Data analytics are becoming increasingly popular in today's corporate world, and as advanced technology continues to streamline the report-creating process, more and more businesses are starting to realize the value of data analytic tools. Many companies now use their data analytical reports for competitive assessment, operational management insights, decision support requirements, financial statement analysis, market research activities and much more. Through proper utilization of these tools stakeholders can keep track of business performance easily within a few clicks! The purpose of this blog post is to provide an overview of how organizations can make full use of data analytic reporting for better visibility into operations as well as trends which impact them.
Let’s start with a clear definition: What exactly are data and data analytics?
Data is the information we use every day, whether for work or personal use. It can be qualitative, meaning it can be good or bad, or quantitative, which is usually continuous and related to measurements.
There are two common ways of representing data: tabular, which provides a quick summary, and graphical, which is becoming more and more popular on the internet. Depending on the complexity involved, data analytics can be divided into four levels - reporting, analysis, predictive analytics, and optimization.
Reporting is concerned with what happened in the past. For example, if we take production as an example, we would be interested in knowing how much we produced yesterday, the day before, or the week before. Next, data analysis moves on to answering why something took place. If the throughput was higher one day or week, data analysis tries to identify the significant variables that affected output.
With predictive analytics, data analysis takes a step forward and answers what can happen if specific ranges of input are controlled. Finally, data optimization focuses on how to make something happen or improve a process that is already taking place. For instance, if we add another piece of equipment, how much performance can be achieved, and at what cost?
To sum up, data analytics is a journey of excellence, and the complexity of analysis increases as we move from reporting to data optimization. Understanding these levels will help us in deciding which data analytics solution will help us achieve our desired results.
Data reporting, or descriptive analytics, translates raw data into information that can identify key areas of strength and weakness within organizations. The focus is on input and output, typically using reports, KPIs, dashboard visuals, and statistics. However, data reporting has its limitations and may not provide deep insights into the information being presented.
For instance, we created a 12-month data reporting dashboard for a client which allowed them to quickly see the budget, forecast, and actual values for their throughput, grades, and recovery rates. Financial statements are another common example of data reporting, typically presented in a table format.
Moving on to data analysis, or diagnostics, the focus is on why certain outcomes occurred and the relationships between input and output variables. Root cause analysis techniques like Five Whys and Fishbone Diagrams are commonly used, along with advanced statistical software like Minitab and data mining tools. Techniques like correlation and regression are also used to measure linear relationships between two continuous variables, although it's important to note that correlation doesn't necessarily imply causation.
Overall, data reporting provides a quick summary of important metrics, while data analysis goes deeper into the why and how behind those metrics. Both are essential components of effective data management and can provide valuable insights for decision making.
Data analysis is a crucial process that enables us to make informed decisions based on factual evidence. It's important to use the right techniques such as ANOVA (analysis of variance), where your input independent variable is discrete and your output (result) is continuous variable. Here are some tips to help you get the most out of your ANOVA analysis:
1. Gather Sufficient and Relevant Data - Before conducting ANOVA analysis, it is important to gather sufficient and relevant data that represents true operating conditions. In the case of copper concentrate recovery, it is ideal to use a minimum of 12 months data in order to capture the effects of different seasons, crew, particle size, throughput, bit grades, and availability.
2. Check for Equal Variance - To ensure that ANOVA analysis produces accurate results, it is important to first check if the different categories of discrete data (crew, season) have equal variance. This can be done by observing if the different datasets overlap each other on a graph as well as checking if the p-value is above 0.05. If they have equal variance, it will ensure that the results produced will be statistically significant and accurate.
3. Perform ANOVA Analysis - Once the data has been collected and variance has been checked, we can begin ANOVA analysis. ANOVA, which stands for analysis of variance, is a statistical tool that determines if the different categories of discrete data have a significant effect on the continuous data, which in this case is copper concentrate recovery. ANOVA produces a graph that shows the average copper concentrate recovery for each category of discrete data, and it determines if the differences between them are statistically significant.
4. Interpret Results - After performing ANOVA analysis, it is important to interpret the results in order to gain insights and make informed decisions. From the ANOVA graph, we can see that copper recovery for fall is between 83 to 85, which is higher than spring's average between 79 to 83. Furthermore, the effect of winter on copper recovery is different compared to spring, which is statistically significant with a p-value less than 0.05. This information can be used to make informed decisions about resource allocation, production planning, and crew management, to maximize copper recovery and profitability.
5. Use Established Data - When conducting ANOVA analysis, it is essential to use established data that you understand well rather than using extensive data that might not provide accurate information following these tips, you can ensure that your ANOVA analysis is , reliable, and provides meaningful insights that can be used to make a positive impact on your business.
In conclusion, it is important to consider the residuals when analyzing data with ANOVA or any other analysis method. This blog post discussed descriptive data analysis which allows us to analyze what has happened and better answer why that happens. We also learned that, in correlation analysis, we can determine which input affects the output. Please look out for my next blogs where I will be discussing the predictive and prescriptive stages of data analytics. It was a pleasure discussing this topic and I hope you found this information insightful! Finally, if you have any further questions about this topic or any of our services, please do not hesitate to contact us! Thank you for your time.