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How to ensure data integrity with analytics testing

Data collection and analysis is critical to ongoing business operations, but maintaining data integrity is an often overlooked problem. Ensuring data integrity is not only a consumer trust issue, but is often also mandated by legal regulations.

Without accurate data, business leaders could make decisions that are slightly (or majorly) misguided. Read on to explore top use cases for analytics testing, the challenges, and how enterprises can ensure data integrity without having to add to their in-house quality assurance team. 

Use cases for analytics testing

Media and Entertainment Industries

In any industry with a digital product for consumers, data is gold. Consumer tech and media industries have the power to collect real data about how customers interact with products and offerings.

Companies can geotarget live stream ad serving and experience testing for any event. Monitor and report issues with vital analytics related to the event page, video, and ad tracking. All data collection and analysis and can help ensure product success, UX, and consumer satisfaction.

Some specific industries that are slated to reap the most benefit from analytics testing are:

  • Broadcasting
  • Digital D2C streaming platforms
  • Social media apps
  • Gaming
  • Live events
  • Consumer tech

Advertising data

It’s useful to track all sorts of user engagement data to understand preferences within the app. Particularly for media companies, this data can inform many decisions — from program cancellations or renewals, to live stream planning, or planning ad campaigns.

However, with data governance laws and regulations coupled with consumer demand for privacy and transparency, analytics testing can often be a bumpy road to navigate.

The unique challenges of analytics testing

Companies have data governance requirements – both internally and externally mandated to comply with during data collection and testing. Data must be systematically gathered and accurately reported according to defined rules, referred to as data governance.

Data governance is the processes and procedures of managing availability, usability, security, and integrity of data.

Internally, companies have set policies for governing data while external laws and privacy regulations aim to ensure consistent, transparent data collection and usage. The EU’s GDPR and the California Consumer Privacy Act are examples of data governance laws that aim to give users more control over personal data collection. 

What’s more, your enterprise might have their own internally developed data governance policies and expectations. So, enterprises not only need to test to ensure that their data is of high quality, but they also need to routinely test for compliance with internal and external regulations. 

Validating data integrity requires constant monitoring by experienced manual testers to check for undercounting of events, missing user engagement, inaccurate attribution, incorrect media metadata, and more. 

Your in-house quality assurance team is most likely concerned with the functionality of your apps. QAs are likely testing user sign in flows, upgrade flows, screen size compatibility, and content availability. 

If you don’t have internal resources dedicated to analytics testing and data integrity, you may not have to build out that capability in house.

How to augment in-house data QA

Analytics testing is a specialized skill that requires testers with data science and analytics tracking experience for marketing, media, product, and other factors crucial to your business. 

It can be more cost effective to augment your in-house QA team with on-demand testers than to hire every capability in house. 

For example, a multinational mass media conglomerate would require continuous analytics testing for every release to validate the integrity of its analytics and data collection. Testlio’s process includes rigorous testing for every page, track, and server calls on videos. Each call is reviewed to ensure it matches the metadata requirements and the accurate volume and order of visible calls. 

How analytics testing fits into media quality

For media companies especially, data analytics testing is a major facet of quality assurance.

  • Video distribution – Test for distribution that matches distributor agreements, such as event start time and content availability. 
  • Live streaming – Practice test runs can help iron out the kinks before the live event, particularly with communication between testers and engineers. You’ll need to test CDNs live across all major metros and markets to spot any outages right away. You’ll also want to test captions, metadata, and latency time
  • Data and analytics – Media companies need to test vital analytics related to event pages, videos, metadata, ad tracking, and usage. Reporting accuracy should also be verified.

You don’t have to manage all of this internally. Testlio offers fully-managed media testers who are geographically dispersed. Customers get the skill of the testers, their devices, their experience, and test managers’ strategy and management. 

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