Interactive Dashboard Design for Manager, Data Analyst and Data Scientist Perspective

2021 ◽  
Author(s):  
Temitope Olubunmi Awodiji

With large amounts of unstructured data being produced every day, organizations are trying to extract as much relevant information as possible. This massive quantity of data is collected from a variety of sources, and data analysts and data scientists use it to create a dashboard that provides a complete picture of the organization's performance. Dashboards are business intelligence (BI) reporting tools that collect and show key metrics and key performance indicators (KPIs) on a single screen, enabling users to monitor and analyse business performance at a glance. An objective assessment of the company's overall performance, as well as of each department, is provided. If each department has access to the dashboard, it may serve as a springboard for future discussion and good decision-making. The goal of this article is to explain in detail the implementation of Dashboard and how it works, which will serve as a blueprint for building an effective dashboard with respect to best practices for dashboard design.

Widya Accarya ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 306-309
Author(s):  
Ismail Setiawan

Seseorang yang ahli dalam keterampilan analisis data hanyalah keterampilan dasar seorang insinyur data. Keahlian statistik digunakan untuk memproses data baca dan tag, serta untuk mengkategorikan data. Karena erat kaitannya dengan pemodelan yang dibuat untuk menguji algoritma pada level data scientist. Model yang dibuat pada fase data scientist digunakan sebagai alat dalam fase business intelligence. Pada tahap akhir ini, eksekusi yang akan dilakukan harus memberikan dampak positif dan keuntungan yang besar bagi sebuah instansi.


Author(s):  
Inta Kotane

To characterise the results of company’s business activities, the term ‘performance’ is used in the foreign scientific and educational literature. Objective and subjective measures are used in the international practice to measure the business performance. In Latvia, evaluation and systematization of these measures has not been carried out, which justifies the necessity of theoretical exploration of the objective and subjective performance measures. The study is based on the analysis of specialised literature and foreign scientific publications on the performance measurement issues. The following general research methods are used in the study: monographic or descriptive research method and comparison method. Aim of the research: to carry out a theoretical study on the objective and subjective measures of business performance. In the result of the research, evaluation and systematization of the objective and subjective performance measures is carried out. Subjective assessment of the company's performance includes the comparison of the company's financial and/or non-financial performance indicators with the competitors'/the main competitor's financial and/or non-financial performance indicators, or the industry’ s average values. Objective assessment of the company's performance determines the necessity of using the information contained in the company's financial statements.


2012 ◽  
pp. 39-43
Author(s):  
Janusz Nesterak ◽  
Bernard Ziębicki

Zarządzanie przedsiębiorstwem we współczesnych warunkach wymaga stosowania zaawansowanych systemów umożliwiających gromadzenie i przetwarzanie informacji do postaci użytecznej w podejmowaniu decyzji zarządczych. Możliwości takie stwarzają systemy klasy Business Intelligence. Systemy te obecnie są już szeroko stosowane w krajowych przedsiębiorstwach. Ostatnio coraz popularniejsze stają się systemy określane mianem Business Performance Management, które są traktowane jako kolejna generacja Business Intelligence. Istota systemów Business Performance Management dotychczas nie była szeroko prezentowane w literaturze krajowej. Część badaczy zajmujących się tą tematyką traktuje wymienione kategorie systemów jako tożsame. W artykule przedstawiono istotę systemów Business Performance Management oraz omówiono różnice pomiędzy tą kategorią rozwiązań i systemami Business Intelligence. Omówiono także elementy tworzące systemy Business Performance Management. Przedstawiono również metodykę oraz korzyści stosowania Business Performance Management w przedsiębiorstwach. (abstrakt oryginalny)


