Discovering Data-Driven Solutions: A Practical Guide for Small Businesses Implementing Data Analytics

In today’s highly competitive business environment, small businesses are under growing pressure to fight for market share. To do this, a competitive advantage must be established to outperform other business competitors both big and small. One way that small businesses can create a competitive advantage is by utilizing their data and applying basic data analytics. Data analytics is the process of collecting and analyzing large sets of raw data using statistical tests to identify patterns and insights to make conclusions that inform business decisions. The use of data analytics is not exclusive to large corporations and can be greatly beneficial to small businesses. Small businesses can gain valuable insights into market trends, customer demographics, purchasing behaviors, internal operations, performance monitoring, and more. Data analytics allows small businesses to harness the capability to create solutions for various business needs at all levels ranging from high-level management decisions down to daily in-store operations. In this paper, I will explore the benefits of implementing data analytics for small businesses and examples of how and where it can be used

Discovering Data-Driven Solutions: A Practical Guide for Small Businesses Implementing Data Analytics

In today’s highly competitive business environment, small businesses are under growing pressure to fight for market share. To do this, a competitive advantage must be established to outperform other business competitors both big and small. One way that small businesses can create a competitive advantage is by utilizing their data and applying basic data analytics. Data analytics is the process of collecting and analyzing large sets of raw data using statistical tests to identify patterns and insights to make conclusions that inform business decisions. The use of data analytics is not exclusive to large corporations and can be greatly beneficial to small businesses. Small businesses can gain valuable insights into market trends, customer demographics, purchasing behaviors, internal operations, performance monitoring, and more. Data analytics allows small businesses to harness the capability to create solutions for various business needs at all levels ranging from high-level management decisions down to daily in-store operations. In this paper, I will explore the benefits of implementing data analytics for small businesses and examples of how and where it can be used

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

What Types of Predictive Analytics are Being Used in Talent Management Organizations?

[Excerpt] Talent management organizations are increasingly deriving insights from data to make better decisions. Their use of data analytics is advancing from descriptive to predictive and prescriptive analytics. Descriptive analytics is the most basic form, providing the hindsight view of what happened and laying the foundation for turning data into information. More advanced uses are predictive (advanced forecasts and the ability to model future results) and prescriptive (“the top-tier of analytics that leverage machine learning techniques … to both interpret data and recommend actions”) analytics (1). Appendix A illustrates these differences. This report summarizes our most relevant findings about how both academic researchers and HR practitioners are successfully using data analytics to inform decision-making in workforce issues, with a focus on executive assessment and selection

HR Analytics: Talent Acquisition

[Excerpt] HR Analytics is becoming increasingly important as new technologies, software and new methods of data collection are revolutionizing the HR function. One area in which analytics tools are particularly flourishing is the talent acquisition space. With an increasingly competitive talent market, talent acquisition presents itself as an area in which analytics tools can greatly supplement decision making for these 3 reasons: here are many measurable, verifiable metrics to measure in terms of sources of talent, candidate qualifications, and the efficacy of the recruitment process here is an abundance of sources from which to collect data (Online sources, interviews, etc.) With the increased importance of sourcing the correct talent, the opportunity to use analytics tools to make better decisions is quite compelling Given these reasons, talent acquisition presents itself as an opportunity for organizations to build their analytics capabilities while driving measurable business outcomes and improvements to their organization. As evidenced in the above graphic, many organizations are already undertaking these changes or considering changes in the near future