The Beauty and the Beast: From Analytics to Execution Technology

Dr. Andreas Müller
November 25, 2021
Data Transformation and Technology Integration

With the rise of the age of data abundance, it is natural for technology like data infrastructure, analytics, and AI platforms that process such data to appear. These new technologies enable to access, transform, and analyze the vast reservoirs of data available to us. However, analyzing and processing data provides us with insights in dashboards and more information involving the worst-case, and such information is useless – unless it can be put to good use.

But what do typical analytical platforms lack?

Modern data analytics platforms that are available and emerging are built to manage data. They ingest a load of numbers and punch out more data that may be but are not necessarily actionable.

As discussed in a recent az16 article on “Emerging Architectures for Modern Data Infrastructure,” the authors point toward the output in dashboards, embedded analytics, and augmented analytics. However, leveraging these outputs to get insights into actionable recommendations, executing, and tracking decisions hasn’t been considered yet. But this is where capturing business value continues and where the rubber hits the road. Let’s take a typical use case: Current data analytics will tell you how much of a product is present in a specific warehouse and predict the future demand pattern. In the best case, it might also provide you with some actionable recommendations if it piles up there. But it will not tell you what marketing and sales strategies to use to get them moving or how best to restock the item following its selling rate, and so on.

Moreover, even if the analytics platform gave you enough data to formulate winning strategies, you would still not have any information on how to execute them. Such execution gaps plague businesses more than you might imagine. However, this is where we can unlock business value from all the insights one might have gathered from analytics and data management.

But the problem is not always on the execution side. Often, the gap is created due to a lack of alignment between analyzing, recommending, executing, and then learning from these loops, making better decisions, and moving on. Systematically making this loop is called closed-loop performance management. My colleagues Christoph Kilger and Boris Reuter discuss this in our video blog about supply chain performance.

However, we still see one quite common roadblock to execution—the age-old paralysis in analysis problem. Here, managers and executives are not ignoring the numbers; rather, they are focusing too much.

So, how to resolve this analysis-paralysis problem?

The analysis-paralysis problem arises from over-thinking and over-analyzing the data that the executive or manager has received. The numerous factors to consider and numerous avenues to assess causes the decision-maker decision paralysis.

Nonetheless, data analytics alone cannot help the person; in fact, the profusion of data from the analytics platform puts them in the strategy limbo.

But what if there was a platform that could analyze this cornucopia of data, run simulations of all possible actions, and choose the best course of action for the executive?

That is what an execution management system does with an analytics system.

Here, managers will no longer have to stew in anxiety and think about what to do as there is an application to help with that.

The power of seamless integration between analytics and execution

There is no doubt that poor alignment and connectivity between analytics and execution is at fault for most execution failures. Integrating the two into a single, seamless chain of events is key to solving the problem at hand.

Let’s look at current execution management systems: most of the time, people start execution with Excel lists, emails, project management tools, and so on. These tools are usually aimed at productivity and are not primarily considering knowledge, experience, and best practices from past initiatives. They essentially focus on progress, not on their impact on the desired result or KPI. This causes a disparity between the indicators from data analytics and the getting-things-done view that many managers have.

Looking at strategic execution systems, Gartner talks about how often market changes between strategy cycles render the strategies useless. A closed-loop, constant feedback system between analytics and the strategic planning and execution machinery could vastly improve if not completely prevent this inefficiency.

Nonetheless, keeping execution connected to analytics at the business object level (atomic) and in real time creates a ripple effect that makes tactical decision-making and strategizing much more effective.

Examples in supply chain management

Coordinating the execution system of the supply chain with data analytics exerts advantages in many areas. For example, having visibility over your supply chain allows you to find the right opportunities at the right times and capture them with tailored strategies.

With such excellent visibility in place today, you can easily map your supply chain and precisely control your stock and stock movement to prevent losses from stockouts or overstocking. Moreover, you can even adjust your strategies on time for better risk management.

Summing up

The gap between analytics and execution is yet to be bridged reliably. Therefore, an application that can either connect the two management systems in real time or combine them into a single platform is the need of the hour. AIOimpact offers an execution management system that supports tactical supply chain decision-making and execution.

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Meet the Writer
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Dr. Andreas Müller
Andreas is an experienced CEO and entrepreneur, building the next generation of supply chain technology leveraging AI-powered decision intelligence. Empowering clients to improve agility and responsiveness of their supply chains, and seize new opportunities in a more uncertain world.


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