Two burning issues arise from the development of predictive platforms when it comes to data sharing. Should you pool data when you choose to platform-share with competitors? Or, should you opt for data retention and deny yourself the potential of performance-boosting technology?
Here are a few questions to help you make that judgment call.
1. Will my company data remain confidential with data pooling?
In theory, a genuine predictive platform guarantees confidentiality of the information it uses. It does this in a comparable way to a market research provider who reveals public opinion trends without disclosing identities or individual beliefs of those polled. A sales process optimization platform functions in the same way. It reveals the most effective factors for winning over new prospects without disclosing the data that led to the calculation. That means specific knowledge of a single transaction between company A and supplier B has no functional value to the platform for adapting sales strategy. By contrast, when the platform is supplied with enough data to give a clear overview of the profile of all the leads likely to respond better to a sales offer, then it can serve up actionable insight.
2. Will my organization be the only one to hold this data?
When it comes to transactional information such as purchase orders or invoices, this is held by both the company in question and any it does business with. In the absence of any special agreement between them, there’s nothing to stop these business parties pooling information concerning you and them via their own predictive platforms, however much you might want to prevent this.
3. Doesn’t this inject an element of competition into the sector from which the data is sourced?
To answer this, think about the way in which rival companies can all be members of one professional association. They share information with it about their market conditions without any distortion of free competition. Predictive platforms are no different. Sidetrade analyses transactional data from its entire client database to predict payment terms for each of your customers. This sharing of data works to the advantage of every user of the platform, even if they’re competitors, by boosting their income streams.
4. How do I know I’ll gain as much from my competitors’ data as they will from mine?
The benefits your company can draw from the predictive platform through data pooling will depend upon how representative the data is as a whole. Let’s suppose that all the businesses using it are very like yours. Then there’s a risk of data duplication, which wouldn’t contribute much to the accuracy of the predictive platform. But the broader the source base of the data, the more it be extrapolated. It’s worth noting though that if you’re using a platform with a large client base, then you’ll get a better payback if your own data contribution is small.
5. To get the most out of a predictive platform, is there any other option besides data pooling?
Designing algorithms for a tailor-made platform would depend on the predictive indicators required, the quality of your organization’s data, and the accessibility of third-party databases. But, bearing in mind the kind of investment that’s needed in terms of data science skills and Big Data infrastructure for the development of predictive software, then data pooling definitely makes most economic sense. That said, in a highly concentrated market, such as a specialist technical field or one where available data is derived only from a limited number of players, the data pooling option might not be as effective or have much value. I can’t imagine data sharing to detect sales opportunities for players in the nuclear industry, for example, making much sense.
Predictive platforms today offer organizations the opportunity to learn from the huge volumes of data they’re gathering. In many cases, they can step up benefits by sharing this customer data. Using a predictive platform is a lot like having your own consultancy firm feeding back expertise gleaned from studies of numerous other companies but without any compromise on data confidentiality.
To learn more, read the first part of the article.