Data-driven Culture Research Articles

INTERPRETING LARGE SCALE NATIONAL LEVEL ASSESSMENT DATA IN MATHEMATICS BY USING RASCH ANALYSIS

Latvia is undergoing a nation-wide curriculum reform in general education, with an aim to help students to develop 21st century skills. In order to successfully implement reform, not only teacher performance in the classroom is important, but also the transformation of the school culture is of high priority. One of the key dimensions that is characteristic for a school as learning organization culture is whether it has data-driven culture and is using data on continuous basis to improve student achievement. Large scale national level assessment data is used for many different purposes, however, this data only rarely is recognised as useful data source for planning actions to improve student achievement at school level. Authors argue that in different grades average performance of students cannot be compared in a meaningful way to develop action plan and evaluate the impact of the initiatives at the school level. It is based on the issues rising from varying difficulty level of the tests and different skills, which are being assessed. The study design is based on in-depth analysis of items of large-scale national level assessment in mathematics, defining minimum level of competency of mathematics and calculating percentage of students in school with minimum level of competence in a cohort. This analysis is conveyed for the students of 3rd, 6th and 9th grade by using Rasch model, thus allowing to effectively monitor the student performance during the general education and use of data to make informed decisions.

Role of Pharmacy Analytics in Creating a Data-Driven Culture for Frontline Management.

Purpose: The purpose of this article is to offer key recommendations based on the authors' experiences for utilizing pharmacy analytics to support moving beyond standard-of-practice operational metrics towards high impact reporting to drive day-to-day decisions for frontline leaders. Summary: There is a continuous and vast amount of data generated through all facets of a health system's daily operations, yet many data elements go unused and fail to contribute to value creation and increased performance at an organizational level. It is critical, therefore, for departments of pharmacy to identify and implement effective strategies to leverage data through robust business analytics and reporting, ensuring managers at every level are provided the information they need to support data-driven decisions and meaningful interventions in the day-to-day operations of the organization. At the authors' institution, development and growth of a dedicated Pharmacy Analytics (PA) team has been instrumental to the pharmacy department for generating value and proactively supporting a business intelligence strategy that focuses on a data-driven management culture. Key recommendations to leverage pharmacy analytics are provided within four overarching themes: building transparency, leveraging synergy, optimizing actionability, and prioritizing partnerships. Conclusion: Through creation of a data-driven management culture, the authors provide recommendations for leveraging pharmacy analytics to reduce costs and impact outcomes across a range of hospital pharmacy operations.

Towards data-driven culture in a Spanish automobile manufacturer: A case study

Purpose: Data-driven decision-making is a growing trend that lots of companies are nowadays willing to adopt. However, the organizational transformation needed is not always as simple and logical as it could seem and the comfort of the old habits can dim the change effort. The purpose of this study is to identify the potential problems that may arise in a real company’s transformation from a traditional intuition-driven decision-making model to a data-driven model. Design/methodology/approach: In order to reach this goal, a single case study method was used. Initially a literature review was conducted to analyze both the importance of the change to a data-driven culture and the process of organizational change. Thus, a case study method was adopted in a company of the automotive sector that included experimentation in the website design decision-making process. Findings: As a result of the case study, it was found that all the most cited risks for the organizational change process commented in the literature appeared in the project. However, even being warned of potential dangers the specific actions to prevent the damages were not trivial.Originality/value: The study presents in detail, the application of an organizational change model in a company. Important insights can be extracted from the specific case of a digitalization performed inside traditional industrial company.

Data science for business: benefits, challenges and opportunities

PurposeThis paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability.Design/methodology/approachInitially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis.FindingsThe most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy.Research limitations/implicationsIn addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities.Practical implicationsTo transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology.Originality/valueA vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.

A Workload-Driven Approach for View Selection in Large Dimensional Datasets

The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of read-intensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads.

Data-driven quality improvement, culture change, and the high reliability journey at a special hospital for people with medically complex developmental disabilities

ABSTRACT The journey towards high-reliability care is challenging for any organization, but particularly so for one whose ethos has historically straddled the healthcare and human services cultures without a strong data-driven quality management program. Such a transition, while bringing with it great strides in patient safety and improvement in quality of care, can also be associated with considerable culture change. We describe a transition journey undergone by a special hospital for those with developmental disabilities to a data-driven quality improvement and patient safety culture on a high reliability journey to deliver exceptionally safe, high quality care to every patient, every day.

