The ‘data advantage’

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The ‘data advantage’

Maximize the return on data

Many businesses are struggling to truly create and capture value from their data investments. Often an integrated strategic angle is missing. Business should therefore start with the intent to design a strategic advantage with data - the ‘data advantage’. Thinking through the role of critical data (and analytics), both from a positioning and resource based school of strategy, is key in this. As a consequence this provides the context to focus on a handful of data (analytics) activities and capabilities that truly matter - ‘less is more’. This then opens the gate for iterative development and quick learning cycles, maximizing the odds of successful execution to create to true value.

By Nils Wolthuis, Leonora Lawson and Eveline Pothoven

Truckloads of data scientists and no business value

Data is seen as the core productivity driver in 21st century business. Over the last decades, businesses massively installed digital technologies (e.g. computer, mobile phone, internet, sensors, business software applications). Slowly, we are entering into a ‘deployment phase’ where a widespread increase of data volume and storage provide opportunities to create value.

Posterchild examples of businesses winning in this ‘deployment phase’ pre-dominantly are so-called ‘tech’ businesses like Alibaba, Uber and Netflix. However, most industries and businesses are lagging. Whilst analytically aware and ambitious, and despite (truckloads of) data scientists, many businesses are struggling to truly create and capture value from their data investments1

Design the ‘data advantage’ - strategic intent

All too often data (and subsequently analytics over the data) is not or poorly integrated in the business’ strategic choices and critical enablers. Taking a strategic angle towards data, as a strategic resource, can improve the odds of true productivity improvement and business value. One should start with intent to truly design a strategic advantage with data – the ‘data advantage’.

Consequently, one should think hard through the role of critical data (and potential of analytics over this data) in positioning the business; what data do we truly need to craft better ‘where will we play? and better ‘how will win?’ choices? This typically requires (creative) combinations between critical internal data, external data and analytics over the (combined) data. Think for example of leading retailers ‘store location strategies’ (combining existing footprint / performance data, highly granular external market / competitor / geographic data and the benchmarking / cluster analytics).

An alternative way, focusing on the business’ resources, is to methodologically screen the business’ data assets and capabilities searching for elements of competitive superiority, scarcity and scalability; which data is truly valuable, unique and can we leverage it? For example, based on the existing transaction data and engineering algorithm on this, Alibaba could launch a (tens of $ billions) micro-loan business (Ant Financial). Based on this they precisely (‘strategic precision’) could discriminate between good and bad lenders and automatically create credit scores achieving a cost advantage vis-à-vis traditional banks.

Executing on the ‘data advantage’ - ‘less is more’

All too often too many, poorly prioritized data (analytics) initiatives fill the agenda. Having the courage to focus on a handful of data (analytics) activities and capabilities frees up the management oversight, the people and the time to execute effectively. This allows true translation of data collection-, analytics-, decision-making to frontline impact.

Many data (analytics) initiatives do not scale and deliver because of organizational silos, lack of talent and adoption in the organization. Therefore, entrepreneurial leadership is critical to drive the execution of data (analytics) initiatives; initiatives should start small and gradually be scaled up to ensure quick learning cycles. For example, first focus on one geographic territory or customer segment and learn, before scaling the initiative. Focus and iterative development maximizes the odds of creating value building a handful of critical data (analytics) activities and capabilities, which together underpin the ‘data advantage’.

1Deloitte: Becoming an AI fueled organization
2Deloitte: IDO Survey 2019
 

More information?

For more information please contact Jorg Schalekamp, Nils Wolthuis, or Leonora Lawson via the contact details below.

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