Paul A Brous

Marijn Janssen
email: janssen@tudelft.nl

Rutger Krans
email: rutger.krans@rws.nl

Data Governance as Success Factor for Data Science

Keywords: Data lake, data governance, data quality, big data, digital transformation, data science, asset management

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

Introduction

More and more, asset management organizations are introducing data science initiatives to support the digital transformation of their business processes [START_REF] Provost | Data Science and its Relationship to Big Data and Data-Driven Decision Making[END_REF]. However, in order for data science to be successful, it is vital that asset management organizations are able to trust the integrity of the digital environment [START_REF]Authenticity in a digital environment[END_REF], [START_REF] Randall | Extreme trust: the new competitive advantage[END_REF]. Managers have, in the past, found it difficult to trust data science products as, for example, the data is often found to be lacking the required quality [START_REF] Lin | The need for a data quality framework in asset management[END_REF]- [START_REF] Wallis | Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries[END_REF]. Furthermore, as suggested by Wallis et al. [START_REF] Wallis | Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries[END_REF], data collections are only as valuable as the data they contain, and users need to be able to trust the data based on the integrity of the data systems and the intrinsic quality of the data. Managers need to be able to trust data science products before they are confident enough to use these products to support their business processes to make crucial decisions [START_REF] Passi | Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects[END_REF]. Examples of these decisions in the asset management domain are maintaining dykes or replacing a bridge. Decisions in these scenarios have long term implications and wrong decisions can be expensive and risky. A lack of trust in data science projects can often be attributed to the lack of data quality, and the success of data science projects is often highly reliant on the quality of the data being used [START_REF] Manco | Fault detection and explanation through big data analysis on sensor streams[END_REF]- [START_REF] Kezunovic | The role of big data in improving power system operation and protection[END_REF]. There is no single factor defining the successful outcomes of a data science project [START_REF] Saltz | Big data team process methodologies: A literature review and the identification of key factors for a project's success[END_REF], [START_REF] Cato | An investigation into the implementation factors affecting the success of big data systems[END_REF], but recently data governance has gained traction by many organizations as being important for ensuring quality and compliance in data science outcomes [START_REF] Saltz | Big data team process methodologies: A literature review and the identification of key factors for a project's success[END_REF], [START_REF] Brous | Governing Asset Management Data Infrastructures[END_REF]. However, it remains unclear how data governance contributes to the success of data science outcomes, leading to calls for more research in this area [START_REF] Saltz | Big data team process methodologies: A literature review and the identification of key factors for a project's success[END_REF], [START_REF] Brous | Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles[END_REF], [START_REF] Yoon | Data reusers' trust development[END_REF]. Data Governance can be defined as "the exercise of authority and control (planning, monitoring and enforcement) over the management of data assets" [START_REF]DAMA-DMBOK: Data Management Body of Knowledge[END_REF] (p. 67), and can provide direct and indirect benefits [START_REF] Ladley | Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program[END_REF]. For example, Brous et al. [START_REF] Brous | Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles[END_REF] showed that adoption of data governance can improve operational efficiency, increase revenue, reduce risk (for example with regards to privacy violations), reduce costs, improve perception of how information initiatives perform, improve acceptance of spending on information management projects, and improve trust in information products.

The main objective of the paper is to understand the role of data governance as a factor for successful data science outcomes. Our main research question therefore asks how does data governance contribute to more successful data science outcomes? This paper analyses a case study in the asset management domain with specific regard for the role of data governance as success factor for data science outcomes. The case under study is managed by Rijkswaterstaat in the Netherlands. Rijkswaterstaat is part of the Dutch Ministry of Infrastructure and Water Management and is responsible for the design, construction, management and maintenance of the main infrastructure facilities in the Netherlands. The paper reads as follows. Section 2 presents the background of literature regarding the relationship between data governance, trust and the digital environment. In Section 3 the methodology of the research is described. Section 4 describes the findings of the case study. Section 5 discusses the findings of the case study and section 6 presents the conclusions.

2

Literature Background

Although more attention has been paid to data governance in the literature in recent years, there have been several calls within the scientific community for more systematic research into data governance and its impact on the business capabilities of organizations [START_REF] Fruehauf | Using The Bolman And Deal's Four Frames In Developing A Data Governance Strategy[END_REF]- [START_REF] Otto | A morphology of the organisation of data governance[END_REF]. Little evidence has been produced so far indicating what actually has to be organized by data governance and what data governance processes may entail [START_REF] Otto | A morphology of the organisation of data governance[END_REF], [START_REF] Morabito | Big Data Governance[END_REF], and many organizations find data governance difficult to implement [START_REF] Mathes | Big data has unique needs for information governance and data quality[END_REF], [START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF]. There appears to be no "one-size-fits-all" approach to data governance [START_REF] Wende | A Contingency Approach to Data Governance[END_REF] and the nuances attached to various domains and organizational types have not yet been extensively described [START_REF] Wang | An integrated data analytics process to optimize data governance of non-profit organization[END_REF], [START_REF] Abraham | Data governance: A conceptual framework, structured review, and research agenda[END_REF]. Furthermore, evidence is scant as to the role data governance plays in ensuring the successful outcomes of data science initiatives [START_REF] Fruehauf | Using The Bolman And Deal's Four Frames In Developing A Data Governance Strategy[END_REF], [START_REF] Hashem | The rise of 'big data' on cloud computing: Review and open research issues[END_REF].

Recent years have witnessed more and more asset management organizations adopting data science initiatives in order to support the digital transformation of their business processes [START_REF] Waller | Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management[END_REF], [START_REF] Berman | Digital transformation: opportunities to create new business models[END_REF], and Van der Aalst [START_REF] Van Der Aalst | Data science in action[END_REF] go so far as to suggest that organizations without a data science capability may not survive. According to Provost and Fawcett [START_REF] Provost | Data Science and its Relationship to Big Data and Data-Driven Decision Making[END_REF] (p.52), data science is "a set of fundamental principles that support and guide the principled extraction of information and knowledge from data". From this perspective, data science encompasses a broad range of knowledge and capabilities such as data-mining and machine learning, which are designed to extract knowledge from data and are important for creating value and moderating risk in data science initiatives. As such, data governance can help organizations make use of data as a competitive asset [START_REF] Morabito | Big Data Governance[END_REF], [START_REF] Madera | The next information architecture evolution: The data lake wave[END_REF]. Data governance aims at maximizing the value of data assets in enterprises [START_REF] Provost | Data Science and its Relationship to Big Data and Data-Driven Decision Making[END_REF], [START_REF] Otto | Data Governance[END_REF]. For example, capturing electric-and gas-usage data every few minutes benefits the consumer as well as the provider of energy. With active governance of big data, isolation of faults and quick fixing of issues can prevent systemic energy grid collapse [START_REF] Malik | Governing Big Data: Principles and practices[END_REF].

