Decide When Analytics Teams Build In-House vs Choose Off-the-Shelf

Choosing between in-house analytics and off-the-shelf tools can shape costs, control, and long-term performance. Experts in the field share practical guidance on what to build, what to buy, and where to draw the line. The result is a clearer way to protect core data while avoiding unnecessary complexity.

Informatics MagazineQ&A · September 28, 2026

Executed this week

Run the whole notebook →

In [1]

Naming a Dish Is the Easy Half: Structuring a Food Database That Actually Reflects a Country's Cuisine

Article · Jose Gaviria · Sep 25

In [2]

Where New Patients Disappear: Funnel Analytics for Private Practices

Article · Victor Smushkevich · Sep 25

In [3]

22 Strategies for International Harmonization of AI Regulations

AI rules are developing differently across borders, creating costly uncertainty for organizations. This article presents 22 practical strategies for aligning regulations, from shared risk standards to verified compliance and cross-border trials. It also includes insights from experts in the field to clarify what effective international cooperation can look like.

Q&A · Informatics Magazine · Sep 25

In [4]

How Health Informatics Is Changing Physical Therapy Care

Article · Ihor Lavrenenko M.S. · Sep 25

In [5]

From Model-Specific Pipelines to Reusable AI Infrastructure: A Practical Framework for Scaling Healthcare AI

Article · Lipsa Senapati · Sep 25

In [6]

Set Clear Data Freshness Expectations and Handle Data Pipeline Outages Without Losing Trust

Data pipeline outages can destroy stakeholder confidence in minutes, but organizations that set transparent freshness expectations and respond with clear incident protocols maintain trust even when systems fail. Industry experts have developed proven frameworks that tie data service levels directly to business risk and implement protective measures that prevent bad data from reaching production tables. These strategies transform pipeline failures from trust-breaking events into manageable incidents with predictable resolution paths.

Q&A · Informatics Magazine · Sep 22

Decide When Analytics Teams Build In-House vs Choose Off-the-Shelf

Executed this week

Naming a Dish Is the Easy Half: Structuring a Food Database That Actually Reflects a Country's Cuisine

Where New Patients Disappear: Funnel Analytics for Private Practices

22 Strategies for International Harmonization of AI Regulations

How Health Informatics Is Changing Physical Therapy Care

From Model-Specific Pipelines to Reusable AI Infrastructure: A Practical Framework for Scaling Healthcare AI

Set Clear Data Freshness Expectations and Handle Data Pipeline Outages Without Losing Trust

Questions in the queue

Decide When Analytics Teams Build In-House vs Choose Off-the-Shelf

Executed this week

Naming a Dish Is the Easy Half: Structuring a Food Database That Actually Reflects a Country's Cuisine

Where New Patients Disappear: Funnel Analytics for Private Practices

22 Strategies for International Harmonization of AI Regulations

How Health Informatics Is Changing Physical Therapy Care

From Model-Specific Pipelines to Reusable AI Infrastructure: A Practical Framework for Scaling Healthcare AI

Set Clear Data Freshness Expectations and Handle Data Pipeline Outages Without Losing Trust

Questions in the queue