In the world of data analytics, diagnostic analytics plays a significant role in getting into depth of a certain event. Unlike other data analysis methods which only provide the analysis result. However, it becomes important to know the root cause of a certain event to be used by businesses in making informative decisions.
Diagnostic analytics take you from surface level observation to in depth analysis in order to address root problems. These data analysis techniques stand beside the traditional method of extracting insights and useful information from available data. In this article, we will understand the importance of diagnostic analytics.
What is Diagnostic Analytics?
Diagnostic Analytics is one of the four types of data analytics that help organisations or businesses understand the reason for occurrence of a certain event or outcome. It resembles a thorough investigation of why a certain event occurred or what triggered it? Let us understand with an example.
Suppose a retail store experienced a sudden drop in sales in the month of July compared to June. Based on the given data we can complete data analysis and produce some of the factors that might have contributed to the loss in sales. With diagnostic analytics we will identify the reason for the occurrence of patterns and trends and draw conclusions.
It is also popularly known as “root analysis”. It goes to the depth of analysis and predicts the reason for the occurrence of the events with logical and data based factual results.
Also, Check What is Predictive Analytics in Data Analysis?
Diagnostic Analytics Key Takeaways
- Diagnostic analytics is a part of data analysis techniques used to find patterns, trends, and relations to explain the occurrence of certain events.
- Diagnostic analytics is also known as root analytics as it is used to evaluate the reason for the occurrence of an event.
- It is used in various sectors such as IT, Manufacturing, Retail, etc.
What is the Significance of Diagnostic Analytics?
Diagnostic analytics provide an in-depth analysis of a certain event or outcome. It aims to unveil the root cause of a behaviour. You can use diagnostic analytics to understand your outcomes, workflow, and operations better.
Diagnostic analytics aim at identifying the issue besides the problems in a workflow or operations of a business or organisation. With the help of informative insights from diagnostic analytics, it becomes easy to find the issue and fix it quickly. With an in-depth analysis it becomes easier to prevent the same threat from occurring again for a long time.
Diagnostic analytics take into account certain parameters and assess the data quality. It also cleans and fixes datasets by analysing and removing anomalies or understanding.
Working of Diagnostic Analytics
Diagnostic analytics is an iterative method where it goes step by step to address the in-depth observation of an event or occurrence.
- Define the objectives: First, you need to outline the objective of a certain event or outcome which you want to understand in depth. The main purpose of diagnostic analytics is to uncover the reason behind the occurrence of an event.
- Collect data: It is important to extract the relevant insights from in depth analysis which might come from various sources such as databases, logs, spreadsheets, PDFs, and other platforms.
- Data Preprocessing: The collected data from the analytics is now processed to ensure the quality and reliability of extracted insights is maintained. It is important to address missing values, and redundant values, remove outliers, and format the data.
- Exploratory Data Analysis: Now the collected insights, trends, or observations are explored to understand and explore the results obtained. Various data visualisation techniques are used to study the data and make informative decisions.
- Hypothesis evaluation: Now all the other theories and factors are studied to provide a reason for the occurrence of an event or outcome.
- Root Cause Analysis: Based on the detailed analysis, get to the base or root of the causes and factors which led to a certain outcome or event. It is important to take direct and indirect factors into consideration.
- Interpretation: All the extracted insights and interpret the results to get a meaningful explanation behind the occurrence of a certain event or result.
- Communicate Findings: Make sure to convey all information collected from the in depth analysis and ensure simpler explanation through effective data visualisation techniques.
Diagnostic Analytics Examples
Check out some of the examples of diagnostic analytics below.
Example 1: Hypothesis Testing
Let us suppose your design engineer made changes to your current theme of your “Buy Now” button. You want to animate your button. Let us analyze its effect on the sales value of a website. Carry out a detailed hypothesis for the topic “Will changing the theme of buttons lead to a higher click through rate.”
With the help of hypothesis, you will collect insights and data related to CTR (Click Through Rate) before and after the changes you made to the button. With the help of root cause analysis you can easily predict whether the impact of changes made was fruitful or not. It is important to give some time like a week or month before collecting the insights.
