manner [START_REF] Aydın | Should We Rely on Intelligence Cycle?[END_REF].

SMI and BDA

Data exist in every sector, economy, organization and user of digital technology [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF], and the amount of generated data continues to grow rapidly. As per existing literature, data can be categorized based on the form, and ownership or access to the data (as per Fig. 2). The formation of unstructured data to structured data is a continuum, including semi-structured data that does not follow a conventional database system [START_REF] Hashem | The rise of "big data" on cloud computing: Review and open research issues[END_REF]. Structured data are stored in fixed fields, such as relational databases and data spreadsheets, whereas unstructured data resides in various sources and formats, such as free-form text, image, video or untagged audio. [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF] Notably, the line between structured and unstructured, relative to internal and external data is indistinct since external data can also be structured or semi-structured.

Business data are often proprietary containing non-disclosure agreements, but firms are realizing the strategic importance of investing in insight based decisionmaking and value co-creation [START_REF] Chang | Understanding the paradigm shift to computational social science in the presence of big data[END_REF]. As data can be a competitive asset, companies need to understand data, which they hold or have access to by inventorying proprietary data and cataloging external data [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF]. Internal data should be organized before acquiring external data [START_REF] Sanders | Big data driven supply chain management : a framework for implementing analytics and turning information into intelligence[END_REF].

Big data definitions have evolved rapidly, resulting to fragmented classifications [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. Using the 3Vs characteristics big data is commonly defined as: "high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decisionmaking, and process automation" [START_REF] Chen | Data-intensive applications, challenges, techniques and technologies: A survey on Big Data[END_REF]. Besides, concepts like veracity, variability and visibility [START_REF] Buyya | Big Data: Principles and Paradigms[END_REF], value and virtual [START_REF] Assunção | Big Data computing and clouds: Trends and future directions[END_REF] are used subject to sector, size and location of the company [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF].

Generating insights from the marketplace to support intelligent decisions requires new analytical tools. Often two categories of analytics are identified as descriptive and predictive. Predictive analytics is a significant aspect of BDA, creating radical new insights and opportunities with third parties via automated algorithms and realtime data analysis [START_REF] Sanders | Big data driven supply chain management : a framework for implementing analytics and turning information into intelligence[END_REF]. The key in creating value via BDA is collaboration between analytics professionals mining and preparing data, conducting statistical operations, building models and programming business applications, and the businesspeople capable to utilize analyses in processes [START_REF]SAS: From Data to Action[END_REF].

BDA applications are increasingly important in strategic sourcing and collaboration. The ability to capture, store, aggregate and analyze data for extracting intelligence is vital for strategic decisions. [START_REF] Sanders | Big data driven supply chain management : a framework for implementing analytics and turning information into intelligence[END_REF] Big data applications in SMI consist of the areas, which provide insights into key supply market characteristics, consisting of emerging technologies, price and cost trends, M&A, capacity requirements and quality and delivery performance. Patents are an important knowledge resource in identifying technology development trends and opportunities, specifically emerging technologies [START_REF] Ma | Analyzing patent topical information to identify technology pathways and potential opportunities[END_REF]. Through descriptive statistics, such as the frequency table, retrieved patent documents can be summarized, using representative variables in order to discover novel data for technology forecasting and analysis [START_REF] Jun | A Technology Valuation Model Using Quantitative Patent Analysis: A Case Study of Technology Transfer in Big Data Marketing[END_REF]. Interest and exchange rates analysis is an important part of price and cost trends prediction and financial risk management in the global and unstable economic environment. Exchange rate movements can be forecasted, for instance, from time series of social media channels, such as Tweet counts [START_REF] Fan | Design, User Experience, and Usability[END_REF]. M&A actions are influenced by competition policies, which can be analyzed and reveal insights from the retail sites as well as the social side via social media analytics solutions [START_REF] Kaye | Data Innovations Help Brands and Retailers Monitor the Competition[END_REF]. Furthermore, user demographics can be utilized to find patterns and create insights into usage clusters in order to create multidimensional segmentations and evaluate capacity requirements [START_REF] Varela | Big data analytics in supply chain management: trends and related research[END_REF]. Finally, geographic information systems facilitate delivery performance monitoring and optimizing by integrating spatial data into regressions or simulations [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF].

