چكيده به لاتين
In the contemporary business landscape, scaling up is crucial for companies, facilitating sustainable growth, enhanced efficiency, and resilience to market fluctuations. This is especially vital in competitive sectors where innovation and adaptability are essential for success. To expedite scaling, many organizations employ various data analysis tools and methods. These tools assist in recognizing patterns and trends, thereby improving decision-making. However, choosing the appropriate tools can be challenging, as companies must navigate a range of options, including data analysis software and management systems tailored to their specific requirements.
A significant issue arises when companies are uncertain about which data analysis methods best support their scaling efforts. Poor choices can result in misguided decisions and lost opportunities. Additionally, ineffective use of recommended methods may hinder the discovery of optimal solutions. The decision-making process is further complicated by the need to choose among descriptive, diagnostic, predictive, and prescriptive data analysis approaches, each with its own advantages. Descriptive methods provide insights into current conditions, highlighting strengths and weaknesses. Diagnostic approaches investigate the causes of events and influential factors. Predictive techniques utilize complex algorithms to forecast future trends, while prescriptive methods offer actionable solutions for enhancing performance and decision-making.
To tackle these challenges, this research identifies key criteria for scaling by reviewing recent literature. A combined fuzzy Analytic Network Process (FANP) model was used to assign weights to these criteria. Effective actions and experiences were compiled from empirical data to bolster the scaling process. The study ranked data analysis methods based on two cases: one using weights from the FANP method and another applying entropy-based weights. The first case analysis assessed the critical role of the four data analysis approaches in accelerating and improving the scaling process through a case study.
In conclusion, this research aims to provide actionable recommendations and strategies for selecting data analysis methods to enhance company scaling, drawing insights from business analysts, risk-taking investors, and startups. This abstract encapsulates the research's goal of delivering valuable guidance for decision-makers pursuing successful scaling.
Key words: Scale-up, Data Analysis, DAPs, FANP, TOPSIS, ENTROPY, Descriptive Approach, Diagnostic Approach, Predictive Approach and Prescriptive Approach
