- Data analytics, like logistics, is transforming before our very eyes.
- Data analytics, like data, is a strategic asset.
- Data analytics will promote and establish data-based decision making.
- Data analytics will advance the “survival of the fittest”, separating successful from unsuccessful businesses.
As we stated in one of our posts, the logistics industry is entering a data-first world. That’s a loaded term to some extent. What it conveys is that data will drive business decisions in the future.
How does data do that?
The short answer is through data analytics and reporting.
Big data analytics and reporting can be descriptive, diagnostic, predictive, and prescriptive. We’ll look at each one and the value each can offer to your business.
Descriptive Data Analytics and Reporting
This represents the first level of data analytics. In logistics it helps to describe the effectiveness of business processes. In doing so it highlights areas doing well and areas in need of improvement.
Another way to look at descriptive data analytics is to determine what happened. That implies the use of historical data. And it lends itself to pre-planned or “canned” reports.
Examples of descriptive data analytics are:
- Logistics Performance
- Order Accuracy
- Inventory Turns
- On-time Shipping
- Transportation Costs
- Warehousing Costs
- Cost of Damaged Goods
- Customer Service
- Customer Wait Time
- Gross Margin Return on Investment
- Supply chain Cost vs. Sales
Again, descriptive data analytics and reporting represents the first level of measurement and reporting. This is your starting point for data analytics.
Diagnostic Analytics and Reporting
In contrast to descriptive data analytics, diagnostic analytics focuses on why something happened. It represents the next level of data analytics and reporting. As such, it is more dynamic or ad hoc. You can also use diagnostics as alerts. It is especially helpful where an unexpected event occurs hampering supply chain operations.
For example, you might use diagnostic analytics to understand why something happened. Diagnostics allow you to use both short-term and long-term data to identify trends.
Diagnostics can help you to improve supply chain effectiveness and efficiency. Think of diagnostic analytics as a drill-down capability that give you deeper insights than descriptive analytics. Diagnostics can help in reducing customer wait time, inventory accuracy, and transportation costs. These are only a few examples.
Predictive Data Analytics and Reporting
Predictive analytics represents yet another level of data analytics and reporting. Think of it as the strategic level. Predictive data analytics and reporting analytics focuses on what could or will happen. That means you can use it to test hypotheses or “what-if” analyses.
You can leverage two kinds of analytics here. You can use data mining and big data. The former focuses only on structured data. Whereas the latter includes structured and unstructured data.
Big data analysis is predictive because it helps reveal insights not otherwise clear. That is, some insights are not obvious in descriptive or diagnostic analytics. It gives you deeper insights. Like descriptive and diagnostic analytics, predictive analytics uses historical data and information.
Its main benefit is that predictive data analytics apply to the enterprise. It is not limited to business processes or functional areas. It is holistic, facilitating big picture analyses.
Prescriptive Data Analytics and Reporting
As the term implies, this form of data analytics and reporting gets at how to implement recommendations. It informs you of what you can do in the future to create a more competitive supply chain.
So, it is prescriptive in nature, identifying options for making changes. This is the fourth level – and most sophisticated one – making it the most beneficial. It is the most valuable because it reveals the future, as it were, about what you can do to gain competitive advantage.
So, the main distinction between predictive analytics and prescriptive analytics is the outcome. Predictive analytics arms you with data and information for making informed decisions.
Prescriptive analytics, meanwhile, presents you with data-based options you can assess. More specifically, it enables comparative analyses of viable options from which to choose.
But getting to prescriptive data analytics and reporting isn’t easy. It is only achieved through a deliberate process. But in taking a step-by-step process, you can simplify your approach.
That briefly explains the four levels of data analytics and reporting enabling data-based decision making.
The Emergence of Data-based Decision Making
You could say the future is here. It is, but we are in the early stages of the future state of decision-based enterprises. We’re probably in the second or third inning. Transformation has started, but it has a long way to go.
That said, the pandemic accelerated this transformation that was already underway. Now, it has a greater sense of urgency as you and your competitors adjust to the residual chaos.
You and your competitors will always need descriptive analytics. You and your competitors most likely have some form of predictive analytics.
Those who don’t are accelerating their adoption of predictive analytics. Finally, as technology delivers greater capabilities, prescriptive analytics is the next frontier.
In a data-first world, you must have a healthy and robust data analytics capability. However robust your analytics, you must keep up with emerging threats and capabilities.
Data analytics, like data, is a strategic asset. But data is only as valuable as your analytics. Soon, businesses will exploit predictive and prescriptive analytics. You should consider adding this to your existing BI capability.
At American Global Logistics, we’ll help you keep pace with the industry’s dynamic changes. We’ll
keep you ahead of your competitors.
If you’re unsure about your next steps, then contact us today. You can’t lose.