Data science can improve asset management decision-making which is needed to facilitate more efficient and secure asset management operations, as well the need for better situational awareness about network disturbances [START_REF] Kezunovic | The role of big data in improving power system operation and protection[END_REF], [START_REF] Waller | Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management[END_REF]. Data science initiatives such as predictive maintenance modelling generally require big data [START_REF] Kezunovic | The role of big data in improving power system operation and protection[END_REF], [START_REF] Chen | Business intelligence and analytics: from big data to big impact[END_REF], [START_REF] Fosso Wamba | How 'big data' can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]. Asset management organizations often choose to implement data lakes using a variety of architectures and technologies to store big data and to make this data available for use. A data lake is "a central repository system for storage, processing, and analysis of raw data, in which the data is kept in its original format and is processed to be queried only when needed" [START_REF] Couto | A mapping study about data lakes: An improved definition and possible architectures[END_REF] (p. 456). Data lakes are different to traditional data warehouses which often have their own native formats and structures as data is stored in its original, raw, format [START_REF] Madera | The next information architecture evolution: The data lake wave[END_REF], [START_REF] Miloslavskaya | Big Data, Fast Data and Data Lake Concepts[END_REF]. Often, the data processing systems which are required to allow the data to be ingested without compromising the data structure are also included in the definition [START_REF] Couto | A mapping study about data lakes: An improved definition and possible architectures[END_REF], [START_REF] Miloslavskaya | Big Data, Fast Data and Data Lake Concepts[END_REF]. The data in the data lake is generally immediately accessible, allowing users to utilize dynamic analytical applications [START_REF] Miloslavskaya | Big Data, Fast Data and Data Lake Concepts[END_REF], [START_REF] Ullah | Big Data in Cloud Computing: A Resource Management Perspective[END_REF]. This immediate accessibility, as well as the retaining of data in its original format presents a number of challenges regarding management of the data lake, including data quality management, data security and access control [START_REF] Madera | The next information architecture evolution: The data lake wave[END_REF], [START_REF] Kroll | Data Science Data Governance [AI Ethics[END_REF], as well as in maintaining compliance with regards to privacy [START_REF] Morabito | Big Data Governance[END_REF], [START_REF] Kroll | Data Science Data Governance [AI Ethics[END_REF]. As such, data governance has increasingly gained popularity as a means of ensuring data quality and maintaining compliance.

Managing data quality is considered by many researchers to be an important reason for adopting data governance (e.g. [START_REF] Wende | A Contingency Approach to Data Governance[END_REF], [START_REF] Otto | Data Governance[END_REF], [START_REF] Khatri | Designing data governance[END_REF]). However, big data can provide asset management organizations with complex challenges in the management of data quality. According to Saha & Srivastava [START_REF] Saha | Data quality: The other face of big data[END_REF], the massive volumes, high velocity and large variety of automatically generated data can lead to serious data quality management issues which can be difficult to manage in a timely manner [START_REF] Hazen | Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications[END_REF]. For example, IoT sensors calibrated to measure the salinity of water may, over time, begin to provide incorrect values due to biofouling. Data science information products often rely on near real-time data to provide timely alerts, and, as such, problems may arise if these data quality issues are not timely detected and corrected.

As well as establishing data management processes which manage data quality, data governance should also ensure that the organization's data management processes are compliant with laws, directives, policies and procedures [START_REF] Wilbanks | Data governance for SoS[END_REF]. For example, Panian [START_REF] Panian | Some practical experiences in data governance[END_REF] states that establishing and enforcing policies and processes around the management of data should be the foundation of effective data governance practice as using big data for data science often raises ethical concerns. Automatic data collection may cause privacy infringements [START_REF] Cecere | Perceived Internet privacy concerns on social networks in Europe[END_REF], [START_REF] Van Den Broek | Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation[END_REF] such as cameras used to track traffic on highways which often record personally identifiable data such as number plates or faces of persons in the vehicles. Data governance processes should ensure that these personally identifiable features are removed before data is shared or used for purposes other than legally allowed. Data governance should therefore establish what specific data privacy policies are appropriate [START_REF] Khatri | Designing data governance[END_REF] and applicable across the organization [START_REF] Malik | Governing Big Data: Principles and practices[END_REF]. For example, Tallon [START_REF] Tallon | Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost[END_REF] states that organizations have a social and legal responsibility to safeguard personal data, whilst Power & Trope [START_REF] Power | The 2006 survey of legal developments in data management, privacy, and information security: The continuing evolution of data governance[END_REF] suggest that risks and threats to data and privacy require diligent attention from organizations.

In summary, asset management organizations often choose to implement data science initiatives such as predictive maintenance and anomaly detection, using methods such as data-mining and machine learning, in order to support the digital transformation of their business processes. Many modern data science methods require big data which is often stored and made available through data lakes. However, asset management organizations are increasingly being faced with challenges which impact the success of data science outcomes, often related to: 1. a lack of trust in the quality of data [START_REF] Saha | Data quality: The other face of big data[END_REF], [START_REF] Hazen | Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications[END_REF], 2. whether or not the data is being used in an ethical way [START_REF] Tallon | Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost[END_REF], and 3. whether or not the management and use of the data is compliant with relevant legislation and internal policies [START_REF] Power | The 2006 survey of legal developments in data management, privacy, and information security: The continuing evolution of data governance[END_REF]. In order to tackle these challenges, data governance assigns responsibilities for decision-making [START_REF] Wende | A Contingency Approach to Data Governance[END_REF], defines processes for monitoring an managing data quality [START_REF] Hazen | Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications[END_REF], and defines policies for monitoring and maintaining compliance with relevant legislation [START_REF] Power | The 2006 survey of legal developments in data management, privacy, and information security: The continuing evolution of data governance[END_REF].

