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We have been working extensively with the analytics maturity curve in recent engagements, particularly focusing on applying this framework to the feedforward and planning functions of businesses, rather than solely concentrating on the traditional feedback aspects. This shift in perspective—leveraging analytics to inform and optimize decisions before investment—is critical for organizations aiming to create and capture value more effectively.
Optimizing Decision-Making with Analytics
The key question is: how can we use analytics to improve decision-making in advance of major investments? By applying the lenses of the analytics maturity curve—descriptive, diagnostic, predictive, prescriptive, and cognitive analytics—we can systematically address opportunities across strategic decision-making, operating models, and delivery frameworks.
However, the challenge lies in laying a solid foundation. Most tools, including many of those in Gartner’s Magic Quadrant, focus heavily on descriptive analytics (what is happening) with occasional forays into diagnostic analytics (why it’s happening). These are valuable but insufficient when the goal is to progress toward higher-value insights like predictive (what will happen) and prescriptive (what actions to take) analytics. And ultimately, the aspiration is to leverage cognitive analytics for more dynamic, AI-driven decision-making.
Bridging the Gap: The Role of Foundational Work
It’s important to emphasize: there is no shortcut to bypass the foundational work required to achieve advanced analytics maturity. Organizations must first ensure that the basics—the descriptive and diagnostic layers—are robust, reliable, and governed effectively. This groundwork is essential for building the capabilities necessary to transition into predictive and prescriptive analytics, which are vital for transforming business planning data into actionable insights.
For example, organizations need to start by addressing fundamental questions such as:
- What is happening in our value chains, operations, and strategies?
- Why are these patterns or challenges occurring?
Once these questions are answered, businesses can then advance to exploring:
- What is likely to happen in the future?
- What actions should we take to optimize outcomes?
The same principle applies to planning data across value models, strategic models, business models, and operating models. The foundational layers must be in place—well-structured, governed, and understood—before moving into the higher-value, AI-driven insights that organizations aspire to achieve.
Leveraging Digital Twins and Advanced Tools
There’s growing discussion in the modeling and architecture space about the potential of digital twins to support this journey. While many current tools focus on descriptive and diagnostic analytics, digital twins offer a promising way to simulate, predict, and optimize outcomes across value creation, strategy, operations, and delivery. By integrating these tools into your business processes, you can create dynamic environments that support feedforward planning and unlock greater value at every stage of the analytics maturity curve.
Driving Business Value with Advanced Analytics
The journey to predictive and prescriptive analytics is not just about adopting new technologies; it’s about a cultural and organizational shift. Businesses must recognize the value of investing in foundational analytics and commit to the hard work required to elevate their planning data. By doing so, organizations can move beyond surface-level insights and start delivering true, actionable intelligence.
If your organization is ready to accelerate its analytics maturity, we’d love to help you take the next steps. Reach out to us to discuss how you can elevate your business planning data and move up the analytics maturity curve to unlock deeper insights and greater value.