Traditional syndicated data is aggregated market data that’s collected by market research firms and sold to businesses with an interest in that market.
For example, a retail brand might purchase and use aggregated behavioral data by Zip code to plan geographically targeted promotions.
Retail and consumer product goods (CPG) companies have been using syndicated data for years, but until recently, only larger companies could afford to do so. Now, thanks to advances in technology, middle market companies can afford to start using syndicated data in their data analytics strategies for better, more informed forecasting and planning.
One reason to consider using syndicated data is that the boundaries of what’s possible with data are expanding. Not only is it now easier and more affordable for smaller companies to use syndicated data, but more types of syndicated data are becoming available. For example, syndicated data is being used to predict broader climate change and how that will impact crop yield, moisture allocation and other factors that influence agricultural planning.
Another change is that the data is more meaningful because it’s now generally available in near real-time. So, instead of evaluating how a promotional program worked 12 weeks after it ended, a company can track near real-time sentiment of its brand and products using syndicated social data.
Five ways to use syndicated data
In the past, data analytics required hiring expensive, highly skilled professionals who would use time- and resource-intensive processes to analyze data. Today, cloud platforms such as Microsoft Dynamics 365 include tools that automate data analytics for business users. This puts the ability to use syndicated data for data analytics into any user’s hands.
At the same time, other advances—such as high-speed networks and application program interfaces (APIs)—have made it easier for new groups to offer syndicated data, which traditionally had come from just a handful of dominant companies.
But as technology continues to evolve and the amount of data generated grows exponentially, there’s nothing that limits any type of third-party data from being syndicated or how businesses use that syndicated data for more informed forecasting and planning.
In the same way, syndicated data could be used for additional risk-testing of any assumption, such as the investment theory of a planned deal. During the due diligence phase, syndicated data can add context and shed light on overlooked factors that should be considered for testing.
Syndicated data can be an essential part of this exploration on the tactical level. Perhaps a team is considering five common behavioral factors to model scenarios about product preferences. What happens when they add a sixth that they hadn’t considered before? They could discover a new indicator that proves beneficial in predicting preference and even influencing strategy.