The propositions of the research are based on the results of the background literature review as well as on existing theory regarding the principles of data governance in asset management organizations and the reasons why asset management organizations choose to implement data governance [START_REF] Brous | Governing Asset Management Data Infrastructures[END_REF], [START_REF] Brous | Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles[END_REF], [START_REF] Brous | Factors Influencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations[END_REF]. The propositions of the research therefore read as follows:

1. Defining clear roles and responsibilities for data management will result in easier generation of business value from data science efforts. 2. Monitoring and managing data quality will result in more useful outcomes from data science efforts. 3. Compliance monitoring and control is a required condition for data science. As discussed above, the literature shows that many organizations have implemented data governance in an attempt to improve trust in data science efforts through the improved management of data quality and compliance to relevant legislation.

Methodology

This paper describes a single case study using a multi-method approach to investigate the role of data governance as success factor for data science. Case study is a widely adopted method for examining contemporary phenomenon such as the adoption of data governance [START_REF] Choudrie | Investigating the research approaches for examining technology adoption issues[END_REF], [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF]. In this research we analyze a single case, following the design of an explanatory case study research proposed by Yin [START_REF] Yin | Case Study Research: Design and Methods[END_REF], including the research question, the propositions for research, the unit of analysis, and the logic linking the data to the propositions. Single case study was selected as being appropriate for this research as there is a need to investigate data governance as success factor for data science in greater detail. In this regard, single case studies may be more appropriate than multiple case studies, as a single case study provides the opportunity to have a deeper understanding of data governance in a specific context [START_REF] Yin | Case Study Research: Design and Methods[END_REF], [START_REF] Gustafsson | Single case studies vs. multiple case studies: A comparative study[END_REF], in this case, data science efforts in the asset management domain. As suggested by Eisenhardt [START_REF] Eisenhardt | Building Theories from Case Study Research[END_REF], the research was contextualized by a review of background literature, identifying the generally accepted roles of data governance in a data science context. The literature background reveals data science initiatives often face a number of challenges, and not all efforts lead to successful outcomes [START_REF] Yoon | Data reusers' trust development[END_REF], [START_REF] Brous | Factors Influencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations[END_REF], [START_REF] Cao | A Trust Model for Data Sharing in Smart Cities[END_REF].

Facing these challenges has led many organizations to adopt data governance as a means of improving the outcomes of data science efforts [START_REF] Brous | Governing Asset Management Data Infrastructures[END_REF]. However, data governance remains a poorly understood concept [START_REF] Mathes | Big data has unique needs for information governance and data quality[END_REF], [START_REF] Kroll | Data Science Data Governance [AI Ethics[END_REF] and its contribution to the success of data science has not been widely researched [START_REF] Kroll | Data Science Data Governance [AI Ethics[END_REF]. As discussed above, our main research question therefore asks how does data governance contribute to more successful data science outcomes? Following Ketokivi & Choi [START_REF] Ketokivi | Renaissance of case research as a scientific method[END_REF], deduction type reasoning augmented by contextual considerations provided the basic logic for the propositions to be tested in a particular context, namely data science in an asset management domain. The data analysis in this research utilizes "within case analysis" [START_REF] Miles | Qualitative Data Analysis: An Expanded Sourcebook[END_REF]. Within case analysis helped us to examine the impact of data governance on the success of data science in a single context. In this case, the unit of analysis was a single data science project in the asset management domain. The case selected was managed and implemented by Rijkswaterstaat, often abbreviated to RWS and referred to as such in this paper. RWS is the Directorate-General for Public Works and Water Management and an operational agency of the Ministry of Infrastructure and Water Management of the Netherlands. RWS is charged with the management and maintenance of the major highways, waterways and shipping lanes in the Netherlands. In order to prepare the organization for the case study research project, RWS was provided with information material outlining the objectives of the project.

Following the suggestions of Yin [START_REF] Yin | Case Study Research: Design and Methods[END_REF], the case study was conducted using a multi-method approach and multiple data sources were used. Methods used are document analysis and face-toface interviews. The interviews were conducted during 2019 taking the form of one-on-one, faceto-face interviews. The interviewees were mainly selected from RWS staff members directly involved in the data science project in various roles, but also included other staff members involved in the governance and management of the data and the monitoring of the data in order to ensure saturation. Secondary data sources included relevant internal documentation, including project reports, data governance workshop reports, and data and information technology strategy documents. Company websites which included relevant data governance information and reports on the data science case were also included. Triangulation of aspects of data governance which contribute to the successful outcome of the data science case was made by listing aspects of data governance found in internal documentation and testing these in the one-on-one interviews. In the interviews the interviewees were asked as to the contribution of these aspects of data governance towards the successful outcome of the project. In the interviews the interviewees were also asked to name other aspects of data governance that may have had a significant contribution to the successful outcome of the data science project but which may have been overlooked.

Case Study Description

RWS is tasked with the management and maintenance of the national public infrastructure including the construction and maintenance of shipping lanes, major waterways (including flood prevention) and national roads and highways. RWS has a spend of approximately €200 million per annum on asphalt maintenance, with operational parameters traditionally focused on traffic safety. In the past this has led to increasing overspend due either to premature maintenance, or to expensive emergency repairs. The prediction of asphalt lifetime based on traditional parameters has been shown to be correct one third of the time. RWS is seeking to reduce these costs by extending the lifespan of asphalt where possible whilst reducing the number of emergency repairs made by adopting data science techniques for the purpose of predictive, "just-in-time" maintenance. Using available big data in a more detailed manner, such as raveling data collected by a Laser Crack Measurement System combined with Weigh-in-Motion data has doubled the prediction consistency. According to RWS officials, improving the accuracy of asphalt lifetime prediction has enabled better maintenance planning which has significantly reduced premature maintenance, improving road safety and cost savings, and reducing the environmental impact due to reduced traffic congestion and a reduction in CO 2 emissions. The data science model uses data related to traditional inspections, historical data generated during the laying of the asphalt, road attribute data and planning data, as well automatically generated, streaming data such as weather data, traffic data, and IoT sensor data. The current model takes about 400 parameters into consideration. According to an RWS official, "this number will only grow, as the (project partners) continue to supply new data". According to RWS, the ultimate goal is a model that can accurately predict the lifespan of a highway. With regards to defining roles and responsibilities RWS has asked the data managers of each of the datasets used in the data science project to each appoint an executive sponsor or data owner. The data owner is a business sponsor. Once ownership is established, the current and desired future situations are assessed in terms of production and delivery. A roadmap is then established which was translated into concrete actions and a delivery agreement is reached. RWS also uses "open" data from external sources. Due to its many open data partnerships, RWS has implemented a policy of providing knowledge, tools and a government-wide contact network in which best practices are shared with other government organizations. These best practices refer to organization of data management, data exchange with third parties, data processing methods and individual training. According to staff members, RWS has implemented data governance for their big data in order to remain "future-proof, agile and to improve digital interaction with citizens and partners". According to an RWS executive manager, "RWS wants to be careful, open and transparent about the way in which it handles big and open data and how it organizes itself". Furthermore, RWS has introduced the policy of assessing and publishing the monetary cost of data assets in order to raise awareness of the importance of data quality management. This means that every RWS process and every RWS organizational unit is encouraged to be aware of its data needs and the incurred costs.

