The much-hoped-for economic upswing, recently predicted by some economists, seems to be stalling for now. The rising number of insolvencies also does not point to a turnaround anytime soon. Estimates suggest there will be at least 20 percent more corporate bankruptcies this year. From more than 1,400 projects, we know that business crises don’t emerge overnight. They typically follow a predictable pattern, and many could be completely avoided if management adhered to a few fundamental principles and activated the right levers at the right time. enomyc author Jan Ulrik Holsten explains what it takes to succeed.
Why are some companies better than others at avoiding crises or achieving a successful turnaround? Based on our experience, the key difference between successful and less successful companies in crisis management lies primarily in how they handle five "barriers."
- Barrier: Turning Data into Information.
Data are raw, unprocessed facts and figures that, in their current form, lack meaning or structure. They serve as the foundation for generating information and knowledge, but are not yet actionable. Information is created when data is contextualized and interpreted. To produce information that is meaningful for decision-making, companies must therefore structure data in a context-specific manner.
- Barrier: Turning Information into Knowledge.
Knowledge is the interconnected and contextualized form of information that emerges through experience, learning, and reflection. With knowledge, individuals and organizations can make decisions not only based on data and information but also in consideration of context-specific issues. This requires, within the framework of (organizational) learning processes, a critical examination of guiding beliefs and the ability to "unlearn".
- Barrier: Moving from Knowledge to Decision-Making.
Decisions are central operations within companies. Existing knowledge forms the basis for decisions, which are typically made under uncertainty. The quality of decisions is directly dependent on the quality of knowledge (as mentioned above). Another challenge in restructuring contexts is recognizing the urgency of decisions when necessary ("sense of urgency").
- Barrier: Moving from Decision to Action.
Technical innovations and changing preference patterns must also be reflected in operational processes, such as assortment optimization. Ongoing reviews and adjustments of the product range are essential to ensure that the offerings remain up-to-date and market-relevant.
- Barrier: Moving from Action to Results and Cash Effects.
Measurable results and cash effects are the outcomes of effective implementation of actions. The quality of implementation is the Achilles' heel of practical measure execution. Consistent management is a key to success, requiring transparency regarding progress and the degree of effect realization.
This understanding aids in both identifying barriers and applying best practices to overcome them. Below, we outline the challenges that need to be addressed in the context of each barrier and highlight approaches that have proven particularly successful.
Addressing the First Barrier: Turning Data into Information
This barrier can have three causes that may pose a challenge for companies, either individually or collectively. These are:
- Data Capture Issues: These lead to the unavailability of data in the required quality (content, scope, detail, validity, timeliness, etc.)
- Structuring Issues: These often arise from the inability to represent data from different systems with the necessary consistency and appropriate "data cut," as well as
- Interpretation Issues: These occur due to insufficient analytical skills, prevailing biases, or limited contextual knowledge, which hinder the ability to recognize patterns and relationships within complex data.
To efficiently address the mentioned issues, companies should utilize approaches that have proven effective in practice and that target specific aspects of the challenges outlined. This includes standardizing data capture through uniform input forms in CRM systems, incorporating mandatory fields and validation rules, or automating data collection using IoT, sensors, and RPA (e.g., automated invoice reading using OCR software). Another proven approach is implementing data quality tools, such as data quality management software.
Overall, and with regard to the overarching question, risk early warning systems based on so-called Early Warning Signals (EWS) play a crucial role in addressing the aforementioned issues. These systems capture data and structure it into information that can effectively signal crisis indicators early on—long before the actual crisis characteristics become measurable or occur.
In the second part of this article, we will explain what is important in the design, implementation, and utilization of risk early warning systems. We will also discuss selected risk early warning indicators and their suitability for predicting specific stages of crises. Are you interested? You can sign up here to receive this information before any other readers.
About the Author
Jan Ulrik Holsten is a Partner at enomyc, responsible for the Sales and Marketing division. He oversees comprehensive turnaround and value enhancement projects as a consultant and interim manager. This article highlights a key solution approach within our consulting portfolio that has proven to be a valuable lever for improving profitability and enhancing competitiveness. Jan Ulrik Holsten also focuses on topics such as Corporate Profit Improvement and Working Capital Management. Learn more about Jan Ulrik Holsten here.
