Leveraging Salesforce Analytics for Enhanced Business Intelligence (original) (raw)

Business Performance Decisions by Social Business Intelligence

International Conference on Informatics in Economy, 2019

The difficulty in interpreting social data through the usual software used by analysts has generated a new segment in the Business Intelligence (BI) market, namely Social Business Intelligence (SBI). The paper investigates the support of SBI in identifying the behavior of customers. Thus, this paper proposes an analysis of the SBI support that facilitates behavioral analysis, and as inputs it were used clicks, link accessing, or participation in online campaigns. The BI Domain supports Social Media (SM) analyzes and uses Extract Transform Load (ETL) technology to process and transform social data into data that can become the basis for business decisions. In this respect, two campaigns, namely conducted by e-mail and Facebook, were analyzed on the model of an insurance company. The aim was to identify which one, from an audience access point of view, is more efficient. The T test was used as a statistical tool, and this research was carried out based on the Facebook Business Manager package. The objective of this paper is to compare the indicators of the two modes of interaction (e-mail vs. Facebook) and to highlight the advantages of using SBI in companies.

Business Intelligence and Analytics for Operational Efficiency

ICMIT 2018, 2018

Typically, business organizations hardly find proper information, processes, and tools need to make responsive decisions at all levels of maangement. Organizations lack clear visibility and insights into the fundamental business situations even after spending heavily on technology, people skills and consultancy. Organizations continuously misunderstand the true story behind the numbers that is a reality that translates directly into a growing number of financial restatements and serious strategic missteps. Despite a generation of investment and efforts, financial analysis and reporting often lack the true standard. The reason might be attributed by the idea of leveraging business intelligence. This is primarily due to lack of awareness about the power of Business Intelligence and Analytics (BIA). The fact is that BIA should be an integral component of every operation of every business as it helps to identify its most profitable customers, trouble spots within the organization, and the return on investment for certain products or services. Hence, BIA is highly demanded by the business organizations as it seeks to improve business outcomes, customer relationships, and operational efficiency by using information. IT-driven application development, access to historical data, and canned business reports are no longer satisfactory now-a-days (Wixom and Watson, 2010 & Lautenbach, et al. 2017). Users want more control, better visualization, higher level of analyzing capabilities, and faster development cycles. Organizations also closely watch emerging technology trends to discover the next competitive advantage in the use of information. Now-a-days, data volumes are growing and the organizations are seeking to tap new sources generated by social media and online customer behaviors. This trend is spurring tremendous interest in better access and analysis of the variety of information available in unstructured and semi-structured content sources. Therefore, there might be a question that how BIA helps in increasing operational efficiency and accuracy in business decisions? Yes, it gives organizations improved visibility into all spending related to analytics and reporting. Companies can leverage this approach to mitigate risks, by better managing credit exposure, creating supply chain flexibility to optimize inventories, and to reduce losses from diversion, counterfeits, revenue leakage, and fraud (Raisinghani, 2003). It is also used to understand the capabilities available in the firm on the state of the art, trends, future market directions, the technologies, and the regulatory environment in which the firm competes and the actions of competitors and the implications of these actions (Negash, 2004).

Business Intelligence and the Transition to Business Analytics

2014

In recent years, several independent studies conducted by different organizations, show that the data extracted from the companies running the business in different industries in their daily work represents a real gold mine. The huge volume of information hide, most of the times, the key to success and provide clues for understanding market trends and the directions of customer needs. The main problem is that the modern world presents the information in a less natural way. Mobile technologies, decreases price of IT equipment (Information Technology), social networks, and the economic crisis have caused major changes in the behavior of both end users and companies. The speed at which information is produced and circulated, large data volumes increased considerably, so that the need to indentify on time new business opportunities and make appropriate decisions increased accordingly.

Augmented Analytics and Modern Business Intelligence Adoption to Maximize Business Value

Journal of University of Shanghai for Science and Technology

The business success is critical for any organization irrespective of its size. Understanding business value and advanced technology capabilities in Business Intelligence (BI) solutions for analysis and decision making is the need of every management, researchers and analysts. Data is available at every corner, at every platform and it is being produced all the way using digital equipment. These complex data sets for analysis and decision-making can be used on various platforms and are more accessible than ever to find insights and explanations. There is an abundance of datasets and data analysts, but organizations are still unable to leverage its potential to the fullest and this perhaps is the failure of most data analytics initiatives. This paper covers the need for Augmented Analytics adoption in companies to maximize business value with respect to added advanced analytics capabilities in BI Tools for effective, accurate and timely decision making, and business analysis.