With regards to data quality, RWS has implemented a data quality framework to improve their control of data quality. RWS staff believe that "the return (of the investment) stands or falls with the quality of data and information". As such, according to RWS staff, the underlying quality of the data and information is of great importance to work in an information-driven way. RWS staff members have suggested that, in the past, a significant amount of production time has often been lost due to inadequate data quality. The RWS data quality management process follows an eight step process which begins by identifying: 1. the data to be produced, 2. the value of the data for the RWS primary processes, and 3. a data owner. RWS has developed an automatic auditing tool (AAT) in combination with a Manual Auditing Tool (MAT) to monitor the quality of the data as a product in order to further improve its grip on data quality. According to RWS staff, the AAT and the MAT ensured that quality measurements were mutually comparable, provided tools for more focused management, and caused a change in the conscious use of data as a strategic asset. Alongside with the AAT, the MAT is considered important as it is not yet possible to automate the monitoring of all data quality dimensions. Data quality measuring is centralized at RWS, the goal being to ensure a standardized working method. However, RWS maintained the policy that every data owner is responsible for improvements to the data management process and the data itself. The RWS data quality framework was based on fitness for use and data quality measurement was maintained according to 8 main dimensions and 47 subdimensions.

With regards to compliance, RWS has translated their data policies and principles into a data agenda in which the opportunities, risks and dilemmas of their data policies and ambitions are identified in advance and are made measurable and practicable. Terms and definitions have been coordinated with the Dutch legal framework related to the environment to ensure compliance. Responsibilities relating to compliance to privacy laws are centralized and RWS has assigned privacy officers to this role. The CIO has the final responsibility for ensuring that privacy and security are managed and maintained, however, business data owners are held accountable for ensuring compliance to dataset specific policy and regulations.

Discussion

Case study methodology was used in this research to identify the role that data governance plays as success factor for data science. The choice for an in-depth, single case study was based on the contemporary nature of both data science and data governance and the need to study data governance as success factor for data science in greater depth. The study was conducted as a single case study and the results should be regarded in this light. Single case study has been criticized in the past due to the difficulty of providing a generalizing conclusion [START_REF] Yin | Case Study Research: Design and Methods[END_REF], [START_REF]Case study as a research method[END_REF]. In order to overcome this, the data collection made use of multiple sources including reports, presentations and face-to-face interviews. More research is recommended in this area to test the applicability of the propositions in other domains and organizational types. The study was conducted in the asset management domain as asset management organizations by nature are often data rich due to the need to monitor the state of the infrastructure assets. This may limit the applicability of the study for domains which are less data intensive, however the essence of generating value from data is likely to be the same in other domains.

5.1

Proposition 1: Defining clear roles and responsibilities for data management will result in easier generation of business value from data science efforts Proposition 1 proposes that data science is likely to generate more business value if responsibilities for data management are clearly defined. RWS has many various open data partners, as well as a large variety of sources from which the data is collected. As a result RWS has experienced difficulties in managing responsibilities for data quality and data management processes. RWS has therefore assumed a leadership role in maintaining a government-wide contact network in which knowledge, tooling and best practices with regards to data management and data sharing are shared with other government organizations. Internally, RWS has assigned business sponsors to assume ownership of datasets so that roles and responsibilities of data management are clearly defined. In order to ensure that sufficient resources are made available for data quality management, RWS has also defined a "price" for each dataset so that business owners are aware of the value of each dataset. This allows the organization to treat the data as a business asset, promoting the need to maintain the expected quality of each dataset.

5.2

Proposition 2: Monitoring and managing data quality will result in more useful outcomes from data science efforts Proposition 2 proposes that data science is more likely to result in useful outcomes if data quality is monitored and controlled. RWS actively monitors their data inputs by means of an "automatic audit tool". RWS has assembled a library of business rules which form the input for the calculation of the data quality. The results of the calculations are displayed in the form of a dashboard which indicates whether the calculated values fall within acceptable limits or not. The acceptable limits are described in the RWS data quality framework which has standardized the calculation and description of data quality throughout RWS. The results of the data quality monitor are used to define which interventions need to be taken in order to achieve the desired levels of quality and also to monitor the effects of the interventions on the data quality. Traditionally, data quality projects at RWS were based on "hearsay" from staff whereby the general feeling was that the quality was below requirements. The AAT has allowed RWS to be more data driven with regards to their data management processes. According to RWS staff, the active monitoring of data quality has led to "identification of gaps in data governance, harmonization of processes across organizational departments, increased awareness and cost savings".

Proposition 3: Compliance monitoring and control is a required condition for data science

Proposition 3 proposes that compliance with relevant legislation is a necessary and required condition for data science. RWS has had a central, IT-centered approach to data privacy to ensure that legal requirements and guidelines regarding the European General Data Protection Regulation (GDPR) are standardized and consistent throughout the organization. RWS has published a transparent list of systems in which personal data is collected, and has published detailed instructions as to how personal data may be viewed and, where necessary, deleted. RWS has appointed privacy and compliance officers to assume this responsibility and has appointed the CIO has the responsible executive sponsor. The monitoring of other compliance related activities is done using the AAT or the MAT. Responsibility for the actions flowing from the results of the AAT or the MAT lies with the data managers and ownership lies with the data sponsor. This hybrid approach allows RWS to standardize compliance processes where possible, whilst also being able to tailor customized solutions for particular data issues. Currently the feasibility of a nationwide data platform for asphalt pavement data is being explored in which easy data accessibility, authorization, storage, scalability, architecture, plateau planning, solution directions and cost estimations are addressed.

