Strategi Penjualan Batako UKM Areta Agung Agung Dengan Marketing Mix Dan Produktivitas Parsial

Small and Medium Enterprises (SME) of Areta Agung is a brick-and-mortar SME in the city of Palembang. The purpose is to increase the productivity of brick sales. The results based on sales data Market Share as much as 24%. Before improving the strategy formulate external and internal factors. Externally using benchmarking data, namely the comparison of similar SME factors and internal results from brainstorming, namely data obtained from the results of SME of Areta Agung. In this study SWOT analysis is used to find out the strengths, weaknesses, opportunities, threats faced by SME. After that, the SWOT matrix generates an improvement strategy, which is then improved using the Marketing Mix (4P). Its implementation includes products, namely receiving services outside of operations. Price is the addition of price variations for shipping outside the city. Promotion, namely making online sales advertisements, sales promotions, publicity, personal selling. The place is to add a building depot subscription. The results obtained by Partial Productivity before preparation were 0.565.

SECTORAL SPECIFICS OF THE TECHNOLOGICAL TRANSFORMATION OF THE RUSSIAN ECONOMY

The article is devoted to the study of the current stage of technological transformation in the aspect of the leading sectors of the Russian economy, which are crucial in the development and strengthening of the country's position in the global technological leadership. Based on a study of current trends in the technological transformation of industries and statistical data analysis, reflecting the main results of the level of technological transformation of leading industries, the assessment of their development in the use and implementation of digital technologies, the most technologically advanced industries were identified. The determining factors of sectoral specifics of technological transformation at the present stage are sectoral characteristics and provision of innovative solutions, the development of digital infrastructure, which forms the features of the technological transformation of industries, its directions and pace of development. It is noted that industries with a high level of concentration, dominated by large businesses with access to significant investment resources, show greater progress in digitalization, and small and medium-sized enterprises lag behind in the rate of implementation of new digital solutions. The main problems of differentiation of industries in the level of technological development, which acts as a barrier to the implementation and development of digital technology in the economic and social spheres, were identified. Effective solutions in this area must involve the formation of appropriate digital infrastructure, comprehensive measures of state support and regulation of the data market, which will increase the level of technological development of industries, including reducing the technological gap between them.

Market Survey Strategy for Sharp Well Economic: A Case Study of Well Planning in Deepwater Exploration Indonesia

A deep-water well is one of the high profile project which is closely related to a high cost project. One of the key success prior starting the project is by conducting the cost estimation (Owner Estimate). For a typical new operation with limited offset data, market survey is one of the way to have the base cost estimation. Performing a market survey for estimating owner estimate prior to perform tender process and also a need for a basis of well cost is critical step in calculating economic of the well or investment decisions during well planning. Many approaches to perform the market survey and sometimes people customize them in order to fit with the purpose. This paper is trying to exercise some of the market survey methodology options and also to describe the impact to the selection of procurement strategy and maturation of cost estimation. The market survey methodology will be presented in details for each options. Experiences while estimating long lead items (LLI) and drilling services costs for a deep-water exploration drilling well were exercised here. The outcomes were compared and assessed. Nevertheless, literature reviews are also performed to enrich understanding and better judgment when estimating the cost. By performing a comprehensive market survey, we have successfully determined the current market condition and accurate cost estimation, which plays crucial roles in the decision making process for the best procurement strategy. Additionally, with the selective and progressive method in the market survey, it has narrowed down the bias cost data, especially for some of the major drilling services contract. The method on the market survey has established also an advantage in the remuneration strategy which leads to a cost saving in the project and also helps to fit in the project timeline.

Secure Internal Data Markets

The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (e.g., between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption.

Assessing Creditworthiness in the Age of Big Data

The purpose of this article is twofold: first, we show how algorithms have become increasingly central to financial credit scoring; second, we draw on this to further develop the anthropological study of algorithmic governance. As such, we describe the literature on credit scoring and then discuss ethnographic examples from two regulatory and commercial contexts: the US and Denmark. From these empirical cases, we carve out main developments of algorithmic governance in credit scoring and elucidate social and cultural logics behind algorithmic governance tools. Our analytical framework builds on critical algorithm studies and anthropological studies where money and payment infrastructures are viewed as embedded in their specific cultural contexts (Bloch and Parry 1989; Maurer 2015). The comparative analysis shows how algorithmic credit scoring takes different forms hence raising different issues in the two cases. Danish banks seem to have developed a system of intensive, yet hidden credit scoring based on surveillance and harvesting of behavioural data, which, however, due to GDPR takes place in restricted silos. Credit scores are hidden to customers, and therefore there has been virtually no public debate regarding the algorithmic models behind scores. In the US, fewer legal restrictions on data trading combined with both widespread and visible credit scoring has led to the development of a credit data market and widespread use of credit scoring by ‘affiliation’ on the one hand, but also to increasing public and political critique on scoring models on the other.

