As businesses begin their journey into data awareness and eventually toward data-driven strategy, they often have many questions to answer. We will answer a lot of those with this article to help arm you with the information needed to make smart purchasing decisions.
Ready to learn about diagnostic analytics and how to put it to good use? Let’s get started.
In this article...
The answer in brief: What is diagnostic analytics?
Diagnostic analytics is a form of data analysis that delves into the “why” behind observed events or outcomes. While descriptive analytics focuses on what has happened, diagnostic analytics seeks to understand the causes and factors that led to those outcomes. It involves examining data to identify patterns, anomalies, and relationships, often using techniques like data drilling, data mining, and correlations.
The goal is to uncover the root causes of a particular event or trend, providing insights that can help in decision-making and future planning. Think of it as a detective trying to piece together the events that led to a particular incident by analyzing clues and evidence.
In general terms, a lot of jargon surrounds the process of how we turn data into something worthwhile, including:
- Business intelligence (BI)
Many of the words we use for these concepts are only partially synonymous with one another, but the core idea is the same.
Data is a resource, one that has to be refined and properly worked for it to take the shape of a useful tool. Data, by itself, doesn’t mean anything or tell us anything. That’s what all the number-crunching and insight-gathering is for. I.e., that’s why we have analytics.
But even when it comes to analytics, there are different types. Or, should we say, different phases. We discuss them in more detail below, but for diagnostic analytics, this is when data is used to identify causality and chains of events. How did we lose/gain sales? What is the cause of increased work accidents?
In business, as in life, there is always a cause and effect. Diagnostic analytics is the tool we use to turn mountains of data into Sherlockian levels of deduction that allow us to properly attribute end results to root causes.
The Types of Analytics
We discussed that analytics covers several approaches/phases/steps/etc.
Here are the four most common, and most relevant to applying analytics to business intelligence:
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
We go into each of these in more detail elsewhere (well, except for diagnostic analytics, which we’re covering here, in case that wasn’t obvious). But below is a very brief summary of each, their purpose, and where they sit in the greater scheme of things.
Descriptive analytics: The “What”
This is the ground level of analytics. After all, you can’t manage, change, or predict what you can’t see, and that’s what descriptive analytics accomplishes. Descriptive takes raw data, scrubs it, and organizes it so that humans can more easily see what the data describes.
Google Analytics for marketing and SEO data, CRM for sales and lead data, and accounting tools like Quickbooks are examples of ways we answer the question, “Have the numbers gone up?”
Descriptive analytics is critical because it allows you to see where things are, where they used to be, and what kind of change the difference between them entails. It doesn’t necessarily tell you “why” or “what comes next.” But without it, you can’t do those things anyway.
Diagnostic analytics: The “How”
This is where the detective work begins.
Diagnostic takes the descriptive data, compares it against historical trends, external market trends, and any other relevant data, and tries to connect the dots.
We’ve technically been doing things like this for decades, though the tech we use to pull it off has become more sophisticated. When the healthcare industry, for example, identifies smoking as a major predictor for lung cancer, they looked at the data, saw how often smokers get cancer compared to non-smokers, and identified a likely causality therein.
Diagnostics is critical between “What are we dealing with?” and “What can we expect?” Once you know what’s causing the issue, you can more effectively plan for it.
Predictive analytics: The “what’s next?”
This leads us to predictive analytics. At this phase, we take the things we’ve learned from scrutinizing the past and the present and apply them to forecast models of what’s yet to come.
Take our smoking example: once we see that at least one major outcome is tied to that precursor, we can start looking for others and making educated guesses. Moving away from that morbid example to something more lighthearted, if we know the mouse has always asked for milk with his cookie, we can either plan on having that milk on hand or withholding said cookie.
Predictive analytics can be used to avoid, mitigate, or properly absorb risks and losses. But it can also refine strategies and accurately pinpoint successful business targets and growth opportunities.
Prescriptive analytics: The “How” to get there
Finally, we reach the endgame. If you can anticipate a future based on the past, it’s only a step or two further to start generating optimized strategies straight from the data and analytics themselves.
The internet has been aflame recently with talk of generative AI and how it impacts the work of creative professionals. If the robots can accurately anticipate which word follows the previous one and what colors the image should use, what does that mean for the humans who normally make those decisions?