Conclusions

In this research paper we analyzed a case study regarding the governance of data in a data lake in the asset management domain to identify factors contributing to the success of using data science. The objective of the case study is to understand the role of data governance as success factor for data science. The case under study is a data science project which predicts the maintenance requirements of asphalt on national highways over time. Three propositions were defined on the basis of existing theory on data governance, namely: 1. defining clear roles and responsibilities for data management will result in easier generation of business value from data science efforts, 2. monitoring and managing data quality will result in more useful outcomes from data science efforts, and 3. compliance monitoring and control is a required condition for data science. These propositions were derived from the literature and confirmed in the case study, suggesting that data governance should be regarded as an important success factor for data science outcomes. The results show that clearly defined ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of compliance requirements are important factors for successful data science outcomes. The results also show that efficient management of compliance may be performed by developing centrally managed, standardized solutions for privacy and security requirements. However, system-specific compliance requirements need to be developed by data managers and these requirements should be owned by a business sponsor who assumes responsibility for these requirements. As such, the results show the data governance is an important success factor for data science outcomes as it ensures that data quality and compliance are effectively managed.


Caro Els

Sara Grobbelaar
email: sssgrobbelaar@sun.ac.za

Denzil Kennon

Complementary Partnerships for SMEs: A Relational Capability Maturity Model from an ecosystem perspective

Keywords: Inter-organisational relationships, SMEs, Relational Capability Maturity Model, Relational capabilities

Inter-organisational relationships have been receiving increased attention in the context of the fourth industrial revolution. Technological advances in connectivity and digitisation are enabling vertically and horizontally integrated networks. The highly technical and dynamic environment in which various types of relationships exist requires a high level of cooperation and transparency between partners. The importance for Small and Medium Sized Organisations (SMEs) to develop and improve their relational capabilities is widely acknowledged. This research paper thus presents a tool and methodology that will enable SMEs to assess and improve these capabilities within the organisation. This paper aims to identify those requirements and practices described in the literature as conducive to sustainable relationship formation and development. A Relational Capability Maturity Model (RCMM) is proposed as a tool that will be able to address the requirements across the various functions of the organisation.

Introduction

Strong technological advances in connectivity and digitisation, are enabling integrated networks of firms, objects and systems. Transparent and flexible ecosystems are forming, cultivating dynamic, collaborative and symbiotic relationships between firms. These ecosystems are shaping new manners to create value. Increasingly, value is being created not only within firms, but rather within the rich interactions between them [START_REF] Schlaepfer | Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies[END_REF], [START_REF] Canning | Business ecosystems come of age[END_REF]. The ecosystem perspective provides a powerful lens through which the transformation in the business landscape can be viewed, by emphasising the growing importance of relationships, partnerships, networks, alliances and collaboration [START_REF] Canning | Business ecosystems come of age[END_REF].

'Ecosystems in a business context' is a concept derived from the biological sciences. Just as biological ecosystems consist of various interdependent species, business ecosystems similarly consist of a variety of interdependent organisations. These dynamic and co-evolving communities create and capture new value through sophisticated models of collaboration and competition [START_REF] Canning | Business ecosystems come of age[END_REF], [START_REF] Iansiti | The Keystone Advantage : What the New Dynamics of Business Ecosystems Mean for Strategy , Innovation , and Sustainability[END_REF]. Multiple players of different types and sizes are brought together to serve markets in ways that are beyond the capacity of any single organisation or any traditional industry.

The complementarity between partners have been identified as a core theme towards ecosystems value creation [START_REF] Ulaga | Capturing value creation in business relationships: A customer perspective[END_REF]. Shared value creation provides complementary benefits to the partners and complementary benefits are what is at the core of a sustainable partnership [START_REF] Blomqvist | Partnering in the Dynamic Environment: The Role of Trust in Asymmetric Technology[END_REF]. Small and large firms have complementary strengths and weaknesses in terms of research and development (R&D). The flexible structures and agile operations of SMEs are especially suitable for the early stages of the innovation process where ideas are created and conceptualised [START_REF] Blomqvist | Partnering in the Dynamic Environment: The Role of Trust in Asymmetric Technology[END_REF], [START_REF] Narula | R&D Collaboration by SMEs: new opportunities and limitations in the face of globalisation[END_REF]. SMEs have a relative advantage in learning and knowledge creation in emerging and risky areas [START_REF] Nieto | Technological collaboration: Bridging the innovation gap between small and large firms[END_REF]. Large firms on the other hand, have existing structures that are suitable for testing, documentation and operation processes that are found at the later stages of innovation [START_REF] Blomqvist | Partnering in the Dynamic Environment: The Role of Trust in Asymmetric Technology[END_REF]. The deep specialisation of SMEs can complement the service offering of large firms where they do not have the internal expertise. Large firms can in turn, expose SMEs to the critical resources and capabilities required to realise innovating ideas [START_REF] Canning | Business ecosystems come of age[END_REF], [START_REF] Nieto | Technological collaboration: Bridging the innovation gap between small and large firms[END_REF].

Complementary benefits can also be found in the product or service offering of the individual firms. New and specialised capabilities are often considered a prerequisite to enable growth to new areas. While large firms usually focus on the products and services with major potential, they are able to access new capabilities from specialist firms, with less capital investment required on their own part [START_REF] Canning | Business ecosystems come of age[END_REF], [START_REF] Blomqvist | Partnering in the Dynamic Environment: The Role of Trust in Asymmetric Technology[END_REF]. For SMEs, this means that they are able to move into the markets of the large firm without acquiring additional capabilities [START_REF] Varadarajan | First-mover advantage in an Internetenabled market environment: conceptual framework and propositions[END_REF]. The advantage of partnerships with firms who have chosen to specialise in those activities, is that these firms are likely to perform the activities better. As a result, every activity is being performed by a tightly focused firm [START_REF] Canning | Business ecosystems come of age[END_REF]. Their first-mover advantage will be enhanced, enabling them to increase their market share [START_REF] Varadarajan | First-mover advantage in an Internetenabled market environment: conceptual framework and propositions[END_REF].