Reaching the full potential of BDA in SMI requires accessing third-party data sources that may be public, through collaboration or purchased, and involve several data types [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF]. Access to external market and internal enterprise data supports supply management by integrating software between enterprises and supply chain partners.

Research framework

In studying the research questions, the importance of integrating external data to the company's context and internal data is recognized. Therefore, a data categorization / integration -based research framework is applied (Fig. 2), as a baseline for the research and a tool for analysis of the empirical data.

Research Methods

Data collection methods in this paper consist of two focus group discussions with purchasing professionals and six qualitative interviews of big data experts (Table 1). Qualitative research is particularly relevant when there are minor previous insights about a phenomenon under study, which indicates that qualitative research is exploratory and flexible due to unstructured questions [START_REF] Eriksson | Qualitative methods in business research[END_REF]. The choice of method was based on the types of research questions [START_REF] Yin | Case study research : design and methods[END_REF]. The practical orientation of big data enabled SMI coupled with a lack of academic research proposes a research approach that includes a strong collaboration between academics and practitioners. We consider focus groups an appropriate method for collecting empirical data, as we can benefit from the participants' practical experience related to SMI and need for SMI. In addition, the approach was chosen because focus groups allow idea generation and strong interaction between members of focus groups, which generates new perspectives and in-depth answers [START_REF] Byers | Focus Groups: A Qualitative Opportunity for Researchers[END_REF]. Focus groups can be defined as 'a research methodology in which a small group of participants gathers to discuss a specified issue under the guidance of a moderator' [START_REF] Wibeck | Learning in focus groups: an analytical dimension for enhancing focus group research[END_REF]. Focus group participants produce qualitative data during a focused discussion to explore particular areas of interest.

The focus groups allowed us to gather empirically relevant data from large number of purchasing professionals. However, as the application of big data was still limited, we added interviews of big data expert to get an understanding of the potential of technology. The selected BDA experts have expertise from a wide range of data engineering and analytics solutions, thus enabling to receive insights into the opportunities of BDA in creating SMI.

Table 1. Overview of the data collection

Method

Objective Number of informants

Role of informants

Focus group discussion 1

To get an understanding of the need for SMI in companies and how companies are performing today Two focus group sessions were organized, in which 35 participants were invited to the first, and 40 to the second one. The participants were purchasing professionals from different companies, representing various industries and positions in the value chain, providing views of both customers and suppliers. Their shared professional background and identity thus enabled discussion in a shared language and facilitated knowledge sharing [START_REF] Byers | Focus Groups: A Qualitative Opportunity for Researchers[END_REF]. In the first focus groups the participants were divided into three groups which discussed three questions in three rounds lasting 25 minutes each:

(1) Why and when SMI is important? (2) How SMI is conducted in your company? By whom? Please tell examples from practice. (3) How SMI capabilities should be developed?

In the second focus group, the participants were mostly purchasing professionals, in addition to a few BDA experts. The participants were divided into six groups, in which 1-2 mediators guided the discussions and made notes of the participants' answers. All of the groups discussed two questions lasting 20 minutes each: (1) Describe an ideal situation of what data from the supply market would you need to reinforce supply management in your company? (2) Which changes in the supply market are most relevant in supply management? How can intelligence be used to prepare for changes in the supply market? Furthermore, two of the groups discussed third question for 10 minutes: (3) Who creates SMI in your company? Who should do it?, and finally one group discussed last question for 10 minutes: (4) Which supply management processes can benefit from descriptive analytics? Which processes require and can benefit from predictive analytics?

