Data Analysis Techniques for Effective Decision Making
Finally, cluster analysis, known also as clustering, is one of the most commonly-utilized data analysis techniques used by data scientists and researchers out there. The main idea of clustering is to find and analyze specific groups of data points within a data set; with the primary goal of finding and sorting any similarities and differences among data – or, to find ‘patterns’ within the data. There are a multitude of applications for cluster analysis.
Data cleaning, as the name suggests, is the process of “cleaning” a data set – that is, ensuring that you have removed or corrected all the records in your data set that are irrelevant, corrupted or inaccurate. Oftentimes, in the process of doing some types of analyses and computer spreadsheets, to datasets, errors and anomalies corrupt the records that you really want to learn from and analyze. This is what we term “bad data” – it’s any data that is uncollected, corrupt or not formatted properly, and without proper data cleaning.
Data sampling is a statistical analysis technique used in data analysis and in particular in the practice of introduction egregious learning from data. The main purpose of data sampling is that it aims to extract a subset of data in order to identify and understand patterns and relationships in the larger data set, and it’s vital in scientific research. By providing a new, well-modulated data set with a handful of randomly-distributed records – as opposed to one comprehensive behemoth of a data set – data sampling makes it easier for analysts and researchers to have conclusive observations and more accurate analyses.
Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is not suitable to be used in its raw format. Thus, data preprocessing translates raw data into a readable format in order to run successful analyses and build machine-learning models.
The ultimate aim of data science is to gain insights and knowledge from any type of data – whether it is structured or unstructured. Similar to data mining, data analysis techniques focus on non-linear models and applications, and in this case, machine learning – the main difference is that data mining is way more useful for professionals in another field seeking data-driven results, as it employs the same methods used in data analysis. However, that doesn’t mean these functions and techniques are mutually exclusive, both help complement one another and aid the performance of a data scientist, or a researcher, or a machine that’s trying to learn from data.
It is important to understand the significance of data analysis in the present scenario. We are living in a data-driven world where data plays a crucial role in making the right decisions. The process of data analysis uses analytical and logical reasoning to gain information from the data. The main purpose of data analysis is to find meaning in data so that the derived knowledge can be used to make informed decisions. At present, it is mandatory to use data-driven techniques and the process of data analysis for critical decision making in business, science, and government. The way data is interpreted and what it is interpreted forms the analysis part in the data science. Many innovation breakthroughs are made through the systematic process of data analysis. This is because data analysis reduces the amount of haphazard and serendipitous discovery. It is not only about discovering something new but it is about discovery that is important to be known at the right time. Data analysis is not about digging out data but it is about exploring and understanding data from varied dimensions and angles. There are many issues and challenges in the process of data analysis such as inaccuracy in results and efficiency in the analysis. However, there are a number of techniques and approaches in overcoming these issues in the data analysis process. Nowadays, with the easy availability of powerful computational software such as R, Python, and Jupyter, data analysis is not limited to large corporations. Almost anyone, including small startups in emerging industries, can benefit from well-conducted data analysis. A new possibility has been opened for innovation and sustainable growth in business. Every day we make hundreds of decisions, some following careful thought and some almost automatic, but data analysis is at the heart of making those decisions that are important, informed, and intelligent. Can we rely on the data? And is the data telling the truth? That explains the main reason that data analysis is so important. Additionally, data analysis focuses our modern and technological society on the appropriate use of quantitative information in addition to the latest technology. Modern society has been just saturated with a huge amount of information. Without the ability to analyze and understand this information, we have trouble making sense of the world around us. Data analysis helps us to disentangle information and knowledge that is important to better understand the world and make valuable contributions in diverse areas. As a result, data analysis comes to pave a way for a new era in modern society. It certainly provides a fundamental shift in the way that we think about technology and its role in society. The impact of data analysis has transformed not only scientific research and discovery…
Data cleaning is a key part of data analysis. It is a method where the existing data is analyzed so that the correct data or the most useful data can be extracted from it. Incomplete or irrelevant pieces of data are corrected or removed from the data set. It identifies and diagnoses the dirty data. “Dirty data” is data that is incorrect, incomplete, inaccurate, or irrelevant, particularly in a computer system. There are different types of dirty data: data that is out of date (data that has not been updated); data that is missing; data that is not accurate; data that is not relevant. There are different ways dirty data happens, for instance, human errors, where the data is entered manually such as spelling mistakes or formatting errors; equipment malfunctions such as computer viruses; software integration (when the software is connected and data is exchanged); duplicate data (the same piece of data is stored in multiple places); obsolete data (the software or data has been updated and old data is not relevant anymore). There are also many different procedures of data cleaning and these may differ according to the type of data worked with, the volume of data, and the operating systems. For example, in data cleaning in Excel, the ‘Filter tool’ may be used to identify specified records and delete them from the active worksheet. By using the filtering feature, we can get a value that we want and then filter out the rows that we do not want. If the specified records are found throughout the data, we have to repeat the process until all are removed. When the clean data is achieved, make sure you do a standard check and close the process. Committed mistakes are avoidable-sometimes, the data is cleaned, but the process is not ended or closed and so the effect is not generated. Data cleaning sounds very tedious but in fact, through advanced technology and improved scientific methodology, the fast pace and smart data cleaning processes have been described and introduced. With data cleaning, big data sets in many industries could be achieved and have an increase in victorious value-stories. For example, a case study from Q-pork which used data cleaning. Q-pork is a large commercial hog farm that uses large volumes of data to make management decisions. The records range from sow breeding to raising piglets. However, the data was dirty and inconsistent. It was difficult to find relevant data and consequently, the data was difficult to use to derive business intelligence. After using data cleaning to clean and validate the production data, nowadays the information is now more valuable when used to make business decisions.