Another very important benefit from a partnership is in the form if organisational support. SMEs often have growth constraints due to undeveloped organisational structures and a lack of management skills [START_REF] Landau | Corporate Partnering Can Spur Innovation[END_REF]. Larger firms often offer their smaller partners resources such as marketing, distribution, manufacturing or training, as well as industry related know-how and expertise [START_REF] Teece | Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy[END_REF]. Large firms may even open up their contact networks to smaller partners and reference customers in the emerging industry. The reputation of the large firm usually has a positive impact on the credibility of their smaller partners. Increased credibility means that the cost of acquiring new customers or partners and sustaining existing ones will reduce [START_REF] Bocconcelli | Interacting with large customers: Resource development in small b2b suppliers[END_REF].

Large firms often pressure smaller partners to increase its competitiveness continuously to produce high quality services or products. SMEs would often receive customer-triggered relationship requirements, resulting in the need to customise technologies and systems specifically to suit their partners [START_REF] Blomqvist | Partnering in the Dynamic Environment: The Role of Trust in Asymmetric Technology[END_REF], [START_REF] Ngugi | Relational capabilities for value co-creation and innovation in SMEs[END_REF]. While these adaptations imply considerable, often non-transferable, investments by one or both parties, it simultaneously provides learning opportunities. The knowledge that SMEs acquire throughout the unique projects can be transferred to other partnerships or developed to products. In this regard, partnerships are seen as a key resource access to valuable organisational, technical and market knowledge held by key customers. This knowledge can then be used to improve and upgrade products and services, production facilities and organisational units and mechanisms [START_REF] Bocconcelli | Interacting with large customers: Resource development in small b2b suppliers[END_REF].

For SMEs to be able to exploit the opportunities from ecosystems, the nature of the support available to SMEs require a shift in focus. The highly technical and dynamic ecosystem environment require a high level of cooperation and transparency between partners [START_REF] Schröder | The Challenges of Industry 4.0 for Small and Medium-sized Enterprises[END_REF]. As a result, the establishment of partnerships and the development of trust simultaneously becomes increasingly critical and complex. For SMEs, who are traditionally known to suffer from severe resource constraints, creating partnerships with larger firms are becoming even more challenging [START_REF] Derrouiche | B2B relationship management: a framework to explore the impact collaboration[END_REF], [START_REF] Inderst | Buyer power and supplier incentives[END_REF]. Partnerships between small and larger firms are often asymmetrical, and SMEs are mostly not equipped to deal with power imbalances due to their lack of resources [START_REF] Blomqvist | Partnering in the Dynamic Environment: The Role of Trust in Asymmetric Technology[END_REF].

For SMEs to be able to establish more sustainable partnerships, they require technical, organisational, and managerial capabilities that can address the challenges presented by dynamic environments and changing relationship requirements [START_REF] Teece | Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy[END_REF]. These relational capabilities enable firms to relate to other firms more successfully, contributing to both their own knowledge and to that of their relationships [START_REF] Johnsen | Interaction capability development of smaller suppliers in relationships with larger customers[END_REF]. This paper aims to identify those requirements and practices described in the literature as conducive to sustainable relationship formation and development.

Methodology

The context of this research is centred predominately on SMEs, which can be considered to be a complex system of cultural, process, and technological components that interact with each other [START_REF] Du Preez | Enterprise Engineering Textbook[END_REF]. The research in this paper therefore followed the constructivist philosophical perspective, and was conducted primarily through an exploratory approach. Jabareen's (2009) conceptual framework analysis (CFA) method formed the foundation upon which the RCF was developed, with specific procedures modified according to the nature and requirements for this study. The CFA method is commonly used to build conceptual frameworks from multiple bodies of knowledge that belong to different disciplines [START_REF] Jabareen | Building a conceptual framework: Philosophy, definitions, and procedure[END_REF]. Due to the ability of the CFA method to clarify conceptual linkages between different domains it was deemed an appropriated method to guide the development of the RCF. The six phases of the CFA method is summarised in Table 1 Table 1: CFA methodology, adapted from Jabareen (2009)

Phase Description Section Phase 1: Extensive reading and categorisation of data

Read and categorise data from the spectrum of multidisciplinary literature regarding the phenomenon in question [START_REF] Jabareen | Building a conceptual framework: Philosophy, definitions, and procedure[END_REF].

Section 3

Phase 2: Identifying concepts

Read and reread the relevant data to discover concepts that are considered to be relevant to partnerships in the context of this study in some way.

Section 3

Phase 3: Deconstructing and categorising concepts

Identify the main attributes, characteristics and assumptions of each concept, and categorise the concepts accordingly.

Section 3

Phase 4: Integrating concepts

Integrate and group together similar concepts to reduce the number of concepts.

Section 4 Phase 5: Synthesis

Synthesise the concepts into a theoretical framework through a repetitive and iterative process.

Section 4 Phase 6: Validate framework

Validate whether the proposed framework and its concepts make sense not only to the researcher but also to other scholars and practitioners.

Not included in this paper

Phase 1 was completed through a systematised literature review to provide an exhaustive review of the literature that is currently available [START_REF] Popay | Rationale and standards for the systematic review of qualitative literature in health services research[END_REF]. The advantages of this method are mainly seen in its rigour and transparent process [START_REF] Bonas | How can systematic reviews incorporate qualitative research? A critical perspective[END_REF]. Data was collected from Scopus. The search was completed by using a combination of the keywords "business ecosystem" and "SME". The search delivered a total of 38 documents, which was then filtered through a series of criteria. After the initial screening process the final 22 documents were critically analysed (See Table 2). "SME" + "business ecosystem"; "Small business" + "business ecosystem"; and "Small firm" + "business ecosystem".

Publication types included Academic journals and conference papers

Publication types excluded Magazines and news articles

Other excluding criteria Foreign language papers; inaccessible papers; papers not relevant to topic; and repetitive papers After the initial screening process the final 22 documents were critically analysed. The data analysis criteria were broken up into two sections namely 1) descriptive statistics, and 2) qualitative criteria.

Through Phase 2, an in-depth review of the research domain has resulted in a comprehensive theory base which contains a large amount of implicit knowledge that needs to be made explicit. Through this review, 114 concepts have been identified that are deemed relevant to the main research objective of this study. Following Jabareen's (2009) CFA method as described in Table 1, each of these concepts was deconstructed into its main attributes and characteristics. This was done by labelling each of the concepts with a relevant theme that describes the attributes, characteristics of and assumptions around each. After each of the concepts has been deconstructed, [START_REF] Jabareen | Building a conceptual framework: Philosophy, definitions, and procedure[END_REF] explains that the concepts should be categorised accordingly. Following the theme allocation of each concept, the concepts that share similar themes were grouped together (Phase 3).

