Data Acquisition | Third-Party Data Strategies

As set out in Strategies for Unlocking the Value of Third-Party Data organizations are faced with both a need and an opportunity to revisit how they acquire and extract value from third-party data. In Solving the 80/20 Data Dilemma, six phases associated with acquiring and extracting value from data were identified: Discover, Assess, Acquire, Adapt, Use, Monetize (optional).

With Discovery and Assessment completed, the acquisition phase begins with a hand-off. The commercial negotiations around pricing and terms, coupled with the creation of legal documents require very different people and skills from the analytical and technical work in the stages before and after. As a result, it is easy for this stage to lack integration, resulting in delays and leading to issues that can impact the actual data being acquired.

Challenges include defining precisely what is required having, in most cases, only accessed a sample. Typically the price of a data product will vary depending on the scope of what you consume, so it can easily become a bargaining chip. Use cases also affect the price, so this is often the focus of much attention and defined tightly, sometimes too tightly, to prevent unauthorized usage. Ironically this limits the potential for data to be trialed and used more widely within an organization as part of an extensible agreement. Due to the length of the sales cycle – at this stage there will have been months of elapsed time and significant investment for both sides – contracts are typically long ranging from 12-36 months. This is a significant commitment, particularly if you have only assessed a sample, leading to more complexity and increasing the overall risk.

This is an area that feels intractable, like the assessment phase, because it is based on widely accepted business processes that make it up. Unlike the assessment phase, there is no option to shift the paradigm by using a technology like a collaborative data exchange, but there are opportunities for improvement.

1. Collaborate effectively. At this stage knowledge and expertise is dispersed across multiple people, departments, and organizations with varying skills and technical understanding. If you can collaborate effectively, it will help to drive faster, better outcomes that enable the use case rather than detract from it.  
2. Clearly drawn lines. An output of the assessment phase should be a clearly articulated scope of what data is required to frame the negotiation. This is easier if a full-corpus trial has been enabled but even on a flawed, sample-based assessment, it’s helpful. This should also include format, frequency of format, method of transfer, and SLAs. In a collaborative data exchange, the desired output can actually be created and productized, which is the most precise and least intensive way of doing this.  
3. Plan for the future. It is in the interest of both parties for new use cases of the data product to be easier to establish than the first. So considering adding use cases such as demos, (full volume) trials, and ‘limited’ innovation in addition to the specific use case that is understood today.

The process of acquiring data involves a significant amount of process that is widely dispersed and challenging to coordinate. By understanding this, being prepared, and enabling future needs, better outcomes can be achieved. Once the data is acquired, attention will quickly shift to adapting it for use.

Authored by Anthony Cosgrove (Co-Founder) at Harbr