Big Data Analytics Capability for Digital Transformation in the Insurance Sector

In order for organisations to generate competitive advantages from big data investments, they need to acquire a unique blend of technology, human skills, financial resources and a data-driven culture. Organisations need to measure their big data analytics capability in order to yield competitive performance. This study sought to examine the relationship between a firm's big data analytics capability (BDAC) and competitive performance through mediating role of dynamic and operational capabilities. To test the proposed research model, we used survey data from 110 employees across 54 insurance companies in Kenya. Using partial least squares structural equation modelling, the results provide evidence that BDAC leads to superior firm performance. Various resources that form big data analytics (BDA) capability have been identified and an instrument to measure BDAC is proposed. The findings from this study provide a roadmap strategy for implementing BDA projects.

Smart Factory Implementation in Morrocan Phosphate Industry

Nowadays, the phosphate industry in Morocco is expecting a crucial round of its development cycle, which is mainly dominated by the digitization challenge. The concept of smart factory is reminiscent of a new way of organizing the production of goods, taking smarter decisions based on environmental variables and optimizing available resources. As for the phosphate industry in Morocco, building smart factories to upgrade the manufacturing processes constitutes a key element in its drive for innovation, growth and sustainable profitability. To successfully build smart factories, phosphate-manufacturing entities should consider some issues with care. This includes fostering IT and data-driven culture among leadership and employees, ensuring security, reliability and stability of IT infrastructure; maintaining the integrity of production processes and IT-controlled processes as well as enhancing IT and data skills and know how. However, there is still a journey to carry out the implementation of smart factories; given the large-scale, systemic transformation, the move requires alongside the lack of Enterprise Reference Model confirmed to fit with the issues of Moroccan phosphate industry. In this paper, we propose a reference model built around three overarching principles: (1) Culture of smart people 4.0, (2) introducing agile organizations, and (3) upgrading IT infrastructure. The paper then makes a case of the transition to smart factory using the proposed model in a pilot entity located at Safi. The results showed that the smart factory implemented yield a range of benefits, such as increased process efficiency, product quality, sustainability, and safety and decreased costs.

Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management

The purpose of this paper is to provide a theory-based explanation for the generation of competitive advantage from Analytics and to examine this explanation with evidence from confirmatory case studies. A theoretical argumentation for achieving sustainable competitive advantage from knowledge unfolding in the knowledge-based view forms the foundation for this explanation. Literature about the process of Analytics initiatives, surrounding factors, and conditions, and benefits from Analytics are mapped onto the knowledge-based view to derive propositions. Eight confirmatory case studies of organizations mature in Analytics were collected, focused on Logistics and Supply Chain Management. A theoretical framework explaining the creation of competitive advantage from Analytics is derived and presented with an extensive description and rationale. This highlights various aspects outside of the analytical methods contributing to impactful and successful Analytics initiatives. Thereby, the relevance of a problem focus and iterative solving of the problem, especially with incorporation of user feedback, is justified and compared to other approaches. Regarding expertise, the advantage of cross-functional teams over data scientist centric initiatives is discussed, as well as modes and reasons of incorporating external expertise. Regarding the deployment of Analytics solutions, the importance of consumability, users assuming responsibility of incorporating solutions into their processes, and an innovation promoting culture (as opposed to a data-driven culture) are described and rationalized. Further, this study presents a practical manifestation of the knowledge-based view.

An empirical study on business analytics affordances enhancing the management of cloud computing data security

The mechanism of business analytics affordances enhancing the management of cloud computing data security is a key antecedent in improving cloud computing security. Based on information value chain theory and IT affordances theory, a research model is built to investigate the underlying mechanism of business analytics affordances enhancing the management of cloud computing data security. The model includes business analytics affordances, decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and the management of cloud computing data security. Simultaneously, the model considers the role of data-driven culture and IT business process integration. It is empirically tested using data collected from 316 enterprises by Partial Least Squares-based structural equation model. Without data-driven culture and IT business process integration, the results suggest that there is a process from business analytics affordances to decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and to the management of cloud computing data security. Moreover, Data-driven culture and IT business process integration have a positive mediation effect on the relationship between business analytics affordances and decision-making affordances of cloud computing data security. The conclusions in this study provide useful references for the enterprise to strengthen the management of cloud computing data security using business analytics.

Using data to maximise event impact and return on investment

The data-driven culture of today’s workforce has made it imperative that marketers implement a data-driven strategy to showcase value and increase the impact of their strategy. Using data, field marketers can curate offline event experiences that will boost brand awareness and engagement, and prove they are affecting the bottom line through tangible, measurable results. This paper walks through how the buyer landscape has shifted because of data and what this means for field marketers. It also provides tried and true examples of specific key performance indicators and metrics that field marketers can apply to their strategy to maximise the influence of their efforts and return on investment, and build trust and confidence between the field marketing team and the greater organisation.

