Diagnostic Analytics: Definition, Examples, and Benefits

Let’s say you purchase a new laptop and decide to download some software — but while they’re downloading, your laptop suddenly freezes and shuts down. A natural question to ask here would be — why did this happen to a brand-new device? Diagnostic Analytics works similarly. It covers the necessary why’s of the events and results that have taken place by deep diving into the datasets and their insights.?

In this article, we will look into the areas below regarding Diagnostic Analytics:?

·?????????Diagnostic Analytics Definition

·?????????Examples of Diagnostic Analytics

·?????????Benefits of Diagnostic Analytics

Diagnostic Analytics Definition

Diagnostic Analytics is defined as the approach used to uncover the reasoning behind certain data results (i.e., events that have taken place). It is a type of analytics — after the descriptive analytics phase — that studies the datasets in detail to identify the reason why something happened. This analytical approach involves techniques like data discovery, data mining, and drill-down to identify any correlations and causations present between a variety of variables related to the event.

Generally, most businesses’ data analyses start with descriptive analytics — it is the basic stage that collects, analyzes, and reports on the datasets for what has already occurred. Examples of these datasets could be a drop in sales for a whole week, a high employee turnover rate, or zero impressions from an ad campaign. With Diagnostic Analytics, however, businesses are able to explore further into the data to explain the reasons — or the why’s — behind these results and insights.

Here’s a simple way to help you picture the importance of Diagnostic Analytics — let’s say you’re selling shoes, specifically women’s wear, through an online platform. As a businessperson, you would naturally keep tabs on how your business is performing — for example, how your daily sales, monthly revenues, and website traffic are doing.

However, if you don’t understand the why’s behind these performances, it would be difficult to identify your key insights, plan your necessary next steps, forecast realistic targets, or strategize a proper approach to realize those goals. For instance, if you didn’t understand the reasoning behind the decrease in sales two months ago, the high bounce rate on your website, the overstocked white shoes in your warehouse, or your high shopping cart abandonment rate, you wouldn’t be able to make the necessary pivots and adjustments in your planning moving forward.

Examples of Diagnostic Analytics

Diagnostic Analytics studies historical and current datasets to explain why something happened in the past. The main objective is to analyze the datasets surrounding these events in an attempt to identify any potential correlations, and henceforth, causations.

Here are two key examples of major industries using Diagnostic Analytics: ?

1. Healthcare

The healthcare industry is one of the most data-driven industries in the world — it analyzes and reports on millions of datasets regarding patients, illnesses, medicines, treatments, insurance claims, payments, employees, and more. It is also a perfect example to show how Diagnostic Analytics is employed.

Let’s say there has been a sudden bottlenecking of patients on the emergency floor within the last few months. Diagnostic Analytics can be employed here to figure out the reason behind this surge — for example, data discovery techniques can be used to collect, evaluate, and mine datasets across multiple variables, such as admission rates, symptoms, number of staff members on duty, availabilities of other hospitals, and more. Then, a quick analysis of the correlations might show that the reason behind these sudden medical surges is due to an ongoing contagious disease, a shortage of staff, or perhaps the closure of nearby healthcare providers.

2. Human Resource (HR)

HR departments interact with data surrounding employees on a daily basis in order to manage and execute processes like hiring, training, resignation, firing, and more. In order to manage employees and their respective welfares properly within the company, HR relies on numerous datasets — both internal (e.g., employee background, performance, engagement, KPIs, etc.) and external (e.g., industry market data, population data, market salary rate, etc.) — to identify the strengths and weaknesses within the company.

For instance, if one of the departments in the company is experiencing a high turnover rate, HR can employ Diagnostic Analytics to discover why so many employees are resigning. First, various datasets from multiple exit interviews, employee feedback submissions, company evaluation ratings on websites, general industry salary rates, and the overall job market size can be coded, queried, and cleaned before entering the data warehouse. Then, diagnostic techniques, like data mining and data discovery, can be leveraged to identify and understand the reasons why employees are leaving the company.???????????

After a detailed analysis, some of the reasons could be due to your company’s less competitive salary packages, fewer employee benefits, or increasing work pressure, or even due to overarching variables, such growing job market opportunities. Once HR has discovered the main justifications, they can then plan the appropriate steps to overcome them.????

Benefits of Diagnostic Analytics

Every business has become increasingly reliant on data across the recent decade. With the help of Diagnostic Analytics tools and techniques, companies can get a deeper understanding of their datasets and the insights produced.

This is why leading Business Intelligence (BI) companies — like Cubeware — have come up with solutions and platforms to implement Diagnostic Analytics tools, thus ensuring that decision-makers have the capabilities to understand their data’s results before taking the next step.

Here are the key benefits of employing Diagnostic Analytics for your business:

1. Obtain customized and specific answers

Diagnostic Analytics analyzes datasets that you deem as relevant to the scope or event. This means that your discoveries are not only more specific to your business (versus the overall market), but also more customized to the particular phenomenon within your business.

For example, if you discovered through reports and analysis results that the sales of women’s shirts have drastically reduced across the last month, Diagnostic tools can help you find answers that are tailored to your business as opposed to the general decline of clothing sales across the industry. Diagnostic Analytics can further target specific sections within your business — for example, the relevant datasets surrounding the marketing campaigns that are involved, recent customer feedback, website traffic on specific product pages, and more. ?????

With that, companies and businesses can then focus on building a targeted strategy that addresses and overcomes specific setbacks. After all, the reason your sales have declined might be due to internal issues, rather than overall market trends.???????

2. Get data-driven explanations, not guesstimations ??????

Diagnostic Analytics relies on hard data and technical tools to arrive at its conclusions. Through techniques like data discovery, data mining, and drill-down, Diagnostic Analytics can process terabytes of data within minutes to look for correlations and causations across a multitude of variables. Essentially, it eliminates the need to guesstimate when it comes to explaining a certain outcome or event. This is incredibly critical for companies who lack the time and resources to execute multiple trial-and-error attempts — for instance, if you realize that an ad campaign is resulting in low impressions, you might be inclined to repeat the same campaign multiple times with small tweaks or adjustments until you achieve your desired outcome.

Without precise and data-driven explanations as to why the campaign is performing the way it is, this tedious, ineffective, and costly process is your only option. On the other hand, with Diagnostic Analytics, you can simultaneously query multiple datasets from past campaigns (with the same targeted audience) in order to identify their success drivers. From there, you can then determine which parts of your current campaign is lacking and take the appropriate steps moving forward — for example, change the visual style, amend the copy, tweak the target audience, and more.???????

3. Avoid repeating past mistakes

One of Diagnostic Analytics’ key aspects is understanding the correlations between different variables related to your outcome. Once you understand the reasoning behind a result, you can then take precautionary measures to avoid similar outcomes in the future. Instead of using Diagnostic Analytics to fix existing problems (such as the aforementioned campaign that was performing poorly), you can use it to circumvent these issues entirely for the future.

For instance, if you already know the reasons underlying why your store experiences low traffic, you can rectify those issues by not repeating the same mistakes when you're opening more store branches. These reasons could be due to complicated floor layouts, disorganized clothes arrangements, poor customer service, or even just non-strategic location planning.?

In a nutshell, Diagnostic Analytics benefits companies in more ways than just understanding the why’s behind business outcomes. By understanding the reasons behind your results, your business can explore new opportunities, anticipate risks and losses, plan suitable approaches, and make the best decisions for your business going forward.

To learn more about data analytics, visit us at www.cubeware.com. In addition to building end-to-end data analytics and BI solutions, Cubeware regularly curates educational articles on the most relevant components of the data analytics industry.