Benefits of Analytics in Strategy Execution

Advanced analytics can be applied, and with remarkable benefits, at almost every step of the Strategy Execution Management process.

Benefits of Analytics in Strategy Execution

Advanced analytics can be applied, and with remarkable benefits, at almost every step of the Strategy Execution Management process.

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The digital revolution is in high gear. Businesses and government bodies are transforming operations as they replace paper-based, transactional processes with digital versions. This digital transformation is enabling improved use of the organization’s internal and customer data through the use of big data analytics. Chief Strategy Officers and their Offices of Strategy Management (OSM) are often the key architects and execution management muscle of this change.

But OSMs often neglect themselves. From our field observations, it is evident that the use of advanced analytics has been poorly developed within strategy departments. Data use has typically been limited to Business Intelligence (BI) tools that monitor KPIs in the form of Balanced Scorecard dashboards. Moreover, despite OSMs allocating resources for advanced analytics to various departments, they themselves have often not fully developed the analytic skills and capabilities needed to make the most out of such solutions. OSMs have therefore not yet integrated advanced analytic aids into their own internal processes, and specifically, in the Strategy Execution Management process that an OSM manages in the organization.

However, advanced analytics can be applied, and with remarkable benefits, at almost every step of the Strategy Execution Management process. In partnership with Palladium, we at Synergy Consulting have defined the role of analytics at every stage of the Execution Premium Process™ (XPP).

The role of Analytics in Stages 1 and 2 of the XPP – develop and translate the strategy 

The most obvious role that advanced analytics can play appears at Stage One and Stage Two of the XPP: 1) develop the strategy and 2) translate the strategy. A strategy, graphically translated into a Strategy Map, is a collection of objectives that are related to each other through cause-effect relationships. A company needs to set and achieve Learning and Growth and Internal Process objectives, also known as enablers, which should drive outcome objectives on Customer and Financial perspectives. Traditionally, the cause-effect relationship was stated as a set of hypotheses based on the corporate strategy. These hypotheses were then quantified in a financial model. However, the driver assumptions and their cause-effect relationships were difficult to accurately quantify in the financial model because they were mainly based on qualitative external and internal analysis during the corporate strategy formulation. With descriptive analytics, organisations are able to use their data volume, data velocity, and data variety to quantify cause and effect between strategic objectives. From this analysis, they are able to translate these qualitative cause-effect links into quantified formulas based on historical data and ensure the numbers and formulas in the financial model are more accurate.

Of course, a financial model reflecting the corporate strategy is intended to quantify the future scenarios. Predictive analytics can be used to quantify what the future could look like. Assuming an organisation achieves its enabler objectives, predictive analytics can determine what success looks like, while generating scenarios and assessing the risks associated with failure. Moreover, the approach means enabler objectives themselves can be defined at the exact levels needed to achieve outcome KPIs and objectives. 

The role of Analytics in Stage 3 of the XPP– align the organisation 

During organisational alignment and strategy cascading, a similar approach can be adopted for the business unit and functional strategies. As these cascaded maps also have cause-effect relationships, the use of descriptive and predictive analytics can play the same role as they do at the corporate level.

The role of Analytics in Stage 4 of the XPP – align operations 

As part of aligning operations, an organisation needs to define and set priorities and targets for the driver processes for each strategic objective. Descriptive analytics can be used to quantify the incremental impact of each percentage improvement in a driver process on the percentage improvement of the linked strategic objective. The resulting and quantified cause-effect relationship helps ensure the targets set for the operational dashboards are in fact those that ultimately help achieve the targets of the linked strategiv objectives.

The role of Analytics in Stage 5 of the XPP – monitor and learn 

A key part of Stage 5 of the XXP is the strategy review. It is used to analyse the root causes of under-performance within any given strategic objective. OSMs frequently make assumptions about the root causes by using subject matter experts from within the organisation. These assumptions and theories are valuable, but should then be tested by the OSM using descriptive analytics to probe whether the numbers actually support the hypothesis. Predictive analytics can then be applied to project how the scorecard of the organisation or function should look like by the end of the next review cycle (assuming all the underlying drivers progress at their current speed). Scenarios can be developed to model how performance could evolve under different circumstances, which in turn aids in prioritising the action plan developed at the end of each quarterly review.

The role of Analytics in Stage 6 of the XPP– test and adapt 

A strategy update is key to Stage 6 of the XPP. The organisation uses data generated since the prior strategy formulation or update to test the validity and accuracy of its cause-effect hypothesis based on descriptive analytics. They can then fine-tune the quantified relationships so that the financial model is more accurate and ensure that achieving a driver target will in fact result in the desired strategic objective.

Final thoughts

Deploying analytics at the strategic level is complex. And, as our approach suggests, it can take a few iterations, a few turns of the wheel to refine any given cause-effect model centred on strategic objectives. But for firms that embrace the challenge, it will pay off, as they will have a better view of the future; and they will be better able to define what needs to be done now.

See if our Strategy Execution Management Consulting Practice can help you.

For more information about the role of Analytics in your organization’s strategy execution management process, download Palladium and Synergy Consulting Group’s White Paper: The XPP and Analytics.

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