Abstract
Organizational design could be understood as a metaphor to appreciate customer needs through segmentation or cross-channel analysis and extrapolation in pervasive value creation environment. Corporations need to calculate moves based on available capacity to produce the products demanded in highly competitive markets. The workforce must possess the required skillsets and brashness critical in getting the job done correctly all the time. The knowledge drawn from data analytics can be applied to develop performance metrics essential in advancing productivity in ubiquitous value making systems. This chapter explores strengthening the significance of data analytics.
TopIntroduction
In Wang and Strong (1996) data analysis is preceded by ensuring that the data is fit for use. They developed a data-quality dimension that is applicable in ensuring that the information deduced is of a high quality and fit for intended use (see Table 1)
Table 1. | Free of Defects | Process Desired Features |
| Accessible | Relevant |
| Accurate | Comprehensive |
| Current | Proper level of detail |
| Consistent with other sources | Easy to read |
| Etc. | Easy to interpret |
Wang and Strong argue that the thrust of second-generation data systems is to consistently identify and prevent the most important root causes of future errors. This require both proper techniques and management infrastructure. Quality data analysis is critical in successful decision-making in highly competitive business environment. Quality analysis begins with the data collection instruments and software employed to analyze the data. Companies could be drowning in data from production, sales or outsourcing contracts, they need to master how to extract and analyze it to make cutting-edge organizational design of the business. Analytics are important in articulating a 360 degree evaluation of how the organization is doing, and what can be done to increase productivity.
TopOrganizational Analytics
Organizational analytics refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive organizational change. Organizational analytics focus on developing new insights and understanding of business performance based on data and statistical methods. The technique is important in pinpointing at the change needed to advance efficiency and effectiveness in successful enterprises (Mupepi, 2017). In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods (Davenport, 2006).
In Watkins Mohr and Kelly (2011), organizational analytics is viewed as a technique that applies extensive use of statistical analysis, including explanatory and predictive modeling and fact-based management to drive organizational design. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, online analytical processing (OLAP), and “alerts.”(Saxena and Srinavasan 2012).
Key Terms in this Chapter
Systems Thinking: Systems thinking involves the use of various techniques to study systems of many kinds.
Differentiation: The process of making products or organization different from others in competition.
Algorithm: An algorithm is a self-contained step-by-step set of procedures applied in solving mathematical or industrial problems.
Value Creation System: A value creation system is a production perspective that describes social and technical resources employed to produce goods and services demanded by customers.
Skunkworks: A project typically developed by a small and loosely structured group of people who research and develop a project primarily for the sake of innovation.
Ubiquitous: Omnipresence or ubiquity is the property of being present everywhere.
Holon: Adapted from Hebrew, implying a self-containing part that is part of a larger system.