What most don’t realize is that there is a much more efficient application of that technology. Sure, you might get decent visual or written content from the algorithms. But it’s a lot easier to play the odds in a gamble is much easier than in a competition. And business decisions, especially at scale, are much more probability-dependent than any creative work.
Prescriptive analytics can go beyond forecasting the future and recommend specific actions and tactics for potential eventualities. Optimized delivery routes, improved inventory systems, and recommended safety protocols—all are within the power of this kind of AI.
Read more: Top Prescriptive Analytics Tools
Why is diagnostic analytics important?
Setting aside its role in achieving predictive and prescriptive analytics and the wish fulfillment of being able to “see the future,” diagnostics is increasingly irreplaceable for any business that wants to maintain stable growth or scale its efforts.
Just having data or even producing descriptive-level reports is not enough. Not when things can be turned upside-down almost overnight. In previous eras, a single drought/famine/epidemic/etc. could completely devastate entire countries. We have farmer’s almanacs for just that reason: the uncertainty that even the near future was, quite literally, a matter of life and death.
Situations aren’t always so dire these days. Still, nearly every business professional in every industry, at every size of organization, would sleep a little more soundly if they had a reliable method of anticipating future events. Or, barring that, at least some way to “show their work,” as it were.
Diagnostics is the tool that does that. Want to demonstrate your value to the ongoing business efforts? Need to prove that recent downturns weren’t your fault? Are you looking to justify additional investment in a solution you believe in?
That is diagnostics.
How to get started
Analytics at every level is something best done with the right tools and techniques. For teams that want to jump in with both feet, you’ll likely need a combination of adequate software and sufficient human expertise. After all, data science is a field of its own.
Bare minimum, you’ll need to reckon with the following three challenges:
Many who begin trying to leverage data don’t realize (at least until they’re up to their elbows in it already) that data isn’t “plug-and-play” by default. As mentioned at the beginning of this article, data is a resource, and like any resource, it has to be processed to be used. As metal ore has to be refined and purified, so does data.
We generate data from many different sources, and it comes in numerous formats and levels of quality. For example, the information aggregated from your CRM may not align with what’s in your billing database. Redundancies, missing details, mismatched formatting, and more all plague the data discovery process.
This process of turning a mess of disorganized data into a single source of truth that can be successfully used is often referred to as “data scrubbing.” And, importantly, not every analytic solution can do it for you. The ones that can may not be designed around push-button simplicity, either. For almost every team, you’ll have to commit to a certain amount of IT investment (be it your own staff or a third-party vendor) to properly hook everything up.
The good news is if you do it right, this can actually be a “set and forget” solution to the problem.
Next, you’ll need the stuff that turns (clean) data into valuable business insights. Business intelligence tools are ostensibly designed for this purpose, but again, not every solution meets every need, and you may require additional human involvement beyond that. Preferably those who are actual experts in working with data.
Many factors will impact the requirements a BI vendor will have to meet to get the job done for you. System complexity, organization size, compliance requirements, etc.
Beyond that, there are concerns such as data warehousing, access management, asset lifecycles, etc.
Last but not least are the people who are often confused by the otherwise elegant simplicity of digital technologies.
All jokes aside, it can be a real struggle to elicit buy-in from every stakeholder involved. IT and I&O teams are usually hesitant to add additional responsibilities to their already overstacked plates. Management isn’t typically happy to add a new expense, even if ongoing costs are negligible once the initial setup is finished.
Team members often struggle to adopt new technology unless the organization as a whole communicates the value and prioritizes the onboarding process.
Worst of all, there are, in some cases, individuals who would rather that you didn’t see the data. Why, you ask? Well, transparency is wonderful…unless you’re attempting to hide something. Full visibility can make that difficult, and those who have enjoyed “working in the dark” may work to counteract analytics initiatives (even if they don’t do it overtly).
Taking the next step
Diagnostic analytics is a critical part of business intelligence, and it can lead to better strategy and better outcomes. Revenue growth, safety improvements, turnover reductions, and much more. Just remember that it’s only the beginning of where analytics can take you.
Looking for the latest in Business Intelligence solutions? Check out our Business Intelligence Software Buyer’s Guide.