Phase 4 of the CFA method requires the concepts that have similarities to be integrated and grouped together. The concepts were integrated by grouping together the themes that had the strongest interrelations. This phase resulted in five main themes, each addressing a critical relational issue related to SMEs in dynamic business ecosystem environments. These themes include (1) goal congruency, (2) trust, (3) collaboration, (4) flexibility, and (5) learning.

Phase 5 of Jabareen's (2009) CFA method requires the concepts to be synthesized into a conceptual framework. This means that the 37 relational capabilities were consolidated into a conceptualization that will enable firms to identify and improve these capabilities. The framework development therefore constitutes two parts. The first which concerns the appropriate structuring into a conceptual framework, and the second which concerns an appropriate methodology needed to use the framework.

Themes and related relational capabilities

While the themes identified represent the requirements that SMEs must be able to meet in their B2B relationships if they operate in business ecosystems, it is necessary to convert each theme into the organisational means through which these relationship requirements can be addressed. The organisational means, referred to as relational capabilities, thus identify certain internal capabilities that SMEs would require to satisfy the relationship requirements. In total, 37 relational capabilities were identified, these are also included in Table 3 Table 3: Summary of main themes and related relational capabilities

Theme

Description Relational capabilities Goal congruency

In B2B relationships, partners work together towards reaching a common goal [START_REF] Archer | Managing business-to-business relationships throughout the e-commerce procurement life cycle[END_REF]- [START_REF] Gadde | Activity Coordination and Resource Combining in Distribution Networks -Implications for Relationship Involvement and the Relationship Atmosphere[END_REF]. The level of goal congruency refers to the possibility for both firms to achieve their goals simultaneously [START_REF] Mackenzie | Partnering Attractiveness in Buyer-Seller Relationships[END_REF]. According to Cuevas, Julkunen and Gabrielsson (2015), goal congruency can be viewed as a prerequisite for developing relationships of trust. If partnerships are goal congruent, the firms will view joint action as mutually beneficial [START_REF] Naudé | Assessing relationship quality[END_REF]. a) Establish shared relationship vision and goals; b) Establish organisational vision and goals; c) Developing partnering strategy; d) Identify potential partners; e) Uphold external reputation; f) Attract complementary partners; and g) Obtain market knowledge.

Trust

Trust is widely associated with successful B2B relationships. Cooperation between partners, as well as the willingness for future collaboration, can arise directly from a strong relationship of trust. Conversely, conflict and uncertainty can be seen as a direct consequence of lack of trust [START_REF] Pruitt | Negotiation behavior[END_REF], [START_REF] Morgan | The commitment-trust theory of relationship marketing[END_REF].

a) Establish trustworthiness through behaviour; b) Assign boundary spanner; c) Measure relationship performance; d) Create and sustain unique value offering; e) Balance investment in relationships; f) Asses relationship risk; and g) Manage intellectual property.

Collaboration

B2B relationships are increasingly involving the sharing of resources, allowing firms to create and share mutual benefits [START_REF] Kandampully | B2B relationships and networks in the Internet age[END_REF]. Firms with complementary capabilities and expertise are connected, providing the opportunity for mutually complementary action in pursuit of a common goal [START_REF] Cunningham | Competitiveness through networks of relationships in information technology product markets[END_REF].

Flexibility

B2B relationships are becoming increasingly agile and adaptive as they have the need to support faster and more flexible responses to constantly changing customer needs. Due to the dynamic business environment, B2B relationships need to be resilient and anti-fragile in order to display self-organising, flexible qualities that are capable of reconfiguring and overcoming shocks and disruptions [START_REF] Canning | Business ecosystems come of age[END_REF]. a) Maintain adaptable and flexible organisational structure; b) Enable product / process experimentation; c) Encourage interdisciplinary knowledge; d) Enable individual reflective capacity; e) Allocate internal resources to relationship; f) Balance relationship portfolios; and g) Establish contracting policy.

Learning Knowledge and data is created and exchanged between partners, offering various opportunities for firms to learn and increase their own internal knowledge. Firms must be able to integrate new data and knowledge within their systems and incorporate it into their internal processes [START_REF] Herdon | Digital Business Ecosystem Prototyping for Agri-food SMEs[END_REF].

a) Manage internal tacit knowledge; b) Manage internal communication and information flow; c) Manage tacit knowledge between partners; d) Define communication channels between partners; e) Externalise data and information; f) Capture, store and retrieve data; g) Analyse data; h) Establish data exploitation strategy; i) Create data security architectures; and j) Determine relationship functional requirements.

A Relational Capability Maturity Framework

While there are various ways to measure process capabilities, the topic of capability improvement often refocuses on the content and guidelines of maturity modelling. Maturity models are well-known and widely used tools that enable users to assess the current state of maturity of capabilities in a certain domain. Maturity models further enables users to identify the strengths and weaknesses of those capabilities, and suggests an improvement plan to increase overall performance.

The concept of maturity can be traced back to quality management when [START_REF] Crosby | Quality is free: The art of making quality certain[END_REF] introduced the idea of maturity stages building on each other [START_REF] Wendler | The maturity of maturity model research: A systematic mapping study[END_REF]. One of the most recognised and most widely used maturity models today is the Capability Maturity Model Integration (CMMI®). The CMMI has its roots in the original Capability Maturity Model® for software (SW-CMM®), which was developed in 1986 in response to a request from the federal government for a method to assess the capability of their software contractors. The Software Engineering Institute (SEI) developed a process maturity framework that would help organisations improve their software processes [START_REF] Humphrey | Three process perspectives: Organizations, teams, and people[END_REF], [START_REF] Paulk | A History of the Capability Maturity Model for Software[END_REF]. The SEI defined the CMMI as "a reference or process model of mature practices in a specified discipline, used to improve and appraise a group's capability to perform that discipline" [START_REF] Höggerl | An introduction to CMMI and its assessment procedure[END_REF].