This work outlines the key types of case study – those which use big data in the business world – and also those examples in the healthcare sector. Through using the work as a guide to an increasingly important area of data science, students and professionals can develop the knowledge and insight required to offer real value to companies and patients in the future. Such values may be realized by making a change in how data is acquired, used, and how certain data can be transferred into valuable knowledge. It is expected that students and professionals are more capable of understanding the market and business trends by working on the right case with the right method of data analysis. This is a differentiator of those who can only provide data figures. Good data analysis work should never be an end in itself but it is put to work in the organization. Various software has been used in the case studies, including Apache Spark, IBM Watson Studio, RapidMiner, Knime, etc.
Well-selected case studies and real scenarios make the work enjoyable and meaningful to readers. It is clear that effective data analysis is the key to the success of a business. In the era of big data, data analysis becomes a critical and long-term strategic activity for any organization, in that there are countless data but needs people to make sense out of it. Through applying techniques of different data analysis, the best possible decisions can be made, and these lead to a direct improvement in productivity and profitability. Especially, guidance of the modern IT generated, such as prescriptive analysis and big data analytics, not only specialists can have greater confidence in decision making but also more innovative and adventurous approaches to business success can be achieved.
Case Study 3: A study of the use of big data in healthcare. The case study analyzes the processes of descriptive and prescriptive analysis, focusing on the current use of data in healthcare and also the possibilities for the future, given both the emerging use of new data techniques and the challenges associated with the information revolution in the sector. This case study really demonstrates the functionality of prescriptive analysis and the way in which predictions can be used to inform healthcare and health decisions.
Case Study 2: A study of the use of data analysis to inform the introduction of a new range of products by established companies and the creation of tailored marketing strategies. The case study focuses on the use of regional data variance and correlation – in other words, how different data trends can be used to understand consumer differences in different areas. It tracks the process of big data analysis, step by step, and ends with a focus on the implications of prescriptive analysis used.
Case Study 1: An exploration of the use of big data used to examine online retail performance and inform decisions around the management of online retail and online marketplaces. The case study explores the processes for descriptive, predictive, and prescriptive analytics used within big data analytics.
Case studies are used to demonstrate the practical uses of data analysis for making effective decisions in the workplace. Three case studies are used: * Descriptive analytics – understanding the current situation * Predictive analytics – using data to help identify future probabilities and trends * Prescriptive analytics – making use of the predictions to make decisions in the real world
In conclusion, the book effectively presents a valid case for the pivotal role of data and its analysis in the decision-making process. It delves into an in-depth analysis of different tools and techniques that can be used for the purpose and asserts that we are living in the age of data. It is also established that with the help of continuous technological advancements, the use and importance of data in effective decision making will continue to rise. The book also sheds light on the three well-known types of data – qualitative data, quantitative data, and categorical data. Moreover, there is a healthy discussion around the various aspects of data quality and the methods to verify and validate data to ensure that the data used is reliable and correct for the purpose of making the decision. The authors assert that the relevance of data is continually growing so much so that it is being referred to as the new oil. This is attributed to the fact that data, like oil, is raw but when it is mined and processed, it becomes valuable information. With the help of real-life examples and case studies, the book effectively emphasizes the real power and importance of data analysis when it is used to support the decision-making process. The book delivers a very practical insight into the subject and it is very informative and useful for beginners in the field of data analysis as well as to those who are more experienced. It is a highly recommended reading for anybody having the inquisitiveness to exploit the ‘gold’ in this new gold rush.