2018 ◽  
Vol 28 (5) ◽  
pp. 1489-1496
Author(s):  
Branislav Stanisavljević

Research carried out in the last few years as the example of companies belonging to the category of medium-size enterprises has shown that, for example, typical enterprises, of the total number of data processed in information of importance for its business, seriously takes into consideration and process only 10% of the observed firms. It is justifiable to ask whether these 10% of the processed and analyzed business information can have an adequate potential or motive power to direct the organization to success that is measured by competitive advantages and on a sustainable basis? Or, the question can be formulated: what happens to the rest, mostly 90% of the information that the enterprise does not transform into a form suitable for business analysis and decision-making. It is precisely the task of business intelligence to find a way to utilize all the data collected and processed in the business decision-making process. In this regard, we can conclude that Business Intelligence is, in fact, the framework title for all tools and / or applications that will enable the collection, processing, analysis, distribution to decision-making bodies in the business system in order to derivate from this information valid business decisions - as the most important and / or most important task of the manager. Of course, from an economic point of view, the best decisions are management decisions that provide a lasting competitive advantage and achieve maximum financial performance. This means that business intelligence actually allows a more complete and / or comprehensive view of the overall business performance of all its parts and subsystems. But the system functions can be measured essential and positive economic and financial performance, as well as the position in the branch of the business to which it belongs, and wider, within the national economy. (Of course, today the boundaries of the national economy have become too crowded for many companies, bearing in mind globalization and competitiveness in the light of organization of work and business function). The advantage of business intelligence as a model, if accepted at the organization level, ensures that each subsystem in the organization receives precisely the information needed to make development decisions, but also decisions regarding operational activities. So, it should be born in mind that business intelligence does not imply that information is shared on some key words, on the contrary, the goal is to look at the context of the business, or in general, and that anyone in the further decision hierarchy can manage exactly the same information that is necessary for achieving excellent business performance. Because, if the insight into the information is not complete, the analysis is based on the description of individual parts, i.e. proving partial performance in the realization of individual information, which can certainly create a space for the loss of the expensive time and energy. Illustratively, if the view, or insight into the information, is not 100%, then all business decision-making is like the song of J.J. Zmaj "Elephant", about an elephant and a blindmen, where everyone feels and act only on the base of the experienced work, and brings judgment on what is what or what can be. As in this song for children, everyone thinks that he touches different animals and when they make claims about what they feel, everyone describes a completely different life. Therefore, business intelligence implies that information is fully considered and it is basically the basis or knowledge base, and therefore the basis of business excellence. In doing so, the main problem is how information is transformed into knowledge and based on it in business decision making. It is precisely in this segment that the main advantage of business intelligence is its contribution to the knowledge and business of the company based on power of knowledge. Therefore, for modern business conditions, it is characteristic that the management of the company is realized on the basis of partial knowledge about stakeholders (buyers, suppliers, competitors, shareholders, governments, institutional framework, legislation), and only a complete overview of managers at the highest level in all these partial interest groups allows managers to have a “boat” called the organization of labor leading a safe hand through the storm, Scile and Haribde threatens to endanger business, towards a calm sea and a safe harbor - called a sustainable competitive advantage based on power and knowledge.


2021 ◽  
Vol 37 (2) ◽  
pp. 244-256
Author(s):  
Ava T. Carcirieri

Academics and practitioners all too often have little or no contact with each other; the practitioner does not know what research exists that can inform their practices, and the academic does not know enough about the institutions they primarily study to make recommendations that are specific enough to inform a concrete practice or policy. I leverage my experiences both as an academic and as a data analyst and domestic violence coordinator at Family Court to outline lessons learned in the field. I detail how my academic training hindered my work as a practitioner, and how practitioners differ in terms of conducting internal research and presenting data and findings. I use my lessons learned and subsequently list several concrete practices that academics can begin to work into their work to increase communication with important stakeholders, and tailor their work to practical systemic improvement. Bridging the gap between academics and practitioners will lead to better research projects, and findings that will be able to actively enact changes within systems that academics focus on.