Big Data and Predictive Analytics Capabilities: A Review of Literature on Its Impact on Firm’s Financial Performance

In the era of the fourth industrial revolution, big data and predictive analytics (BDPA) capabilities considered as one of the significant resources that enable a firm to gain competitiveness. Nowadays, the advancement of technology information increases the difficulty of the firm to manage vast amounts of structured and unstructured data. The excessive growth between data captured and the firm’s capabilities to manage, process, analyze, and transfer the big data to actionable knowledge and value still challenges many firms in the competitive market. Besides, the lack of tangible resources, technical skills, management skills, organizational learning, and data-driven culture are some of the challenges for firms to apply analytics approach to support the data processing process. These situations led to poor decision making by the firm and result in high operation cost and lower profitability. However, the situation will be different if the firm able to manage BDPA capabilities in the right way. Based on the identified problems, this study aims to review the impact of BDPA capabilities on a firm’s financial performance. This study is expected to enhance the body of knowledge on BDPA capabilities and the firm’s financial performance. This study also provides information regarding the importance of BDPA capabilities on the firm’s financial performance to industrial practitioners. An empirical study on this subject matter is suggested for future researchers, especially in Malaysia manufacturing industry.

Data ratcheting and data-driven organisational change in transport

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration.

Data is Not Scarce, But Oil Companies Hoard It As If It Were

A growing number of oil industry leaders are saying that data sharing across the industry is needed, but change is coming slowly. Recently an executive from Shell said that “unprecedented data transparency” will be needed for the oil business to adapt to rapid change. That call for action came from Mike DeWitt, vice president of operated upstream wells for Shell, during the keynote speech at the SPE/IADC International Drilling Conference, where he asked: “Is data really proprietary?” Afterward, some delegates who heard the speech said they were struck by the call for openness in a business where that is generally not the case. DeWitt said after the talk that while the need for change is apparent, little progress has been made. He added that Shell is supporting initiatives for more sharing. There are multiple barriers to overcome, according to a panel at CERA-Week, an annual conference of oil and gas industry executives. Data-driven decision making represents a challenge to how engineers have historically made decisions in a business where companies shared as little as possible about wells and operations. “Pivoting to a data-driven culture is hard enough. Now we’re trying to change the mindset from competing to partnering,” said Kentaro Kawamori, vice president and chief digital officer for Chesapeake Energy. Digital decision analysis is a break from doing things based on experience and intuition. Companies often man-age information assuming that revealing data will be giving away valuable knowledge. “Data sharing is important,” Kawamori added. “The context of what is around it is equally important. We should get to the point of sharing more data, but we are all looking for competitive advantage.” Finding and Producing The urge to hoard data may be explained by a widely quoted comment that “data is the next oil.” Both things can be valuable. But there are fundamental differences in how one extracts value from hydrocarbons in the ground and data in the cloud. Oil prices are impossible to predict, but the world’s voracious appetite for energy ensures it has value. Data’s value depends on how it is used. Having enough of the right data can help engineers find ways to reduce drilling costs, eke out more oil from the ground, or renegotiate a service contract based on productivity rather than the number of days worked. Or it can be stored away and never looked at again, only contributing to the cost of cloud storage. “Looking at data as oil may be like the tail wagging the dog,” said Dave Mabee, global innovation manager for ConocoPhillips. The company creates value by finding and producing oil and gas. Its people use data and analytics to help create that value. “Thinking of data as the next oil may lead to focusing on doing things for their own sake rather than creating value,” he said.

Understanding Data-Driven Organizational Culture: A Case Study of Family League of Baltimore

Human service organizations have long been encouraged to utilize performance measurement to track their efficiency and effectiveness. But recent trends move beyond the measurement of outcomes to the management of large, often messy, datasets surrounding programmatic impact . Given the resources required for tracking performance measures, organizational data has the potential to be of value for nonprofit organizations (i.e., grants, fundraising), but can data be considered an organizational value? Using a multimethod case study of Family League, a human service organization in Baltimore, Maryland, this research found that there are varying attitudes about efforts to be data driven among staff of the organization suggesting that data as an organizational value has not yet been solidified. Utilizing Family League as an example, this research explores how organizations can promote an internal culture that clarifies the meanings of data and the incorporation of data into decision-making processes. This research adds a fourth pillar to Scott’s Institutional Theory that seeks to promote knowledge utilization as the basis of order within an organization.

