Today’s data-driven world demands a new approach to business analytics. Gone are the days of backroom analysis and “stubby pencil” decision-making. What executives need now are actionable insights on their terms and their timing. In particular, organizations no longer can afford to conduct business analytics using a fragmented, siloed approach, disconnected from decision-makers. Moreover, we need to stop generating hundreds of mindless, disjointed Business Intelligence (BI) reports and cease participating in long, drawn-out planning cycles that produce few insights. What is needed is a seamless Business Analytics Continuum that synchronizes descriptive, diagnostic, predictive, and prescriptive analytics for corporate executives.

In this article, I’ll introduce you to the Business Analytics Continuum and its criticality for data-driven decision-making. Additionally, I’ll describe how the full spectrum of business analyses need to work together to provide both reflective and forward-looking insights. Lastly, I’ll describe how this Business Analytics Continuum synergizes analyses to enable these capabilities to work together within an organization’s decision intelligence framework.

1. Unlocking the Power of the Business Analytics Continuum: Understanding Its Criticality for Data-Driven Decision-Making.

Advanced technologies are supercharging and fundamentally changing the entire premise of business analytics. Traditionally, business analytics was the domain of analysts such as statisticians, mathematicians, finance, and planners. Now with advanced, inexpensive computing power and cheap data storage, executives at all levels can work directly with analytical capabilities to derive faster, better insights for superior decision-making. As a result, business analytics no longer needs to be a series of disjointed, piecemeal activities governed by scarce, slow-moving analytical resources. Now, business analytics can take on a new decision intelligence framework, a continuum, to drive data-driven decision-making. 

a. Revisiting the Purpose of Business Analytics.

First, let’s revisit what is the purpose of business analytics. As described by NetSuite, the purpose of business analytics is to “… transform data into insights, so companies can use the data to drive better-informed decisions and actions”. I like this description as it clearly relegates business analytics to a subservient role within the decision-making process. Indeed, business analytics only truly exists to provide insights to decision-makers. In the past, business analytics could not meet its full potential and fell short many times in meeting executives’ real world needs. Hence, many executives today use Excel spreadsheets and “stubby” pencils to support corporate decisions that in many cases can make or break companies.

Now, even more so, corporate executives need business analytics that can match their operational tempo in making superior decisions. Indeed in this era of digitalization, businesses are accelerating how fast they make decisions and take action just to survive. Today’s leaders can’t afford to wait days or weeks for analytics teams to process data and make recommendations. What business executives need are analytics that they can work with directly across all three of their executive decision horizons: operational, tactical, and strategic. Specifically, this includes:

Business Analytics Needs to Support Three Decision Horizons 

• Operational. The need for immediate insights for day-to-day decisions. 
• Tactical. Require timely insights for tactical planning to help department heads make informed choices about quarterly goals and resource allocation. 
• Strategic. Lastly, the need for relevant insights to feed long-range planning and complex business decisions.

b. The Need to Fully Incorporate Business Analytics Into the Decision-Making Process.

Without a doubt, business analytics needs to be a single, coherent capability that both mutually complements each type of analysis and delivers timely insights to the decision-making process. Today, business analytics does provide value, but it is not reaching its full potential in empowering decision-makers. For instance, below I list the four major types of analytics and how they work in isolation.

Types of Business Analytics Working In Isolation

Supply Chain Analytics Types and The Way They Work To Better Empower Decision-Making

• Descriptive. It shows what’s happening now (like current inventory levels). For instance, this type of analysis may render its results in the form of a Business Intelligence (BI) report.
• Diagnostic. This type of analysis explains why it happened (such as identifying why demand pattern shifts).
• Predictive. In this case, the analytics forecasts what could happen next (potential stockouts).
• Prescriptive. Lastly, this analytics suggests what actions to take (optimal reorder points).

So for the most part, organizations use different types of business analytics in isolation. Moreover, this piecemeal deployment of Business Analytics continues today, even in this digital age. From my perspective, this is more of a case of habit chained to technology constraints that no longer exists. Now with fast computers, cheap storage, and AI, Business Analytics can form an interconnected, synergistic smart analytical capability that turns raw data into actionable, timely insights that meet the decision needs of executives.

c. The Business Analytics Continuum Framework.

Indeed, it is time for us to think of Business Analytics as a continuum, a comprehensive approach that encompasses the full range of analytics, from descriptive to prescriptive. It enables organizations to gain a deeper understanding of their operations, customers, and markets, ultimately driving better decision-making. By treating business analytics as a continuum, organizations can unlock the full potential of their data and make informed decisions that drive business growth.

For example of a Business Analytics Continuum, see Gartner’s Analytics Continuum below. It provides an excellent depiction of how analytics works within a decision intelligence framework. Specifically, this chart shows how each type of analytics supports both business decision-making and implementation as well as how they can mutually support each other, tied together by a common goal.

