By: Expert Panel, Forbes Council Members
To compete in a fast-moving, global marketplace, businesses must be able to make quick, informed decisions—and that requires robust, accurate data. Internal data offers critical insights into an organization’s operations and customer behavior. Combining it with public data can reveal broader market context, helping leaders identify consumer trends, demographic shifts and other emerging patterns.
But realizing the value of public data takes more than simply adding it to an existing dataset. Below, members of Forbes Technology Council share best practices—and cautionary advice—for organizations looking to strengthen decision-making through the strategic use of internal and public data.
1. Ensure Insights Are Actionable
Don’t dump combined datasets on every desk. Transform public-internal data fusion into simple, actionable insights anyone can use. Your store manager doesn’t need raw demographic data—they need “foot traffic will spike 40% this weekend due to local events.” Your product team doesn’t need social media firehoses—they need “X is causing frustration.” Make insights self-serve and decision-ready for everyone. – Marc Fischer, Dogtown Media LLC
2. Shift From Reactive To Proactive
When you combine internal data with trusted live public data, you must shift from being reactive to proactive. To navigate emerging risks, it’s critical to have a complete, real-time view of what’s happening inside your business and what’s unfolding externally in the world around you. You can then turn insight into action by operationalizing the data and building the appropriate workflows. – Brian Gumbel, Dataminr
3. Integrate Population Averages With Individual Data
Learn how to integrate data from population averages that you see on the internet or in industry publications with individual-level data that you observe in your company. For example, a healthcare company may have information on drug effectiveness from a clinical publication, but for its trial, it’s observing patient-level outcomes. If you learn how to do that well, you may be able to enroll fewer patients! – Eric Novik, Generable Inc.
4. Enhance Decisions Through Cross-Validation
Both data types are important in their own right but are made stronger with validation from the other. Public data enhances decision-making by showcasing both similarities and differences between your operation and the outside world. It enables both the acceptance and mitigation of things coming down the line. – Georgia Leybourne, Linnworks
5. Start With The Decision
Start with the decision, not the data. If you view the process through that lens, the data will likely be more relevant and of higher quality, as you’re using public data sources based on impact, not just availability. This strategy is also likely to lead to faster adoption as stakeholders see the value of leveraging public and private data to drive the decision outcomes they care about. – Igor Rikalo, o9 Solutions
6. Anchor Integration In Business Objectives
Always anchor the integration of public data in clear business objectives. Ensure the external datasets directly complement or challenge your internal information. Focus on robust data mapping and validation—this prevents misleading correlations and ensures actionable insights, ultimately transforming combined data into strategic clarity and stronger, data-driven decisions. – Aravind Nuthalapati, Microsoft
7. Prioritize Internal Data Preparation
Public data may seem easier to use in the short term because of its accessibility. It takes time to sort, cleanse and properly categorize internal data. However, when the proper care is taken to prepare the internal data, it will typically produce more reliable and helpful results. Advice: Don’t take shortcuts—do the hard work to incorporate your internal data, as it will pay off in the end. – Amy Brown, Authenticx
8. Establish A Clear Goal And Seamless Data-Sharing Systems
Start with a clear goal and modern, cloud-native systems that enable seamless data sharing. When agencies combine internal data—like dispatch and records—with public sources such as demographics and traffic data, the real value comes from context: understanding what the data says and what it means. Through intelligent data insights come enhanced operations, transparency and community safety. – Matthew Polega, Mark43
9. Surface Internal Bias
Internal data often hides bias from historical decisions. Use public datasets not just for validation, but also to teach your systems and teams where internal blind spots lie. For example, if your sales projections ignore demographic shifts seen in census data, you’re forecasting in a vacuum. Making public data a dissenting voice sharpens objectivity in high-stakes decisions. – Jagadish Gokavarapu, Wissen Infotech
10. Implement A Data Governance Plan
Setting up a data governance plan is imperative. A data governance plan ensures that the right data is being mixed in at the right time. While businesses may gain greater insight from public agency data by combining it with their internal data, they must ensure both data sources have been properly cleansed, standardized and aligned for proper assessment. – Asad Khan, LambdaTest Inc.
11. Work Toward Higher-Quality Knowledge
Recognize that increasingly higher-quality knowledge—not just data—is essential for achieving quality decision support, especially as you start to leverage generative AI. Knowledge is a different game altogether and has its own practices and challenges. – Leonard Lee, neXt Curve
12. Connect Disparate Data To Scale AI
Building a connected data foundation is essential for scaling artificial intelligence capabilities in any organization. In the consumer packaged goods industry, for instance, this means integrating zero-party, first-party, second-party and third-party data with internal data sources. – Deepak Jose, Niagara Bottling
13. Compare Before You Combine
Don’t combine external data with internal data for metrics. Start by comparing the two to identify any variances in baseline values. And always take outside data with a grain of salt. – Jeff Bruce, Quality Claims Solutions
14. Use External Benchmarks To Add Context To Internal Performance
Public data provides a broader view, while internal data delivers detailed insights. The real value emerges when you use external benchmarks to give context to your internal performance. For instance, analyzing how your customer retention compares to industry churn trends can reveal whether a dip is unique to your business or reflective of a larger market movement. – Pratik Badri, JPMorgan Chase & Co.
15. Mitigate Privacy And Security Risks
When you combine public and internal data, you amplify privacy and security risks. More data points mean more vulnerability to breaches and the risk of noncompliance with regulations like the GDPR. The solution lies in a multifaceted approach: Classify your data, use role-based access controls, and anonymize or pseudonymize data (that is, strip away or mask personal identifiers where possible). – Konstantin Klyagin, Redwerk
16. Normalize And Align Data
Prior to analysis, make sure that your internal data and public data are normalized and contextually aligned. This improves the relevance of insights for strategic decision-making, permits accurate comparisons and lessens bias. – Jyoti Shah, ADP
17. Use Context-Aware AI
Use context-aware AI to fuse public and internal data and significantly boost decision accuracy. For example, Google’s AI fuses market signals with sales data, dynamically prioritizing relevance. Align KPIs and validate sources to drive predictive strategies and achieve big savings in a high-stakes analytics market. – Durga Krishnamoorthy, Cognizant Technology Solutions
18. Define Clear Objectives And Use Cases
To effectively combine public and internal data, begin by defining clear business goals and use cases. Know the questions you aim to answer and the insights you seek. This focus ensures relevant integration. Maintain strong data quality and governance to handle complexity, mitigate risks and support informed, evidence-based decision-making throughout the organization. – Dileep Rai, Hachette Book Group
19. Build A Dynamic Knowledge Engine
Treat public data as fresh, real-time signals and internal data as structured knowledge (think decision logic, tribal workflows and proprietary context). Build a composite knowledge engine that indexes, vectorizes and fuses both dynamically based on use cases. This enables context-rich, time-sensitive decisions powered by both domain-specific and adaptive intelligence. – Karen Kim, Human Managed
20. Focus On Context Engineering
The key is context engineering: Thoughtfully link public data points to your internal metrics. Don’t just merge; understand why a public trend matters to your specific internal operations. This deeper connection reveals hidden insights, strengthens predictions and empowers smarter, data-driven decisions. Establish clear data lineage for all integrated sources. – Anil Pantangi, Capgemini America Inc.
Original story: https://www.forbes.com/councils/forbestechcouncil/2025/08/04/how-to-combine-internal-and-public-data-for-smarter-decisions/
