I am working in an MNC RMG company as an IT manager. My responsibilities include recruiting, selecting, orienting, and training employees, and recommending information technology strategies, policies, and procedures by evaluating organization outcomes. I am also involved in preserving assets by implementing disaster recovery and backup procedures and information security and control structures. I choose to upskill in data science and business analytics to learn the use of data to solve complex problems, increasing market demand and it being a lucrative career choice for career advancement.
I had to cater to a customer from the Philippines who asked our firm to look into policy research and planned on utilizing data and statistical approaches. To predict models and techniques, I constructed all of my models using advanced statistics and machine learning. There were several concerns with respect to untrustworthy data, data not available at the chosen aggregate level, prolonged data processing times, estimate procedure technological concerns, technological problems with the data-collecting procedure and data accessibility. I utilized Machine Learning, Artificial intelligence and Statistical Analysis to work around the problems and devise effective solutions. I sought advice from renowned policy academics who confirmed the use of mentioned tools.
The main data-related issue was that their chosen industries were not well-represented in the sample, making sectoral estimates unreliable. The problem of dependability is also raised in the breakdown of manufacturing costs. However, because there are no widely available techniques for authenticating respondents and responses, researchers are forced to utilize whatever data is available. The lengthy data processing time was another issue.
Finally, after doing the relevant surveys and employing statistical analytics, I concluded that improving future planning helped my organisation in breaking down the manufacturing cost by 1% since the dependability was reduced. Some of the key considerations were estimating variance in complex surveys, estimating a small area, deduction from a complex survey, total survey design method, errors in control and non-sampling and use of monitoring device. The availability of research material was obtained through the Internet. Correspondence with other survey practitioners and researchers may be facilitated through electronic mail that can be delivered practically anywhere in the globe and survey newsgroups. An automated indexing system and public domain materials on the Internet may also make it easier to access research and other technical articles. Statistical studies in general will benefit from this.
The exercise helped with process improvement and automation, leading to increased efficiency and cost savings. By analyzing customer data, organizations can gain a deeper understanding of their customers and tailor their products and services to better meet their needs. Data analytics can help organizations identify and mitigate potential risks, such as fraud or supply chain disruptions, leading to a more stable and secure business.