The structure of the framework developed in this article needs to address multiple dimensions of relational capability throughout various parts of an organisation. Furthermore, complex interrelations exist between the relational capabilities. For this reason, it was decided to construct the framework along two dimensions, the organisational construct and the relational construct. The structure is largely based on the structure of the Innovation Capability Maturity Model (ICMM) as developed by [START_REF] Essmann | Toward Innovation Capability Maturity[END_REF]. The ICMM guides its users to address the maturity of innovation capability, while considering the multiple dimensions of innovation, and the different parts of the organisation that is affected. The model is also designed with the applicability and practicality factor in mind. Relational capabilities share various fundamental aspects with innovation capabilities in the sense that it is multi-dimensional, dynamic and complex. The ICMM is consequently considered to be a suitable reference to structure relational capabilities. The structure that forms the Relational Capability Framework (RCF) is displayed in Figure 1.

Structure outline

The framework is structured along three dimensions. The dimensions include (1) Relational Capability construct, (2) Organisational Construct and (3) Relational Capability Maturity. The three dimensions are summarised in Table 4.

Table 4: Three dimensions of the RCF

Relational Capability construct Relationship Lifecycle

Relationships are not static, it rather evolves through a series of stages. This capability area refers to the practices, procedures and activities that are executed at the initiation of the relationship, through the growth phases of the relationship and the continuous interaction in the relationship.

Knowledge and Information

The transfer of knowledge between partners and the subsequent learning within the organisation is a fundamental part of B2B relationships. This process area addresses the capabilities to identify, acquire and manage knowledge. Also included is the organisations ability to capture, manage and utilise valuable data that is accumulated in the relationship.

Organisational Structure

The infrastructure, resources, strategy, policies and management necessary to support the relationships, and knowledge and information requirements.

Organisational Construct Strategy & Objectives

The management response to uncertain environments, it includes the mission, vision and objectives. It provides targets and goals for the processes to steer the organisation in a particular direction.

Organisation & Management

The formal structure and governance of the organisation that is defined with the purpose of fulfilling the strategy and objectives.

Function & Processes

The activities that are performed within the organisation that drives the organisation closer to fulfilling its objectives.

Relational Capability Maturity [39] Level 1: Initial

Processes are mostly ad hoc and chaotic. A stable environment to support processes are not provided.

Level 2: Managed

The need for relational capabilities have been identified and defined. Foundation to implement processes have been created. Process adherence is periodically evaluated.

Level 3: Defined

Practices, procedures and tools have been defined and implemented. Outputs are consistent.

Level 4: Measured

Focus is managing and improving process performance. Activities and resources are integrated and aligned within organisation. Processes are continuously monitored and evaluated.

Level 5: Optimised

Synchronisation and institutionalisation of activities and processes. Organisation continually improves its processes based on a quantitative understanding of its business objectives and performance needs.

Relational capabilities

The relational capabilities are at the core of the RCF. These capabilities were therefore categorised into the structure as it has been described in the previous section. The resulting two-dimensional face of the framework is displayed in Error! Reference source not found. Each of the capabilities was assigned an Organisational construct area and a Relational construct item which best defines the capability. For example, based on its representative code (RL/SO1), capability 'Establish shared relationship vision and goals' is assigned to the Strategy and objectives area as it addresses the direction in which the relationships are steered. At the same time, it is assigned to the Relationship opportunities item, as it involves searching and identifying new opportunities, as well as determining the possible implications of these opportunities. The remainder of the capabilities are distributed in a similar manner. Each of the remaining relational capabilities were similarly categorised into the structure of the framework.

Conclusion

This paper presents a model that describes the relational capabilities of small firms at three levels of detail. The 36 identified relational capabilities relates to the various aspects of the organisation through the organisational construct, providing a holistic view of the challenges associated with B2B relationships. At the same time, the granularity of the model allows that capability issues to be addressed incrementally and in part. The nature of the model is thus suitable for SMEs, for whom large, expensive and time-consuming projects are often not a feasible option. Future research to evaluate the maturity of the capability requirements in a wide range of firms is proposed. This would highlight the capability requirements as a firm develops. The aim is to enable ownermanagers to improve their relational capability requirements proactively, and ultimately improve their ability to establish and sustain beneficial partnerships.

  a) Interpret and contextualise partner diversity; b) Understand partner requirements; c) Identify mutual opportunities; d) Adapt to relationship; e) Create joint knowledge; and f) Leverage external resources.

Figure 1 :

 1 Figure 1: RCF structure, adapted from[START_REF] Essmann | Toward Innovation Capability Maturity[END_REF]

Table 2 : Systematized review criteria

 2

	Criteria	Description
	Search engine	Scopus
	Latest search date	31 May 2017
	Search terms

Table 5 : Relational Capability Requirements categorised into construct

 5

	Organisational	Strategy & Objectives	Organisation & Man-	Function & Process
	Construct		agement
	Relational Construct
		Relationship	RL/SO1 -Establish	RL/OM1 -Interpret	RL/FP1 -Identify mutual op-
		Opportuni-	shared relationship vi-	and contextualise part-	portunities
		ties	sion and goals	ner diversity
		Relationship	RL/SO2 -Create and	RL/OM2 -Allocate	RL/FP2 -Adapt to relationship
		Development	sustain unique value of-	internal resources to
			fering	relationship
			RL/SO3 -Leverage ex-
	Relationship Lifecycle	Relationship Environment Interaction Process	ternal resources RL/SO4 -Develop partnering strategy RL/SO5 -Establish contracting policy	RL/OM3 -Obtain market knowledge RL/OM4 -Uphold external reputation RL/OM5 -Assign boundary spanner	RL/FP3 -Identify complemen-tary partners RL/FP4 -Attract complemen-tary partners RL/FP5 -Define communica-tion channels between partners
		Relationship	KI/SO1 -Understand	KI/OM1 -Manage in-	KI/FP1	-	Create	joint
		Learning	partner requirements	tellectual property	knowledge
				KI/OM2 -Manage
				tacit knowledge be-
				tween partners
	Knowledge & Information	Organisa-tional learn-ing Data	KI/SO2 -Manage inter-nal tacit knowledge KI/SO3 -Establish data exploitation strategy	KI/OM3 -Determine relationship functional requirements KI/OM4 -External-ise data & information	KI/FP2 -Manage internal com-munication & information flow KI/FP3 -Create data security architectures KI/FP4 -Capture, store & re-trieve data KI/FP5 -Analyse data