The degree of accuracy with which we are now able to predict weather patterns is improving with every passing day. This is an impressive application of data, computer science and mathematics, especially considering the ever-changing weather dynamic, and gives real value to individuals and organisations alike.
At the same time, as business markets become increasingly competitive/dynamic, an increasing level of importance is placed on managing and understanding external competitive factors. Sadly, too few businesses have a structured process for collating and deriving real-time insight from this data (their business planning process is often reliant on an internal view of the world fixed every 12 months).
How can we apply learning from weather forecasting in a business landscape?
A weather forecast takes a real-time stream of external data points and is continuously revised.
- How often does your business review its competitive position?
- How often is that updated?
- How often is the plan reviewed by the executive team?
- What is happening in your company’s competitive landscape?
- Do the executive team have real-time access/insight into that market intelligence?
- Is that data presented in a visually engaging way?
- How often does the business plan get reviewed?
- Does it evolve as the market dynamics change?
- What external data is used to drive the business plan?
- What does a perfect client profile look like?
- How many of those businesses exist in the landscape and what are the engagement points?
- How do you monitor the sales triggers within those businesses?
The tools to enable strategic (and scalable) planning do exist and enable you to review the competitive business landscape on a continuous basis, but they are often located in disparate departments (Finance, Marketing, Sales, etc.) and not shared centrally.
A clear outlook
Accurate weather forecasting is reliant on data collection, data integration, modelling/prediction, and data visualisation. A quick WiKi search, explains:
“Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. As data intelligence, modelling, compute performance and data visualisation methods have improved so too has predictability.”
How can you integrate these elements into your strategic plan to better respond to market conditions, and deliver improved and more predictable business performance?
Discover the possibilities of strategic planning with the IQBlade Platform.
by Ben Abraham.