Commercial data has always been a strategic asset. Unfortunately, it's often not managed like one. It's often treated as transactional information, transient tactical data to fuel short term insight, campaigns, and promotions. Data quality, data integrity, veracity and interpretation are often open to question. Consequently, many businesses are squandering value and failing to build valuable operational and intangible assets using data, especially proprietary data.
The explosion in data availability
More data then ever is available to businesses, but not all data is either urgent or important. Forecast predict that 5G, IoT and AI will accelerate the growth in data availability. Connected devices, sensory networks, new content, deeper, more granular operational data mean that there will be plenty data available. That's a blessing and a curse as most organisations don’t have well-developed plans to maximise value from the data the have already.
Most commercial organisations have not aligned their data needs or data collection to the key disciplines of commercial strategy and operations. While there are many sources of data and the volume of data is increasing, this lack of assignment and alignment is creating an operational gap that is likely to increase. This gap between data availability and management means data assets are often sub-optimally managed and under-developed.
The most valuable data is not that which is generally available or available to a few it’s proprietary data, developed from proprietary sources or derived for available sources. It’s this kind of data that’s an asset, providing the basis for insight that’s difficult or impossible for competitors to replicate.
Data as an asset
Many businesses see data as transactional, transient, a means to an end, Fewer, see it as an asset, something to be curated and developed, an investment. Both are right. Data is a useful source of transaction and strategic insight. The key is knowing which data is useful or valuable in both circumstances.
In a transactional sense it can help develop insight into intent, inform personalisation and drive conversion. In a strategic sense it can help share strategies, inform choice, and build adaptability. In both cases the right data is an asset, the wrong data is a liability. However, there’s a lot of data around, it’s not all useful, meaningful, or accurate. It can often be disorganised, incomplete and inaccurate. Commercial and customer data is no exception.
Data is a strange asset, in many ways it’s in a class of its own, in most circumstances it’s not limited to a single owner. It’s easily copied, shared, and used. This is why proprietary data is so important, why it should be protected and why it’s arguably the most important source of insight
Data architecture and AdaptomyDNA
Before any business can think about the collection and quality of data, about data assets, there needs to be a framework or architecture to understand why and when certain data is valuable, what that data can be used for, how it drives customer and commercial value and the accountabilities and responsibilities for commercial data asset management. AdaptomyDNA provides that framework.
AdaptomyDNA is a collection of over 70 commercial disciplines, an adaptive model, an integrated framework, a unified architecture to assign data and information*. It helps design and deliver commercial strategy and operations and it’s a way to develop meaningful metrics and build deeper insight into customer and commercial value and the efficiency and effectiveness of commercial operations.
*We have completed approximately 25% of the data assignments for AdaptomyDNA disciplines, that’s about 23 disciplines, starting with Sense and Re-think clusters. The full map is due for completion by the end of March 2024.Each discipline, the detailed processes, and the clusters to which they belong have been assigned data. The data is classified in three ways and is used to drive specific metrics, measures and insight associated with one or more disciplines and their processes. Within DNA Data Asset Management is discrete discipline within the Renew cluster.
AI and commercial data
Increasing use of Artificial Intelligence in commercial strategy and operations amplifies the need for consistent, accurate**, reliable data, rationalisation, and optimisation of data sources. Ensuring data integrity at source, verifying and testing data from all sources is critical to the success of any AI strategy. This is difficult to achieve without a clear architecture or framework. AdaptomyDNA provides a framework to cluster and test data, to optimise data collection across a range of disciplines and processes.
Proprietary customer and commercial data, derived data and insight are key assets. These assets can only be build over time if data and information sources are carefully assigned and curated. They can only be managed effectively if the business is clear about data value and how that data informs strategy and operations. AI can exploit these assets to provide differential advantage to customers and the business, but only if the data and information is consistently managed.
**Conventional wisdom suggests that more accurate data is more valuable, but often data that’s 90% accurate is a useful as that which is 100% accurate and is costs much less to produce. However, with the advent of AI this rule may no longer hold.
Data context, patterns, and history
Commercial and customer data is context sensitive. It changes over time and circumstance, loses its relevancy, it decays. Its currency is important, so too are the patterns it creates over time. Even subtle changes in the state of data can reveal important customer or market insight that can offer real differential advantage. Today, traditional statistical tools, predictive modelling and AI are being combined to uncover these insights using a combination of available and proprietary data sources. Again, it’s critical to understand the relevancy of source data and these insights to specific commercial disciplines, strategies, operations, and business outcomes.
This is especially important when considering the current value, value growth and investment in data assets. Data that’s valuable to one organisation at a point in time may not be valuable to another. It can be challenging to identify and value data assets. The main challenge is attribution the value of specific data to the outcome. This is why an overarching framework is key, it help management assignment and allocation to specific disciplines and processes, the metrics and measures associated with the discipline and the accountabilities. It’s not a perfect science, but it’s a starting point.
Key takeaways
Unifying data across commercial strategy and operations obviously has efficiency benefits, more accurate, timely information. It can also help increase effectiveness through reliable data that truly informs decision-making. However, the real benefit for any organisation lies in building deep, unique and proprietary insight that informs specific strategies, tactics and operations, insight that delivers more customer and commercial value, insight that aligned to particular disciplines. This is hidden advantage, asset value that is difficult if not impossible for competitors to replicate.
Know where value is derived from data (have a framework)
Understand the disciplines, processes and accountabilities around customer and commercial data management
Develop and curate non-proprietary and proprietary data sources
Develop adaptive methods and tools to assign and manage commercial and customer data to specific strategic disciplines and operations
Manage data as an intangible or intellectual asset - an investment
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