What is Quantitative Analytics?
Quantitative analytics is a process that aims to measure business data, financial data, online activity, or any other kind of information in order to draw conclusions. It uses mathematical models and algorithms that are programmed into computer software to guide the analyst’s decision making process. There are many different approaches to quantitative analytics, but the fundamental goal is always the same; provide insight for organizational decision-making. The general purpose behind quantitative analysis is to evaluate an entire range of options by asking questions about specific variables. By applying a series of predetermined standards, a decision can be reached in a more rational and comprehensive manner. It will allow the manager to base their business decisions on facts & data rather than hunches or feelings.
What Qualifies as Quantitative Analytics?
In order to measure something quantitatively, it has to be defined beforehand, or ‘quantified’ . In the context of quantitative analytics this means that there has to be a clear metric for analyzing an event. For example, a metric might be a sales figure, or a daily revenue report. By compiling the same data into a format that can be measured via a computer program, an analyst can quantify their findings. This allows the analyst to work out the causes and effect of certain variables, as well as pinpoint exactly how important they are.
Background and development of Quantitative Analysis
Quantitative Analytics has been around for hundreds of years. It can be traced back to 1614, when statisticians started using Logarithms to measure the samples of different variables in order to come up with a more accurate sample size. In the 1900s, statisticians discovered that instead of going about this whole new way to measure variables, they could measure a very specific variables within a dataset. This is where the idea of numerical measures, such as percentiles and z-scores came about.
At first, quantification was used more for a variety of applications. In the 1940s, algorithmic quantification was introduced. The idea behind algorithmic quantification is that using computers can assist in data gathering and organization.e. In the 1960s, data warehousing also came into play. Data warehousing is a system of storing data on a large scale that can be accessed on demand. Data was being stored in a more organized fashion, which led to the advent of database management systems. In the 1980s, database management systems were used more to consolidate data rather than store it. In the 1990s, a new form of quantitative analysis was introduced: Business Analytics. Business Analytics started with the idea behind data warehousing and data modeling. It tried to put quantification into a more visible and relevant application.
The field of quantitative analysis has been around since the beginning of computing. However, in recent years, there has been a steady rise in the use and popularity of quantitative analysis in business and society in general. Many organizations use quantitative analysis in order to gain an advantage over their rivals and make better decisions. One of the main reasons organizations use quantification is due to the fact that it can be helpful during already existing trends. Quantification is usually used after the data has already been gathered. Quantification can also provide an advantage in determining trends, which can help to predict future trends before they take place. Quantitative analysis has grown to be so significant that APQC (the Association for Operations Management and Quantitative Analysis) was created in 2002 in order to promote the use of standard techniques of quantifying business practices on a global scale.
The quantitative analysis field is constantly growing and changing as time passes. The field itself has been able to adapt, incorporate trends as they come about, and take them as a whole. The world of quantitative analysis will never be the same as it was in the 1950s, but that doesn’t mean that there isn’t still much room for future growth and development. There are still many people looking into how to grow the field of quantitative analysis and change it for the better. For example, there is a lot of talk around “Big Data”. This is basically data of extremely large quantities. An example of Big Data would be of people using social media on a regular basis. However, there are people who don’t agree that Big Data is an appropriate name for this new growth in the field. They say that “Big Data” is actually more of a buzzword than anything.
What Qualifies as a Business Analytics Solution? Top-Down vs. Bottom-Up
The first step is to define a point in time for when the analyst begins the process. The most common time frames used are monthly, quarterly, and yearly. The main idea is that by analyzing a specific period in time, you will be able to understand patterns and trends in your data more clearly. Using the data from the previous period, the current analysis is able to analyze and evaluate data for improvement. This can be expanded to a multi-step process, where analysis happens annually and yearly off-seasons.
This type of approach uses a “Top Down” approach, meaning that it generally follows well-established methods and best practices. This means that the analysis is generally tried through and through and has been proven to work over time. A Top-Down approach is often used when the business has a visible, well-defined need. This might be something like a specific goal that needs to be reached by a certain date. It can also be used in situations where there is very clear and defined objectives that need to be met. Because Top-Down approaches are quite structured and adhered to, it helps to ensure that the analytics process is as repeatable as possible and the same results can be obtained by an analyst at any point in time.
Bottom-Up methods are the exact opposite of top-down methods. Rather than using existing techniques, the Bottom-Up approach is about experimenting in an iterative fashion. It’s all about trial and error; the idea is that the way in which you analyze your data will often be quite different depending on what kind of data you’re working with, and how it is structured. This means that there is no one set formula for how an analyst should go about analyzing their data. The important thing is that the outcomes are still of high quality.
There are different approaches that can be taken to quantitative analytics, including:
1) Business Analytics is a type of quantitative analytics which is used to help organizations make decisions based on historical data. Business analytics seeks to answer questions by looking at the business, market, and competition in an objective, non-emotional fashion. Business analytics can be used to discover trends in historical data which may not be obvious or obvious on first glance. Over time, business analytics can be used to give organizations a competitive advantage over rivals Over the course of multiple years.
2) Data Mining is a type of quantitative analysis that looks for trends which have already happened rather than predicting trends that might happen in the future. As opposed to forecasting, data mining is more predictive in nature. Data mining is used to discover patterns and make an accurate prediction. Data mining analyzes large amounts of past or existing data and looks for trends or patterns that can be used to make a prediction about the future. This type of analysis can be used in conjunction with other forms of analysis such as forecasting and forensics.
3) Forecasting is similar to data mining; it seeks trends that have already arisen. However, much of the time forecasting is used to predict the future. This type of analysis uses previous trends, data, and statistics to predict trends in the coming future. It can give an accurate prediction if the data being analyzed is reliable. If the data being used for analysis is not reliable or accurate, then it may not give an accurate prediction.
4) Inferential Statistics uses patterns that are similar to Data mining and forecasting. The difference with inferential statistics is that it tries to explain why the data is acting in certain ways. Inferential statistics looks at what factors are causing changes in the data rather than simply trying to predict what will happen next.
Quantitative analysis can provide organizations with an advantage over their rivals in many different ways. One of the main ways is by understanding trends that already exist. This helps companies to make better decisions about the future rather than guess what will happen based on historical data. Another way Quantitative analysis helps businesses is by predicting changes that may take place in the near future. The ability to predict trends can give businesses an advantage over their rivals by allowing them to take action before something bad happens. Quantitative Analysis can provide organizations with more accurate predictions than other forecasting methods because it looks for trends that have already occurred. Fintalent, the hiring and collaboration platform for tier-1 Strategy and M&A consultants has a pool of expert Quantitative Analysts and consultants. By employing complementing tools like Qualitative Analysis and Econometric Modelling and Analysis, Fintalent’s Quantitative Analysts and Experts can give a business a comfortable edge over rivals enabling them make comfortable predictions about the future.
