How to supercharge data analytics with syndicated data

Jul 25, 2021
Data & digital services Digital evolution Data analytics

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.


Leading indicators

Sometimes syndicated data is used for long-term planning, but it also could be used for shorter-term forecasting and planning. During the pandemic crisis, for instance, agility was critical, so retailers used syndicated data of econometric factors as leading indicators for forecasting which products and product mixes to offer three, six and 12 months out.


Intelligent forecasting

Adding syndicated data to existing data sets can provide enough data to use machine learning (ML) models for more intelligent forecasts or to improve the efficacy of existing ML models. When intelligent data models are fed a wider set of signals, the resulting insights are more data driven and more predictive.


Siting and capacity planning

For capital-intensive companies such as industrials and retail store chains, syndicated data can provide additional signals about where demand will be, as well as where necessary resources—such as employees with the right skills—are more likely to be. This can be particularly important as remote work becomes more common and as more weather-related events unexpectedly disrupt operations and supply chains.


Testing models and assumption

One of the risks of using data analytics is having a biased model because that could make the resulting analysis incomplete or skewed. Using third-party data sets to test the model could help teams identify bias and make adjustments. If an indicator is dramatically different when syndicated data is added, for instane, that could be an indication of bias.



Forbes estimates that the amount of data created, captured, copied and consumed in the world grew by 5,000% from 2010 to 2020. Indications are that this rate is accelerating, and technology for performing data analytics is becoming even faster and easier to use. We are living in a golden age of unearthing new knowledge because of these trends, and part of any data analytics strategy should be to grow organizational knowledge and opportunities.

Start from a strong position

Having a strong data infrastructure is the baseline for effectively adding syndicated data to your data analytics strategy. Although modern applications make it easier to perform data analytics, these are only one part of an effective data strategy.

Being able to securely absorb, manage and share syndicated data with the right people is also essential, and these tasks usually fall on the shoulders of organizations. This can be a heavy lift for many midmarket companies, so some choose to partner with business and technology consultants such as RSM to help them design and deploy an effective data strategy, or they use that advisor’s managed services to help with the workload.

Now’s a good time to review your data infrastructure and data analytics strategy to determine whether your organization is prepared to take advantage of all the benefits of using syndicated data.

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