Example 2: Sales Analysis
Suppose you work for a retail company and you want to collect insights on the success of your ads campaign. Collect data and insights on sales before and after implementing sales campaigns through various paid methods.
The detailed diagnostic analytics can identify the data points and outliers to understand the effect of a sales campaign and understand the relationship between spending on advertisement and the sales made.
Benefits of Diagnostic Analytics
Diagnostic analytics is an important part of data analytics which puts pressure on the reasons behind the occurrence of certain events and results. When you know the reason behind an event occurrence you can better plan your next move and make informative decisions for your company’s growth. With the root cause analysis of diagnostic analytics it becomes easier
- To solve long overdue problems or understand the bottlenecks in a workflow or operation
- Mitigate risks that might harm the growth of a business.
- Improve the efficiency and productivity of the process after exploratory analytics.
Collecting data insights from various sources is not enough until you can use the available information to understand the reason behind the occurrence of trends, events, or other observations. Diagnostic analytics help specialists rise from surface level exploration to in-depth analysis.
Businesses can rise over their competitors with detailed diagnostic analytics of the current trends and events. If interpreted properly they might use this analysis to take their future decision more carefully to generate higher revenues and rank over the others in the same sector.
Also Check, Difference between Business Analytics and Data Analytics?
Challenges of Diagnostic Analytics
Finding the relationship between two distinct events might become a tedious task, especially with large datasets. There are various parameters and factors responsible for the occurrence of a certain event and if any parameter goes unnoticed then it might differentiate the actual result from the obtained result.
It is important to ensure the quality of available datasets and ensure that the data is fit for diagnostic analytics. Any unreliable or false insights can alter the outcome and predict false outcomes.
Overall skilled professionals and knowledge is required to conduct diagnostic analytics. Knowledge of advanced analytics and tools is important to conduct the analysis successfully.
Sectors that use Diagnostic Analytics
Many businesses are maintaining their online presence and competing against others. Data plays a major role in almost all sectors and hence diagnostic analytics can help businesses get an edge over the others.
- Healthcare: Identifies causes of patient readmissions or treatment inefficiencies.
- Finance: Detects reasons for fraudulent transactions or financial discrepancies.
- Retail: Analyzes sales drops, customer behavior, and inventory issues.
- Manufacturing: Finds root causes of production delays or quality defects.
- Telecommunications: Investigate customer churn and network performance issues.
- Marketing: Assesses why marketing campaigns underperform.
- Logistics: Pinpoints causes of shipment delays or supply chain bottlenecks.
- Energy: Identifies reasons for outages or energy consumption spikes.
- Education: Diagnoses factors affecting student performance or dropout rates.
- Sports: Analyzes player performance decline or game strategy issues.
- Government: Explores causes behind rising unemployment or policy failures.
- Hospitality: Investigates reasons for guest dissatisfaction or operational inefficiency.
- E-commerce: Analyzes cart abandonment or website user experience issues.
- Technology: Diagnoses system crashes or software performance issues.
- Insurance: Identifies trends in claims and assesses underlying risk factors.
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Diagnostic Analytics FAQs
Q1. What is diagnostic analytics?
Ans: Diagnostic analytics is an important part of data analytics method which is used to get the reason behind the occurrence of a certain event or outcome. It is an in-depth analysis of the available data which help businesses in making informative decisions.
Q2. What is the purpose of diagnostic analytics?
Ans: Diagnostic analytics help identify, detect and provide a reason for the occurrence of a certain event while analyzing data. It helps to understand customer behaviour, data trends, and help in making well informed data driven decisions.
Q3. What is an example of diagnostic analytics?
Ans: Diagnostic analytics is used to predict the reason behind occurrence of a certain event after analysing the collected data insights. Suppose you want to know the reason for the fall in sales of your product or your business for a specific time period you can use diagnostic analytics to obtain a detailed report of sales before and after the fall to know the reason behind the fall of sales.
Q4. Why is diagnostic analytics important?
Ans: Diagnostic analytics is important as it is used to get to the root cause of occurrence of a certain event or trend. The organisation can use these reports to take their next step with better precision.
