At Information Evolution we see many companies that invest in AI, analytics, and automation tools, but overlook the fundamentals of their data supply chains. A few simple rules (and the right partner) can help firms eliminate process bottlenecks, access data in once inaccessible silos, and ensure that there are no blind spots in the data that could undermine confidence in your product’s value.
Source Data: You can have a SaaS product with outstanding features and functionality, but if the data underlying it is inaccurate or incomplete your users’ results will be disappointing, and this poses a very real risk to the service’s long-term viability. Identifying high-value primary sources of information and finding ways to add unique value to them is fundamental to your service’s success.
Extraction: Most data-intensive software services retrieve data from multiple sources and then combine that information into a unified database. This means that your supply chain also will likely require the disambiguation of similar records and proper appending of provenance metadata.
Filtering: Efficient removal of irrelevant records and/or data makes systems run faster and is therefore essential to cost-efficiency and timeliness.
QA: Humans in the loop are required to review high-priority and complex records. Not only does this provide a crucial ‘feedback loop’ for evaluating the effectiveness of the extraction and filtering processes, but it also gives you a marketing edge over those competitors taking shortcuts by eliminating humans in the loop.
Delivery: Flexible delivery options at the end of the data supply chain are essential to ensure the proper ingestion of data into your content management system.
Having a well-designed data supply chain that runs efficiently and can be easily adjusted over time is a strong barrier to entry for potential competition. Each stage of the process can be super-charged with AI tools and custom applications, making your position in your market more formidable with each passing year.