2. The Four Pillars of Business Analytics for Gaining Both Reflective and Forward-Thinking Insights.

To better understand business analytics types, let’s dig deeper, comparing their differences and similarities. Specifically, the four pillars of business analytics – descriptive, diagnostic, predictive, and prescriptive – provide decision-makers with both reflective and forward-thinking insights. Although they rely on historical data, each type serves a distinct purpose. First, descriptive and diagnostic analytics focus on past events, while predictive and prescriptive analytics look to the future. Without a doubt, understanding the primary question each type addresses helps determine its role in the decision-making process and where it fits within the broader data analytics landscape. See below:

Different Forms of Data Analytics – Their Focus

• Reflecting on Past Events and Anomalies
  • Descriptive – What Happened?
  • Diagnostic – Why Did This Happen?

• Forward Thinking Toward Future Trends, Events, and Actions
  • Predictive – What Is Most Likely To Happen?
  • Prescriptive – What Action Should We Take?

3. Synergizing Business Analytics: How Different Types Work Together Within the Decision-Making Process.

For corporate executives to make superior decisions in this data-driven age, they need a decision intelligence framework that incorporates a Business Analytics Continuum. This is the only way to create a seamless decision-making process that is driven by data and insights. This synergy enables businesses to respond quickly to changing market conditions and make informed decisions that drive long-term success. Below is a breakout and description of each business analytics type working together as a continuum to enable superior decision-making.

a. Descriptive Analytics – What Happened?

In this day-and-age, descriptive analytics more and more uses digital input in order to scour the business environment to confirm the status quo, identify trends, and discover anomalies. For instance, businesses routinely use Business Intelligence (BI) reports and dashboards for this type of analytics. As a result of a descriptive analysis, a decision-maker may identify items of interest such as a developing trend or anomaly. Subsequently, this type of analysis will trigger other types of analytics such as diagnostics. 

b. Diagnostics Analytics – Why Did This Happen?

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

In many cases, diagnostic analytics is triggered as a result of descriptive analytics such as the identification of something out of tolerance within the business environment. As a result, a decision-maker can initiate a diagnosis of the situation. Also, automation or even AI agents can trigger this type of analytics based on a pre-defined key performance indicator (KPI) or some other type of measure. 

Once triggered, a diagnostics analytics focuses on such things as identifying root causes, determining the “why” behind a trend, or validating hypotheses. As a result of a diagnostics, a decision-maker or even automation may trigger further analytics such as predictive or prescriptive. On the other hand, a diagnosis may support a decision not to pursue a particular issue and move on to more pressing matters. For more information, see my article, The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance.

c. Predictive Analytics – What Is Most Likely To Happen?

Predictive analytics does make forecasts about the future, but it is not a crystal ball. Indeed, its purpose is to help executives make better decisions now in order to favorably affect desired outcomes in the future. Again as with other types of analytics, it can be initiated by a decision-maker, standard operating procedures, or even AI agents. What’s more, predictive analytics can be triggered by other analytics types such as descriptive, diagnostics, or even prescriptive analytics. For more information, see my article,  Predictive Analytics Types: The Best Opportunities For Supply Chains.

Predictive Analytics Types: The Best Opportunities For Supply Chains

d. Prescriptive Analytics – What Action Should We Take?

Prescriptive Analytics in Supply Chains: It Advises What’s the Best Thing to Do, Why, and How to Make It Happen

Prescriptive Analytics is a lot more than a recommendation engine. To illustrate, most of us are familiar with shopping recommenders that make product buy suggestions on an ecommerce site. On the other hand, Prescriptive Analytics does much more. Specifically, it can use advanced algorithms to recommend a specific course of action, explain why it is the best, and provide details on how to implement it.

Additionally, it is a forward looking analytics like predictive, but instead of forecasting likely outcomes, it helps you choose the best option along with an action plan to implement. Again, prescriptive analytics also works in concert with other types of analytics. For more information, see my article, Prescriptive Analytics in Supply Chains: It Advises What’s the Best Thing to Do, Why, and How to Make It Happen.

Conclusion.

To conclude, today’s data-driven world demands a new approach to business analytics. Gone are the days of backroom analysis and “stubby pencil” decision-making. Moreover, we need to stop generating hundreds of mindless, disjointed Business Intelligence (BI) reports and cease participating in long, drawnout planning cycles that produce few insights. What is needed is a seamless Business Analytics Continuum that synchronizes descriptive, diagnostic, predictive, and prescriptive analytics for corporate executives. Without a doubt, organizations need to leverage a Business Analytics Continuum that synergizes analyses, enabling an organization’s analytical capabilities to work together within a decision intelligence framework. For more references on Business Analytics and Decision Intelligence, see below.

This Is What Decision Intelligence Technology Is And Know What Its Not

More References.

• ThoughtSpot’s article, What is business analytics? The complete guide for analysts
• Agile Decision Intelligence: High-Velocity Analytics To Best Empower Executives In A Quickly Changing World
• An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes
• This Is What Decision Intelligence Technology Is And Know What Its Not

Agile Decision Intelligence: High-Velocity Analytics To Best Empower Executives In A Quickly Changing World

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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