In addition, we conducted BDA expert interviews to receive valuable insights to creating SMI using BDA. The discussions concentrated mainly on the potential and benefits BDA can create for business through supply management, enabling strategic collaboration with particular partners. Qualitative interviews enable tailoring for the purposes of the research objectives and questions of the study. Three of the interviewees represent BDA solution providers to study analytics solutions and customers' needs for SMI. Furthermore, three interviewees of BDA experts were selected from the academic field to study the current research and knowledge regarding the subject. The interviews were recorded and transcribed for further analysis.

Data coding and analysis was conducted through coding, using a qualitative data analysis software NVivo. The first focus group discussions were analysed by use of coding; identifying the needs and methods for SMI. The analyses of second focus group discussion strongly utilized the framework derived from related literature (Fig. 2). The BDA experts' interviews were analysed in order to discover opportunities for creating SMI using BDA, and analysis use cases to reinforce strategic collaboration.

Data triangulation was ensured through different data collection methods as well as analysis from different perspectives. Data was analysed by two researchers from the perspective of strategic sourcing and one of the researchers brought the point of view of knowledge management into the analysis.

Findings

Based on the empirical research, it was discovered that collaboration should be threefold in order to harness the opportunities of BDA in SMI. According to focus group discussions SMI enables (long-term) strategic collaboration with suppliers and other external partners for joint development opportunities and innovative solutions. Secondly, internal collaboration between business units is required for information sharing and strategic alignment. Thirdly, the interviews of BDA experts revealed that collaboration between supply management professionals and big data analysts is a prerequisite for harnessing the full potential of big data solutions to support informed decision-making in networked business environment. This requires analytical mindset and receptive attitude from the supply management professionals and business understanding from the big data analysts. Supply management professionals perceived strategic alignment between business units and suppliers, as well as actions such as joint development opportunities with suppliers as fundamentals for innovation and strategic collaboration.

The company representatives in the focus groups considered SMI to be critical in predicting and identifying changes in the supply base and supply markets among existing and new partners. SMI can be used to find and select new suppliers, and evaluate current suppliers as well as make choices such as when to change partners. SMI is important for reacting to changes in the supplier base, and for anticipating risks, for example connected to raw material availability. Supply risk management, comprising of for example country and image risks, was considered important in all of the second focus groups. Ecosystems may change much faster than the focal company, thus frequently producing new valuable business opportunities by finding new partners. In specific, the ability to scan new supply / partner markets was considered important in new business development, requiring becoming familiar with new supplier bases connected to for instance new geographical areas. Furthermore, SMI allows rapid identification of new opportunities. Up-to-date knowledge about new technologies and capabilities may be important in contributing to innovation and business development.

Moreover, the BDA experts emphasized the importance of going further than just monitoring changes in the markets. One of the interviewees stated: "It is not enough that the system gives an alert, but it needs to justify why it was distributed and what should be done." The system should recommend further actions, or in some cases even automatically solve the issue. Value from BDA derives from applying the analyzed information to decision-making and actions. In supply management, the value from BDA can be divided into backward and forward oriented as well as reactive and proactive processes. Due to the dynamic and complex business environment and supply markets, management of change and staying ahead of market fluctuations is crucial. Forecasting the future and being aware of current market conditions and potential risks via SMI enables proactive actions and fast reactions to unprecedented events. However, as per one of the BDA experts: "In a very critical part is that the one who is using the information understands what it is, and that is the biggest challenge." If the extracted insights are not converted into intelligence across business units, value from the analysis will be unexploited. Hence, collaboration between business people and analysts is important, in collaboration with external partners and across business units for acquiring knowledge and identifying development opportunities.

As today, SMI in the sample companies was still immature and not systematic. The use of BDA was very limited and minor. Internal data in relational databases was mostly not sufficiently organized, and knowledge sharing between business units was infrequent. Segregation of "traditional data" in relational databases and big data was indistinct, even though as per the BDA experts, one of the most important aspects is integrating data from diverse legacy systems and external market data. The research framework (Fig. 2) is utilized to analyze the BDA expert interviewees to present a According to the research findings, the supply markets consist of a wide range of data and information, so before acquiring the data it is important to define what data are needed, why they are needed and how they are utilized to support supply management processes. Hence, as per the big data experts "Asking the right questions is vitally important". One of the most appeared pre-knowledge was recognizing future market trends and megatrends, but also their influence on suppliers' strategy and value proposal. Access to this type of data and information requires collaboration across networks.