A foundation for building a data-driven culture

As the world is becoming more digital, connected and intelligent, there is a growing desire among companies to become more data-driven. The unstoppable appetite for higher speed, sound and image resolution in this digital revolution is accompanied by an exponential increase in the volume of data. The key challenge, however, is how to use such data to serve our goals better. It is unwise to rely solely on technologies to cope with this challenge; rather, organisations must strike a balance between people and technologies. Technologies are here to help humans to take decisions, not take responsibility for decision-making. Championing this transformation in organisational culture is not easy and it is essential to lead by example. This article provides a foundation for building a data-driven culture and serves as a guideline for datadriven champions and other individuals who want to make such a change. It provides a generic framework on how data can play a critical role in research, problem solving and decision-making; provides an overview of common challenges; and suggests mitigations for daily practice.

Corporate Attitudes towards Big Data and Its Impact on Performance Management: A Qualitative Study

This paper investigates the organizational challenges raised by Big Data and its impact on the business environment with a focus on performance management. We investigate managers’ perceptions, understanding, and attitudes relating to Big Data and its analytics, in terms of opportunities, extent, limitations, challenges, and implications, with specific reference to performance management. The research methodology we adopt is grounded theory: we develop a reflection guide based on research questions covering the impact and challenges of a data-driven culture on business, and the impact on performance management and the decision-making process. The results obtained from senior executives from 21 Romanian companies leads to a conceptual model that distils the major areas arising from the responses and the interrelationships between them. These reveal several key areas of managerial relevance and suggest fruitful action. In particular, we find that the most critical areas requiring intervention lie in the area of awareness and understanding, goal setting, assessing benefits and limitations, learning to trust data, and commitment to an embedded data-driven culture. In addition to changes within organizations themselves, there are also implications for other stakeholders, such as education providers.

Practical data collection and extraction for big data applications in radiotherapy.

The capture of high-quality treatment data and outcomes is necessary in order to learn from our clinical experiences with big data analytics. In radiotherapy, there are several practical challenges to overcome. Practical aspects of data collection are discussed pointing to a need for a culture change in clinical practice to one that captures structured patient-related data in routine care in a prospective manner. Radiation dosimetry and the contoured anatomy must also be captured routinely to represent the best estimate of delivered radiation. The quality and integrity present in the data are critical which poses opportunities to introduce electronic validity checking to improve them. Similarly, data completeness and methods and technology to improve the efficiency and sufficiency of data capture can be introduced. In the manuscript, the types of clinical data are discussed including patient reports, images, biospecimens, treatments, and symptom management. With a data-driven culture, the realization of a learning health system is possible unlocking the potential of big data and its influence on clinical decision-making and hypothesis generation.

Understanding the impact of business analytics on innovation

Advances in Business Analytics in the era of Big Data have provided unprecedented opportunities for organizations to innovate. With insights gained from Business Analytics, companies are able to develop new or improved products/services. However, few studies have investigated the mechanism through which Business Analytics contributes to a firm's innovation success. This research aims to address this gap by theoretically and empirically investigating the relationship between Business Analytics and innovation. To achieve this aim, absorptive capacity theory is used as a theoretical lens to inform the development of a research model. Absorptive capacity theory refers to a firm's ability to recognize the value of new, external information, assimilate it and apply it to commercial ends. The research model covers the use of Business Analytics, environmental scanning, data-driven culture, innovation (new product newness and meaningfulness), and competitive advantage. The research model is tested through a questionnaire survey of 218 UK businesses. The results suggest that Business Analytics directly improves environmental scanning which in turn helps to enhance a company's innovation. Business Analytics also directly enhances data-driven culture that in turn impacts on environmental scanning. Data-driven culture plays another important role by moderating the effect of environmental scanning on new product meaningfulness. The findings demonstrate the positive impact of business analytics on innovation and the pivotal roles of environmental scanning and data-driven culture. Organizations wishing to realize the potential of Business Analytics thus need changes in both their external and internal focus.

So you want to be a data-driven advancement shop?

The modern alumni and development organisation is awash with data. On a daily basis, our digital feeds are filled with content that inspires us with analytics and data visualisations for higher education advancement. Advancement executives are embracing their growing reliance on data, seeing it as a strategic tool and openly advocating the need for their organisations to be 'data driven' yet, sadly, the rhetoric does not always translate to the expected strategic value. Executives can be left feeling underwhelmed by their organisation's utilisation of data and advanced analytics. Perhaps, our ability to fully embrace sophisticated analytics is hampered by a sometimes overlooked, more fundamental advancement function' systems and data management. This paper has no 'wow factor'. No high-end analytics, no predictive models and no impressive data visualisations. Instead, it aims to help establish and/or improve the data and systems function within an advancement organisation. The paper presents some practical steps that are beginning to see a data culture seed and flourish in the Division of Alumni and Development at the University of Sydney. It shines a spotlight on some of the issues, processes, frameworks and management artefacts that may assist an organisation to incrementally build their data-driven culture.