Buy vs. Build: What Kind of Data Integration Will Best Serve Your Business?

To navigate the complexities of Asian markets, international brands need as much data as possible. But while the average brand has between 8-10 data sources in Asia, accessing and harnessing that data proves challenging.

This is because connecting data sources with internal systems – like CRMs or Logistics software – requires complex integrations. To enable comprehensive access to their Asian data, brands require roughly 60 different point-to-point integrations.

Brands facing these problems have two options: either build their own integrations in-house, or buy an off-the-shelf solution. In this article, we’re going to compare those two options.

Time, Effort and Opportunity Costs

Developing a single point-to-point integration in-house involves a number of steps. You must:

  • Obtain developer access to the data source, which can cause delays and communication problems
  • Explore the data available and design a model that will ensure you get the information you need
  • Set up a connector framework
  • Design an update and delete strategy
  • Test the connector and validate the data

Ultimately, this process could take anywhere between 3-6 months; it will usually require two full-time data engineers.

In contrast, an off-the-shelf solution can be plugged straight into your systems in 15 minutes – with no labour on your part. And while this is clearly a huge saving in terms of effort, it’s also important to appreciate the value that time saving presents.

Consider, for example, the opportunity costs inherent in waiting up to 6 months to begin harvesting and using data. Competitors will be streets ahead by this point, rendering your data far less valuable.

Not only that: the time of your engineers could be spent on developing innovative data models and processes that will give you a competitive advantage – not just building data integrations.

Financial Cost

Given the amount of time and technical expertise involved in creating point-to-point integration from scratch, it’s hardly surprising that the costs add up. While off-the-shelf solutions generally demand a single monthly fee which can be easily budgeted for, in-house development involves extremely steep upfront costs – and that’s before you start factoring in on-going maintenance.

Payscale estimates that the average yearly salary of a data scientist ranges from US $66,000 to US $134,000.[1] Assuming your integration is completed as fast as possible (3 months) by a single data scientist working at the lowest end of this scale – you’re still looking at $16,500 for a single integration.

That figure could easily end up ballooning into the six-figures. And while bigger brands may literally be able to afford the high upfront costs associated with building integrations in-house, it’s important to consider whether those resources could be better spent elsewhere.

In many cases, businesses write-off these costs as unavoidable. But given the existence of off-the-shelf solutions, it is becoming harder to justify spending six-figures on an integration.

Maintenance and Future Complexity

As platforms evolve and update, your integrations will require adjustments. And while off-the-shelf solutions resolve these issues for you, brands are entirely responsible for financial and overseeing their own maintenance with in-house integrations.

This means paying technical people to develop solutions; monitoring your integrations to ensure you respond immediately; and risking disruption if errors are made.

Worse still, as you , the number of platforms you require integrations for will grow. The resulting complexity will create more work, more confusion and more disruption.

Introducing INTEGRAT3’s Single API Integration

INTEGRAT3 exists to make accessing your data in Asia easy. Our single, simple-to-use API allows you to connect with all major Asian platforms – instantly unlocking growth potential for ambitious international brands.

If you’d like to hear more about how we solve your data problems in Asia, request a demo today.

[1] https://www.payscale.com/research/US/Job=Data_Scientist/Salary

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