Discussion and Conclusions

As per recognized in the literature ( [START_REF] Iloranta | kirjoittaja: Hankintojen johtaminen : ostamisesta toimittajamarkkinoiden hallintaan[END_REF]; [START_REF] Handfield | Organizational Structure and Application of Supply Market Intelligence[END_REF]; [START_REF] Jones | Supply Market Intelligence for Procurement Professionals: Research, Process[END_REF]) and verified by the purchasing professionals, SMI is an important capability for the firm, as firms need comprehensive knowledge and understanding of the opportunities available in the market to find the best partners and combination of capabilities within the CN. However, despite of its evident managerial relevance, SMI is still little researched topic. This research contributes to our understanding of the opportunities of BDA in creating systematic SMI to reinforce strategic collaboration, and to the understanding of knowledge as a strategic resource for the forming of strategic CN. Therefore, the term collaborative big data intelligence is utilized in order to highlight the need for involvement of multiple parties. The radical new source of value and competitive advantage in supply management originates in the intersection of different sources of data, for instance, comparing negotiated prices to market prices and the causal connections of price fluctuations to a company's profitability. With the expertise of a BDA solution provider, and businesses capability to absorb analyses, it is possible to reach more mature BDA and receive comprehensive results.

The results of this paper suggest that decision-making support is one of the most critical benefits that can be achieved through collaborative big data intelligence. Threefold collaboration is needed for reaching the full potential of BDA in SMI for acquiring knowledge and recognizing collaboration opportunities with suppliers. Collaboration between analysts and business people is supported by literature [START_REF]SAS: From Data to Action[END_REF], but this research contributes to recognizing collaboration opportunities with external partners. Once an opportunity is recognized, SMI can be used to indicate the win-win situations to external partners. By utilizing big data technologies, multidimensional segmentations based on defined criteria enable, for instance, evaluating capacity requirements, user demographics, and supplier suitability for a particular company.

According to the big data experts, some of the biggest advantages of BDA are advanced algorithms that can automate routine tasks, leaving more time for strategic decisions that require human input. In addition, collaborative big data intelligence provides visibility and openness to processes, allowing perceiving defects and initiating improvements. Also previous literature has proved that there is great potential to benefit from BDA by collecting and analyzing the data, and utilizing them to detect risks [START_REF] Fan | Design, User Experience, and Usability[END_REF]. Correspondingly, supply risk management, linked to recognizing needs and opportunities, was perceived as one of the most important aspects according to the purchasing management professionals. Implemented by analysts, BDA makes it possible to receive automatic alerts of threats and changes, or correspondingly opportunities that require actions. However, simply alerts are not enough, but reasons and understanding of the alerts is required for intelligent actions. BDA can provide suggestions for action plans, but at present human intervention is still required for most critical decisions and processes. BDA can be used to analyze the past via historical data, describe the present in real time and forecast the future. Instead of focusing only on the figures of spend and savings, more forward oriented approach of supply pipeline management should be applied. Utilizing BDA enables to detect risks and opportunities, and proactively develop the pipeline and networks. Ability to accomplish backward focused cost-follow up is needed for moving to forward oriented forecasting.

As in any empirical research, the results of the present study cannot be interpreted without taking into account its limitations. Thus, this study provides many interesting avenues for further research. It may, for instance, take place in the form of multiple case studies of leading firms and their CN in order to more fully understand the potential of SMI.

Fig. 2 .

 2 Fig. 2. Data categorization / integration -based research framework (Source: modified from [24]; [23]; [22])

more precise framework for pre-knowledge categorization in the context of SMI (Fig. 3). 2, some examples of pre-knowledge according to the supply management professionals and BDA experts are presented.