Business Analytics: A Simplified Review

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Business analytics is primarily about getting the most out of data. Data has lately been dubbed "the new oil" rather than the "sludge of the information era." While data can be used to develop new products and services, identify market niches, and spot new opportunities, it is also notoriously amorphous and difficult to extract value from. It involves different steps to get the insights from the data present majorly involving approaches like Aligning strategy, desired behaviors, and business performance management with analytical activities and capabilities is necessary to derive value from data. This article uses both conventional and qualitative research methods to examine the expanding body of work on business analytics (BA).In this paper, an attempt is being made to review several viewpoints on how business analytics is defined and how it relates to business intelligence. Additionally, we highlight business education and demonstrate how business analytics are applied in both company and industrial sectors. I. INTRODUCTION Big data is a fast-growing discipline being used to define and analyze huge quantities of data present in various forms organized, semistructured, and non-structured data from multiple enormous and complex sources. Data is rapidly growing in every sector, making it a quickly growing to enforce used to define and analyze massive volumes of data present in various forms of organized, semistructured, and non-structured data from various immense and complicated sources. The method also necessitates the use of sophisticated data-processing technologies and advanced analytic programs. Big data has changed the way businesses and organizations operate. Companies of all sizes and industries may benefit from big data applications. According to corporate groups, such benefits might boost productivity, revenue, and growth.1 Many organizations are implementing big data tools and complicated statistical applications to improve quality in areas such as operations, customer happiness or satisfaction from the deployed process, and loyalty, as well as to strengthen overall standards of corporate governance and combat malicious activities such as fraud, cyber attacks, embezzlement, and other financial crimes, which have recently increased. Apart from that, big data has a variety of business applications. High-quality essays on both theoretical and practical knowledge of big data in business will be included in this special issue.2 Statistical procedures (analysis of variance (ANOVA, tables and charts, and so on), data software applications (data mining, sorting routines), and market methodological approaches are all used to explore, visualize, identify, and further communicate the patterns or trends existing in data (linear programming). Broadly said, analytics is the conversion of data into useful knowledge. Analytics is an older term that today refers to a wide number of disciplines, not only business. A notable example of how analytics can be employed is the collecting and translation of meteorological data into statistics, which are then used to anticipate weather patterns.3 Business analytics is described as the art of discovering insight via the use of complex mathematical, analytical, machine learning, and network science methodologies, as well as a variety of data and expert knowledge. It aids in speedy decision-making. Business analytics may be considered a tool for resolving problems and making decisions. Indeed, business analytics is a subgroup of analytics that employs the utilization of tools, techniques, and other statistical ideas to solve more complicated business problems. Analytics is often used by businesses to explain, predict, and improve their performance. As data grows, it contains insights that, when used properly, may result in productive outputs and provide value for the firm. Because of its growing popularity as a term, analytics is being used to replace a variety of previously popular ideas such as intelligence, mining, and discovery. For example, business intelligence is now known as business analytics, whereas customer intelligence is now known as customer analytics, Web mining is now known as Web analytics, and knowledge discovery is now known as data analytics. Because of the number, diversity, and speed with which data is generated-i.e., big data-modern analytics may necessitate a significant amount of computation, as well as the tools, methodologies, and algorithms used in analytics projects. The special issue on big data in company or organization accepted five accomplishments in the areas of business innovativeness in the big data era, non-structured big data analytical methods and techniques in firms, advanced analytical approach for business analytics in big data, geospatial deep insight for retail proposition using similarity metric, and big data as well as modifications through interactive data visualisation.4

A Functional Model of Social Media and its Application to Business Intelligence

2010

The marketing departments of the major business intelligence (BI) software vendors have been quick to associate their products with the popular term 'Web 2.0,' branding the new releases of their product suites 'BI 2.0.' This paper argues that beyond its value as a device to enhance sales and marketing, the functions typically found in Web 2.0 web sites can be usefully applied to BI applications. It explores the application and role of Web 2.0 concepts within BI applications. The paper develops a simple framework to help understand the collaboration that is afforded by Web 2.0 applications. It classifies the functions that are provided in social media platforms to foster user collaboration and contribution. The framework is then used to examine how these forms of collaboration can be used to create more effective and 'active' BI applications.

Sustained business competitive advantage with data analytics

International Journal of Business and Data Analytics, 2019

In today's highly networked and digital business, data is the only gold element with extensive capability to create business value which we have never seen in the traditionally executed business. This new business world is stimulated by analytics and its ability to make the most of data for sustained competitive advantage. The organisations need to adapt new technologies to prevent the wearing out of the existing knowledge and sustain competitively for a long period according to the increasing customer demands. Analytics has the power to forecast future happenings based on some data which is available to the organisation, so making use of the data with analytics. However, new technology, here data analytics, alone will be of little use if the strategic managers lack to understand the business context in which the forecasted knowledge is useful and help in sustained competitive advantage. A strong framework needs to be in place to integrate analytics with the knowledge base of the organisation along with skilled managers. This research paper is an attempt to provide a theoretical framework and its empirical analysis to apply analytics effectively so as to remove uncertainties in business for its sustainability.