In today’s fast-paced digital business world, agility is crucial. Companies must swiftly adapt to changing conditions and base their decisions on solid data to stay ahead of the competition. Diagnostic analytics is a powerful tool that businesses need to operate effectively and with agility. This superior approach enables companies to quickly understand why something is happening. Moreover, it not only identifies the root cause of anomalies or unusual events but also assesses their impact on the business. As a result, diagnostic insights empower companies to make better decisions, especially in rapidly changing business environments. In this article, I’ll look at how diagnostic analytics is critical for businesses and ways it can be best used within an agile decision cycle.

1. Diagnostic Analytics: An Unique Decision Tool That Roots Out Why Things Happen.

Many times analytics methods often just focus on descriptive analytics (what happened) and predictive analytics (what will happen). However, these methods do not excel at answering the question, “why did it happen?” This is where diagnostic analytics comes in. It allows businesses to dig deep into problems to conduct effective root cause analysis, addressing underlying issues rather than just symptoms. Additionally, it helps organizations to fully understand risks and validate assumptions. Moreover, this type of analytics excels at diagnosing the impact of key trends and events. For a more detailed discussion on diagnostic analytics, see my article, The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance

2. The OODA Loop: An Agile Business Decision Cycle Demands a New Level of Analytical Nimbleness.

A Data Analytics Perspective To Better Empower Supply Chain Managers

Today, most, if not all, businesses are going through digital transformations. So, to remain competitive within this data-intensive, connected landscape, businesses will also need to change the way they make business decisions. Further, these changes also extend to data analytics. Indeed, now in this age of digitalization it is possible for business leaders to make informed decisions according to their own timing. Indeed, businesses no longer need to be chained to rigid, lockstep processes of laborious information gathering and analysis.

Without a doubt, businesses can now adopt an agile decision framework to make faster, better decisions. Indeed, this is rapid decision-making that both improves service offerings and reduces costs. Most importantly, in this age of digitalization, business agility is needed not just to be competitive, but to survive. To illustrate how rapid decision-making and analytics can work, below is an example of an agile decision framework. This lists the major phases of the OODA (Observe, Orient, Decide, Act) Loop decision framework.

Agile Decision-Making: The OODA (Observe, Orient, Decide, Action) Loop 

1. Observe Phase: Find Out What is Happening Using BI-Based Descriptive Analytics.
2. Orient Phase: Prioritize Rapidly to Diagnose (Diagnostic Analytics) Pressing Problems and Size Up Opportunities.
3. Decide Phase: Analyze What Can Happen (Predictive) and What Action Should Be Taken (Prescriptive), and Determine Best Option.
4. Act Phase: Tell the Organization the Decision and Intended Outcomes, Monitor Progress, and Use Business Agility to Adjust as Changes Occurs.
5. Repeat the OODA Loop: This Business Agility Cycle Is an Iterative Process Using Continuous Feedback for Optimal Decision-Making.

“He who can handle the quickest rate of change survives”

Col. John Boyd

So, the the OODA Loop is an agile decision framework that businesses can leverage to practice Business Agility as well as diagnostic analytics. For a more detail description of this decision framework, see my article, OODA – Enabling Business Agility: The Best Way To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles.

OODA – Enabling Business Agility: The Best Way To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles

3. Orient on the Problem and Opportunities: The Use of Diagnostic Analytics Within an Agile Decision Cycle.

In the world of agile business decision making, the ability to quickly orient on problems or opportunities is paramount. When an anomaly arises, a key event occurs, or a new task is assigned, businesses must be able to rapidly diagnose the situation and determine the best course of action. This is where diagnostic analytics comes in. By providing an effective approach through root cause analysis and impact assessment, diagnostic analytics enables businesses to make swift, data-driven decisions that drive optimization and success. Below, I’ll explain how diagnostic analytics is used within a business’ agile decision cycle. Also, I’ll look at how businesses leverage diagnostics to turn insights into action.

a. A Need for a Diagnosis Arises Due to an Anomaly, Event, or Tasking.

Within an agile decision cycle, diagnostic analytics is like a rapid assessment tool that is used when a decision-maker needs to know the specifics on why something happened. Within the OODA Loop framework, this is the Orient stage of the decision cycle. Also, in many cases this need for diagnostics is based on the results of descriptive analytics such as BI dashboards where changes of situations are detected. In other instances, diagnostics is needed as a result of a new initiative or change in business objectives resulting in a task to test an hypothesis or validate an assumption. To list, below are examples of when diagnostics are needed within an agile decision cycle.

Things That Can Trigger the Need for Diagnostic Analytics

• Anomaly.  For example, a sudden spike in website traffic.
• Event. For instance, a competitor launched a new product.
• Tasking.  A business needs to research buying habits of a target audience to confirm the viability of a new marketing campaign. 

b. Conduct Diagnostic Analytics: Analyze Root Cause and Assess Impact to Organization.

The Best Impact Assessment Approach that will Quickly Orient You for Better Business Decisions

After recognizing the need for a diagnosis, it’s time to carry out the diagnostic analytics process itself. This can involve such things as collecting relevant data, using statistical and machine learning methods to find patterns and connections, and investigating the causes behind an anomaly, event, or task. Diagnostic analytics also examines the potential impacts on the organization. By measuring these impacts and prioritizing based on risk and opportunity, businesses can concentrate their efforts where they will be most valuable. Analyzing both root causes and impacts is what makes diagnostic analytics a powerful tool in agile decision-making. For more details on impact assessments, see my article, The Best Impact Assessment Approach that will Quickly Orient You for Better Business Decisions.

c. Assess Need for a Decision or the Need for More Analysis.

After conducting the diagnostic analytics task, ideally businesses are left with a wealth of insights into the root causes and potential impacts of the situation at hand. To put it another way, they have a good understanding of “why something happened”. However, these insights are only valuable if they can be translated into action. So, armed with this information, the decision-maker determines whether the insights from the diagnostics warrant an immediate decision, or if further analysis is needed. To list, below are the post-diagnostic choices for the decision-maker:

Post-Diagnostics Next Steps.

• No Action Needed. In this case, the diagnostics results in the conclusion that no action needs to be taken. This could be because the event or anomaly was not significant enough to warrant action. Also, it could be because the diagnostics determined that it was a “false alarm”.
• More Analysis Needed. This may be a case where the diagnostics did not provide enough information or confidence to go forward with a decision. Thus, more research is needed. It also could be a case where the diagnostics uncovered other problems or opportunities. So, this would lead to analytics being applied to this new area of interest.
• Move Forward to a Decision. Lastly, the diagnostics does provide enough insights to move to the next stage of the decision cycle. In this case within the OODA Loop process, the Orient step of the decision cycle is complete and the decision-maker moves on to the Decide phase.  Thus, the diagnostics has provided the decision-maker an understanding of “what happened” and the confidence to move forward with a decision.

More References.

For more information on business analytics and decision tools to power agile decision-making, see my article, An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes. Also, for a more detailed discussion on diagnostic analytics, see Analytics8’s article, How Can My Business Use Diagnostic Analytics to Turn Insights into Actions? Lastly, for more on the agile decision framework, OODA Loop, see Unvarnished Facts’ article, The Thrilling OODA Loop: The Way To Shatter Your Competitor’s Decision Cycle And More Breakthrough Concepts.

The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

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