2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Linda A. Leon ◽  
Kala Chand Seal ◽  
Zbigniew H. Przasnyski ◽  
Ian Wiedenman

The explosive growth of business analytics has created a high demand for individuals who can help organizations gain competitive advantage by extracting business knowledge from data. What types of jobs satisfy this demand and what types of skills should individuals possess to satisfy this huge and growing demand? The authors perform a content analysis of 958 job advertisements posted during 2014-2015 for four types of positions: business analyst, data analyst, data scientist, and data analytics manager. They use a text mining approach to identify the skills needed for these job types and identify six distinct broad competencies. They also identify the competencies unique to a particular type of job and those common to all job types. Their job type categorization provides a framework that organizations can use to inventory their existing workforce competencies in order to identify critical future human resources. It can also guide individual professionals with their career planning as well as academic institutions in assessing and advancing their business analytics curricula.


2017 ◽  
Vol 16 (1) ◽  
pp. 61-75
Author(s):  
V Murugaiah ◽  
Raghavendra Prasanna Kumar

Indian Railways is aware of the need to be up-to-datewith modern technological developments and best globalpractices to cash on the growing opportunity in freight and passenger business and provide the desired level of service to its passengers and customers. In this research paper, an attempt is made to evaluate the overall performance of Indian railways over the last ten years &to study the comparative position of train accidents and measures to improve safety by Indian Railways. The outcome of the study will explain in detail about the efficiency of Indian railways. Indian railways has always shown keen interest in taking advantage of technology to provide safety for the passengers.


2020 ◽  
Vol 1 (2) ◽  
pp. 12-24
Author(s):  
Paolo Bongarzoni

As automation increasingly influences businesses, digitalization technologies and tools such as artificial intelligence, machine learning, etc., become essential to support the definition and implementation of strategy activities aimed at improving businesses' competitiveness in the digital, cloud-based, and data-driven world. Since this business growth corresponds to an enormous increase in the data volumes, it is fundamental for businesses to adopt several digital solutions in their strategy process together with a tailored digital strategy embedded in their strategic plan. The purpose of this article is to critically analyse the classic strategy activities' latest trends/needs and how they could be properly addressed by the available digital technologies. Finally, for every activity are mentioned some best practices tools and software, supported by management consultants, since they trigger a high return on investment in term of the time savings, less dedicated resources, and final business performance.


2021 ◽  
Vol 14 (8) ◽  
pp. 388
Author(s):  
Ilse Svensson de Jong

Measuring innovation is a challenging but essential task to improve business performance. To tackle this task, key performance indicators (KPIs) can be used to measure and monitor innovation. The objective of this study is to explore how KPIs, designed for measuring innovation, are used in practice. To achieve this objective, the author draws upon literature on business performance in accounting and innovation, yet moves away from the functional view. Instead, the author focuses explicitly on how organizational members, through their use of KPIs in innovation, make sense of conflicting interpretations and integrate them into their practices. A qualitative in-depth case study was conducted at the innovation department of an organization in the process industry that operates production sites and sales organizations worldwide. In total, 28 interviews and complementary observations were undertaken at several organizational levels (multi-level). The empirical evidence suggests that strategic change, attributed to commoditization, affects the predetermined KPIs in use. Notably, these KPIs in innovation are used, despite their poor fit to innovation subject to commoditization. From a relational perspective, this study indicates that in innovation, KPIs are usually complemented by or supplemented with other information, as stand-alone KPIs exhibit a significant degree of incompleteness. In contrast to conventional studies in innovation and management accounting, this study explores the use of key performance indicators (KPIs) in innovation from an interpretative perspective. This perspective advances our understanding of the actual use of KPIs and uncovers the complexity of accounting and innovation, which involve numerous angles and organizational levels. Practically, the findings of this study will inform managers in innovation about the use of KPIs in innovation and the challenges individual organizational members face when using them. In innovation, KPIs appear to be subjective and used in unintended ways. Thus, understanding how KPIs are used in innovation is a game of reading between the lines, and these KPIs can be regarded as misfits.


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