Not everything that counts can be counted and not everything that can be counted counts.” Albert Einstein.

When we measure strategy, there are no absolute indicators, they are not a comprehensive response, they should become a basis for starting a face-to-face conversation between the strategy team and the execution team, which could be carried out around the actions. described below:

• Cross-cutting experimentation to improve the effectiveness of initiatives.
• Master in data science.
• Alignment of the analysis strategy and operational analytics.
• Adaptive Synchronization.

Continuously testing the hypotheses of the strategy, resides in the creation of an advanced capacity, risk reduction, qualitative and quantitative measurement (analytical). To test an initiative we proceed to decompose it into a set of probable hypotheses (business model framework, where each component represents a hypothesis to be tested), these hypotheses cover 3 types of risk, the first thing is that customers are interested, if they really find the initiative useful, the second is the feasibility that can be built, and the third is if the initiative generates profits.

The experiments are the means to test these hypotheses, each experiment must generate evidence and insights, which allow learning and deciding, that is where we find the quantitative (analytical) and qualitative (insights) analysis, based on that evidence and these insights can be adapt the initiative, pivot, or continue testing other aspects of it, if the evidence supports the direction.

The management team must review how well the organization captures the external movements of the market / customer and is able to synchronize the internal and external exchange rates, this begins with the creation of the capacity in advanced analytical causality, which is related to the data science, which in turn is based on a platform that allows the disciplined registration of tests, experimentation and learning of initiatives, to increase the probability that their result will be greater than or equal to the proposed objective and if not proceed to adapt according to customer needs.

The alignment of the analysis strategy with operational analytics is based on the fact that agility requires data to be effective, which demands systematic organizational (internal) and market (external) intelligence to understand when and how to adapt. Which does not mean the voluminous collection of data, but which are the most relevant data, and adequately offer the correct insights and decision makers, regardless of the levels, functions and organizational limits. It also requires a different pace for organizational performance management and strategic decision making.

Strategic moves must be triggered by an external event (move by a competitor or customer or, better yet, early identification of a trend). At the same time, there must be clarity around the strategic options and the positioning of the company (which are maintained in the longer term).

The information systems, the behavior of the business and the company must be continuously synchronized and adapted to the environmental conditions and the opportunities that arise, in order to maximize the benefits of technology, market opportunities, knowledge and experience. .

If the synchronization is not carried out, the company could find itself in serious problems, such as a decrease in competitiveness in the market, where the client can find products with higher quality, at a lower cost, and better alternatives in the market.

The need for synchronization is a never ending process and the basis for change. The change is managed to ensure that the most competent resources are involved at the right time, to produce solutions that best meet the needs and expectations of customers, when these solutions are ready for the market.

Miguel Martínez, March 31th, 2021.

References:

• Ismail, I., Palao, F., y Michelle, L. (2019). Transformación Exponencial. México D.F.: Bubok Publishing SL.
• Lyngso, S. (2014). Agile strategy management: Techniques for continuous alignment and improvement. Boca Raton: CRC Press.
• Fernando, R. (2019). Agile strategy: How to create a strategy ready for anything. Harlow, England: Pearson.
• Lambert, D. (2020). Practical Guide to Agile Strategy Execution: Design, Architect, Prioritize, and Deliver your Corporate Future Successfully. CA: Independently published.
• Wiraeus, D., y Creelman, J. (2019). Agile strategy management in the digital age: How dynamic balanced scorecards transform decision making, speed and effectiveness. Cham, Switzerland: Palgrave Macmillan.
• Porter, D. T., y Porter, M. E. (2015). Estrategia competitiva: Técnicas para el análisis de los sectores industriales y de la competencia. México D.F.: Grupo Editorial Patria.