Measuring the impact of publicly funded open innovation programmes: the case of Data Market Services Accelerator

One of the current goals of the European Commission is to stimulate the development and uptake of data and AI technologies in the economy. Substantial funding has been invested in programmes that help startups and small-medium enterprises (SMEs) to assimilate the latest technical and regulatory trends. In order to assess the efficacy and impact of such initiatives, their specific social and economic objectives must be taken into consideration. Our paper proposes a generalisable mixed-methods approach for assessing the impact of publicly funded innovation programmes across multiple dimensions, including their effect on the market, fundraising capabilities of companies, innovation, and socio-economic aspects. We apply this framework to evaluate the recent performance of the Data Market Services (DMS) Accelerator, a current programme funded by the European Commission. In addition to assessing how DMS has been able to meet its objectives, our examination alerts other similar programmes of the challenges in assessing specific outcomes such as standardisation and long-term legal strategy in the fields of data innovation.

Features of Big Data market risk identification

The paper hypothesizes that the dynamic digitalization of the economy, based on the benefits of using Big data, accelerates the use in management and production processes of technologies offered by this market. However, it is noted that the acquisitions of the Big data market also exacerbate the socio- economic contradictions between countries with developed market economies and institutionally underdeveloped countries, which include Ukraine. The authors of the study proposed to identify the Big data market with such key indicators as total revenue, number of Internet users, losses of Big Data market participants from data leakage. Thus, the high rates of development of the Big data market in terms of growth of total market income in 8,03 times during 2011 — 2020 were witnessed. The main segments of the Big Data market (service segment, software segment and service segment) were identified. It is established that the largest share of the Big Data market is occupied by the services segment (37,5% in 2020). From 2021, the growth rate of the software segment is expected to exceed other segments of this market — hardware and services. The results of the analysis of the Big Data market by M. Porter’s five forces model show that the most important of the competitive forces in the market is a high level of competition, in which market participants are encouraged to focus on potential needs and expectations of their customers to strengthen bases of differentiation and clear positioning of the services. According to the results of the SWOT-analysis of the Big Data market, the following strengths were identified: business expansion due to the increase in the amount of information it owns; increasing the number of customer reviews through social networks; establishing strategic partnerships with suppliers, dealers and other stakeholders through the use of Big Data; permanent training of employees to maintain the competitiveness of organizations; established IT system of the enterprise, which promotes faster adoption of effective management decisions; high incomes due to effective management decisions, possession of market research results through Big Data technologies. The identified market opportunities are: population growth, which means an increase in the number of potential consumers and the amount of data collected; growth in the number of enterprises that implement e-commerce in their activities; growth of active consumers due to the integration of Big Data into social networks; increasing the share of automated processes, which helps reduce costs; growing popularity of IT specialization in universities; globalization of the economy, which allows companies to expand their activities to other countries. On the basis of identified strengths of the market and its capabilities, the following strategic directions of development are proposed: entry of enterprises into new markets due to market globalization and effective implementation of Big Data technologies; use of social networks to collect consumer data and involve them in Big Data processes; improvement of the e-commerce system of enterprises due to the capabilities of well-established IT systems of the enterprise with the capabilities of Big Data; reduction of product prices due to cost optimization and effective interaction with contractors. The results of the research allowed to identify the following main risks of the industry: destruction of data confidentiality; collection of false data, infringement of intellectual property of a third party, etc. The formed risk matrix indicates that the most significant risks of this market are the reduction of information security of the entity due to hacker attacks (probability of 50%, significant damage) and the destruction of data confidentiality (probability of 25%, significant damage). Qualitative interpretation of risks in the Big Data market allowed to characterize the impact of adverse factors of internal and external environments, namely: insufficient unreliability of cloud storage for data storage; high level of distrust of data carriers to companies using Big Data; high staff turnover in the market, etc. Assessing the risk of data loss due to hacker attacks allowed to identify it as a risk of a high level of importance (6 p.). Based on the obtained result, it is concluded that the activities of Big Data market participants are vulnerable to possible hacker intrusions and require more effective measures to ensure reliable data protection of enterprises and their customers.