Five Hidden Costs of Working with Alternative Data

Alternative data gives enterprises a competitive advantage, but the cost of integrating it into business workflows can be higher than expected.

Alternative data sources are now embedded in business processes across departments. According to a 2022 Lowenstein Sandler survey, 92% of investment organizations—from hedge funds to venture capital—use alternative data to inform decisions, and they expect usage to increase. These data often come from exhausted business processes such as social‑media activity, satellite imagery, location tracking, credit‑card transactions, and web scraping.

Although alternative data can be used throughout an organization—from marketing to finance—the IT department typically owns and manages third‑party data. A 2019 Forrester Research study found that 56% of alternative‑data acquisition is overseen by CIOs and CDOs.

Procuring, storing, and managing alternative data presents new challenges for IT managers and can generate significant, often hidden, costs. The article identifies five such challenges and suggests ways to mitigate their impact.

Vendor Selection Costs

Lowenstein’s survey shows that 61% of alternative‑data users cite vendor selection as their primary concern. The cost stems from the time‑intensive process of reviewing providers and ensuring data quality, especially when data become core to business processes and are not easily replaceable. Buyers need confidence that vendors will continue supplying data in the foreseeable future.

One mitigation strategy is to join industry alliances that identify reliable data sources, allowing companies with similar needs to share ideas and best practices.

Finding Adequately Skilled Staff

A Quanthub survey reported a shortage of 250,000 data scientists in 2020, and by late 2022, Indeed listed only 2,700 data‑science openings in the UK. The scarcity drives up salaries and makes retention difficult. Data scientists are not the only personnel needed to integrate alternative data; Forrester recommends employing “data hunters” who track viable alternative sources and verify their accuracy and completeness. Munich Re, for example, employs a team of 20 data hunters.

Potential solutions include upskilling existing employees—who already understand business needs—and partnering with universities for internships or graduate training programs to build a talent pipeline.

Determining Data Ownership

The unconventional nature of alternative data can make verifying ownership more difficult than with mature, trusted suppliers. When multiple sources are combined before purchase, untangling provenance can be complex, and licensing, intellectual‑property, and data‑privacy regulations may pose additional challenges.

Choosing trustworthy suppliers that provide transparency about their procurement methods, or relying on internal data where possible, can reduce these risks.

Updating Models to Handle Alternative Data

Maintaining data models to ensure consistency and error handling is a major, often underestimated cost; Idera estimates that maintenance consumes 50‑80% of development budgets. Adding new data sources further strains tight budgets.

Careful initial data modeling and incorporating flexibility into model design can smooth the integration process.

Appropriate Tools for Storing Alternative Data

One‑quarter of Lowenstein respondents indicated a lack of tools and technologies for storing alternative data. Issues arise from inconsistent update frequencies, APIs, and formats across sources. Cleaning data to keep models running smoothly can be costly. The expanding array of storage options—from on‑premises to cloud and hybrid solutions—adds another layer of complexity and expense.

As data continue to provide competitive advantage, understanding that while many alternative‑data sources have low access costs, the effort to make them fit existing workflows can involve substantial hidden expenses is essential.